uuid
int64 0
6k
| title
stringlengths 8
285
| abstract
stringlengths 22
4.43k
|
|---|---|---|
3,900 | Large-scale image retrieval using transductive support vector machines | In this paper, we propose a new method for large-scale image retrieval by using binary hierarchical trees and transductive support vector machines (TSVMs). We create multiple hierarchical trees based on the separability of the visual object classes, and TSVM classifier is used to find the hyperplane that best separates both the labeled and unlabeled data samples at each node of the binary hierarchical trees (BHTs). Then the separating hyperplanes returned by TSVM are used to create binary codes or to reduce the dimension. We propose a novel TSVM method that is more robust to the noisy labels by interchanging the classical Hinge loss with the robust Ramp loss. Stochastic gradient based solver is used to learn TSVM classifier to ensure that the method scales well with large-scale data sets. The proposed method significantly improves the Euclidean distance metric and achieves comparable results to the state-of-the-art on CIFAR10 and MNIST data sets, and significantly outperforms the state-of-the-art hashing methods on more challenging ImageCLEF 2013, NUS-WIDE, and CIFAR100 data sets. (C) 2017 Elsevier Inc. All rights reserved. |
3,901 | Sn-Based Perovskite Halides for Electronic Devices | Because of its less toxicity and electronic structure analogous to that of lead, tin halide perovskite (THP) is currently one of the most favorable candidates as an active layer for optoelectronic and electric devices such as solar cells, photodiodes, and field-effect transistors (FETs). Promising photovoltaics and FETs performances have been recently demonstrated because of their desirable electrical and optical properties. Nevertheless, THP's easy oxidation from Sn2+ to Sn4+ , easy formation of tin vacancy, uncontrollable film morphology and crystallinity, and interface instability severely impede its widespread application. This review paper aims to provide a basic understanding of THP as a semiconductor by highlighting the physical structure, energy band structure, electrical properties, and doping mechanisms. Additionally, the key chemical instability issues of THPs are discussed, which are identified as the potential bottleneck for further device development. Based on the understanding of the THPs properties, the key recent progress of THP-based solar cells and FETs is briefly discussed. To conclude, current challenges and perspective opportunities are highlighted. |
3,902 | Fuzzy rule based unsupervised sentiment analysis from social media posts | In this paper, we compute the sentiment of social media posts using a novel set of fuzzy rules involving multiple lexicons and datasets. The proposed fuzzy system integrates Natural Language Processing techniques and Word Sense Disambiguation using a novel unsupervised nine fuzzy rule based system to classify the post into: positive, negative or neutral sentiment class. We perform a comparative analysis of our method on nine public twitter datasets, three sentiment lexicons, four state-of-the-art approaches for unsupervised Sentiment Analysis and one state-of-the-art method for supervised machine learning. Traditionally, Sentiment Analysis of twitter data is performed using a single lexicon. Our results can give an insight to researchers to choose which lexicon is best for social media. The fusion of fuzzy logic with lexicons for sentiment classification provides a new paradigm in Sentiment Analysis. Our method can be adapted to any lexicon and any dataset (two-class or three-class sentiment). The experiments on benchmark datasets yield higher performance for our approach as compared to the state-of-the-art. (C) 2019 Elsevier Ltd. All rights reserved. |
3,903 | Personal Fabrication: Patrick Baudisch and Stefanie Mueller Talk About Physical Natives | Personal fabrication is celebrating technological break-throughs that will enable us to easily generate new physical forms and shapes. It democratizes the capability of building physical things and may usher in the era of physical natives. Stefanie Mueller and Patrick Baudisch critically review the state of the art and speculate on the future use and users of personal fabrication, while highlighting corresponding research challenges for the HCI community. |
3,904 | Improving Wearable-Based Activity Recognition Using Image Representations | Activity recognition based on inertial sensors is an essential task in mobile and ubiquitous computing. To date, the best performing approaches in this task are based on deep learning models. Although the performance of the approaches has been increasingly improving, a number of issues still remain. Specifically, in this paper we focus on the issue of the dependence of today's state-of-the-art approaches to complex ad hoc deep learning convolutional neural networks (CNNs), recurrent neural networks (RNNs), or a combination of both, which require specialized knowledge and considerable effort for their construction and optimal tuning. To address this issue, in this paper we propose an approach that automatically transforms the inertial sensors time-series data into images that represent in pixel form patterns found over time, allowing even a simple CNN to outperform complex ad hoc deep learning models that combine RNNs and CNNs for activity recognition. We conducted an extensive evaluation considering seven benchmark datasets that are among the most relevant in activity recognition. Our results demonstrate that our approach is able to outperform the state of the art in all cases, based on image representations that are generated through a process that is easy to implement, modify, and extend further, without the need of developing complex deep learning models. |
3,905 | Mono- and Dual-Regulated Contractive-Expansive-Contractive Deep Convolutional Networks for Classification of Multispectral Remote Sensing Images | Deep convolutional neural networks (CNNs) are the state-of-the-art methods in the domain of classification of remote sensing (RS) data. However, traditional CNN models suffer from huge computational costs in learning land-use and land-cover features, particularly in large-scale RS problems. To address this issue, we propose a reliable mono- and dual-regulated contractive-expansive-contractive (MRCEC/DRCEC) CNN for scene based multispectral (MS) image classification. The proposed technique increases the accuracy of learning and minimizes the loss in the feature maps by incorporating the CEC approach in the classification. Extensive experiments conducted on the Sentinel-2 EuroSATallbands dataset pointed out that the proposed model outperforms the state-of-the-art models such as EfficientNet-B0, RESNet-50, and EfficientNet-B7. |
3,906 | Hardening Principles and Characterization of an Optocoupler Including a Vertical Cavity Surface Emitting Laser | This paper reports the hardening principles and the first characterization of an optocoupler structure that uses a Vertical Cavity Surface Emitting Laser (VCSEL) as a light emitter. Current Transfer Ratio (CTR) measurements carried out before and after proton irradiations show that this architecture improves the radiation hardness by a factor of 10 or more above the present state-of-the-art hardened optocouplers. |
3,907 | Insights into the effect mechanism of back-mixing inoculation on sewage sludge biodrying process: Biodrying characteristics and microbial community succession | Back mixing was frequently used to replace conventional bulking agenting, however, however, the internal effect mechanism was unclear. This study compared four bulking agents: mushroom residue (MR), MR + primary BM (BM-P), BM-P, and secondary BM (BM-S). The effect mechanism of back mixing (BM) inoculation was assessed based on biodrying performance and microbial community succession. Four trials (Trial A, Trial B, Trial C, and Trial D) reached maximum temperatures of 61.9, 68.8, 73.7, and 69.9 °C on days 6, 3, 2, and 2, respectively. Application of BM increased pile warming rate and resulted in higher temperatures. Temperature changes and microbial competition lead to decline in microbial diversity and richness during the biodrying process. Microbial diversity increased of four biodried products. The number of microorganisms shared by Trial A, Trial B, Trial C, and Trial D were 90, 119, 224, and 300, respectively. The addition of BM improved microbial community stability, and facilitating the initiation of biodrying process. Microbial genera that played an important role in the biodrying process included Ureibacillus, Bacillus, Sphaerobacter, and Tepidimicrobium. Based on these results, it was concluded that BM was efficient method to enhanced the microbial activity and reduced the usage of bulking agent. |
3,908 | Systematic review on ensuring the global food security and covid-19 pandemic resilient food systems: towards accomplishing sustainable development goals targets | Covid-19, one of the most critical and widespread global pandemics, has resulted in extraordinary risk corollaries engulfing millions of people's lives and has caused an unprecedented economic downturn while amplifying food insecurity. A systematic review of 132 scientific communications was performed over a 15-year period, using articles from the ScienceDirect and Web of Science databases (2006-2021). In addition, 24 policy briefs, country papers, and publications from the UN, WHO, FAO, and OECD were cited. The aim of this paper is to provide a comprehensive review of existing literature on the adverse effects of the Covid-19 pandemic on agricultural food systems, as well as potential strategies for building robust, resilient, and sustainable food systems to ensure global food security, safety, and endeavors regarding future global emergencies, as well as new research policies while achieving SDG targets. This would fill a research gap while also having long-term implications for health, agricultural, and food resilience policy development in a rapidly changing world. Covid-19 demonstrates how human, animal, and environmental health are all interconnected, emphasizing the need for one health legislation and a paradigm shift in planetary health. Furthermore, it identifies potential mechanisms for rebuilding better systems by shifting priorities toward policy coherence, innovative food system governance, re-engineering market access, and nexus thinking in the food system approach. According to our findings, the COVID-19 posed unavoidable impediments to achieving SDG targets for food security and household poverty. |
3,909 | Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis | Lung cancer is the leading cause of cancer deaths worldwide and early diagnosis of lung nodule is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accurate lung nodule benign-malignant classification and attribute score regression. However, this is quite challenging due to the considerable difficulty of lung nodule heterogeneity modeling and the limited discrimination capability on ambiguous cases. To solve these challenges, we propose a Multi-Task deep model with Margin Ranking loss (referred as MTMR-Net) for automated lung nodule analysis. Compared to existing methods which consider these two tasks separately, the relatedness between lung nodule classification and attribute score regression is explicitly explored in a cause-and-effect manner within our multi-task deep model, which can contribute to the performance gains of both tasks. The results of different tasks can be yielded simultaneously for assisting the radiologists in diagnosis interpretation. Furthermore, a Siamese network with a margin ranking loss is elaborately designed to enhance the discrimination capability on ambiguous nodule cases. To further explore the internal relationship between two tasks and validate the effectiveness of the proposed model, we use the recursive feature elimination method to iteratively rank the most malignancy-related features. We validate the efficacy of our method MTMR-Net on the public benchmark LIDC-IDRI dataset. Extensive experiments show that the diagnosis results with internal relationship explicitly explored in our model has met some similar patterns in clinical usage and also demonstrate that our approach can achieve competitive classification performance and more accurate scoring on attributes over the state-of-the-arts. Codes are publicly available at: https://github.com/CaptainWilliam/MTMR-NET. |
3,910 | Pyrazolopyrimidines: Potent Inhibitors Targeting the Capsid of Rhino- and Enteroviruses | There are currently no drugs available for the treatment of enterovirus (EV)-induced acute and chronic diseases such as the common cold, meningitis, encephalitis, pneumonia, and myocarditis with or without consecutive dilated cardiomyopathy. Here, we report the discovery and characterization of pyrazolopyrimidines, a well-tolerated and potent class of novel EV inhibitors. The compounds inhibit the replication of a broad spectrum of EV in vitro with IC50 values between 0.04 and 0.64 μM for viruses resistant to pleconaril, a known capsid-binding inhibitor, without affecting cytochrome P450 enzyme activity. Using virological and genetics methods, the viral capsid was identified as the target of the most promising, orally bioavailable compound 3-(4-trifluoromethylphenyl)amino-6-phenylpyrazolo[3,4-d]pyrimidine-4-amine (OBR-5-340). Its prophylactic as well as therapeutic application was proved for coxsackievirus B3-induced chronic myocarditis in mice. The favorable pharmacokinetic, toxicological, and pharmacodynamics profile in mice renders OBR-5-340 a highly promising drug candidate, and the regulatory nonclinical program is ongoing. |
3,911 | Domain Adaptive Box-Supervised Instance Segmentation Network for Mitosis Detection | The number of mitotic cells present in histopathological slides is an important predictor of tumor proliferation in the diagnosis of breast cancer. However, the current approaches can hardly perform precise pixel-level prediction for mitosis datasets with only weak labels (i.e., only provide the centroid location of mitotic cells), and take no account of the large domain gap across histopathological slides from different pathology laboratories. In this work, we propose a Domain adaptive Box-supervised Instance segmentation Network (DBIN) to address the above issues. In DBIN, we propose a high-performance Box-supervised Instance-Aware (BIA) head with the core idea of redesigning three box-supervised mask loss terms. Furthermore, we add a Pseudo-Mask-supervised Semantic (PMS) head for enriching characteristics extracted from underlying feature maps. Besides, we align the pixel-level feature distributions between source and target domains by a Cross-Domain Adaptive Module (CDAM), so as to adapt the detector learned from one lab can work well on unlabeled data from another lab. The proposed method achieves state-of-the-art performance across four mainstream datasets. A series of analysis and experiments show that our proposed BIA and PMS head can accomplish mitosis pixel-wise localization under weak supervision, and we can boost the generalization ability of our model by CDAM. |
3,912 | High performance scalable image coding | A high performance scalable image coding algorithm is proposed. The salient features of this algorithm are the ways to form and locate the significant clusters. Thanks to the list structure, the new coding algorithm achieves fine fractional bit-plane coding with negligible additional complexity. Experiments show that it performs comparably or better than the state-of-the-art coders. Furthermore, the flexible codec supports both quality and resolution scalability, which is very attractive in many network applications. |
3,913 | Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model | Hand, foot, and mouth disease (HFMD) is an important public health problem and has received concern worldwide. Moreover, the coronavirus disease 2019 (COVID-19) epidemic also increases the difficulty of understanding and predicting the prevalence of HFMD. The purpose is to prove the usability and applicability of the automatic machine learning (Auto-ML) algorithm in predicting the epidemic trend of HFMD and to explore the influence of COVID-19 on the spread of HFMD. The AutoML algorithm and the autoregressive integrated moving average (ARIMA) model were applied to construct and validate models, based on the monthly incidence numbers of HFMD and meteorological factors from May 2008 to December 2019 in Henan province, China. A total of four models were established, among which the Auto-ML model with meteorological factors had minimum RMSE and MAE in both the model constructing phase and forecasting phase (training set: RMSE = 1424.40 and MAE = 812.55; test set: RMSE = 2107.83, MAE = 1494.41), so this model has the best performance. The optimal model was used to further predict the incidence numbers of HFMD in 2020 and then compared with the reported cases. And, for analysis, 2020 was divided into two periods. The predicted incidence numbers followed the same trend as the reported cases of HFMD before the COVID-19 outbreak; while after the COVID-19 outbreak, the reported cases have been greatly reduced than expected, with an average of only about 103 cases per month, and the incidence peak has also been delayed, which has led to significant changes in the seasonality of HFMD. Overall, the AutoML algorithm is an applicable and ideal method to predict the epidemic trend of the HFMD. Furthermore, it was found that the countermeasures of COVID-19 have a certain influence on suppressing the spread of HFMD during the period of COVID-19. The findings are helpful to health administrative departments. |
