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5,700 | Arctic Arts with Pride: Discourses on Arctic Arts, Culture and Sustainability | There has been growing interest in Arctic arts and culture as well as in sustainability among artists, researchers, and policy makers. However, until recently, the comprehension of Arctic arts and culture within the framework of sustainable development has remained vague. In this study, by analysing diverse debates from the Arctic Arts Summit 2019 in Rovaniemi, we investigate how the arts and culture sector promotes Arctic sustainability. An analysis of abstracts, conclusions, blogs and newspaper articles reflecting the presentations, art events, exhibitions and dialogues showed that the discourse on sustainability is organised around five themes: (1) global politics and ecological crises as part of the cultural politics of the Arctic; (2) indigenous and non-indigenous Arctic arts and culture; (3) 'handmade' and the material culture of the Arctic; (4) place-making, revitalisation and regional development; and (5) economy and sustainability. These partly interlinked themes have relevance for policy making, defining principles for arts and culture funding, artistic practice and research on the Arctic. In addition, education and artistic training are important for all of the five themes; therefore, resources for educational institutions are crucial for the sustainable future of the Arctic. Arts, culture and education have the potential to empower people in the Arctic, increase cultural pride, educate and inform global audiences and create connectedness between the past, present and future. Arts, culture and education contribute to Arctic sustainability. |
5,701 | Evaluation of the automated reference toolset as a method to select reference plots for oil and gas reclamation on Colorado Plateau rangelands | Rangelands are typically characterized by low precipitation and low biomass which makes them susceptible to disturbance and difficult to reclaim. These characteristics become a management issue when considering the widespread and significant impact of oil and gas development on rangelands. Reclamation from this land use involves the complexities of dealing with multiple state and federal agencies, private landowners, and their sometimes conflicting rules. Reference plots (e.g., nearby undisturbed sites) can help with these issues by providing an objective context for reclamation planning. They are selected to provide a comparison that is similar to a reclamation site in most aspects except for the disturbance activity. This allows for the relative condition of the reclamation site to be determined. Because selection of reference plots is normally expert-driven on a site-by-site basis, it can be time consuming and thus ineffective in helping to meet reclamation goals over large landscapes. The Automated Reference Tool (ART) was developed to improve the efficiency and efficacy of reference plot selection. The ART improves reference plot selection through remote sensing and indicators of land potential by selecting reference plots of similar land potential to the reclamation site based on soil texture, topography, and geology. We evaluated the ART in the context of well-pad reclamation to determine if ART-selected plots were appropriate to use as reference when compared to an existing reference plot network. We applied the ART to reclamation sites managed by the Bureau of Land Management's (BLM) White River Field Office, Colorado which had existing expert-selected reference plots. We found that the ART-selected reference plots and their matching expert-selected reference plot had similar large-scale vegetative cover characteristics (total foliar: R-2 = 0.34, p-value = 0.0012) and dissimilar finer-scale cover characteristics (plant diversity: R-2 = 0.079, p-value = 0.15). In addition, we detected similarities in their soil water content (R-2 = 0.43, p-value<0.001), depth to restricting layer (RMSD = 21.90), and rock fragment (RMSD = 19.99). These results demonstrate that ART could be a useful tool for managers to help meet their reclamation goals over large landscapes, but it is not a complete automation of the reference selection process. |
5,702 | Super-Resolution Blind Channel-and-Signal Estimation for Massive MIMO With One-Dimensional Antenna Array | In this paper, we study blind channel-and-signal estimation by exploiting the burst-sparse structure of angular-domain propagation channels in massive MIMO systems. The state-of-the-art approach utilizes the structured channel sparsity by sampling the angular-domain channel representation with a uniform angle-sampling grid, a.k.a. virtual channel representation. However, this approach is only applicable to uniform linear arrays and may cause a substantial performance loss due to the mismatch between the virtual representation and the true angle information. To tackle these challenges, we propose a sparse channel representation with a super-resolution sampling grid and a hidden Markovian support. Based on this, we develop a novel approximate inference based blind estimation algorithm to estimate the channel and the user signals simultaneously, with emphasis on the adoption of the expectation-maximization method to learn the angle information. Furthermore, we demonstrate the low-complexity implementation of our algorithm, making use of factor graph and message passing principles to compute the marginal posteriors. Numerical results show that our proposed method significantly reduces the estimation error compared to the state-of-the-art approach under various settings, which verifies the efficiency and robustness of our method. |
5,703 | Feature-Preserving Noise Removal | Conventional image restoration algorithms use transform-domain filters, which separate the noise from the sparse signal among the transform components or apply spatial smoothing filters in real space whose design relies on prior assumptions about the noise statistics. These filters also reduce the information content of the image by suppressing spatial frequencies or by recognizing only a limited set of shapes. Here we show that denoising can be efficiently done using a nonlinear filter, which operates along patch neighborhoods and multiple copies of the original image. The use of patches enables the algorithm to account for spatial correlations in the random field whereas the multiple copies are used to recognize the noise statistics. The nonlinear filter, which is implemented by a hierarchical multistage system of multilayer perceptrons, outperforms state-of-the-art denoising algorithms such as those based on collaborative filtering and total variation. Compared to conventional denoising algorithms, our filter can restore images without blurring them, making it attractive for use in medical imaging where the preservation of anatomical details is critical. |
5,704 | Local performing arts and recovery from the Great East Japan earthquake and tsunami: A descriptive qualitative study | The purpose of this study is to understand the role of local performing arts in disaster recovery after the 2011 Great East Japan earthquake and tsunami. A qualitative descriptive methodology was used to obtain insights into local people's experiences and perspectives. Three main methods were used to collect data: semi-structured group interviews with five traditional preservation societies including 13 participants, as well as interviews with two key informants, a questionnaire survey with 53 respondents, and field observation and visual data. The study setting was an area called Karakuwa, which is located in Miyagi Prefecture. Four themes with 11 associated sub-themes were generated: 1. Locality, 2. Connections with other areas, 3. Connections within the community, and 4. Psychosocial support. Findings indicated that local performing arts are embedded in people's lives and are based on knowledge and experiences that have been transmitted through generations. Although the people experienced the tsunami, they gradually began to perform again within the community as a means of regaining their identity and encouraging themselves and others. In addition, they began to perform to non-locals to show their appreciation for the assistance they had received and in attempts to increase the number of people involved in the activities. A key implication for policy is that, in disaster-recovery efforts, it is necessary to invest in local performing arts in order to help people restore their ways of life and promote ties among locals and connections with non-locals. |
5,705 | Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation | In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods. |
5,706 | Hybrid absorbing boundary conditions based on fast nonuniform grid integration for nonconvex scatterers | Novel hybrid (local-global) absorbing boundary conditions (ABCs) art proposed for the truncation of computational domains infinite-method analysis of open-region problems. The pertinent boundary integration is accelerated by the nonuniform grid-interpolation scheme, resulting in a fast algorithm,for treating electrically large objects with largely concave outer boundaries. (C) 2004 Wiley Periodicals, Inc. |
5,707 | LDC: Lightweight Dense CNN for Edge Detection | This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC. |
5,708 | State-of-the art CAPEX data for water electrolysers, and their impact on renewable hydrogen price settings | Within the framework of the Hydrogen Implementing Agreement (HIA) of the International Energy Agency (IEA), a new Task 38 was started early 2016, entitled "Power-to-Hydrogen and Hydrogen-to-X: System Analysis of techno-economic, legal and regulatory conditions". Within this framework, a specific task force was set-up for the compilation of state-of-the-art technical and economical data on large-scale water electrolyser systems, both based on PEM and alkaline technology. The objectives set forward have been twofold. Firstly, to offer policy makers and industry with comprehensive trends and guidelines for further electrolyser cost reduction (CAPEX, in Euro/kW) into the MW-scale. Secondly, to provide objective technological & economic arguments for converging towards a realistic electrolytic (and hence renewable) H-2 market price (in Euro/kg). This should help water electrolysis to become competitive with SMR technology for (local) H-2 production, and hence to start making H-2 a competitive fuel. (C) 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. |