3,914 | Ownership psychology as a cognitive adaptation: A minimalist model | Ownership is universal and ubiquitous in human societies, yet the psychology underpinning ownership intuitions is generally not described in a coherent and computationally tractable manner. Ownership intuitions are commonly assumed to derive from culturally transmitted social norms, or from a mentally represented implicit theory. While the social norms account is entirely ad hoc, the mental theory requires prior assumptions about possession and ownership that must be explained. Here I propose such an explanation, arguing that the intuitions result from the interaction of two cognitive systems. One of these handles competitive interactions for the possession of resources observed in many species including humans. The other handles mutually beneficial cooperation between agents, as observed in communal sharing, collective action and trade. Together, these systems attend to specific cues in the environment, and produce definite intuitions such as "this is hers", "that is not mine". This computational model provides an explanation for ownership intuitions, not just in straightforward cases of property, but also in disputed ownership (squatters, indigenous rights), historical changes (abolition of slavery), as well as apparently marginal cases, such as the questions, whether people own their seats on the bus, or their places in a queue, and how people understand "cultural appropriation" and slavery. In contrast to some previous theories, the model is empirically testable and free of ad hoc stipulations. |
3,915 | Efficient and High-Purity Sound Frequency Conversion with a Passive Linear Metasurface | Despite the significance for wave physics and potential applications, high-efficiency frequency conversion of low-frequency waves cannot be achieved with conventional nonlinearity-based mechanisms with poor mode purity, conversion efficiency, and real-time reconfigurability of the generated harmonic waves in both optics and acoustics. Rotational Doppler effect provides an intuitive paradigm to shifting the frequency in a linear system which, however, needs a spiral-phase change upon the wave propagation. Here a rotating passive linear vortex metasurface is numerically and experimentally presented with close-to-unity mode purity (>93%) and high conversion efficiency (>65%) in audible sound frequency as low as 3000 Hz. The topological charge of the transmitted sound is almost immune from the rotational speed and transmissivity, demonstrating the mechanical robustness and stability in adjusting the high-performance frequency conversion in situ. These features enable the researchers to cascade multiple vortex metasurfaces to further enlarge and diversify the extent of sound frequency conversion, which are experimentally verified. This strategy takes a step further toward the freewheeling sound manipulation at acoustic frequency domain, and may have far-researching impacts in various acoustic communications, signal processing, and contactless detection. |
3,916 | State of the art of osmotic membrane bioreactors for water reclamation | In the last few years, extensive research has been dedicated to development of a novel osmotic membrane bioreactor (OMBR), which combines high-retention osmotic separation and biological reactions in a single vessel. Although promising results have been reported in the literature, some challenges associated with applications of OMBR remain unresolved at the present stage of development, including lack of a high performance forward osmosis (FO) membrane, identification of an ideal draw solute and effect of salt accumulation on biological activity. Therefore, this paper attempts to provide a comprehensive review of state of the art of OMBR for water and wastewater reclamation. (C) 2012 Elsevier Ltd. All rights reserved. |
3,917 | Residue behavior and risk assessment of afidopyropen and its metabolite M440I007 in tea | Afidopyropen, a novel insecticide, is highly effective against piercing insects such as the tea leafhopper. The residual levels of afidopyropen and M440I007 in tea cultivation, processing, and brewing were studied. During tea cultivation, afidopyropen dissipated faster in fresh tea shoots in the rainy season (T1/2 of 1.2-2.5 d) than that in the dry season (T1/2 of 3.1-4.4 d); afidopyropen was metabolized into M440I007, the level of which peaked in 1 d, and degraded rapidly (over 90 %) afterward 3 d. The green tea processing steps had little effect on decreasing the afidopyropen residue (PF of 0.90-1.18). Low infusion rates of afidopyropen (16.7 %-17.7 %) and M440I007 (4.1 %-6.2 %) were observed from dry green tea to infusion; furthermore, the risk of ingesting afidopyropen from drinking tea was low, with the risk quotient values < 0.0001. This study can offer guidance on the rational application of afidopyropen in tea plants. |
3,918 | AIParsing: Anchor-Free Instance-Level Human Parsing | Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level. To address these two issues, we have designed an instance-level human parsing network which is anchor-free and solvable on a pixel level. It consists of two simple sub-networks: an anchor-free detection head for bounding box predictions and an edge-guided parsing head for human segmentation. The anchor-free detector head inherits the pixel-like merits and effectively avoids the sensitivity of hyper-parameters as proved in object detection applications. By introducing the part-aware boundary clue, the edge-guided parsing head is capable to distinguish adjacent human parts from among each other up to 58 parts in a single human instance, even overlapping instances. Meanwhile, a refinement head integrating box-level score and part-level parsing quality is exploited to improve the quality of the parsing results. Experiments on two multiple human parsing datasets (i.e., CIHP and LV-MHP-v2.0) and one video instance-level human parsing dataset (i.e., VIP) show that our method achieves the best global-level and instance-level performance over state-of-the-art one-stage top-down alternatives. |
3,919 | On Exploiting Concurrent Transmissions Through Discernible Interference Cancellation | This paper represents the design, feasibility evaluation, and performance validation of ICMR, a novel cross layer protocol that can maximize concurrent transmissions and avoid data frame interference in wireless networks, achieving a higher throughput comparing with the 802.11 standard and other state-of-the-art protocols. Observations on the 802.11 standard reveal that nodes around both the transmitter and receiver of the ongoing link waste concurrent transmission opportunities, degrading the network throughput dramatically. A state-of-the-art protocol IRMA is proposed to improve the network throughput through exploiting concurrent transmissions at the transmitter side. In this paper, a new ICMR protocol focuses on the receiver side to further improve the network throughput, through exploiting discernible interference cancellation, a physical layer mechanism that can successfully detect data frames when collided by control frames. We analyze the concurrent transmission opportunities of one link from the transmitter's transmission opportunities and the receiver's reception opportunities, then formulate the opportunities and give theoretical analysis to indicate that ICMR will have a higher opportunity over other protocols. Feasibility of the discernible interference cancellation mechanism is demonstrated through experiment results based on USRP2, and the throughput improvement of ICMR comparing with the other protocols is confirmed through simulations based on ns-2. |
3,920 | A 1.2-V Dynamic Bias Latch-Type Comparator in 65-nm CMOS With 0.4-mV Input Noise | A latch-type comparator with a dynamic bias pre-amplifier is implemented in a 65-nm CMOS process. The dynamic bias with a tail capacitor is simple to implement and ensures that the pre-amplifier output nodes are only partially discharged to reduce the energy consumption. The comparator is analyzed and compared to its prior art in terms of energy consumption and input referred noise voltage. First-order equations are presented that show how to optimize the pre-amplifier for low noise and high gain. Both the dynamic bias comparator and the prior art are implemented on the same die and measurements show that the dynamic bias can reduce the average energy consumption by about a factor 2.5 for the same input-equivalent noise at an input common-mode level of half the supply voltage. |
3,921 | Fusing Sorted Random Projections for Robust Texture and Material Classification | This paper presents a conceptually simple, and robust, yet highly effective, approach to both texture classification and material categorization. The proposed system is composed of three components: 1) local, highly discriminative, and robust features based on sorted random projections (RPs), built on the universal and information-preserving properties of RPs; 2) an effective bag-of-words global model; and 3) a novel approach for combining multiple features in a support vector machine classifier. The proposed approach encompasses the simplicity, broad applicability, and efficiency of the three methods. We have tested the proposed approach on eight popular texture databases, including Flickr Materials Database, a highly challenging materials database. We compare our method with 13 recent state-of-the-art methods, and the experimental results show that our texture classification system yields the best classification rates of which we are aware of 99.37% for Columbia-Utrecht, 97.16% for Brodatz, 99.30% for University of Maryland Database, and 99.29% for Kungliga Tekniska hogskolan-textures under varying illumination, pose, and scale. Moreover, the proposed approach significantly outperforms the current state-of-the-art approach in materials categorization, with an improvement to classification accuracy of 67%. |
3,922 | Do Appetite Traits Mediate the Link between Birth Weight and Later Child Weight in Low-Income Hispanic Families? | Background: Birth weight and appetite traits (ATs) are important early life determinants of child weight and obesity. Objectives: The aim of this study is to examine whether (1) birth weight-for-gestational age z-scores (BWGAzs) were associated with ATs at child age 2 years and (2) ATs mediated the link between BWGAzs and weight-for-age z-scores (WFAzs) at child ages 3 and 4 years among Hispanic children. Methods: We conducted a secondary longitudinal analysis of data from the Starting Early Program of low-income, Hispanic mother-child pairs. ATs were assessed using the Child Eating Behavior Questionnaire at age 2 years. Child birth weight was obtained from medical records. Birth weight, sex, and gestational age were used to generate BWGAzs with Fenton growth curves. WFAz was calculated based on the CDC 2000 growth charts. Regression and mediation analyses were used to explore associations between BWGAzs, ATs, and WFAzs. Results: Infants with higher BWGAzs had significantly lower Satiety Responsiveness (B = -0.10) and Food Fussiness (B = -0.13) scores at age 2 years and higher WFAzs at ages 3 (B = 0.44) and 4 (B = 0.34) years. Lower Satiety Responsiveness at age 2 years was associated with higher WFAzs at ages 3 (B = -0.11) and 4 (B = -0.34; all p < 0.01) years. Lower Satiety Responsiveness partially mediated the positive relationship between birth weight and child WFAzs at ages 3 and 4 years. Conclusions: Children with higher birth weight and lower Satiety Responsiveness scores may be at higher risk of developing obesity in childhood. Further research is needed to understand the mechanisms through which birth weight influences child appetite. Clinical Trial Registration: ClinicalTrials.gov: NCT01541761. |
3,923 | Secure Hashing-Based Verifiable Pattern Matching | Verifiable pattern matching is the problem of finding a given pattern verifiably from the outsourced textual data, which is resident in an untrusted remote server. This problem has drawn much attention due to a large number of applications. The state-of-the-art method for this problem suffers from low efficiency. To enable fast verifiable pattern matching, we propose a novel scheme in this paper. Our scheme is based on an ordered set accumulator data structure and a newly developed verifiable suffix array structure, which only involves fast cryptographic hash computations. Our scheme also supports fast multiple-occurrence pattern matching. A striking feature of our proposed scheme is that our scheme works even with no secret keys, which ensures public verifiability. We conduct extensive experiments to evaluate the proposed scheme using Java. The results show that our scheme is orders of magnitude faster than the state-of-the-art work. Specifically, our scheme with public verifiability only costs a preprocessing time of 47 s (merely one-time off-line cost during outsourcing), a search time of 30 mu s, a verification time of 149 mu s, and a proof size of 2760 bytes for a verifiable pattern matching query with pattern length 200 on 10-million long textual data which consists of sequences of two-byte, Unicode characters in Java. |
3,924 | Ganglioside Microdomains on Cellular and Intracellular Membranes Regulate Neuronal Cell Fate Determination | Gangliosides are sialylated glycosphingolipids (GSLs) with essential but enigmatic functions in brain activities and neural stem cell (NSC) maintenance. Our group has pioneered research on the importance of gangliosides for growth factor receptor signaling and epigenetic regulation of NSC activity and differentiation. The primary localization of gangliosides is on cell-surface microdomains and the drastic dose and composition changes during neural differentiation strongly suggest that they are not only important as biomarkers, but also are involved in modulating NSC fate determination. Ganglioside GD3 is the predominant species in NSCs and GD3-synthase knockout (GD3S-KO) revealed reduction of postnatal NSC pools with severe behavioral deficits. Exogenous administration of GD3 significantly restored the NSC pools and enhanced the stemness of NSCs with multipotency and self-renewal. Since morphological changes during neurogenesis require a huge amount of energy, mitochondrial functions are vital for neurogenesis. We discovered that a mitochondrial fission protein, the dynamin-related protein-1 (Drp1), as a novel GD3-binding protein, and GD3 regulates mitochondrial dynamics. Furthermore, we discovered that GM1 ganglioside promotes neuronal differentiation by an epigenetic regulatory mechanism. Nuclear GM1 binds with acetylated histones on the promoters of N-acetylgalactosaminyltransferase (GalNAcT; GM2 synthase) as well as on the NeuroD1 genes in differentiated neurons. In addition, epigenetic activation of the GalNAcT gene was detected as accompanied by an apparent induction of neuronal differentiation in NSCs responding to an exogenous supplement of GM1. GM1 is indeed localized in the nucleus where it can interact with transcriptionally active histones. Interestingly, GM1 could induce epigenetic activation of the tyrosine hydroxylase (TH) gene, with recruitment of nuclear receptor related 1 (Nurr1, also known as NR4A2), a dopaminergic neuron-associated transcription factor, to the TH promoter region. In this way, GM1 epigenetically regulates dopaminergic neuron specific gene expression. GM1 interacts with active chromatin via acetylated histones to recruit transcription factors at the nuclear periphery, resulting in changes in gene expression for neuronal differentiation. The significance is that multifunctional gangliosides modulate lipid microdomains to regulate functions of important molecules on multiple sites: the plasma membrane, mitochondrial membrane, and nuclear membrane. Versatile gangliosides could regulate functional neurons as well as sustain NSC functions via modulating protein and gene activities on ganglioside microdomains. |
3,925 | Adoptive Transfer of Renal Allograft Tolerance in a Large Animal Model | Our recent studies in an inbred swine model demonstrated that both peripheral and intra-graft regulatory cells were required for the adoptive transfer of tolerance to a second, naïve donor-matched kidney. Here, we have asked whether both peripheral and intra-graft regulatory elements are required for adoptive transfer of tolerance when only a long-term tolerant (LTT) kidney is transplanted. Nine highly-inbred swine underwent a tolerance-inducing regimen to prepare LTT kidney grafts which were then transplanted to histocompatible recipients, with or without the peripheral cell populations required for adoptive transfer of tolerance to a naïve kidney. In contrast to our previous studies, tolerance of the LTT kidney transplants alone was achieved without transfer of additional peripheral cells and without strategies to increase the number/potency of regulatory T cells in the donor. This tolerance was systemic, since most subsequent, donor-matched challenge kidney grafts were accepted. These results confirm the presence of a potent tolerance-inducing and/or tolerance-maintaining cell population within LTT renal allografts. They suggest further that additional peripheral tolerance mechanisms, required for adoptive transfer of tolerance to a naïve donor-matched kidney, depend on peripheral cells that, if not transferred with the LTT kidney, require time to develop in the adoptive host. |
3,926 | Retreatment of a recurrent giant aneurysm of the internal carotid artery after treatment with a flow-diverting stent | Flow-diverting stents (FDSs) have proven advantageous for the treatment of large, fusiform, and dissecting aneurysms that are otherwise difficult to treat. Retreatment strategies for recurrent large or giant aneurysms after FDSs are limited to overlapping implantation of an additional FDS or definitive occlusion of the parent vessel. We report a recurrent giant aneurysm that was initially treated with an FDS with coils and was successfully treated with an additional FDS. Visual symptoms due to the mass effect of the recurrent aneurysm were completely resolved, and follow-up digital subtraction angiography revealed complete obliteration of the aneurysm. Additional FDS implantation for the retreatment of incompletely occluded aneurysms after the initial FDS treatment may be feasible and safe. Further studies are required to validate these results. |
3,927 | D-galactose-induced cardiac ageing: A review of model establishment and potential interventions | Cardiovascular disease (CVD) is highly prevalent in an ageing society. The increased incidence and mortality rates of CVD are global issues endangering human health. There is an urgent requirement for understanding the aetiology and pathogenesis of CVD and developing possible interventions for preventing CVD in ageing hearts. It is necessary to select appropriate models and treatment methods. The D-galactose-induced cardiac ageing model possesses the advantages of low mortality, short time and low cost and has been increasingly used in the study of cardiovascular diseases in recent years. Therefore, understanding the latest progress in D-galactose-induced cardiac ageing is valuable. This review highlights the recent progress and potential therapeutic interventions used in D-galactose-induced cardiac ageing in recent years by providing a comprehensive summary of D-galactose-induced cardiac ageing in vivo and in vitro. This review may serve as reference literature for future research on age-related heart diseases. |
3,928 | Fast Retinomorphic Event-Driven Representations for Video Gameplay and Action Recognition | Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks. In such framework, besides the regular ConvNets responsible for RGB frame inputs, a second network is introduced to handle the temporal representation, usually the optical flow (OF). However, OF or other task-oriented flow is computationally costly, and is thus typically pre-computed. Critically, this prevents the two-stream approach from being applied to reinforcement learning (RL) applications such as video game playing, where the next state depends on current state and action choices. Inspired by the early vision systems of mammals and insects, we propose a fast event-driven representation (EDR) that models several major properties of early retinal circuits: (1) log-arithmic input response, (2) multi-timescale temporal smoothing to filter noise, and (3) bipolar (ON/OFF) pathways for primitive event detection. Trading off the directional information for fast speed (>9000 fps), EDR enables fast real-time inference/learning in video applications that require interaction between an agent and the world such as game-playing, virtual robotics, and domain adaptation. In this vein, we use EDR to demonstrate performance improvements over state-of-the-art reinforcement learning algorithms for Atari games, something that has not been possible with pre-computed OF. Moreover, with UCF-101 video action recognition experiments, we show that EDR performs near state-of-the-art in accuracy while achieving a 1,500x speedup in input representation processing, as compared to optical flow. |
3,929 | A Fast and Accurate Algorithm for Nuclei Instance Segmentation in Microscopy Images | Nuclei instance segmentation within microscopy images is a fundamental task in the pathology work-flow, based on that the meaningful nuclear features can be extracted and multiple biological related analysis can be performed. However, this task is still challenging because of the large variability among different types of nuclei. Although deep learning(DL) based methods have achieved state-of-the-art results in nuclei instance segmentation tasks, these methods are usually focus on improving the accuracy and require support of powerful computing resources. In this paper, we joint the detection and segmentation simultaneously, and propose a fast and accurate box-based nuclei instance segmentation method. Mainly, we employ a fusion module based on the feature pyramid network(FPN) to combine the complementary information of the shallow layers with deep layers for detection the nuclear location by bounding boxes. Subsequently, we crop the feature maps according to the bounding boxes and feed the cropped patches into an U-net architecture as a guide to separate clustered nuclei. The experiments show that the proposed approach outperforms prior state-of-the-art methods, not only on accuracy but also on speed. The source code will be released at: https://github.com/QUAPNH/Nucleiseg. |
3,930 | Memory-efficient architecture for FrWF-based DWT of high-resolution images for IoMT applications | This paper proposes a simple low memory architecture for computing discrete wavelet transform (DWT) of high-resolution (HR) images on low-cost memory-constrained sensor nodes used in visual sensor networks (VSN) or Internet of Multimedia Things (IoMT). The main feature of the proposed architecture is the novel data scanning technique that makes memory requirement independent of the image size. The proposed architecture needs only (30S) words of memory, where S is the number of parallel processing units and a critical path delay (CPD) equal to the delay of a multiplier (T-m). Furthermore, a multiplierless version of this architecture is also proposed which reduces the CPD to T-a<T-m (where T-a is the delay of an adder). In order to evaluate their effectiveness, the proposed architectures are coded in HDL and implemented on same FPGA board. Their performance is also compared with other state-of-the-art low memory DWT architectures. The experimental results show the superiority of the proposed architectures in terms of memory and CPD compared to existing DWT architectures. Moreover, the reduction in CPD to T-a indicates that the operating frequency can be scaled up by several factors and can be chosen depending upon the application. Compared to one of the best state-of-the-art DWT architecture, proposed multiplierless architecture (with S = 4) needs 57.37% less LUT's and 64.39% less flip-flops for HR image of dimension 2048 x 2048. Moreover, the proposed architecture needs no LUTRAM and DSP, whereas the existing architecture requires 3264 LUTRAM and 24 DSP's. Thus the proposed multiplierless architecture is superior to the existing state-of-the-art architecture and is suitable for IoMT/VSNs. |
3,931 | Superpixel segmentation: A benchmark | Various superpixel approaches have been published recently. These algorithms are assessed using different evaluation metrics and datasets resulting in discrepancy in algorithm comparison. This calls for a benchmark to compare the state-of-the-arts methods and evaluate their pros and cons. We analyze benchmark metrics, datasets and built a superpixel benchmark. We evaluated and integrated top 15 superpixel algorithms, whose code are publicly available, into one code library and, provide a quantitative comparison of these algorithms. We find that some superpixel algorithms perform consistently better than others. Clustering based superpixel algorithms are more efficient than graph-based ones. Furthermore, we also introduced a novel metric to evaluate superpixel regularity, which is a property that superpixels desired. The evaluation results demonstrate the performance and limitations of state-of-the-art algorithms. Our evaluation and observations give deep insight about different algorithms and will help researchers to identify the more feasible superpixel segmentation methods for their different problems. |
3,932 | Image Super-Resolution Using Aggregated Residual Transformation Networks With Spatial Attention | For a long time, previous studies focused on convolutional neural network depth and width to improve accuracy in image super-resolution tasks. Although overall performance has grown as going deeper and going wider over time, until recently, these two elements start to show saturating accuracy and we meet a marginal utility. To address this problem, model efficiency is improved successfully by introducing two effective strategies, multi-cardinality, and spatial attention, to image super-resolution from high-level vision tasks. We propose a novel and efficient architecture aggregated residual attention network (ARAN) and set a new state-of-the-art model efficiency. According to the multi-cardinality strategy, we use group convolutions in each basic module. Moreover, we apply spatial attention blocks as gate units to capture detailed information in input images, which can be the counterparts of their basic modules. Extra representation ability is demonstrated compared with both the same sized enhanced deep super-resolution network (EDSR) baseline model and currently the-state-of-the-art cascading residual network (CARN). The experiments suggest the effectiveness of these two missing strategies previously. Especially, in the aspect of model efficiency, ARAN exceeds almost all the medium sized models currently. Code and pre-trained models are publicly available on the github: https://github.com/Xingrun-Xing/ARAN. |
3,933 | Mosaic-Based Color-Transform Optimization for Lossy and Lossy-to-Lossless Compression of Pathology Whole-Slide Images | The use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. In particular, color transforms are needed to exploit the very high degree of inter-component correlation and obtain competitive compression performance. Even though the state-of-the-art color transforms remove some redundancy, they disregard important details of the compression algorithm applied after the transform. Therefore, their coding performance is not optimal. We propose an optimization method called mosaic optimization for designing irreversible and reversible color transforms simultaneously optimized for any given WSI and the subsequent compression algorithm. Mosaic optimization is designed to attain reasonable computational complexity and enable continuous scanner operation. Exhaustive experimental results indicate that, for JPEG 2000 at identical compression ratios, the optimized transforms yield images more similar to the original than the other state-of-the-art transforms. Specifically, irreversible optimized transforms outperform the Karhunen-Loeve Transform in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to 3.8 dB), and the accuracy of computer-aided nuclei detection tasks (F1 score up to 0.04 higher). In addition, reversible optimized transforms achieve PSNR, HDR-VDP-2, and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB, and 0.025, respectively, when compared with the reversible color transform in lossy-to-lossless compression regimes. |
3,934 | RESEARCH ON CROSS CULTURAL AWARENESS IN THE NEW ERA OF COLLEGE MUSIC TEACHING FROM THE PERSPECTIVE OF ECOLOGY | In school education, the music course is a highly practical art course. It is an art course that takes aesthetics as the core and improves students' music literacy and aesthetic taste. With the emergence of the "interconnection +" era, the innovative research of music teaching must be continuously deepened, changing the teaching methods of traditional music classrooms and injecting new vitality into the reform of music teaching. Guided by the thought of ecological civilization, with the construction of a beautiful China as the research background, this paper studies the cross-cultural awareness of the new era of university music teaching. From the perspective of ecology, it proposes a coordinated development model. The research results show that: crosscultural awareness can enhance the identity and tolerance of different cultures, so as to better promote the exchange, communication and development of different music and cultures. |
3,935 | Acute Bilateral Swelling of the Parotid Gland After General Anesthesia in Lateral Decubitus Position | A rare but well-known anesthetic side effect is acute parotid gland enlargement after general anesthesia, sometimes known as anesthesia mumps or acute post-operative sialadenitis. Acute dehydration, obstruction of glandular excretory ducts caused by the position of the patient, and/or medications such as atropine that increase saliva viscosity have all been proposed as potential causes, while the specific cause is still unknown. We present a case of a 41-year-old patient who underwent a right open anatrophic pyelolithotomy for a staghorn calculus in the left lateral decubitus position and had swelling in the right and left preauricular and postauricular regions, which had progressed to the angle of the mandible post-operatively. |
3,936 | Natural language processing: state of the art, current trends and challenges | Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of Natural Language Generation followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP. |
3,937 | Effect of Low Over-Sampling Rate on a 64Gbaud/DP-16QAM 100-km Optical Link | We experimentally demonstrated a one-span 100-km transmission of 64Gbaud/DP-16QAM signal using a state-of-the-art InP-based coherent transmitter and receiver, a 1-sample/symbol DAC without pulse shaping, and a 1.25-sample/symbol ADC/DSP. Various system performance penalties related to the low over-sampling rates have been investigated. |
3,938 | State-of-the-art and trends in scalable video compression with wavelet-based approaches | Scalable Video Coding (SVC) differs form traditional single point approaches mainly because it allows to encode in a unique bit stream several working points corresponding to different quality, picture size and frame rate. This work describes the current state-of-the-art in SVC, focusing on wavelet based motion-compensated approaches (WSVC). It reviews individual components that have been designed to address the problem over the years and how such components are typically combined to achieve meaningful WSVC architectures. Coding schemes which mainly differ from the space-time order in which the wavelet transforms operate are here compared, discussing strengths and weaknesses of the resulting implementations. An evaluation of the achievable coding performances is provided considering the reference architectures studied and developed by ISO/MPEG in its exploration on WSVC. The paper also attempts to draw a list of major differences between wavelet based solutions and the SVC standard jointly targeted by ITU and ISO/MPEG. A major emphasis is devoted to a promising WSVC solution, named STP-tool, which presents architectural similarities with respect to the SVC standard. The paper ends drawing some evolution trends for WSVC systems and giving insights on video coding applications which could benefit by a wavelet based approach. |