5,709 | Steganography in JPEG2000 compressed images | Information hiding in JPEG2000 compressed images is investigated in this research. The challenges of covert communication in this state-of-the-art image codec are analyzed and a steganographic scheme is then proposed to reliably embed high-volume data into the JPEG2000 bit-stream. A special mode of JPEG2000 is employed, and its usage and functions are explained and justified. Experimental results are given to demonstrate the performance of the proposed algorithm. |
5,710 | A low latency implementation of a non-uniform partitioned convolution algorithm for room acoustic simulation | Finite impulse response convolution is one of the most widely used operations in digital signal processing field for filtering operations. Low computationally demanding techniques are essential for calculating convolutions with low input/output latency in real scenarios, considering that the real-time requirements are strictly related to the impulse response length. In this context, a complete overview of the state of the art relative to the algorithms for fast computation of convolution is described here. Then, a novel perceptual approach employed to reduce the computational cost of fast convolution algorithms is here presented. It is based on the pre-processing of a selected impulse response and it allows to further reduce the number of complex multiplications considering the energy decay relief and the absolute threshold of hearing, as psychoacoustic constraints. Several results are reported in terms of computational cost and perceived audio quality in order to prove the effectiveness of the proposed approach also introducing comparisons with the existing techniques of the state of the art. |
5,711 | Cryo-EM structures of light-harvesting 2 complexes from Rhodopseudomonas palustris reveal the molecular origin of absorption tuning | The genomes of some purple photosynthetic bacteria contain a multigene puc family encoding a series of α- and β-polypeptides that together form a heterogeneous antenna of light-harvesting 2 (LH2) complexes. To unravel this complexity, we generated four sets of puc deletion mutants in Rhodopseudomonas palustris, each encoding a single type of pucBA gene pair and enabling the purification of complexes designated as PucA-LH2, PucB-LH2, PucD-LH2, and PucE-LH2. The structures of all four purified LH2 complexes were determined by cryogenic electron microscopy (cryo-EM) at resolutions ranging from 2.7 to 3.6 Å. Uniquely, each of these complexes contains a hitherto unknown polypeptide, γ, that forms an extended undulating ribbon that lies in the plane of the membrane and that encloses six of the nine LH2 αβ-subunits. The γ-subunit, which is located near to the cytoplasmic side of the complex, breaks the C9 symmetry of the LH2 complex and binds six extra bacteriochlorophylls (BChls) that enhance the 800-nm absorption of each complex. The structures show that all four complexes have two complete rings of BChls, conferring absorption bands centered at 800 and 850 nm on the PucA-LH2, PucB-LH2, and PucE-LH2 complexes, but, unusually, the PucD-LH2 antenna has only a single strong near-infared (NIR) absorption peak at 803 nm. Comparison of the cryo-EM structures of these LH2 complexes reveals altered patterns of hydrogen bonds between LH2 αβ-side chains and the bacteriochlorin rings, further emphasizing the major role that H bonds play in spectral tuning of bacterial antenna complexes. |
5,712 | Tracking Transformer Tap Position in Real-Time Distribution Network Power Flow Applications | The current injection power flow method is commonly used by the power distribution industry in real-time distribution management systems. The inclusion of reactive power control adjustments that simulate local voltage controllers is known to create multiple power flow solutions. The stateof-the-art for simulating local voltage controllers computes the most likely operational solution by considering the different precedences of the controlled devices, based on their speeds of response. This paper presents a novel sensitivity based approach for simulating local controllers, which directly accounts for both controller interactions and the way in which the system state is reached. Numerical results are reported on networks with up to 3146 nodes and voltage dependent load models; the results show that the proposed approach yields probable power How solutions that are virtually identical to those given by an implementation of the state-of-the-art time-coordinated method and with a speed-up factor around three on the large network cases. |
5,713 | Toward Performing Image Classification and Object Detection With Convolutional Neural Networks in Autonomous Driving Systems: A Survey | Nowadays Convolutional Neural Networks (CNNs) are being employed in a wide range of industrial technologies for a variety of sectors, such as medical, automotive, aviation, agriculture, space, etc. This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way. Layer-based details of CNNs along with parameter and floating-point operation number calculations are outlined. Using an evolutionary approach, the majority of the outstanding image classification CNNs, published in the open literature, is introduced with a focus on their accuracy performance, parameter number, model size, and inference speed, highlighting the progressive developments in convolutional operations. Results of a novel investigation of the convolution types and operations commonly used in CNNs are presented, including a timing analysis aimed at assessing their impact on CNN performance. This extensive experimental study provides new insight into the behaviour of each convolution type in terms of training time, inference time, and layer level decomposition. Building blocks for CNN-based object detection are also discussed, such as backbone networks and baseline types, and then representative state-of-the-art designs are outlined. Experimental results from the literature are summarised for each of the reviewed models. This is followed by an overview of recent ADSs related works and current industry activities, aiming to bridge academic research and industry practice on CNNs and ADSs. Design approaches targeted at solving problems of automakers in achieving real-time implementations are also proposed based on a discussion of design constraints, human vs. machine evaluations and trade-off analysis of accuracy vs. size. Current technologies, promising directions, and expectations from the literature on ADSs are introduced including a comprehensive trade-off analysis from a human-machine perspective. |
5,714 | Analysis of Cell Cycle and DNA Compaction Dependent Subnuclear Distribution of Histone Marks | In eukaryotes, the organization of DNA wrapped around histones regulates DNA-dependent processes. Changes in epigenetic modifications modulate the compaction of DNA into chromatin and, thus, regulate DNA metabolism in time and space. Hence, to catalog the spatiotemporal epigenetic information and its relation to the dynamic nuclear landscape is of paramount importance. Here, we present a method, based on FiJi and the statistical image analysis tool nucim(R), to classify in 3D the nuclear DNA compaction in single interphase cells. We, furthermore, mapped the distribution of (epi)genetic marks and nuclear proteins/processes to the compaction classes along with their dynamics over the cell cycle. These techniques allow to catalog and quantify the dynamic changes in the epigenome in space and time and in single cells. |
5,715 | Risk Factors for Mortality among Adult HIV/AIDS Patients Following Antiretroviral Therapy in Southwestern Ethiopia: An Assessment through Survival Models | Introduction: Efforts have been made to reduce HIV/AIDS-related mortality by delivering antiretroviral therapy (ART) treatment. However, HIV patients in resource-poor settings are still dying, even if they are on ART treatment. This study aimed to explore the factors associated with HIV/AIDS-related mortality in Southwestern Ethiopia. Method: A non-concurrent retrospective cohort study which collected data from the clinical records of adult HIV/AIDS patients, who initiated ART treatment and were followed between January 2006 and December 2010, was conducted, to explore the factors associated with HIV/AIDS-related mortality at Jimma University Specialized Hospital (JUSH). Survival times (i. e., the time from the onset of ART treatment to the death or censoring) and different characteristics of patients were retrospectively examined. A best-fit model was chosen for the survival data, after the comparison between native semi-parametric Cox regression and parametric survival models (i. e., exponential, Weibull, and log-logistic). Result: A total of 456 HIV patients were included in the study, mostly females (312, 68.4%), with a median age of 30 years (inter-quartile range (IQR): 23-37 years). Estimated follow-up until December 2010 accounted for 1245 person-years at risk (PYAR) and resulted in 66 (14.5%) deaths and 390 censored individuals, representing a median survival time of 34.0 months (IQR: 22.8-42.0 months). The overall mortality rate was 5.3/100 PYAR: 6.5/100 PYAR for males and 4.8/100 PYAR for females. The Weibull survival model was the best model for fitting the data (lowest AIC). The main factors associated with mortality were: baseline age (> 35 years old, AHR = 3.8, 95% CI: 1.6-9.1), baseline weight (AHR = 0.93, 95% CI: 0.90-0.97), baseline WHO stage IV (AHR = 6.2, 95% CI: 2.2-14.2), and low adherence to ART treatment AHR = 4.2, 95% CI: 2.5-7.1). Conclusion: An effective reduction in HIV/AIDS mortality could be achieved through timely ART treatment onset and maintaining high levels of treatment adherence. |
5,716 | LWIR Strained-Layer Superlattice Materials and Devices at Teledyne Imaging Sensors | Strained-layer superlattices (SLS) based on type II InAs/Ga(In)Sb materials are a rapidly maturing technology and are theoretically predicted to exceed the dark-current performance of state-of-the-art HgCdTe. A substantial effort is underway at Teledyne Imaging Sensors in the development of SLS materials for infrared focal-plane arrays. In this paper, we describe state-of-the-art materials, device research and characterization, along with testing results for long-wavelength infrared SLS devices based on double-heterostructure and p(+)-B-n architectures, having n-on-p and p-on-n polarities, respectively. Detector materials exhibited excellent morphological and crystalline characteristics, and electro-optical characterization demonstrated performance comparable to the state of the art. |