3,939 | ICC plus plus : Explainable feature learning for art history using image compositions | Image compositions are helpful in the study of image structures and assist in discovering the semantics of the underlying scene portrayed across art forms and styles. With the digitization of artworks in recent years, thousands of images of a particular scene or narrative could potentially be linked together. However, manually linking this data with consistent objectiveness can be a highly challenging and timeconsuming task. In this work, we present a novel approach called Image Composition Canvas (ICC ++ ) to compare and retrieve images having similar compositional elements. ICC ++ is an improvement over ICC, specializing in generating low and high-level features (compositional elements) motivated by Max Imdahl's work. To this end, we present a rigorous quantitative and qualitative comparison of our approach with traditional and state-of-the-art (SOTA) methods showing that our proposed method outperforms all of them. In combination with deep features, our method outperforms the best deep learning-based method, opening the research direction for explainable machine learning for digital humanities. We will release the code and the data post-publication. |
3,940 | IGBT History, State-of-the-Art, and Future Prospects | An overview on the history of the development of insulated gate bipolar transistors (IGBTs) as one key component in today's power electronic systems is given; the state-of-the-art device concepts are explained as well as an detailed outlook about ongoing and foreseeable development steps is shown. All these measures will result on the one hand in ongoing power density and efficiency increase as important contributors for worldwide energy saving and environmental protection efforts. On the other hand, the exciting competition of more maturing Si IGBT technology with the wide bandgap successors of GaN and SiC switches will go on. |
3,941 | Novel SIL1 mutations cause cerebellar ataxia and atrophy in a French-Canadian family | Two French-Canadian sibs with cerebellar ataxia and dysarthria were seen in our neurogenetics clinic. The older brother had global developmental delay and spastic paraplegia. Brain MRIs from these two affected individuals showed moderate to severe cerebellar atrophy. To identify the genetic basis for their disease, we conducted a whole exome sequencing (WES) investigation using genomic DNA prepared from the affected sibs and their healthy father. We identified two mutations in the SIL1 gene, which is reported to cause Marinesco-Sjögren syndrome. This study emphasizes how the diagnosis of patients with ataxic gait and cerebellar atrophy may benefit from WES to identify the genetic cause of their condition. |
3,942 | Spatial modeling reveals nuclear phosphorylation and subcellular shuttling of YAP upon drug-induced liver injury | The Hippo signaling pathway controls cell proliferation and tissue regeneration via its transcriptional effectors yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ). The canonical pathway topology is characterized by sequential phosphorylation of kinases in the cytoplasm that defines the subcellular localization of YAP and TAZ. However, the molecular mechanisms controlling the nuclear/cytoplasmic shuttling dynamics of both factors under physiological and tissue-damaging conditions are poorly understood. By implementing experimental in vitro data, partial differential equation modeling, as well as automated image analysis, we demonstrate that nuclear phosphorylation contributes to differences between YAP and TAZ localization in the nucleus and cytoplasm. Treatment of hepatocyte-derived cells with hepatotoxic acetaminophen (APAP) induces a biphasic protein phosphorylation eventually leading to nuclear protein enrichment of YAP but not TAZ. APAP-dependent regulation of nuclear/cytoplasmic YAP shuttling is not an unspecific cellular response but relies on the sequential induction of reactive oxygen species (ROS), RAC-alpha serine/threonine-protein kinase (AKT, synonym: protein kinase B), as well as elevated nuclear interaction between YAP and AKT. Mouse experiments confirm this sequence of events illustrated by the expression of ROS-, AKT-, and YAP-specific gene signatures upon APAP administration. In summary, our data illustrate the importance of nuclear processes in the regulation of Hippo pathway activity. YAP and TAZ exhibit different shuttling dynamics, which explains distinct cellular responses of both factors under physiological and tissue-damaging conditions. |
3,943 | The feasibility, validity, and reliability of strain measures in the iliotibial band during isolated muscular contractions | The iliotibial band (ITB) is a unique anatomical structure that transmits forces from two in-series muscles across the lateral knee. Little is known about how force is transmitted, via ITB strain, in response to muscle activation. We have developed a technique to measure the strain through the distal ITB during isolated contractions of the tensor fascia latae (TFL) muscle, using a Kanade-Lucas-Tomasi ultrasound image tracking algorithm. Here we report: 1) the validity of this method to track ITB tissue displacement; 2) the reliability of tracking ITB strain across multiple contractions (intra-probe placement), tracking attempts (intra-operator), data collection sessions (inter-probe placement), and tracking operators (inter-operator); and 3) the feasibility of this approach to assess differences in strain produced during different TFL contraction levels. Our method was valid for tracking ITB displacement and could be used to determine tissue strain due to isolated muscle contraction. Our method was most reliable when a single operator tracked trials without replacing the ultrasound transducer and when averaging across multiple stimulations. Our method was also able to detect changes in ITB strains resulting from differing levels of muscle activation. In the future, this method could be used to assess how factors like posture and ITB region affect the strain found in the distal ITB. |
3,944 | Thoracoscopic Clockwise Lobectomy May Be a Stylized Procedure for Treating Children with Congenital Lung Malformations | Background: Thoracoscopic lobectomy is a challenging procedure in children with congenital lung malformations (CLMs). This study aims to evaluate the safety and efficacy of thoracoscopic clockwise lobectomy (TCL) in CLMs in children and its potential to be a stylized procedure. Methods: All patients with CLMs who received TCL from 2015 to 2019 in our hospital were retrospectively reviewed. Clinical information was extracted from medical records, including patient demographics, operative details, and outcomes. Results: A total of 184 patients with a median age of 6.8 months (range, 3-156) and a median weight of 9 kg (range, 6-45) received TCL. Lesions were all located in the lower lobe and included congenital pulmonary airway malformation (n = 133), intralobar sequestration (n = 44), bronchiectasis (n = 4), and congenital lobar emphysema (n = 3). The mean (±standard deviation [SD]) operating time was 46 ± 7.5 minutes (range, 35-113). The mean (±SD) blood loss was 3.5 ± 0.8 mL (range, 1-60). Three patients converted to thoracotomy, and 162 patients did not have a chest tube placed. The postoperative course was uneventful in all patients except 2 patients who developed air leaks and 23 patients who developed a mild fever. The median length of postoperative hospital stay was 2 days. A total of 163 patients were followed up for more than 1 year without any complications. Conclusion: TCL is suitable for lower lobectomy and is safe and effective in standard and complicated thoracoscopic lobectomy. It could be recommended as a stylized procedure in treating children with CLMs. |
3,945 | Lightweight Image Super-Resolution by Multi-Scale Aggregation | Ultra-high-definition display technology is widely used in broadcasting, but there is a huge contradiction between its ultra-high-resolution content and short storage. Super-Resolution (SR) can effectively alleviate this contradiction. Recently, State-of-the-art image SR approaches leveraging Deep Convolutional Neural Networks (DCNNs) have demonstrated high-quality reconstruction performance. However, most of them suffer from large model parameters, which restricts their practical application. Besides, image SR for large scaling factors (e.g., x8) is a tricky issue when the parameters diminish. To remedy these issues, we propose the Lightweight Multi-scale Aggregation Network (LMAN) for the image SR, which works well for both small and large scaling factors with limited parameters. Specifically, we propose a Group-wise Multi-scale Block (GMB) in which a group convolution is exploited for extracting and fusing multi-scale features before a channel attention layer to obtain discriminative features. Additionally, we present a novel Hierarchical Spatial Attention (HSA) mechanism to jointly and adaptively fuse local and global hierarchical features for high-resolution image reconstruction. Extensive experiments illustrate that our LMAN achieves superior performance against state-of-the-art methods with similar parameters and in particular for large scaling factors such as 4 x and 8x. |
3,946 | Nano-read-across predictions of toxicity of metal oxide engineered nanoparticles (MeOx ENPS) used in nanopesticides to BEAS-2B and RAW 264.7 cells | The demand for nutrients and new technologies has increased with population growth. The agro-technological revolution with metal oxide engineered nanoparticles (MeOx ENPs) has the potential to reform the resilient agricultural system while maintaining the security of food. When utilized extensively, MeOx ENPs may have unintended toxicological effects on both target and non-targeted species. Since limited information about nanopesticides' pernicious effects is available, in silico modeling can be done to explore these issues. Hence, in the present work, we have applied computational modeling to explore the influence of metal oxide nanoparticles on the toxicity of bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells to bridge the data gap relating to the toxicity of MeOx NPs. Initially, partial least squares (PLS) regression models were developed applying the Small Dataset Modeler software (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) using four datasets having effective concentration (EC50%) as the endpoints and employing only periodic table descriptors. To further explore the predictions, we applied a read-across approach using the descriptors selected in the QSAR models. Also, the inter-endpoint cytotoxicity relationship modeling (quantitative toxicity-toxicity relationship or QTTR) was conducted. It was found that the result obtained by nano-read-across provided a similar level of accuracy as provided by QSAR. The information derived from the PLS models of both the cell lines suggested that metal cation formation, and bond-forming capacity influence the toxicity whereas the presence of metal has an influential impact on the ecotoxicological effects. Thus, it is feasible to design safe nanopesticides that could be more effective than conventional analogs. |
3,947 | Capital budgeting methods among Sweden's largest groups of companies. The state of the art and a comparison with earlier studies | This study presents a general description of the state of the art of capital budgeting methods used by Swedish corporations. It is based on a questionnaire sent to 528 companies selected from 500 of the largest Swedish corporations and some from the O-list of companies. The response rate is 24.4%. The study shows that the public sector companies are most frequent users of discounted cash flow (DCF) methods. The payback method is the most used in all industries; also matters size of company. The use of the net present value method has increased over the years, although the use of DCF methods is generally unchanged. Value-based management models have been introduced in the listed companies. Another conclusion is that tradition is an important factor explaining the choice of capital budgeting method. Large companies within the manufacturing industry use DCF methods more often than other companies. In general, the companies seem unconcerned with the tax consequences of capital budgeting decisions. It would appear as if the public companies have not adopted capital budgeting practices supporting shareholder value increases. (C) 2002 Elsevier Science B.V. All rights reserved. |
3,948 | Competitive Normalized Least-Squares Regression | Online learning has witnessed an increasing interest over the recent past due to its low computational requirements and its relevance to a broad range of streaming applications. In this brief, we focus on online regularized regression. We propose a novel efficient online regression algorithm, called online normalized least-squares (ONLS). We perform theoretical analysis by comparing the total loss of ONLS against the normalized gradient descent (NGD) algorithm and the best off-line LS predictor. We show, in particular, that ONLS allows for a better bias-variance tradeoff than those state-of-the-art gradient descent-based LS algorithms as well as a better control on the level of shrinkage of the features toward the null. Finally, we conduct an empirical study to illustrate the great performance of ONLS against some state-of-the-art algorithms using real-world data. |
3,949 | Isolation of segmented filamentous bacteria from complex gut microbiota | Segmented filamentous bacteria (SFB) modulate the ontogeny of the immune system, and their presence can significantly affect mouse models of disease. Until recently, the inability to successfully culture SFB has made controlled studies on the mechanisms by which these bacteria exert their influence problematic. Here, we report a new method for selecting SFB from complex microbial mixtures, providing researchers a simple and cost-effective means to prepare pure infective inocula for prospective studies and also to compare individual SFB isolates. |
3,950 | Thymoquinone ameliorates testicular tissue inflammation induced by chronic administration of oral sodium nitrite | Although sodium nitrite has been widely used as food preservative, building bases of scientific evidence about nitrite continues to oppose the general safety in human health. Moreover, thymoquinone (TQ) has therapeutic potential as antioxidant, anti-inflammatory, antibacterial and anticancer. Therefore, we investigated the effects of both sodium nitrite and TQ on testicular tissues of rats. Forty adult male Sprague Dawley rats were used. They received either 80 mg kg(-1) sodium nitrite or 50 mg kg(-1) TQ daily for twelve weeks. Serum testosterone was measured. Testis were weighed and the testicular tissue homogenates were used for measurements of tumour necrosis factor (TNF)-α, interleukin (IL)-1β, IL-4, IL-6, IL10, caspase-3, caspase-8 and caspase-9. Sodium nitrite resulted in significant reduction in serum testosterone concentration and elevation in testis weight and Gonado-Somatic Index. We found significant reduction in testicular tissues levels of IL-4 and IL-10 associated with elevated levels of TNF-α, IL-1β, IL-6, caspase-3, caspase-8 and caspase-9. In conclusion, chronic oral sodium nitrite induced changes in the weight of rat testis accompanied by elevation in the testicular tissue level of oxidative stress markers and inflammatory cytokines. TQ attenuated sodium nitrite-induced testicular tissue damage through blocking oxidative stress, restoration of normal inflammatory cytokines balance and blocking of apoptosis. |
3,951 | P2R2: Parallel Pseudo-Round-Robin arbiter for high performance NoCs | Networks-on-Chip (NoCs) play an important role in the performance of Chip Multi-Processors (CMPs). Providing the desired performance under heavy traffics imposed by some applications necessitates NoC routers to have a large number of Virtual Channels (VCs). Increasing the number of VCs, however, will add to the delay of the critical path of the arbitration logic, and hence restricts the clock frequency of the router. In order to make it possible to enjoy the benefits of having many VCs and keep the clock frequency as high as that of a low-VC router, we propose Parallel Pseudo-Round-Robin (P2R2) arbiter. Our proposal is based on processing multiple groups of requests in parallel. Our experimental results show that the proposed scheme can beat the state-of-the-art arbiter design by up to 12.5% and 6.8% in terms of saturation rate and zero-load latency, respectively, under synthetic traffic patterns. These results also demonstrate a 29.5% improvement in average packet latency in Splash-2 applications in favor of P2R2 with respect to the state-of-the-art arbiter. (C) 2014 Elsevier B.V. All rights reserved. |
3,952 | Fast Compression of Large Semantic Web Data Using X10 | The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of 2.6 - 7.4x and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications. |
3,953 | Highly Robust Observer Sliding Mode Based Frequency Control for Multi Area Power Systems with Renewable Power Plants | This paper centers on the design of highly robust observer sliding mode (HROSM)-based load frequency and tie-power control to compensate for primary frequency control of multi-area interconnected power systems integrated with renewable power generation. At first, the power system with external disturbance is model in the state space form. Then the state observer is used to estimate the system states which are difficult or expensive to measure. Secondly, the sliding mode control (SMC) is designed with a new single phase sliding surface (SPSS). In addition, the whole system asymptotic stability is proven with Lyapunov stability theory based on the linear matrix inequality (LMI) technique. The new SPSS without reaching time guarantees rapid convergence of high transient frequency, tie-power change as well as reduces chattering without loss of accuracies. Therefore, the superiority of modern state-of-the-art SMC-based frequency controllers relies on good practical application. The experimental simulation results on large interconnected power systems show good performance and high robustness against external disturbances when compared with some modern state of art controllers in terms of overshoots and settling time. |
3,954 | Hypertension and Dyslipidaemia in Argentina: Patient Journey Stages | Cardiovascular disease (CVD) leads to one-third of all deaths in Argentina. To implement patient-centric strategies for reducing CVD burden, available data on hypertension and hypercholesterolemia patients at different stages of their journey: awareness, screening, diagnosis, treatment, adherence, and control were analysed. A semi-systematic review in peer-reviewed databases (EMBASE and MEDLINE) and unstructured sources such as Google Scholar, Argentine Ministry of Health, and World Health Organization websites was conducted till 06.07.2021 for hypertension and dyslipidemia. English articles published in 2010-2021, depicting patient journey data for hypertension or hypercholesterolemia of the nationally representative adult population of Argentina were included. Thesis abstracts, letters to the editor, editorials, and case studies were excluded. No limits were used for unstructured sources. Weighted or simple means were estimated for patient journey stages. Out of 296 and 1257 articles retrieved for hypertension and hypercholesterolemia, respectively, five articles were retained for each of the conditions. The estimates for hypertension and hypercholesterolemia, respectively, were 46.6% and 30.7% for prevalence, 61.6% and 37.3% for awareness, 97.5% and ≥80% for screening, 64.1% and 28.9% for diagnosis, and 49.7% and 36.6% for treatment, and 19.9% and 20% for overall control. Adherence data were not available for hypercholesterolemia, while the same for hypertension was 50.4%. Various determinants are responsible for low adherence such as patient-level barriers, physician-related barriers, and health system-related issues. The review reveals that hypertension and hypercholesterolemia are poorly controlled in Argentina. Although further studies with more accurate data are needed to confirm these results, they should alert the medical community and the public health institutions to take urgent corrective actions. |
3,955 | Interfacing Power System and ICT Simulators: Challenges, State-of-the-Art, and Case Studies | With the transition toward a smart grid, the power system has become strongly intertwined with the information and communication technology (ICT) infrastructure. The interdependency of both domains requires a combined analysis of physical and ICT processes, but simulating these together is a major challenge due to the fundamentally different modeling and simulation concepts. After outlining these challenges, such as time synchronization and event handling, this paper presents an overview of state-of-the-art solutions to interface power system and ICT simulators. Due to their prominence in recent research, a special focus is set on co-simulation approaches and their challenges and potentials. Further, two case studies analyzing the impact of ICT on applications in power system operation illustrate the necessity of a holistic approach and show the capabilities of state-of-the-art co-simulation platforms. |
3,956 | Reduced search space mechanism for solving constrained optimization problems | Over the last few decades, a considerable number of evolutionary algorithms (EAs) have been proposed for solving constrained optimization problems (COPs). As for most of these problems, the optimal solution exists on the boundary of the feasible space, we aim to focus the search process around the boundary. In this paper a new concept, called reduced search space (R2S), is introduced. In the process, we first identify active constraints, based on the current solutions, and then define R2S around those constraint's boundaries. However, the search may be conducted either in the entire R2S or in some portions of it. To judge the impact of this concept, we have incorporated it with a number of state-of-the-art algorithms, and we have comprehensively tested it on three sets of benchmark test functions, namely, 24 test functions taken from IEEE CEC2006, 18 test functions with 10D and 301) taken from IEEE CEC2010 and 10 test functions taken from IEEE CEC2011. The results show that our proposed mechanism significantly improves the performances of state-of-the-art algorithms. (C) 2017 Elsevier Ltd. All rights reserved. |
3,957 | Compressive Sensing and Recovery for Binary Images | We propose a method for compressive sensing and recovery of binary images. To achieve this, we combine two ideas: in the sensing step, ordered aperture patterns are employed instead of random aperture patterns, and in the recovery step, a dense reconstruction scheme replaces sparse reconstruction. We demonstrate that this approach is more effective for binary images than the state-of-the-art algorithms relying on random sensing and sparse reconstruction. |
3,958 | Facial reanimation using free partial latissimus dorsi muscle transfer: Single versus dual innervation method | The aim of the present study was to analyze the consequences of partial free latissimus dorsi muscle flap with nerve splitting technique (Partial LD transfer) for facial reanimation and compare outcomes according to innervation method (singer versus dual innervation). Patients with complete unilateral facial paralysis underwent either the single (ipsilateral masseteric nerve only) or dual (ipsilateral masseteric nerve plus contralateral buccal branch of the facial nerve) nerve innervation method for facial reanimation. An assessment was carried out to compare the outcomes between the single and dual innervation. Total of 21 patients were involved in this study. In the single innervation group, 7 out of 8 patients developed a voluntary smile. However, none were able to achieve a spontaneous smile. On the other hand, 9 out of 13 patients developed a voluntary smile and 3 out of 13 patients achieved a spontaneous smile. The mean increases of smile excursion assessed by Emotrics software and Terzis grades showed no significant differences between two groups. Within the limitations of the study it seems that partial LD transfer approach utilizing the dual innervation method has a positive effect on achieving a spontaneous smile and could be a valuable option for facial reanimation. |
3,959 | Transcriptome Analysis of Protein Kinase MoCK2, which Affects Acetyl-CoA Metabolism and Import of CK2-Interacting Mitochondrial Proteins into Mitochondria in the Rice Blast Fungus Magnaporthe oryzae | The rice pathogen Magnaporthe oryzae causes severe losses to rice production. Previous studies have shown that the protein kinase MoCK2 is essential for pathogenesis, and this ubiquitous eukaryotic protein kinase might affect several processes in the fungus that are needed for infection. To better understand which cellular processes are affected by MoCK2 activity, we performed a detailed transcriptome sequencing analysis of deletions of the MoCK2 b1 and b2 components in relation to the background strain Ku80 and connected this analysis with the abundance of substrates for proteins in a previous pulldown of the essential CKa subunit of CK2 to estimate the effects on proteins directly interacting with CK2. The results showed that MoCK2 seriously affected carbohydrate metabolism, fatty acid metabolism, amino acid metabolism, and the related transporters and reduced acetyl-CoA production. CK2 phosphorylation can affect the folding of proteins and especially the effective formation of protein complexes by intrinsically disordered or mitochondrial import by destabilizing soluble alpha helices. The upregulated genes found in the pulldown of the b1 and b2 mutants indicate that proteins directly interacting with CK2 are compensatorily upregulated depending on their pulldown. A similar correlation was found for mitochondrial proteins. Taken together, the classes of proteins and the changes in regulation in the b1 and b2 mutants suggest that CK2 has a central role in mitochondrial metabolism, secondary metabolism, and reactive oxygen species (ROS) resistance, in addition to its previously suggested role in the formation of new ribosomes, all of which are processes central to efficient nonself responses as innate immunity. IMPORTANCE The protein kinase CK2 is highly expressed and essential for plants, animals, and fungi, affecting fatty acid-related metabolism. In addition, it directly affects the import of essential mitochondrial proteins into mitochondria. These effects mean that CK2 is essential for lipid metabolism and mitochondrial function and, as shown previously, is crucial for making new translation machinery proteins. Taken together, our new results combined with previously reported results indicate that CK2 is an essential protein necessary for the capacities to launch efficient innate immunity responses and withstand the negative effects of such responses necessary for general resistance against invading bacteria and viruses as well as to interact with plants, withstand plant immunity responses, and kill plant cells. |
3,960 | Multimodal Target Detection by Sparse Coding: Application to Paint Loss Detection in Paintings | Sparse representation based methods have demonstrated their superior performance in target detection tasks compared to more traditional approaches such as matched subspace detectors and adaptive subspace detectors. However, the existing sparsity-based target detection methods were mostly formulated for and validated on a single imaging modality (sometimes with multiple spectral bands). In many application domains, including art investigation, multimodal data, acquired by different sensors are readily available, and yet, efficient processing techniques for such data are still scarce. In this paper, we propose a sparsity-based multimodal target detection method that processes jointly the information from multiple imaging modalities in a kernel feature space, and making use of the spatial context. We develop our target detector such to be robust to errors in labelled data, which is especially important in applications like digital painting analysis, where pixel-wise manual annotations are unreliable. We apply the proposed method to a challenging application of paint loss detection in master paintings and we demonstrate its effectiveness on a case study with multimodal acquisitions of the Ghent Altarpiece. |
3,961 | Highly polarized single-chip ELED sources using oppositely strained MQW emitters and absorbers | Integrated polarizer components with polarization extinctions >40 dB are desirable for state-of-the-art photonic integrated circuits. We demonstrate >60-dB polarization extinction from a single-chip InGaAsP-InP broadband source by combining an edge light-emitting diode consisting of compressively strained quantum wells (QWs) with an absorber consisting of tensile strained QWs. A 600-pm polarizer exhibits only 5 dB of insertion loss. |
3,962 | How has the state-of-the-art for quantification of landscape pattern advanced in the twenty-first century? | Context Landscape ecology was founded on the idea that there is a reciprocal relationship between spatial pattern and ecological processes. I provide a retrospective look at how the state-of-the-art of landscape pattern analysis has changed since 1998. Objectives My objective is to show how pattern analysis techniques have evolved and identify some of the key lessons learned. Results The state-of-the-art in 1998 was derived from information theory, fractal geometry, percolation theory, hierarchy theory and graph theory, relying heavily on the island-patch conceptual model using categorical maps, although point-data analysis methods were actively being explored. We have gradually winnowed down the list of fundamental components of spatial pattern, and have clarified the appropriate and inappropriate use of landscape metrics for research and application. We have learned to let the objectives choose the metric, guided by the scale and nature of the ecological process of interest. The use of alternatives to the binary patch model (such as gradient analysis) shows great promise to advance landscape ecological knowledge. Conclusions The patch paradigm is often of limited usefulness, and other ways to represent the pattern of landscape properties may reveal deeper insights. The field continues to advance as illustrated by papers in this special issue. |
3,963 | Aesthetic Visual Quality Assessment of Paintings | This paper aims to evaluate the aesthetic visual quality of a special type of visual media: digital images of paintings. Assessing the aesthetic visual quality of paintings can be considered a highly subjective task. However, to some extent, certain paintings are believed, by consensus, to have higher aesthetic quality than others. In this paper, we treat this challenge as a machine learning problem, in order to evaluate the aesthetic quality of paintings based on their visual content. We design a group of methods to extract features to represent both the global characteristics and local characteristics of a painting. Inspiration for these features comes from our prior knowledge in art and a questionnaire survey we conducted to study factors that affect human's judgments. We collect painting images and ask human subjects to score them. These paintings are then used for both training and testing in our experiments. Experimental results show that the proposed work can classify high-quality and low-quality paintings with performance comparable to humans. This work provides a machine learning scheme for the research of exploring the relationship between aesthetic perceptions of human and the computational visual features extracted from paintings. |
3,964 | Radial Basis Functions With Adaptive Input and Composite Trend Representation for Portfolio Selection | We propose a set of novel radial basis functions with adaptive input and composite trend representation (AICTR) for portfolio selection (PS). Trend representation of asset price is one of the main information to be exploited in PS. However, most state-of-the-art trend representation-based systems exploit only one kind of trend information and lack effective mechanisms to construct a composite trend representation. The proposed system exploits a set of RBFs with multiple trend representations, which improves the effectiveness and robustness in price prediction. Moreover, the input of the RBFs automatically switches to the best trend representation according to the recent investing performance of different price predictions. We also propose a novel objective to combine these RBFs and select the portfolio. Extensive experiments on six benchmark data sets (including a new challenging data set that we propose) from different real-world stock markets indicate that the proposed RBFs effectively combine different trend representations and AICTR achieves state-of-the-art investing performance and risk control. Besides, AICTR withstands the reasonable transaction costs and runs fast; hence, it is applicable to real-world financial environments. |