5,717 | Benthic studies adjacent to Sakhalin Island, Russia, 2015 II: energy content of the zoobenthos in western gray whale feeding grounds | The waters adjacent to the northeastern coast of Sakhalin Island, Russia, are an important feeding ground for the endangered western gray whale. Data on the energy available to foraging whales from their prey resources is required for researchers interested in modeling the bioenergetics of whale foraging, but little energy content information is available for the benthic prey communities of gray whales in this region. In this study, we describe the energy density (ED), biomass, and total energy availability (ED × biomass) of benthic prey sampled from two gray whale foraging areas adjacent to Sakhalin Island: the nearshore and offshore feeding areas. ED varied almost seven-fold among benthic taxa, ranging from 1.11 to 7.62 kJ/g wet mass. Although there was considerable variation within most prey groups, amphipods had the highest mean ED of all of groups examined (5.58 ± 1.44 kJ/g wet mass). Small sample sizes precluded us from detecting any seasonal or spatial differences in mean ED within or among taxa; however, mean biomass in the offshore feeding area was, in some cases, an order of magnitude higher than mean estimates in the nearshore feeding area, resulting in higher mean total energy available to foraging gray whales offshore (958-3313 kJ/m2) compared to nearshore (223-495 kJ/m2). While the proportion of total energy accounted for by amphipods was variable, this prey group generally made up a higher proportion of the total energy available in the benthos of the offshore feeding area than in the benthos of the nearshore feeding area. Data presented here will be used to inform bioenergetics modeling of the vital rates of mature females in an effort to improve understanding of population growth limits for western gray whales. |
5,718 | A Ring Oscillator-Based PUF With Enhanced Challenge-Response Pairs | Physical unclonable functions (PUFs) are powerful security primitives that provide cheap and secure solutions for security-related applications. Strong PUFs provide a large set of challenge-response pairs (CRPs) and are suitable for device authentication. Weak PUFs produce a small number of CRPs and can be used for key extraction. In this paper, we propose a novel method to enhance the CRP set of traditional ring oscillatorbased PUFs (RO-PUFs). RO-PUFs are one of the most reliable types of PUFs and the best fit to implement on the field-programmable gate arrays. To the best of our knowledge, our method provides the maximum number of CRPs compared with the state of the art. In addition, the number of response bits that can be extracted by our method for each challenge is n - 1 times more than the state of the art, where n is the number of ROs. The large number of response bits results in the authentication of more devices and generation of more keys. Evaluation of the PUF responses produced by applying our method shows a significant improvement in unpredictability and randomness compared with the related works. Moreover, we show that the responses are unique and reliable. |
5,719 | Computational modeling to assist in the discovery of supramolecular materials | Computational modeling is increasingly used to assist in the discovery of supramolecular materials. Supramolecular materials are typically primarily built from organic components that are self-assembled through noncovalent bonding and have potential applications, including in selective binding, sorption, molecular separations, catalysis, optoelectronics, sensing, and as molecular machines. In this review, the key areas where computational prediction can assist in the discovery of supramolecular materials, including in structure prediction, property prediction, and the prediction of how to synthesize a hypothetical material are discussed, before exploring the potential impact of artificial intelligence techniques on the field. Throughout, the importance of close integration with experimental materials discovery programs will be highlighted. A series of case studies from the author's work across some different supramolecular material classes will be discussed, before finishing with a discussion of the outlook for the field. |
5,720 | An Oil Well Dataset Derived from Satellite-Based Remote Sensing | Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University-Oil Well Object Detection Version 1.0 (NEPU-OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing. |
5,721 | Reliability Issues in State-of-the-Art Microfluidic Biochips: A Survey | Microfluidic biochips are a prominent class of lab-on-a-chip, i.e. LoC system, guided with some activation sequences devised for a bioprotocol to manipulate fluids used in biomedical and biochemical laboratory syntheses. This paper is a survey of reliability issues for state-of-the-art microfluidic biochips. First, this paper presents the basic structure and working principles for different types of available microfluidic biochips. After that, it elaborates on a systematic analysis of security and reliability issues in performing different fluidic operations using microfluidic biochips. This paper also presents the current attack models and types with their scenarios in different remedy actions, both proactive and reactive. Finally, this paper concludes with future research directions so that the effects due to attacks on biochips will be minimized within the stipulated time for a reliable and sustainable microfluidic biochip world. |
5,722 | Extraembryonic tissue in chelicerates: a review and outlook | The formation of extraembryonic membranes (EEMs) contributes to the proper development of many animals. In arthropods, the formation and function of EEMs have been studied best in insects. Regarding the development of extraembryonic tissue in chelicerates (spiders and relatives), most information is available for spiders (Araneae). Especially two populations of cells have been considered to represent EEMs in spiders. The first of these potential EEMs develops shortly after egg deposition, opposite to a radially symmetrical germ disc that forms in one hemisphere of the egg and encloses the yolk. The second tissue, which has been described as being extraembryonic is the so-called dorsal field, which is required to cover the dorsal part of the developing spider germ rudiment before proper dorsal closure. In this review, we summarize the current knowledge regarding the formation of potential extraembryonic structures in the Chelicerata. We describe the early embryogenesis of spiders and other chelicerates, with a special focus on the formation of the potential extraembryonic tissues. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'. |
5,723 | Adversarial Deep Tracking | A number of visual tracking methods achieve the state-of-the-art performance based on deep learning recently. However, most of these trackers utilize the deep neural network in regression task or classification task separately. In this paper, we propose an adversarial deep tracking framework. The framework is composed of a fully convolutional Siamese neural network (regression network) and a discriminative classification network. Then, we jointly optimize the regression network and the classification network by adversarial learning. In the uniform framework, the regression network and classification network can be trained end-to-end as a whole using large amounts of video training data sets. During the testing phase, the regression network generates a response map which reflects the location and the size of the target within each candidate search patch, and the classification network discriminates which response map is the best in terms of the corresponding template patch and candidate search patch. In addition, we propose an attention visualization algorithm for our tracker, and it reflects the area that attracts the attention of our tracker during tracking. The experimental results on three large-scale visual tracking benchmarks (OTB-100, TC-128, and VOT2016) demonstrate the effectiveness of the proposed tracking algorithm and show that our tracker performs comparably against the state-of-the-art trackers. |
5,724 | Model for Detection of Masquerade Attacks Based on Variable-Length Sequences | A masquerader is an attacker who gains illegitimate access to a user's account. Masquerade detection is one of the key problems of intrusion detection systems. Deep learning models that obtained state-of-the-art results in masquerade detection have failed to exhibit very high detection performance when data samples contain limited information. Alternatively, computationally cheaper and more memory-efficient traditional machine learning models suffer from less robust features, which hinders them in achieving high detection performance. The contributions of this article are as follows: we introduce new features of variable-length UNIX command sequences (i.e., weighted occurrence frequencies of different orders) and integrate these features into an extended Markov-chain-based variable-length model. The detection performance of our model is evaluated on three publicly available and free datasets: Schonlau (SEA), Purdue (PU), and Greenberg. The results demonstrate that our model significantly improves the true positive rate (TPR), false positive rate, receiver operator characteristic, and threshold variance compared to the baselines (other Markov-chain-based variable-length models). Furthermore, in terms of the TPR, the proposed method is superior to a state-of-the-art deep learning model that uses a convolutional neural network on the PU and Greenberg datasets and a state-of-the-art sequence-alignment-hidden Markov model on the SEA dataset. Moreover, the proposed method is much more lightweight than the state-of-the-art models in terms of computational and memory complexity, and thus more suitable for real-time masquerade detection. |
5,725 | PoolNet plus : Exploring the Potential of Pooling for Salient Object Detection | We explore the potential of pooling techniques on the task of salient object detection by expanding its role in convolutional neural networks. In general, two pooling-based modules are proposed. A global guidance module (GGM) is first built based on the bottom-up pathway of the U-shape architecture, which aims to guide the location information of the potential salient objects into layers at different feature levels. A feature aggregation module (FAM) is further designed to seamlessly fuse the coarse-level semantic information with the fine-level features in the top-down pathway. We can progressively refine the high-level semantic features with these two modules and obtain detail enriched saliency maps. Experimental results show that our proposed approach can locate the salient objects more accurately with sharpened details and substantially improve the performance compared with the existing state-of-the-art methods. Besides, our approach is fast and can run at a speed of 53 FPS when processing a $300 \times 400$300x400 image. To make our approach better applied to mobile applications, we take MobileNetV2 as our backbone and re-tailor the structure of our pooling-based modules. Our mobile version model achieves a running speed of 66 FPS yet still performs better than most existing state-of-the-art methods. To verify the generalization ability of the proposed method, we apply it to the edge detection, RGB-D salient object detection, and camouflaged object detection tasks, and our method achieves better results than the corresponding state-of-the-art methods of these three tasks. Code can be found at http://mmcheng.net/poolnet/. |
5,726 | Accelerated reweighted nuclear norm minimization algorithm for low rank matrix recovery | In this paper we propose an accelerated reweighted nuclear norm minimization algorithm to recover a low rank matrix. Our approach differs from other iterative reweighted algorithms, as we design an accelerated procedure which makes the objective function descend further at every iteration. The proposed algorithm is the accelerated version of a state-of-the-art algorithm. We provide a new analysis of the original algorithm to derive our own accelerated version, and prove that our algorithm is guaranteed to converge to a stationary point of the reweighted nuclear norm minimization problem. Numerical results show that our algorithm requires distinctly fewer iterations and less computational time than the original one to achieve the same (or very close) accuracy, in some problem instances even require only about 50% computational time of the original one, and is also notably faster than several other state-of-the-art algorithms. (C) 2015 Elsevier B.V. All rights reserved. |
5,727 | Hybrid Feature Model for Emotion Recognition in Arabic Text | In recent years, research into developing state-of-the-art models for Arabic natural language processing tasks has gained momentum. These models must address the added difficulties related to the nature and structure of the Arabic language. In this paper, we propose three models, a human-engineered feature-based (HEF) model, a deep feature-based (DF) model, and a hybrid of both models (HEF& x002B;DF) for emotion recognition in Arabic text. We evaluated the performance of the proposed models on the SemEval-2018, IAEDS, and AETD datasets by comparing the performances of those models on each emotion label. We also compared the model performances with those of other state-of-the-art models. The results show that the HEF& x002B;DF model outperformed the DF and HEF models on all datasets. The DF model performed better than the HEF model on the SemEval-2018 and AETD datasets, while the HEF model performed better than the DF model on the IAEDS dataset. The HEF& x002B;DF model outperformed the state-of-the-art models in terms of accuracy, weighted-average precision, weighted-average recall, and weighted-average F-score on the AETD dataset and in terms of accuracy, macro-averaged precision, macro-averaged recall, and macro-averaged F-score on the IAEDS dataset. It also achieved the best macro-averaged F-score and the second-best Jaccard accuracy and micro-averaged F-score on the SemEval-2018 dataset. |
5,728 | Gestational malnutrition, hyperemesis gravidarum, and Wernicke's encephalopathy: What is missing? | Hyperemesis gravidarum (HG), or the severe nausea and vomiting of pregnancy, is one of the most dreaded complications of gestation, affecting between 1.5% and 3.0% of pregnant women. From the late 1800s to the mid-1980s, the etiology was frequently cited to have psychological and/or-later-perhaps hormonal origins, which have numbered at least 10. Current research has unearthed a genetic basis for HG that implicates growth differentiation factor 15, insulin-like growth factor binding protein 7, and hormone receptors (namely, glial cell line-derived neurogenic factor family receptor alpha-like and the progesterone receptor). Whatever the origins of this disease, it has caused immeasurable physiological and psychological damage to women, their fetuses, and their families. The psychological trauma includes a high rate of suicidal ideation as well as posttraumatic stress disorder. Whereas the healthcare costs are substantial for the mother with HG, the lifetime costs to the neonate include that which accompanies reduced employment earnings related to cognitive compromise. Another devastating outcome of severe HG can be Wernicke's encephalopathy (WE), which has a high fetal and maternal mortality rate. Our study explored 18 current reports of HG and WE. We highlighted additional presenting features we believe also accompany, and sometimes replace, the classically taught triad components of WE: ataxia, confabulation, and nystagmus. We agree with the conclusion made by Sheehan and Ironside in 1939 that thiamin alone may not reverse WE, and we offer possible explanations. Lastly, we offer suggestions for remediation. |
5,729 | Modelling biological puncture: a mathematical framework for determining the energetics and scaling | Biological puncture systems use a diversity of morphological tools (stingers, teeth, spines etc.) to penetrate target tissues for a variety of functions (prey capture, defence, reproduction). These systems are united by a set of underlying physical rules which dictate their mechanics. While previous studies have illustrated form-function relationships in individual systems, these underlying rules have not been formalized. We present a mathematical model for biological puncture events based on energy balance that allows for the derivation of analytical scaling relations between energy expenditure and shape, size and material response. The model identifies three necessary energy contributions during puncture: fracture creation, elastic deformation of the material and overcoming friction during penetration. The theoretical predictions are verified using finite-element analyses and experimental tests. Comparison between different scaling relationships leads to a ratio of released fracture energy and deformation energy contributions acting as a measure of puncture efficiency for a system that incorporates both tool shape and material response. The model represents a framework for exploring the diversity of biological puncture systems in a rigorous fashion and allows future work to examine how fundamental physical laws influence the evolution of these systems. |
5,730 | A Long Short-Term Memory Network for Sparse Spatiotemporal EEG Source Imaging | EEG inverse problem is underdetermined, which poses a long standing challenge in Neuroimaging. The combination of source-imaging and analysis of cortical directional networks enables us to noninvasively explore the underlying neural processes. However, existing EEG source imaging approaches mainly focus on performing the direct inverse operation for source estimation, which will be inevitably influenced by noise and the strategy used to find the inverse solution. Here, we develop a new source imaging technique, Deep Brain Neural Network (DeepBraiNNet), for robust sparse spatiotemporal EEG source estimation. In DeepBraiNNet, considering that Recurrent Neural Network (RNN) are usually "deep" in temporal dimension and thus suitable for time sequence modelling, the RNN with Long Short-Term Memory (LSTM) is utilized to approximate the inverse operation for the lead field matrix instead of performing the direct inverse operation, which avoids the possible effect of the direct inverse operation on the underdetermined lead field matrix prone to be influenced by noise. Simulations on various source patterns and noise conditions confirmed that the proposed approach could actually recover the spatiotemporal sources well, outperforming existing state of-the-art methods. DeepBraiNNet also estimated sparse MI related activation patterns when it was applied to a real Motor Imagery dataset, consistent with other findings based on EEG and fMRI. Based on the spatiotemporal sources estimated from DeepBraiNNet, we constructed MI related cortical neural networks, which clearly exhibited strong contralateral network patterns for the two MI tasks. Consequently, DeepBraiNNet may provide an alternative way different from the conventional approaches for spatiotemporal EEG source imaging. |
5,731 | Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks With Adversarial Traces | Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity, even when the traffic is protected by a VPN or an anonymity system like Tor. Leveraging a deep-learning classifier, a WF attacker can gain over 98% accuracy on Tor traffic. In this paper, we explore a novel defense, Mockingbird, based on the idea of adversarial examples that have been shown to undermine machine-learning classifiers in other domains. Since the attacker gets to design and train his attack classifier based on the defense, we first demonstrate that at a straightforward technique for generating adversarial-example based traces fails to protect against an attacker using adversarial training for robust classification. We then propose Mockingbird, a technique for generating traces that resists adversarial training by moving randomly in the space of viable traces and not following more predictable gradients. The technique drops the accuracy of the state-of-the-art attack hardened with adversarial training from 98% to 42-58% while incurring only 58% bandwidth overhead. The attack accuracy is generally lower than state-of-the-art defenses, and much lower when considering Top-2 accuracy, while incurring lower bandwidth overheads. |