3,965 | Deep learning-regularized, single-step quantitative susceptibility mapping quantification | The purpose of the current study was to develop deep learning-regularized, single-step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A deep learning-regularized, single-step QSM quantification model, named SS-POCSnet, was trained with datasets created using the QSM synthesis approach in QSM reconstruction challenge 2.0. In SS-POCSnet, a data fidelity term based on a single-step model was iteratively applied that combined the spherical mean value kernel and dipole model. Meanwhile, SS-POCSnet regularized susceptibility maps, avoiding underestimating susceptibility values. We evaluated the SS-POCSnet on 10 synthetic datasets, 24 clinical datasets with lesions of cerebral microbleed (CMB) and calcification, and 10 datasets with multiple sclerosis (MS).On synthetic datasets, SS-POCSnet showed the best performance among the methods evaluated, with a normalized root mean squared error of 37.3% ± 4.2%, susceptibility-tuned structured similarity index measure of 0.823 ± 0.02, high-frequency error norm of 37.0 ± 5.7, and peak signal-to-noise ratio of 42.8 ± 1.1. SS-POCSnet also reduced the underestimations of susceptibility values in deep brain nuclei compared with those from the other models evaluated. Furthermore, SS-POCSnet was sensitive to CMB/calcification and MS lesions, demonstrating its clinical applicability. Our method also supported variable imaging parameters, including matrix size and resolution. It was concluded that deep learning-regularized, single-step QSM quantification can mitigate underestimating susceptibility values in deep brain nuclei. |
3,966 | Facial image super-resolution guided by adaptive geometric features | This paper addresses the traditional issue of restoring a high-resolution (HR) facial image from a low-resolution (LR) counterpart. Current state-of-the-art super-resolution (SR) methods commonly adopt the convolutional neural networks to learn a non-linear complex mapping between paired LR and HR images. They discriminate local patterns expressed by the neighboring pixels along the planar directions but ignore the intrinsic 3D proximity including the depth map. As a special case of general images, the face has limited geometric variations, which we believe that the relevant depth map can be learned and used to guide the face SR task. Motivated by it, we design a network including two branches: one for auxiliary depth map estimation and the other for the main SR task. Adaptive geometric features are further learned from the depth map and used to modulate the mid-level features of the SR branch. The whole network is implemented in an end-to-end trainable manner under the extra supervision of depth map. The supervisory depth map is either a paired one from RGB-D scans or a reconstructed one by a 3D prior model of faces. The experiments demonstrate the effectiveness of the proposed method and achieve improved performance over the state of the arts. |
3,967 | Methodology focused on the selection of construction operations for the standardization of work with an emphasis on the occupational safety criterion | This article indicates that work standardization is an effective tool for the improvement of occupational safety in any process performed by people. Work standardization has an impact on improving productivity, quality and engaging employees in improving current working methods. The article shows that in the construction industry there are problems concerning the selection of operations for work standardization due to the specificity of the operations performed there. As a result, work standardization is not a common methodology used in the construction industry, which may be one of the reasons for the greater number of accidents and near misses when compared to the manufacturing industry. The article presents the author's safety-complication-frequency (SCF) model for the selection of operations for work standardization, which is dedicated to the construction industry. The SCF model enables operations with the highest priority in terms of implementation for work standardization to be selected. |
3,968 | ASL-3DCNN: American sign language recognition technique using 3-D convolutional neural networks | The communication between a person from the impaired community with a person who does not understand sign language could be a tedious task. Sign language is the art of conveying messages using hand gestures. Recognition of dynamic hand gestures in American Sign Language (ASL) became a very important challenge that is still unresolved. In order to resolve the challenges of dynamic ASL recognition, a more advanced successor of the Convolutional Neural Networks (CNNs) called 3-D CNNs is employed, which can recognize the patterns in volumetric data like videos. The CNN is trained for classification of 100 words on Boston ASL (Lexicon Video Dataset) LVD dataset with more than 3300 English words signed by 6 different signers. 70% of the dataset is used for Training while the remaining 30% dataset is used for testing the model. The proposed work outperforms the existing state-of-art models in terms of precision (3.7%), recall (4.3%), and f-measure (3.9%). The computing time (0.19 seconds per frame) of the proposed work shows that the proposal may be used in real-time applications. |
3,969 | From Materials to Technique: A Complete Non-Invasive Investigation of a Group of Six Ukiyo-E Japanese Woodblock Prints of the Oriental Art Museum E. Chiossone (Genoa, Italy) | In the present work, a complete non-invasive scientific investigation of six Utagawa Kunisada's woodblock prints (nishiki-e) belonging to the Oriental Art Museum "E. Chiossone" (Genoa, Italy), was performed in situ. The campaign started with high resolution multiband imaging (visible, multiband fluorescence, near infrared) followed by reflectance transformation imaging (RTI) to characterize and highlight the peculiar printing techniques and the condition of the support. Then fiber optics reflectance spectroscopy (FORS), spectrofluorimetry, Raman and reflectance Fourier-transform infrared (FTIR) spectroscopies were successfully applied in synergy for the investigation of the printing materials (pigments, binders, support). The results obtained represent a set of very important information for these never-before-studied works of art, useful to the different professionals involved: historians, conservators and curators. The materials identified were completely in agreement with those traditionally used in the Edo period in the 19th century, while the computational imaging technique RTI gave an additional amount of information in terms of surface characterization that could not be overlooked when studying these works of art. RTI data were further processed to enhance the texture visualization. |
3,970 | Transcriptome analysis provides novel insights into the immune mechanisms of Macrobrachium nipponense during molting | Molting is a basic physiological behavior of the Oriental river prawn (Macrobrachium nipponense), however, the gene expression patterns and immune mechanisms during the molting process of Oriental river prawn are unclear. In the current study, the gene expression levels of the hepatopancreas of the Oriental river prawn at different molting stages (pre-molting, Prm; mid-molting, Mm; and post-molting, Pom) were detected by mRNA sequencing. A total of 1721, 551, and 1054 differentially expressed genes (DEGs) were identified between the Prm hepatopancreas (PrmHe) and Mm hepatopancreas (MmHe), MmHe and Pom hepatopancreas (PomHe) and PrmHe and PomHe, respectively. The results showed that a total of 1151 DEGs were annotated into 316 signaling pathways, and the significantly enriched immune-related pathways were "Lysosome", "Hippo signaling pathway", "Apoptosis", "Autophagy-animal", and "Endocytosis". The qRT-PCR verification results of 30 randomly selected DEGs were consistent with RNA-seq. The expression patterns of eight immune related genes in different molting stages of the Oriental river prawn were analyzed by qRT-PCR. The function of Caspase-1 (CASP1) was further investigated by bioinformatics, qRT-PCR, and RNAi analysis. CASP1 has two identical conserved domains: histidine active site and pentapeptide motif, and the expression of CASP1 is the highest in ovary. The expression levels of triosephosphate isomerase (TPI), Cathepsin B (CTSB) and Hexokinase (HXK) were evaluated after knockdown of CASP1. This research provides a valuable basis to improve our understanding the immune mechanisms of Oriental river prawns at different molting stages. The identification of immune-related genes is of great significance for enhancing the immunity of the Oriental river prawn, or other crustaceans, by transgenic methods in the future. |
3,971 | Induction heating in transversal magnetic field - mathematical modeling and experimental verification | Based upon a state-of art on induction heating an analysis of induction devices for surface heating of thin workpieces in transverse flux magnetic field was done in the paper. A short overview of calculation methods for such induction heating systems applied for was elaborated. (Induction heating in transversal magnetic field - mathematical modeling and experimental verification). |
3,972 | RPEOD: A Real-Time Pose Estimation and Object Detection System for Aerial Robot Target Tracking | Pose estimation and environmental perception are the fundamental capabilities of autonomous robots. In this paper, a novel real-time pose estimation and object detection (RPEOD) strategy for aerial robot target tracking is presented. The aerial robot is equipped with a binocular fisheye camera for pose estimation and a depth camera to capture the spatial position of the tracked target. The RPEOD system uses a sparse optical flow algorithm to track image corner features, and the local bundle adjustment is restricted in a sliding window. Ulteriorly, we proposed YZNet, a lightweight neural inference structure, and took it as the backbone in YOLOV5 (the state-of-the-art real-time object detector). The RPEOD system can dramatically reduce the computational complexity in reprojection error minimization and the neural network inference process; Thus, it can calculate real-time on the onboard computer carried by the aerial robot. The RPEOD system is evaluated using both simulated and real-world experiments, demonstrating clear advantages over state-of-the-art approaches, and is significantly more fast. |
3,973 | Spiking Deep Residual Networks | Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional artificial neural networks (ANNs). They possess the potential of achieving energy-efficient machine intelligence while keeping comparable performance to ANNs. However, it is still a big challenge to train a very deep SNN. In this brief, we propose an efficient approach to build deep SNNs. Residual network (ResNet) is considered a state-of-the-art and fundamental model among convolutional neural networks (CNNs). We employ the idea of converting a trained ResNet to a network of spiking neurons named spiking ResNet (S-ResNet). We propose a residual conversion model that appropriately scales continuous-valued activations in ANNs to match the firing rates in SNNs and a compensation mechanism to reduce the error caused by discretization. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet 2012 with low latency. This work is the first time to build an asynchronous SNN deeper than 100 layers, with comparable performance to its original ANN. |
3,974 | Mental Health Risks Among Informal Waste Workers in Kathmandu Valley, Nepal | Informal waste workers are a vulnerable population group who are often socio-economically marginalized and disadvantaged, with more likelihood of experiencing ill health than the general population. To explore the determinants of mental ill health in this group, we conducted a cross-sectional survey of 1278 informal waste-workers in Nepal in 2017, using a demographic health assessment questionnaire and a modified Patient Health Questionnaire (PHQ-9). We looked at the potential associations between various exposure factors and mental health outcomes and found that 27.4% of waste-workers had depressive symptoms, more likely to be reported by female (OR 2.290), older person (OR 7.757), divorced/separated (5.859), and those with ill health (OR 2.030), or disability (OR 3.562). Waste-workers with access to social protection (OR 0.538) and financial savings (OR 0.280) were less likely to have depressive symptoms. There are key risk factors that may enable identification of particularly vulnerable persons within this group and also protective factors that may help improve their mental health resilience. |
3,975 | Historic building materials from Alhambra: Nanoparticles and global climate change effects | Advanced microscopy analyses are capable of revealing particles that are existent in the Alhambra, Spain, a UNESCO World Heritage Site, at minimal levels owing to the detailed directing on ultra-fine (UFPs) and nano-particles (NPs) of importance. This applied technique is one of the most used to natural samples. Between the diverse archaeological places in the planet, Alhambra is one of the most important representations of Hispano-Islamic architecture and art significant Muslim virtuosity in its conclusive European periods, thus helping to offer scientific material about the still little studied Islamic art. In this study, advanced microscopy analyses has been applied to understand minor portions of UFPs and NPs of pollutants in external walls exposed to weathering. Several materials identified by X-Ray Diffraction can be detected using HR-TEM/FE-SEM and vice versa. The occurrence of anglesite, gypsum, hematite containing PHEs, and several organic compounds associated with modifications due to moisture and pollution was also demonstrated. (C) 2019 Elsevier Ltd. All rights reserved. |
3,976 | Primary membranous nephropathy: an endless story | Primary membranous nephropathy (PMN) is an autoimmune disease caused by the attack of autoantibodies against podocyte antigens leading to the in situ production of immune complexes. However, the etiology is unknown and the pathogenesis is still far from being completely elucidated. MN is prevalently idiopathic or primary, but in about 20-30% of cases it is secondary to chronic infections, systemic diseases, exposure to drugs, or malignancy. The differentiation between primary and secondary MN may be difficult, particularly when MN precedes signs and symptoms of the original disease, as in some cases of cancer or systemic lupus erythematosus. The natural course of PMN is variable, but in the long term 40-60% of patients with nephrotic syndrome progress to end-stage renal disease (ESRD) or die from thrombotic or cardiovascular events. PMN is a treatable disease. Patients with asymptomatic proteinuria should receive supportive care. Immunosuppressive treatments should be given to patients with nephrotic syndrome or risk of progression. The most frequently adopted treatments rely on cyclical therapy alternating steroids with a cytotoxic agent every other month, i.e., rituximab at different doses, or calcineurin inhibitors plus low-dose steroids. A good rate of response may be obtained but relapses can occur. Randomized controlled trials, with adequate size, long-term follow-up, and fair definition of endpoints are needed to identify treatment with the best therapeutic index. |
3,977 | Graph and Rank Regularized Matrix Recovery for Snapshot Spectral Image Demosaicing | Snapshot spectral imaging (SSI) is a cutting-edge technology for enabling the efficient acquisition of the spatio-spectral content of dynamic scenes using miniaturized platforms. To achieve this goal, SSI architectures associate each spatial pixel with a specific spectral band, thus introducing a critical trade-off between spatial and spectral resolutions. In this paper, we propose a computational approach for the recovery of high spatial and spectral resolution content from a single exposure or a small number of exposures. We formulate the problem in a novel framework of spectral measurement matrix completion and we develop an efficient low-rank and graph regularized method for SSI demosaicing. Furthermore, we extend state-of-the-art approaches by considering more realistic sampling paradigms that incorporate information related to the spectral profile associated with each pixel. In addition to reconstruction quality, we also investigate the impact of recovery on subsequent analysis tasks, such as classification using state-of-the-art convolutional neural networks. We experimentally validate the merits of the proposed recovery scheme using synthetically generated data from indoor and satellite observations and real data obtained with an Interuniversity MicroElectronics Center (IMEC) visible range SSI camera. |