5,732 | The outcomes of psychiatric inpatients by proportion of experienced psychiatrists and nurse staffing in hospital: New findings on improving the quality of mental health care in South Korea | Readmission rates for mental health care are higher in South Korea than other Organization for Economic Development (OECD) countries. Therefore, it is worthwhile to continue investigating how to reduce readmissions. Taking a novel approach, we determined the relationship between psychiatrist experience and mental health care readmission rates. We used National Health Insurance claim data (N=21,315) from 81 hospitals to analyze readmissions within 30 days of discharge for "mood disorders" or "schizophrenia, schizotypal and delusional disorders" during 2010-2013. In this study, multilevel models that included both patient and hospital-level variables were analyzed to examine associations with readmission. Readmissions within 30 days of discharge accounted for 1079 (5.1%) claims. Multilevel analysis demonstrated that the proportion of experienced psychiatrists at a hospital was inversely associated with risk of readmission (OR: 0.79, 95% CI: 0.74-0.84 per 10% increase in experienced psychiatrists). Readmission rates for psychiatric disorders within 30 days of discharge were lower in hospitals with a higher number of nurses (OR: 0.95, 95% CI: 0.94-0.96 per 10 nurses). In conclusion, health policymakers and hospital managers should make an effort to reduce readmissions for psychiatric disorders and other diseases by considering the role that physician experience plays and nurse staffing. |
5,733 | Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT | The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vision problems, although substantial challenges remain in the detection of malignant nodules due to the lack of large training data sets. In this paper, we propose a multi-view knowledge-based collaborative (MV-KBC) deep model to separate malignant from benign nodules using limited chest CT data. Our model learns 3-D lung nodule characteristics by decomposing a 3-D nodule into nine fixed views. For each view, we construct a knowledge-based collaborative (KBC) submodel, where three types of image patches are designed to fine-tune three pre-trainedResNet-50 networks that characterize the nodules' overall appearance, voxel, and shape heterogeneity, respectively. We jointly use the nine KBC submodels to classify lung nodules with an adaptive weighting scheme learned during the error back propagation, which enables the MV-KBC model to be trained in an end-to-end manner. The penalty loss function is used for better reduction of the false negative rate with a minimal effect on the overall performance of the MV-KBC model. We tested our method on the benchmark LIDC-IDRI data set and compared it to the five state-of-the-artclassificationapproaches. Our results show that the MV-KBC model achieved an accuracy of 91.60% for lung nodule classification with an AUC of 95.70%. These results are markedly superior to the state-of-the-art approaches. |
5,734 | Embedded Night-Vision System for Pedestrian Detection | Assistive vision-based solutions for the driver extend the capabilities of human vision and support safe travel. Unfortunately, their widespread usage is generally limited to expensive cars. Interestingly, a high price is most likely a derivative of the costs incurred in the research instead of the value of hardware components. In the article we show that a mobile system for pedestrian detection in severe lighting conditions can be build using state of the art algorithms and widely available hardware. The proposed night-vision system for pedestrian detection processes thermal images using a proprietary ODROID XU4 microcomputer under Ubuntu MATE operating system. We applied a cascade object detector for the task of human silhouette detection in context of thermal imagery and contrasted the results with the state of the art deep learning approach. The experiments conducted prove effectiveness of the proposed solution. |
5,735 | Anomaly Detection With Particle Filtering for Online Video Surveillance | With growing security threats, many online and offine frameworks have been proposed for anomaly detection in video sequences. However, existing online anomaly detection techniques are either computationally very expensive or lack desirable accuracy. This research work proposes a novel particle filtering based framework for online anomaly detection which detects video frames with anomalous activities based upon the posterior probability of activities in a video sequence. The proposed method also detects anomalous regions in anomalous video frames. We propose novel prediction and measurement models to accurately detect anomalous video frames and anomalous regions in video frames. Novel prediction model for particle prediction and likelihood model for assigning weights to these particles are proposed. These models efficiently utilise variable sized cell structure which creates variable sized sub-regions of scenes in video frames. Furthermore, they efficiently extract and utilise information from the video frame in the form of size, motion and location features. The proposed framework is tested on UCSD and LIVE datasets and compared with the existing state-of-the-art algorithms in the literature. The proposed anomaly detection algorithm outperforms the state-of-the art algorithms in terms of reduced Equal Error Rate (EER) with comparatively lesser processing time. |
5,736 | State-of-the-art in force and tactile sensing for minimally invasive surgery | Haptic perception plays a very important role in surgery. It enables the surgeon to feel organic tissue hardness, measure tissue properties, evaluate anatomical structures, and allows him/her to commit appropriate force control actions for safe tissue manipulation. However, in minimally invasive surgery, the surgeon's ability of perceiving valuable haptic information through surgical instruments is severely impaired. Performing the surgery without such sensory information could lead to increase of tissue trauma and vital organic tissue damage. In order to restore the surgeon's perceptual capability, methods of force and tactile sensing have been applied with attempts to develop instruments that can be used to detect tissue contact forces and generate haptic feedback to the surgeon. This paper reviews the state-of-the-art in force and tactile sensing technologies applied in minimally invasive surgery. Several sensing strategies including displacement-based, current-based, pressure-based, resistive-based, capacitive-based, piezoelectric-based, vibration-based, and optical-based sensing are discussed. |
5,737 | An efficient ir approach based semantic segmentation | Content Based Image Retrieval (CBIR) is the task of finding similar images from a query one. The state of the art mentions two main methods to solve the retrieval problem: (1) Methods dependent on visual description, for example, bag of visual words model (BoVW), Vector of Locally Aggregated Descriptors (VLAD) (2) Methods dependent on deep learning approaches in particular convolutional neural networks (CNN). In this article, we attempt to improve the CBIR algorithms with the proposition of two image signatures based on deep learning. In the first, we build a fast binary signature by utilizing a CNN based semantic segmentation. In the second, we combine the visual information with the semantic information to get a discriminative image signature denoted semantic bag of visual phrase. We study the performance of the proposed approach on six different public datasets: Wang, Corel 10k, GHIM-10K, MSRC-V1,MSRC-V2, Linnaeus. We significantly improve the mean of average precision scores (MAP) between 10% and 25% on almost all the datasets compared to state-of-the-art methods. Several experiments achieved on public datasets show that our proposal leads to increase the CBIR accuracy. |
5,738 | Fuzzy Logic and Additive Wavelet-Based Panchromatic Sharpening | A fuzzy logic and additive wavelet-based image fusion scheme is proposed. The scheme injects high-frequency information from a high-resolution panchromatic image into a low-resolution multispectral image taking both the intensity levels of each band and spatial information of panchromatic image into consideration. The proposed scheme preserves both spatial and spectral information. Quantitative analysis performed on the Ikonos and Quickbird data sets demonstrates that the proposed scheme outperforms state-of-the-art multiresolution image fusion schemes. |
5,739 | Art painting detection and identification based on deep learning and image local features | Many art paintings are placed in film scenes or TV programs as decoration. To prevent using unauthorized copyrighted art paintings, we propose a method that combines a deep learning based object detector and hand-crafted image local features to identify copyrighted art paintings from images that contain them. The object detector is trained with our collected data to be able to detect art paintings. If a query image is input, the object detector will detect the art painting regions, then, the copyrighted art paintings can be identified by matching image local features between the art painting regions and the original copyrighted art paintings that have already been stored in advance. To test the ability of the proposed method from different aspects, we prepared four different kinds of test images: Famous, Monitor Easy, Monitor Hard, and Print. Finally, we provide a practicability analysis of our method based on the experimental results on these test images. Additionally, compared with Scale Invariant Feature Transform (SIFT), our approach outperformed by more than 20%. |
5,740 | Dynamic Statistical-Timing-Analysis-Based VLSI Path Delay Test Pattern Generation | Nanoscale VLSI systems are subject to increasingly significant performance variability. Accurate timing analysis and effective silicon-based performance verification techniques are critical to successful nanoscale VLSI design. The state-of-the-art statistical static timing analysis (SSTA) techniques cannot capture performance variability due to primary inputs and sequential element states which, however, is critical to path delay test generation. In this paper, we present the first dynamic statistical timing analysis-based VLSI path delay test pattern generation technique. We observe that VLSI timing analysis and power estimation target the same signal toggling activity. By leveraging the existing power estimation techniques, we have developed signal-probability-based statistical timing analysis (SPSTA), and SPSTA-based VLSI delay test pattern generation (SPSTA-DTPG) techniques. Our experimental results based on ISCAS' 89 benchmark circuits show that the state-of-the-art statistical static timing analysis-based delay test pattern generation (SSTA-DTPG) achieves an average of 47.32%, 45.14%, and 57.98%, SPSTA-DTPG achieves an average of 57.41%, 61.43%, and 68.05%, while signal probability-based statistical timing analysis-based delay test pattern generation with (test pattern) compaction (SPSTA-DTPG-C) achieves an average of 83.09%, 87.48% and 90.30% coverage of the top 50, 100, and 200 timing-critical paths, respectively. |
5,741 | Sex-specific behavioral effects of acute exposure to the neonicotinoid clothianidin in mice | Although neonicotinoids are among the major classes of pesticides that affect mammalian nervous systems, little is known about sex differences in their effects. This study aimed to examine whether the neurobehavioral effects of a neonicotinoid, clothianidin (CLO), differed between sexes. Male and female C57BL/6N mice were orally administered CLO (5 or 50 mg/kg) at or below the chronic no-observed-adverse-effect-level (NOAEL) and subjected to behavioral tests of emotional and learning functions. Changes in neuroactivity in several brain regions and the concentrations of CLO and its metabolites in blood and urine were measured. Acute CLO exposure caused sex-related behavioral effects; decreases in locomotor activities and elevation of anxiety-like behaviors were more apparent in males than in females. In addition, male-specific impairment of short- and long-term learning memory by CLO exposure was observed in both the novel recognition test and the Barnes maze test. Male-dominant increases in the number of c-fos positive cells were observed in the paraventricular thalamic nucleus in the thalamus and in the dentate gyrus in the hippocampus, which are related to the stress response and learning function, respectively. The concentrations of CLO and most metabolites in blood and urine were higher in males. These results support the notion that male mice are more vulnerable than females to the neurobehavioral effects of CLO and provide novel insights into the risk assessment of neonicotinoids in mammalian neuronal function. |
5,742 | Radiative efficiency of state-of-the-art photovoltaic cells | Maximum possible photovoltaic performance is reached when solar cells are 100% radiatively efficient, with different photovoltaic technologies at different stages in their evolution towards this ideal. An external radiative efficiency is defined, which can be unambiguously determined from standard cell efficiency measurements. Comparisons between state-of-the-art devices from the representative cell technologies produce some interesting conclusions. Copyright (c) 2011 John Wiley & Sons, Ltd. |
5,743 | Effect of germinated and heat-moisture treated ancient wheat on some quality attributes and bioactive components of noodles | Utilization of germinated and heat-moisture treated wheat in noodle formulation to improve bioactive components and technologic quality was investigated. Flour of untreated, germinated, heat-moisture treated germinated wheat varieties (Esperia, einkorn and emmer) were used at five ratios (0-60 %) in noodles according to (3 × 3 × 5) × 2 factorial design. Forty-five different noodle formulations were produced. Cooking loss values of the noodles raised with increasing ratios of germinated wheat flours. Heat-moisture treatment partially decreased the cooking loss of noodle prepared with germinated wheat flour. The ash, protein, phytic acid, total yellow pigment, total phenolic content, minerals (except K) and antioxidant activity values of the noodle increased with the inclusion of ancient wheat compared to modern wheat. These increases were much more evident with the use of germinated flours of modern and ancient wheat. While the germination process increased the bioactive components content, the heat-moisture treatment improved the technological quality of the noodle prepared from germinated wheat. |
5,744 | Improved de-interleaving algorithm of radar pulses based on dual fuzzy vigilance ART | As a core part of the electronic warfare (EW) system, de-interleaving is used to separate interleaved radar signals. The de-interleaving algorithm based on the fuzzy adaptive resonance theory (fuzzy ART) is plagued by the problems of premature saturation and performance improving dilemma. This study proposes a dual fuzzy vigilance ART (DFV-ART) algorithm to address these problems and make the following improvements. Firstly, a correction method is introduced to prevent the network from prematurely saturating; then, the fuzzy vigilance models (FVM) are constructed to replace the conventional vigilance parameter, reducing the error probability in the overlapping region; finally, a dual vigilance mechanism is introduced to solve the performance improving dilemma. Simulation results show that the proposed algorithm could improve the clustering accuracy (quantization error dropped 60%) and the de-interleaving performance (clustering quality increased by 10%) while suppressing the excessive proliferation of categories. |
5,745 | Perioperative Telemedicine Utilization Among Geriatric Patients Being Evaluated for Abdominal Wall Reconstruction and Hernia Repair | Introduction: Perioperative telemedicine services have increasingly been utilized for ambulatory care, although concerns exist regarding the feasibility of virtual consultations for older patients. We sought to review telemedicine encounters for geriatric patients evaluated at a hernia repair and abdominal wall reconstruction center. Methods: A retrospective review of telemedicine encounters between May 2020 and May 2021 was performed. Patient characteristics and encounter-specific outcomes were compared among geriatric (older than65 years old) and nongeriatric patients. Clinical care plans for encounters were reviewed to determine potential downstream care utilization. Patient-derived benefits related to time saved in travel time was calculated using geo-mapping. Outcomes for postoperative encounters were assessed to determine if complication rates differed between geriatric and nongeriatric populations. Results: A total of 313 telemedicine encounters (geriatric: 41.9%) were conducted among 251 patients. Reviewing preoperative factors for hernia care, geriatric patients presented with higher rates of recurrent or incisional hernias (87.9% vs. 70.7%, p < 0.01). Potential travel time was longer for geriatric patients (104 min vs. 42 min, p = 0.03) in the preoperative setting. No differences in clinical care plans were found. Only 8.6% of preoperative encounters resulted in recommendations for supplemental in-person evaluation. Operative plans were coordinated for 42.5% of all preoperative telemedicine encounters. There was no difference in complication rate between geriatric and nongeriatric patients (p > 0.05) in the postoperative setting, with no complications directly attributable to telemedicine-based care. Conclusions: Telemedicine-based evaluations appear to function well among geriatric patients seeking hernia repair and abdominal wall reconstruction. Clinical care plans rendered following telemedicine-based encounters are appropriate with a low rate of supplemental in-person evaluations. Telemedicine use resulted in significantly more time saved in commuting to and from clinic for geriatric patients. |
5,746 | Amaretto: An Active Learning Framework for Money Laundering Detection | Monitoring financial transactions is a critical Anti-Money Laundering (AML) obligation for financial institutions. In recent years, machine learning-based transaction monitoring systems have successfully complemented traditional rule-based systems to reduce the high number of false positives and the effort needed to review all the alerts manually. Unfortunately, machine learning-based solutions also have disadvantages: while unsupervised models can detect novel anomalous patterns, they are usually characterized by a high number of false alarms; supervised models, instead, usually offers a higher detection rate but require a large amount of labeled data to achieve such performance. In this paper, we present Amaretto, an active learning framework for money laundering detection that combines unsupervised and supervised learning techniques to support the transaction monitoring processes by improving the detection performance and reducing the compliance management costs. Amaretto exploits novel selection strategies to target a subset of transactions for investigation, making more efficient use of the feedback provided by the analyst. We perform the experimental evaluation on a synthetic dataset provided by the industrial partner, which simulates the profiles of clients trading in international capital markets. We show that Amaretto outperforms state-of-the-art solutions by reducing money laundering risk and improving detection performance. In particular, we compare state-of-the-art unsupervised and supervised techniques commonly used in the AML domain with the ones implemented in this work. We show that the Isolation and Random Forests of Amaretto perform best in the task under analysis, with an AUROC of 0.9 for the first (20% better on average) and a detection rate of 0.793 for the second (30 % better on average). In addition, they are characterized by lower investigation costs computed in terms of the daily number of transactions to be examined and the number of false positives and false negatives. Finally, we compare Amaretto against a state-of-the-art active learning fraud detection system, achieving better detection performances and lower costs in all the analyzed scenarios. Worth mentioning, Amaretto improves the detection rate up to 50 % and reduces the overall cost by 20% in the most realistic scenario under analysis. |
5,747 | Interp-SUM: Unsupervised Video Summarization with Piecewise Linear Interpolation | This paper addresses the problem of unsupervised video summarization. Video summarization helps people browse large-scale videos easily with a summary from the selected frames of the video. In this paper, we propose an unsupervised video summarization method with piecewise linear interpolation (Interp-SUM). Our method aims to improve summarization performance and generate a natural sequence of keyframes with predicting importance scores of each frame utilizing the interpolation method. To train the video summarization network, we exploit a reinforcement learning-based framework with an explicit reward function. We employ the objective function of the exploring under-appreciated reward method for training efficiently. In addition, we present a modified reconstruction loss to promote the representativeness of the summary. We evaluate the proposed method on two datasets, SumMe and TVSum. The experimental result showed that Interp-SUM generates the most natural sequence of summary frames than any other the state-of-the-art methods. In addition, Interp-SUM still showed comparable performance with the state-of-art research on unsupervised video summarization methods, which is shown and analyzed in the experiments of this paper. |