3,978 | Oral arsenic and retinoic acid for high-risk acute promyelocytic leukemia | Acute promyelocytic leukemia (APL) has become curable over 95% patients under a complete chemo-free treatment with all-trans retinoic acid (ATRA) and arsenic trioxide in low-risk patients. Minimizing chemotherapy has proven feasible in high-risk patients. We evaluated oral arsenic and ATRA without chemotherapy as an outpatient consolidation therapy and no maintenance for high-risk APL. We conducted a multicenter, single-arm, phase 2 study with consolidation phases. The consolidation therapy included Realgar-Indigo naturalis formula (60 mg/kg daily in an oral divided dose) in a 4-week-on and 4-week-off regimen for 4 cycles and ATRA (25 mg/m2 daily in an oral divided dose) in a 2-week-on and 2-week-off regimen for 7 cycles. The primary end point was the disease-free survival (DFS). Secondary end points included measurable resident disease, overall survival (OS), and safety. A total of 54 participants were enrolled at seven centers from May 2019. The median age was 40 years. At the median follow-up of 13.8 months (through April 2022), estimated 2-year DFS and OS were 94% and 100% in an intention-to-treat analysis. All the patients achieved complete molecular remission at the end of consolidation phase. Two patients relapsed after consolidation with a cumulative incidence of relapse of 6.2%. The majority of adverse events were grade 1-2, and only three grade 3 adverse events were observed. Oral arsenic plus ATRA without chemotherapy was active as a first-line consolidation therapy for high-risk APL.Trial registration: chictr.org.cn number, ChiCTR1900023309. |
3,979 | Flow Adversarial Networks: Flowrate Prediction for Gas-Liquid Multiphase Flows Across Different Domains | The solution of how to accurately and timely predict the flowrate of gas-liquid mixtures is the key to help petroleum and other related industries to reduce costs, improve efficiency, and optimize management. Although numerous studies have been carried out over the past decades, the problem is still significantly challenging due to the complexity of multiphase flows. This paper attempts to seek new possibilities for multiphase flow measurement and novel application scenarios for state-of-the-art machine learning (ML) techniques. Convolutional neural networks (CNNs) are applied to predict the flowrate of multiphase flows for the first time and can achieve promising performance. In addition, considering the difference between data distributions of training and testing samples and its negative impact on prediction accuracy of the CNN models on testing samples, we propose flow adversarial networks (FANs) that can distill both domain-invariant and flowrate-discriminative features from the raw input. The method is evaluated on dynamic experimental data of different multiphase flows on different flow conditions and operating environments. The experimental results demonstrate that FANs can effectively prevent the accuracy degradation caused by the gap between training and testing samples and have better performance than state-of-the-art approaches in the flowrate prediction field. |
3,980 | Bottleneck-Stationary Compact Model Accelerator With Reduced Requirement on Memory Bandwidth for Edge Applications | State-of-the-art compact models such as MobileNets and EfficientNets are structured using a linear bottleneck and inverted residuals. Hardware architecture using a single dataflow strategy fails to balance the required memory bandwidth with the given computational resources. This work presents a heterogeneous dual-core accelerator that performs a block-wise pipelined process as a unit using a bottleneck-stationary (BS) dataflow. The BS greatly relieves the requirement on DRAM bandwidth and on-chip SRAM capacity. A look-behind-only attention is also proposed as a co-optimized algorithm. Compared to the state-of-the-art hardware scheme, the proposed accelerator demonstrates a reduction of 1.8-2.9 x in latency and 2.2-3 x in energy consumption, respectively.For verification, the accelerator with a 16-bit integer precision was implemented using 28nm CMOS process. Measurements show energy efficiencies of 0.5-to-3.75 TOPS/W in a supply voltage range of 0.55-to-1.15V. |
3,981 | Pentoxifylline treatment had no detrimental effect on sperm DNA integrity and clinical characteristics in cases with non-obstructive azoospermia | The aim of this study was to assess the consequences of treatment with pentoxifylline (PTX), an inducer of sperm motility, on sperm DNA fragmentation (SDF) and clinical characteristics in non-obstructive azoospermia (NOA) patients. The pilot study included 15 NOA patients. Half of each sperm sample before and after rapid freezing, was treated with PTX (3.6 mM /l, 30 min) as the PTX group and the remaining samples were considered as the control. SDF and sperm motility were assessed in each group. The clinical study comprised 30 fresh testicular sperm extractions (TESE) and 22 post-thawed TESE intracytoplasmic sperm injection cycles. Half of the mature oocytes from each patient were injected with PTX-treated spermatozoa and the remaining oocytes were injected with non-treated spermatozoa. Fertilization was assessed at 16 h post injection. Embryo transfer was carried out on day 2 after fertilization. Chemical pregnancy was assessed 2 weeks after transfer. PTX was found to significantly increase (P < 0.05) sperm motility. There was an insignificant difference in SDF rates between the groups (P > 0.05). In patient ovaries given fresh TESE, there was not any significant difference in clinical characteristics (P > 0.05). In patient ovaries given post-thawed TESE, there was a significant difference in the number of 2PN and in embryo formation (P < 0.05). Differences in the results of chemical pregnancy were insignificant (P > 0.05) between the groups. In addition, there was not any correlation between DNA fragmentation index and sperm motility and laboratory outcomes. Therefore, obtaining viable spermatozoa using PTX was more effective in post-thawed TESE regime patients in terms of 2PN and in embryo formation, deprived of damaging effects on sperm DNA integrity. |
3,982 | On the Value of Oversampling for Deep Learning in Software Defect Prediction | One truism of deep learning is that the automatic feature engineering (seen in the first layers of those networks) excuses data scientists from performing tedious manual feature engineering prior to running DL. For the specific case of deep learning for defect prediction, we show that that truism is false. Specifically, when we pre-process data with a novel oversampling technique called fuzzy sampling, as part of a larger pipeline called GHOST (Goal-oriented Hyper-parameter Optimization for Scalable Training), then we can do significantly better than the prior DL state of the art in 14/20 defect data sets. Our approach yields state-of-the-art results significantly faster deep learners. These results present a cogent case for the use of oversampling prior to applying deep learning on software defect prediction datasets. |
3,983 | A Complex Convolution Kernel-Based Optical Displacement Sensor | During the last decade, a variety of optical displacement measurement techniques have been developed, commercialized and widely used. This paper presents a new type of optical displacement sensor that improves on the prior art with higher sub-pixel resolution, better accuracy, and better computation efficiency. The Complex Convolution Kernel-Based Optical Sensor (KBOS) developed in this paper leverages the phase-magnitude representation of a complex valued kernel function to achieve a superior optical displacement sensor for 2D motion measurements. The sensor's performance and properties are quantified in terms of its ability to compute the marker motions. Evaluation of this metric will explain how the sensor is able to achieve superior performance over the state of the art. The design is then validated with a set of experiments over a range of operating conditions to demonstrate the effectiveness of the approach over other existing displacement measurement techniques. Finally, the sensor's capability is compared qualitatively against other state of the art optical displacement sensors found in the literature. The sensor is shown to possess a subpixel resolution of between 1/100 and 1/2500 of a pixel depending upon spatial sampling density. The sensors dynamic range was investigated and shown to be only limited by the camera sampling speeds. |
3,984 | Microcrystalline Silicon Photodiode For Large Area NIR Light Detection Applications | This letter reports on microcrystalline silicon near infrared (NIR) photodiode detector. The fabricated device shows dynamic ratio of 200 at 850 nm wavelength per 0.2 mW/cm(2) of incident power density at reverse bias voltage of -1 V with response time of 400 mu s. The dynamic ratio achieved here is 15 times higher than the state of art large area inorganic a-SiGe: H phototransistor and twice the one for state of art organic cyanine based NIR detector. The speed of this device is 40 times faster than the a-SiGe: H detector. The overall advantage of high dynamic ratio and fast response alongside with compatibility with standard a-Si: H thin film transistor industry makes this device suitable for large area NIR detection applications. |
3,985 | A Chopped Neural Front-End Featuring Input Impedance Boosting With Suppressed Offset-Induced Charge Transfer | Modern neuromodulation systems typically provide a large number of recording and stimulation channels, which reduces the available power and area budget per channel. To maintain the necessary input-referred noise performance despite growingly rigorous area constraints, chopped neural front-ends are often the modality of choice, as chopper-stabilization allows to simultaneously improve (1/f) noise and area consumption. The resulting issue of a drastically reduced input impedance has been addressed in prior art by impedance boosters based on voltage buffers at the input. These buffers precharge the large input capacitors, reduce the charge drawn from the electrodes and effectively boost the input impedance. Offset on these buffers directly translates into charge-transfer to the electrodes, which can accelerate electrode aging. To tackle this issue, a voltage buffer with ultra-low time-averaged offset is proposed, which cancels offset by periodic reconfiguration, thereby minimizing unintended charge transfer. This article explains the background and circuit design in detail and presents measurement results of a prototype implemented in a 180 nm HV CMOS process. The measurements confirm that signal-independent, buffer offset induced charge transfer occurs and can be mitigated by the presented buffer reconfiguration without adversely affecting the operation of the input impedance booster. The presented neural recorder front-end achieves state of the art performance with an area consumption of 0.036 mm (2), an input referred noise of 1.32 mu V-rms (1 to 200 Hz) and 3.36 mu V-rms (0.2 to 10 kHz), power consumption of 13.7 mu W from 1.8 V supply, as well as CMRR and PSRR >= 83 dB at 50 Hz. |
3,986 | ARTS: Automotive Repository of Traffic Signs for the United States | Traffic signs recognition (TSR) is a crucial sub-domain of computer vision, particularly relevant to autonomous vehicles and autonomous driver-assistance systems (ADAS). TSR systems can further augment efforts in many other applications, such as highway asset maintenance and management. Despite the relative success of detectors with hand-crafted features in Europe, most, if not all, non-deep-learning based systems are not scalable to accurately recognize a large subset of traffic signs in the United States. The recent works in the domain of object detection using machine learning have shown the necessity of deep neural networks (DNNs) in TSR, whereby a DNN can learn features automatically without hand-crafting them. Due to the lack of datasets in the U.S. and the inefficient use of traditional methods for traffic sign recognition (TSR) in the U.S., we created the Automotive Repository of Traffic Signs (ARTS), a new dataset for traffic signs recognition in the U.S. ARTS covers a wide range of sign-types, including Regulatory, Guide, Warning, and Temporary signs as defined in the Manual on Uniform Traffic Control Devices (MUTCD). It also features geospatial data to localize signs using their GPS coordinates. Benchmarks are presented to assess the performance of state-of-the-art deep learning based detectors. |
3,987 | Vision models fine-tuned by cinema professionals for High Dynamic Range imaging in movies | Many challenges that deal with processing of HDR material remain very much open for the film industry, whose extremely demanding quality standards are not met by existing automatic methods. Therefore, when dealing with HDR content, substantial work by very skilled technicians has to be carried out at every step of the movie production chain. Based on recent findings and models from vision science, we propose in this work effective tone mapping and inverse tone mapping algorithms for production, post-production and exhibition. These methods are automatic and real-time, and they have been both fine-tuned and validated by cinema professionals, with psychophysical tests demonstrating that the proposed algorithms outperform both the academic and industrial state-of-the-art. We believe these methods bring the field closer to having fully automated solutions for important challenges for the cinema industry that are currently solved manually or sub-optimally. Another contribution of our research is to highlight the limitations of existing image quality metrics when applied to the tone mapping problem, as none of them, including two state-of-the-art deep learning metrics for image perception, are able to predict the preferences of the observers. |
3,988 | Bayesian Deep-Learning-Based Health Prognostics Toward Prognostics Uncertainty | Deep-learning-based health prognostics is receiving ever-increasing attention. Most existing methods leverage advanced neural networks for prognostics performance improvement, providing mainly point estimates as prognostics results without addressing prognostics uncertainty. However, uncertainty is critical for both health prognostics and subsequent decision making, especially for safety-critical applications. Inspired by the idea of Bayesian machine learning, a Bayesian deep-learning-based (BDL-based) method is proposed in this paper for health prognostics with uncertainty quantification. State-of-the-art deep learning models are extended into Bayesian neural networks (BNNs), and a variational-inference-based method is presented for the BNNs learning and inference. The proposed method is validated through a ball bearing dataset and a turbofan engine dataset. Other than point estimates, health prognostics using the BDL-based method is enhanced with uncertainty quantification. Scalability and generalization ability of state-of-the-art deep learning models can be well inherited. Stochastic regularization techniques, widely available in mainstream software libraries, can be leveraged to efficiently implement the BDL-based method for practical applications. |
3,989 | Carbon-negative synthetic biology: challenges and emerging trends of cyanobacterial technology | Global warming and climate instability have spurred interest in using renewable carbon resources for the sustainable production of chemicals. Cyanobacteria are ideal cellular factories for carbon-negative production of chemicals owing to their great potentials for directly utilizing light and CO2 as sole energy and carbon sources, respectively. However, several challenges in adapting cyanobacterial technology to industry, such as low productivity, poor tolerance, and product harvesting difficulty, remain. Synthetic biology may finally address these challenges. Here, we summarize recent advances in the production of value-added chemicals using cyanobacterial cell factories, particularly in carbon-negative synthetic biology and emerging trends in cyanobacterial applications. We also propose several perspectives on the future development of cyanobacterial technology for commercialization. |
3,990 | Self-Taught Feature Learning for Hyperspectral Image Classification | In this paper, we study self-taught learning for hyperspectral image (HSI) classification. Supervised deep learning methods are currently state of the art for many machine learning problems, but these methods require large quantities of labeled data to be effective. Unfortunately, existing labeled HSI benchmarks are too small to directly train a deep supervised network. Alternatively, we used self-taught learning, which is an unsupervised method to learn feature extracting frameworks from unlabeled hyperspectral imagery. These models learn how to extract generalizable features by training on sufficiently large quantities of unlabeled data that are distinct from the target data set. Once trained, these models can extract features from smaller labeled target data sets. We studied two self-taught learning frameworks for HSI classification. The first is a shallow approach that uses independent component analysis and the second is a three-layer stacked convolutional autoencoder. Our models are applied to the Indian Pines, Salinas Valley, and Pavia University data sets, which were captured by two separate sensors at different altitudes. Despite large variation in scene type, our algorithms achieve state-of-the-art results across all the three data sets. |