5,748 | Iterative grouping median filter for removal of fixed value impulse noise | Due to the limitation of existing filters in detection and removal of fixed value impulse noise, the authors propose an iterative grouping median filter (IGMF) according to the characteristics of noise intensity and distribution. It sorts the noise-free pixels in neighbourhood by intensity, divides the sorted pixels into groups depending on the intensity differences of adjacent pixels, and finally takes the median of the maximum group as the intensity of noisy pixel. This noise removal strategy is performed iteratively and takes full advantage of the previous denoising results. Experiments show that IGMF outperforms the existing state-of-the-art filters in terms of visual perception, peak signal to noise ratio and structural similarity index at various noise densities. |
5,749 | ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification | Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small. To ensure good generalizability of deep networks under small sample sizes, learning discriminative features is crucial. To this end, several loss functions have been proposed to encourage large intra-class compactness and inter-class separability. In this paper, we propose to enhance the discriminative power of features from a new perspective by introducing a novel neural network termed Relation-and-Margin learning Network (ReMarNet). Our method assembles two networks of different backbones so as to learn the features that can perform excellently in both of the aforementioned two classification mechanisms. Specifically, a relation network is used to learn the features that can support classification based on the similarity between a sample and a class prototype; at the meantime, a fully connected network with the cross entropy loss is used for classification via the decision boundary. Experiments on four image datasets demonstrate that our approach is effective in learning discriminative features from a small set of labeled samples and achieves competitive performance against state-of-the-art methods. Code is available at https://github.com/liyunyu08/ReMarNet. |
5,750 | Data analytic approach for bankruptcy prediction | Bankruptcy prediction problem has been intensively studied over the past decades. From traditional statistical models to state of the art machine learning models, various predictive models are developed and applied to various datasets. However, models that use machine learning are not used in the field of business, for two main reasons. First, the prediction accuracy does not far exceed the statistical models and second, the results are not interpretable. In this study, we focused on solving the skewness which is a characteristic of financial data. By solving this problem, we obtained 17% average improvement in AUC over existing models. To address the second shortcoming, we analyze the importance of features identified by the XGBoost model. The interpretation of the model differs among categories of data. Our bankruptcy prediction model has high predictive accuracy with clear explanations and is therefore directly applicable to the industry. (C) 2019 Elsevier Ltd. All rights reserved. |
5,751 | Can Reverse Nearest Neighbors Perceive Unknowns? | A novel open set classifier is presented in this work, where the neighborhood of a test instance is determined using the principles of Reverse k-nearest neighbors (Rk NN). The RkNN count of an instance can have any non-negative value less or equal to the size of the training set. While dealing with an open dataset, consisting of known and unknown classes, the zero count can provide a possible solution for detecting the unknown class. Positive RkNN count along with the nearest Rk NN distance information are used to determine the known class classifications. Experiments are carried out on ten realworld datasets, with various openness values on five state-of-the-art open set learners and the proposed scheme. Their performance is measured on three evaluating metrics namely accuracy, average F-1 over known and unknown classes, and Known class F-1. Empirical results indicate comparable to superior performance delivered by the proposed method over the state-of-the-art approaches on all but one dataset. |
5,752 | The Value of Art in Persuasive Marketing Communication and Its Sustainable Effect on the Country of Origin | The value of art in persuasion integrates the country's culture and art according to marketing communication. Our research examined the impact of artistic communication values on customers' behavior as a sustainable effect on the country of origin by applying two hypotheses. These hypotheses were tested on 689 respondents, mainly from Romania. This marketing communication analysis indicated that persuasion had more value for customers when ethical rhetorical arguments, dialects, poetry, essays, poems, and fairy tales were linked to a country's culture, such as tradition, national values, and heritage, or were combined with art, such as paintings, sculptures, and music. The results demonstrated that artistic communication influenced consumer behavior positively, having a sustainable effect on the country of origin. |
5,753 | Anti-IgLON5 disease: a novel topic beyond neuroimmunology | Anti-IgLON5 disease is a recently defined autoimmune disorder of the nervous system associated with autoantibodies against IgLON5. Given its broad clinical spectrum and extremely complex pathogenesis, as well as difficulties in its early diagnosis and treatment, anti-IgLON5 disease has become the subject of considerable research attention in the field of neuroimmunology. Anti-IgLON5 disease has characteristics of both autoimmunity and neurodegeneration due to the unique activity of the anti-IgLON5 antibody. Neuropathologic examination revealed the presence of a tauopathy preferentially affecting the hypothalamus and brainstem tegmentum, potentially broadening our understanding of tauopathies. In contrast to that seen with other autoimmune encephalitis-related antibodies, basic studies have demonstrated that IgLON5 antibody-induced neuronal damage and degeneration are irreversible, indicative of a potential link between autoimmunity and neurodegeneration in anti-IgLON5 disease. Herein, we comprehensively review and discuss basic and clinical studies relating to anti-IgLON5 disease to better understand this complicated disorder. |
5,754 | ACtivE: Assembly and CRISPR-Targeted in Vivo Editing for Yeast Genome Engineering Using Minimum Reagents and Time | Thanks to its sophistication, the CRISPR/Cas system has been a widely used yeast genome editing method. However, CRISPR methods generally rely on preassembled DNAs and extra cloning steps to deliver gRNA, Cas protein, and donor DNA. These laborious steps might hinder its usefulness. Here, we propose an alternative method, Assembly and CRISPR-targeted in vivo Editing (ACtivE), that only relies on in vivo assembly of linear DNA fragments for plasmid and donor DNA construction. Thus, depending on the user's need, these parts can be easily selected and combined from a repository, serving as a toolkit for rapid genome editing without any expensive reagent. The toolkit contains verified linear DNA fragments, which are easy to store, share, and transport at room temperature, drastically reducing expensive shipping costs and assembly time. After optimizing this technique, eight loci proximal to autonomously replicating sequences (ARS) in the yeast genome were also characterized in terms of integration and gene expression efficiencies and the impacts of the disruptions of these regions on cell fitness. The flexibility and multiplexing capacity of the ACtivE were shown by constructing a β-carotene pathway. In only a few days, >80% integration efficiency for single gene integration and >50% integration efficiency for triplex integration were achieved on Saccharomyces cerevisiae BY4741 from scratch without using in vitro DNA assembly methods, restriction enzymes, or extra cloning steps. This study presents a standardizable method to be readily employed to accelerate yeast genome engineering and provides well-defined genomic location alternatives for yeast synthetic biology and metabolic engineering purposes. |
5,755 | Woodblock image decomposition of Chinese new year paintings | Woodblock printed Chinese new year (WNY) painting has been a popular art form in Chinese folk culture. To make a WNY painting involves carving images on woodblocks and printing colors using woodblocks. Although thousands of WNY paintings were preserved, the ten-year national survey reveals that a great number of woodblocks were damaged or lost. In this paper, we study a novel problem of decomposing woodblock images from WNY paintings, which currently requires a tremendous amount of manual labor. We also find that the state-of-the-art methods of natural image segmentation generate poor results in our application. Instead of using sophisticated schemes, we develop a simple yet robust decomposition approach, which contains the extraction of line block image and the separation of color block images. The effectiveness of the proposed approach is validated through both quantitative evaluation and visual quality comparison with six state-of-the-art methods on multiple WNY paintings. |
5,756 | The Upper Paleolithic rock art of Ukraine between here and nowhere | The complex of Kamyana Mohyla is the westernmost rock art location of the Eurasian Steppe and the largest accumulation of cave art sites in the Eastern Europe. So far it has been believed that the complex contains the Upper Paleolithic cave art images as well as portable art collection that resemble the instances of Upper Paleolithic worldview. Though this belief lacked the support of archaeological context and chronological attribution it remained neither proved nor disputed. However, the application of digital photogrammetric tools allowed to perform the sub-millimeter surface modeling of the rock art objects and to re-examine and reconsider the engravings that were previously attributed to Pleistocene. The modeling results presented in this article revealed the complete absence of figurative images for the collection of portable art specimens and the dubious character of those for the cave art one. Therefore, the whole collection should be reconsidered, studied and attributed according to the state of the art and contemporary archaeological record in the region. This contribution attempts to think over the possible Upper Paleolithic origin of the motifs from Kamyana Mohyla in the light of new data and proposes three hypotheses towards the understanding of the rock art assemblage from one of the caves in the complex. |