3,991 | Perfusion of microvascular free flaps in head and neck reconstruction after prior neck dissection and irradiation | Microvascular free flaps are frequently used for head and neck reconstruction after prior neck dissection (ND) and neck irradiation (RTX). The aim of this study was to investigate the influence of ND and RTX on flap perfusion as a critical factor for flap success. Overall, 392 patients reconstructed with a microvascular fasciocutaneous flap (FF) or perforator flap (PF) in the head and neck region between 2011 and 2020 were analysed retrospectively. Flap perfusion measured intraoperatively and postoperatively with the O2C tissue oxygen analysis system was compared between patients who had received neither ND nor RTX (controls), patients who had received ND but no RTX (ND group), and patients who had received both ND and RTX (ND+RTX group). Intraoperative and postoperative flap blood flow was decreased in FFs in ND group patients compared to controls (median 66.3 AU vs 86.0 AU, P = 0.023; median 73.5 AU vs 93.8 AU, P = 0.045, respectively). In the multivariable analysis, these differences showed a tendency to persist (P = 0.052 and P = 0.056). Flap success rates were similar in control patients, ND patients, and ND+RTX patients (98.7%, 94.0%, and 97.6%, respectively). Flap perfusion is not reduced in FFs and PFs in patients who have undergone ND or ND and RTX. This indicates that neck dissection and neck irradiation should not be contraindications for microvascular free flap reconstruction. |
3,992 | A Triple-Loop Inductive Power Transmission System for Biomedical Applications | A triple-loop wireless power transmission (WPT) system equipped with closed-loop global power control, adaptive transmitter (Tx) resonance compensation (TRC), and automatic receiver (Rx) resonance tuning (ART) is presented. This system not only opposes coupling and load variations but also compensates for changes in the environment surrounding the inductive link to enhance power transfer efficiency (PTE) in applications such as implantable medical devices (IMDs). The Tx was built around a commercial off-the-shelf (COTS) radio-frequency identification (RFID) reader, operating at 13.56 MHz. A local Tx loop finds the optimal capacitance in parallel with the Tx coil by adjusting a varactor. A global power control loop maintains the received power at a desired level in the presence of changes in coupling distance, coil misalignments, and loading. Moreover, a local Rx loop is implemented inside a power management integrated circuit (PMIC) to avoid PTE degradation due to the Rx coil surrounding environment and process variations. The PMIC was fabricated in a 0.35-pm 4M2P standard CMOS process with 2.54 mm(2) active area. Measurement results show that the proposed triple-loop system improves the overall PTE by up to 10.5% and 4.7% compared to a similar open- and single closed-loop system, respectively, at nominal coil distance of 2 cm. The added TRC and ART loops contribute 2.3% and 1.4% to the overall PTE of 13.5%, respectively. This is the first WPT system to include three loops to dynamically compensate for environment and circuit variations and improve the overall power efficiency all the way from the driver output in Tx to the load in Rx. |
3,993 | MulNet: A Flexible CNN Processor With Higher Resource Utilization Efficiency for Constrained Devices | Leveraging deep convolutional neural networks (DCNNs) for various application areas has become a recent inclination of many machine learning practitioners due to their impressive performance. Research trends show that the state-of-the-art networks are getting deeper and deeper and such networks have shown significant performance increase. Deeper and larger neural networks imply the increase in computational intensity and memory footprint. This is particularly a problem for inference-based applications on resource constrained computing platforms. On the other hand, field-programmable gate arrays (FPGAs) are becoming a promising choice in giving hardware solutions for most deep learning implementations due to their high-performance and low-power features. With the rapid formation of various state-of-the-art CNN architectures, a flexible CNN hardware processor that can handle different CNN architectures and yet customize itself to achieve higher resource efficiency and optimum performance is critically important. In this paper, a novel and highly flexible DCNN processor, MulNet, is proposed. MulNet can be used to process most regular state-of-the-art CNN variants aiming at maximizing resource utilization of a target device. A processing core with multiplier and without multiplier is employed to achieve that. We formulated optimum fixed-point quantization format for MulNet by analyzing layer-by-layer quantization error. We also created a power-of-2 quantization for multiplier-free (MF) processing core of MulNet. Both quantizations significantly reduced the memory space needed and the logic consumption in the target device. We utilized Xilinx Zynq SoCs to leverage the one die hybrid (CPU and FPGA) architecture. We devised a scheme that utilizes Zynq processing system (PS) for memory intensive layers and the Zynq programmable logic (PL) for computationally intensive layers. We implemented modified LeNet, CIFAR-10 full, ConvNet processor (CNP), MPCNN, and AlexNet to evaluate MulNet. Our architecture with MF processing cores shows the promising result, by saving 36%-72% on-chip memory and 10%-44% DSP48 IPs, compared to the architecture with cores implemented using the multiplier. Comparison with the state of the art showed a very promising 25-40x DSP48 and 25-29x on-chip memory reduction with up to 136.9-GOP/s performance and 88.49-GOP/s/W power efficiency. Hence, our results demonstrate that the proposed architecture can be very expedient for resource constrained devices. |
3,994 | Assessing expert system-assisted literature reviews with a case study | Given the large numbers of publications in software engineering, frequent literature reviews are required to keep current on work in specific areas. One tedious work in literature reviews is to find relevant studies amongst thousands of non-relevant search results. In theory, expert systems can assist in finding relevant work but those systems have primarily been tested in simulations rather than in application to actual literature reviews. Hence, few researchers have faith in such expert systems. Accordingly, using a realistic case study, this paper assesses how well our state-of-the-art expert system can help with literature reviews. The assessed literature review aimed at identifying test case prioritization techniques for automated UI testing, specifically from 8,349 papers on IEEE Xplore. This corpus was studied with an expert system that incorporates an incrementally updated human-in-the-loop active learning tool. Using that expert system, in three hours, we found 242 relevant papers from which we identified 12 techniques representing the stateof-the-art in test case prioritization when source code information is not available. These results were then validated by six other graduate students manually exploring the same corpus. Without the expert system, this task would have required 53 h and would have found 27 additional papers. That is, our expert system achieved 90% recall with 6% of the human effort cost when compared to a conventional manual method. Significantly, the same 12 state-of-the-art test case prioritization techniques were identified by both the expert system and the manual method. That is, the 27 papers missed by the expert system would not have changed the conclusion of the literature review. Hence, if this result generalizes, it endorses the use of our expert system to assist in literature reviews. |
3,995 | SwICS: Section-Wise In-Text Citation Score | Since past several years, finding relevant documents from plethora of web repositories has become prime attention of the scientific community. To find out relevant research articles, state-of-the-art techniques employ content, metadata, citations, and collaborative filtering based approaches. Among all of them, citation based approaches hold strong potential because most of the time, authors cite relevant papers. Bibliographic coupling is one of the well-known citation based approaches for recommending relevant papers. In this paper, we present an approach SwICS that harnesses number of common references between pair of documents as similarity measure whereas the distribution of in-text citations within the text are not analyzed. The proposed approach explores the in-text citation frequencies within contents of the paper and in-text citation patterns between different logical sections for bibliographically coupled papers. For evaluation, the employed data set contains 1150 research documents are obtained from a well-known autonomous citation index known as: CiteSeer. A comprehensive user study is conducted to build a gold standard for comparing the proposed approach. The approach is compared with the state-of-the-art bibliographic coupling and content similarity based techniques. The comparison results revealed that proposed approach significantly performs better than the contemporary approaches. The comparison result with gold standard yielded an average of 0.73. The average gain achieved by the proposed approach is 60% from state-of-the-art: bibliographic coupling. Whereas, the correlation between gold standard and content based approach remains 20%. The proposed approach can play a significant role for search engines and citation indexers in terms of improving the quality of their results. |
3,996 | Compressed Collective Sparse-Sketch for Distributed Data-Parallel Training of Deep Learning Models | Distributed data-parallel training (DDP) is prevalent in large-scale deep learning. To increase the training throughput and scalability, high-performance collective communication methods such as AllReduce have recently proliferated for DDP use. However, these approaches require long communication periods with increasing model sizes. Collective communication transmits many sparse gradient values that can be efficiently compressed to reduce the required training time. State-of-the-art compression approaches do not provide mergeable compression for AllReduce and lack convergence bounds. We present a sparse sketch reducer (S2Reducer), a sparsity-preserving sketch based collective communication method. S2Reducer preserves gradient sparsity and reduces communication costs via a bitmap informed count sketch structure and adapts to efficient AllReduce operators. We tune the count sketch organization to minimize the hash conflicts in a fixed-size budget. We prove that our method has the same convergence rate as vanilla data-parallel training and a much smaller communication overhead than those of state-of-the-art methods. We implement a GPU-accelerated S2Reducer for the Ring All-Reduce-based DDP system. We perform extensive evaluations against four state-of-the-art methods across seven deep learning models. Our results show that S2Reducer converges to the same accuracy as that of state-of-the-art approaches while reducing the sparse communication overhead by up to 86% and achieving a speedup of up to 3.5x in distributed training. |
3,997 | Critical Current Distributions of Recent Bi-2212 Round Wires | Bi2Sr2CaCu2O8+x (Bi-2212) is the only high-field, high-temperature superconductor (HTS) capable of reaching a critical current density J(c) (16T, 4.2 K) of 6500A center dot mm(-2) in the highly desirable round wire (RW) form. However, state-of-the-art Bi-2212 conductors still have a critical current density (J(c)) to depairing current density (J(d)) ratio around 20 to 30 times lower than that of state-of-the-art Nb-Ti or REBCO. Previously, we have shown that recent improvements in Bi-2212 RW Jc are due to improved connectivity associated with optimization of the heat treatment process, and most recently due to a transition to a finer and more uniform powder manufactured by Engi-Mat. One quantitative measure of connectivity may be the critical current (I-c) distribution, since the local I-c in a wire can vary along the length due to variable vortex-microstructure interactions and to factors such as filament shape variations, grain-to-grain connectivity variations and blocking secondary phase distributions. Modeling the experimental V-I transition measured on a low resistance shunt as a complex sum of voltage contributions of individual filament and wire sub-sections allows a numerical extraction of the I-c distribution from the d(2)V/dI(2) treatment of the V-I curves. Here we compare similar to 0.1 m length I-c distributions of Bi-2212 RWs with recent state-of-the-art very high-J(c) Engi-Mat powder and lower J(c) and older Nexans granulate powder. We do find that the I-c spread for Bi-2212 wires is about twice the relative standard of high-J(c) Nb-Ti well below H-irr. We do not yet see any obvious contribution of the Bi-2212 anisotropy to the I-c distribution and are rather encouraged that these Bi-2212 round wires show relative I-c distributions not too far from high-J(c) Nb-Ti wires. |
3,998 | Clinical characteristics of steroid-responsive but dependent chronic graft-versus-host disease: a multicenter retrospective analysis | Chronic graft-versus-host disease (cGVHD) is a long-term complication of allogeneic hematopoietic stem cell transplantation. The clinical importance of long-term corticosteroid dependency in steroid-responsive cGVHD is undetermined. We retrospectively reviewed the data of 120 consecutive patients who received systemic steroid therapy for cGVHD between January 2007 and December 2018 at three institutions. Among patients with steroid-responsive cGVHD, those who successfully tapered off corticosteroids within 1 year were defined as the early withdrawal group (EW-cGVHD) and others were defined as the dependent group (Dp-cGVHD). Twenty-six patients were classified as EW-cGVHD and 55 as Dp-cGVHD. The proportion of men was significantly higher and performance status was significantly better in EW-cGVHD. The 5-year overall survival and cGVHD recurrence-free survival rates were significantly higher in EW-cGVHD than Dp-cGVHD (96% vs. 68%, p = 0.017 and 84% vs. 41%, p = 0.002, respectively). While the relapse-free survival rate did not differ significantly (84% vs. 65%, p = 0.15), the proportion of patients requiring readmission, mainly due to cGVHD recurrence or infection, was significantly increased in Dp-cGVHD (38% vs. 84%, p < 0.001). In summary, steroid dependency in cGVHD for more than 1 year was significantly associated with poor transplant outcomes. |
3,999 | Long noncoding RNA Pvt1 promotes the proliferation and migration of Schwann cells by sponging microRNA-214 and targeting c-Jun following peripheral nerve injury | Research has shown that long-chain noncoding RNAs (lncRNAs) are involved in the regulation of a variety of biological processes, including peripheral nerve regeneration, in part by acting as competing endogenous RNAs. c-Jun plays a key role in the repair of peripheral nerve injury. However, the precise underlying mechanism of c-Jun remains unclear. In this study, we performed microarray and bioinformatics analysis of mouse crush-injured sciatic nerves and found that the lncRNA Pvt1 was overexpressed in Schwann cells after peripheral nerve injury. Mechanistic studies revealed that Pvt1 increased c-Jun expression through sponging miRNA-214. We overexpressed Pvt1 in Schwann cells cultured in vitro and found that the proliferation and migration of Schwann cells were enhanced, and overexpression of miRNA-214 counteracted the effects of Pvt1 overexpression on Schwann cell proliferation and migration. We conducted in vivo analyses and injected Schwann cells overexpressing Pvt1 into injured sciatic nerves of mice. Schwann cells overexpressing Pvt1 enhanced the regeneration of injured sciatic nerves following peripheral nerve injury and the locomotor function of mice was improved. Our findings reveal the role of lncRNAs in the repair of peripheral nerve injury and highlight lncRNA Pvt1 as a novel potential treatment target for peripheral nerve injury. |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.