5,757 | Constructing Dual-Transport Pathways by Incorporating Beaded Nanofillers in Mixed Matrix Membranes for Efficient CO2 Separation | Mixed matrix membranes (MMMs) have attracted significant attention in the field of CO2 separation because MMMs have potential to overcome an undesirable "trade-off" effect. In this study, the beaded nanofillers of ZIF-8@aminoclay (ZIF-8@AC) were synthesized using an in situ growth method, and they were doped into a Pebax MH 1657 (Pebax) matrix to fabricate MMMs for efficient CO2 separation. The beaded structure was formed by ZIF-8 particles joined together during the process of AC coating on the ZIF-8 surface. ZIF-8@AC played a vital role in the improvement of gas separation performance. It was mainly attributed to the following reasons: First, the inherent micropores of ZIF-8 constructed the internal pathways for gas transport in the beaded nanofillers, benefiting the improvement of gas permeability. Second, the staggered AC layers constructed the external pathways for gas transport in the beaded nanofillers, increasing the tortuosity of gas transport for larger molecules and favoring the improvement of gas selectivity. Therefore, the internal and external pathways of ZIF-8@AC co-constructed the dual-transport pathways for CO2 transport in MMMs. In addition, the abundant amino groups of the beaded nanofillers provided abounding carriers for CO2, facilitating CO2 transport in the dual-transport pathways. Therefore, the CO2 separation performance of Pebax/ZIF-8@AC-1 MMMs was significantly improved. The CO2 permeability and CO2/CH4 separation factor of Pebax/ZIF-8@AC-1-7 MMM were 620 ± 10 Barrer and 40 ± 0.4, which were 2.3 and 1.6 times those of a pure Pebax membrane, respectively. Furthermore, the CO2/CH4 separation performance of Pebax/ZIF-8@AC-1-7 MMM overcame successfully the "trade-off" effect and approached the 2019 upper bound. It is a novel strategy to design a beaded nanofiller doped into MMMs for carbon capture. |
5,758 | Multi-Grained Random Fields for Mitosis Identification in Time-Lapse Phase Contrast Microscopy Image Sequences | This paper proposes a multi-grained random fields (MGRFs) model for mitosis identification. To deal with the difficulty in hidden state discovery and sequential structure modeling in mitosis sequences only containing gradual visual pattern changes, we design the graphical structure to transform individual sequence into a set of coarse-to-fine grained sequences conveying diverse temporal dynamics. Furthermore, we propose the corresponding probabilistic model for joint temporal learning and feature learning. To deal with the non-convex formulation of MGRF, we decompose model training into two sub-tasks, layer-wise sequential learning of both temporal dynamics and visual feature and new layer generation by graph-based sequential grouping, and optimize the model by alternating between them iteratively. The proposed method is validated on very challenging mitosis data set of C3H10T1/2 and C2C12 stem cells. Extensive comparison experiments demonstrate its superiority to the state of the arts. |
5,759 | State-of-the-Art in 360 degrees Video/Image Processing: Perception, Assessment and Compression | Nowadays, 360 degrees video/image has been increasingly popular and drawn great attention. The spherical viewing range of 360 degrees video/image accounts for huge data, which pose the challenges to 360 degrees video/image processing in solving the bottleneck of storage, transmission, etc. Accordingly, the recent years have witnessed the explosive emergence of works on 360 degrees video/image processing. In this article, we review the state-of-the-art works on 360 degrees video/image processing from the aspects of perception, assessment and compression. First, this article reviews both datasets and visual attention modelling approaches for 360 degrees video/image. Second, we survey the related works on both subjective and objective visual quality assessment (VQA) of 360 degrees video/image. Third, we overview the compression approaches for 360 degrees video/image, which either utilize the spherical characteristics or visual attention models. Finally, we summarize this overview article and outlook the future research trends on 360 degrees video/image processing. |
5,760 | Nonrigid Registration of Ultrasound and MRI Using Contextual Conditioned Mutual Information | Mutual information (MI) quantifies the information that is shared between two random variables and has been widely used as a similarity metric for multi-modal and uni-modal image registration. A drawback of MI is that it only takes into account the intensity values of corresponding pixels and not of neighborhoods. Therefore, it treats images as "bag of words" and the contextual information is lost. In this work, we present Contextual Conditioned Mutual Information (CoCoMI), which conditions MI estimation on similar structures. Our rationale is that it is more likely for similar structures to undergo similar intensity transformations. The contextual analysis is performed on one of the images offline. Therefore, CoCoMI does not significantly change the registration time. We use CoCoMI as the similarity measure in a regularized cost function with a B-spline deformation field and efficiently optimize the cost function using a stochastic gradient descent method. We show that compared to the state of the art local MI based similarity metrics, CoCoMI does not distort images to enforce erroneous identical intensity transformations for different image structures. We further present the results on nonrigid registration of ultrasound (US) and magnetic resonance (MR) patient data from image-guided neurosurgery trials performed in our institute and publicly available in the BITE dataset. We show that CoCoMI performs significantly better than the state of the art similarity metrics in US to MR registration. It reduces the average mTRE over 13 patients from 4.12 mm to 2.35 mm, and the maximum mTRE from 9.38 mm to 3.22 mm. |
5,761 | Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects | Wind, as a sustainable and affordable energy source, represents a strong alternative to traditional energy sources. However, wind power is only one of the options, together with other renewable energy sources. Consequently, the core concerns for wind turbine manufacturers and operators are to increase its reliability and decrease costs, therefore enhancing commercial competitiveness. Among typical failure modes of wind turbines, fatigue is a common and critical source. Given the significance of fatigue reliability in wind turbine structural integrity, reliable probabilistic fatigue theories are necessary for design scheme optimization. By reducing the expenses on manufacturing, operation, and maintenance in reliability-and cost-optimal ways, the cost of energy can be significantly reduced. This study systematically reviews the state-of-the-art technology for fatigue reliability of wind turbines, and elaborates on the evolution of methodology in wind load uncertainty modelling. In addition, fatigue reliability assessment techniques on four typical components are summarized. Finally, discussions and conclusions are presented, intending to provide direct insights into future theoretical development and meth-odological innovation in this field. |
5,762 | Evaluation of the Increase in Serum Calcium Levels After Unilateral Adrenalectomy | Introduction This study aims to investigate the prevalence and characteristics of patients with elevated serum calcium due to adrenal insufficiency after unilateral adrenalectomy. Methods The study included 76 patients who underwent unilateral adrenalectomy from January 2012 to November 2021 and did not have an additional etiologic factor for hypercalcemia, During the postoperative period, the highest calcium value in six months was taken into account as the postoperative value. Calcium values were corrected according to the albumin value. Results Of the 76 patients included in the study, serum calcium levels were higher in six patients (7.9%) after adrenalectomy. Unlike the others, a decrease in glomerular filtration rate (GFR) and an increase in serum creatinine values were detected in the postoperative period in this patient group. In this patient group, the corrected calcium level detected an average increase of 1.3 mg/dL. Conclusion After unilateral adrenalectomy, hypercalcemia may occur due to adrenal insufficiency. It should also be considered that there may be a decrease in GFR and increased creatinine in these patients. |
5,763 | Climate change and the nonlinear impact of precipitation anomalies on income inequality | Climate anomalies, such as floods and droughts, as well as gradual temperature changes have been shown to adversely affect economies and societies. Although studies find that climate change might increase global inequality by widening disparities across countries, its effects on within-country income distribution have been little investigated, as has the role of rainfall anomalies. Here, we show that extreme levels of precipitation exacerbate within-country income inequality. The strength and direction of the effect depends on the agricultural intensity of an economy. In high-agricultural-intensity countries, climate anomalies that negatively impact the agricultural sector lower incomes at the bottom end of the distribution and generate greater income inequality. Our results indicate that a 1.5-SD increase in precipitation from average values has a 35-times-stronger impact on the bottom income shares for countries with high employment in agriculture compared to countries with low employment in the agricultural sector. Projections with modeled future precipitation and temperature reveal highly heterogeneous patterns on a global scale, with income inequality worsening in high-agricultural-intensity economies, particularly in Africa. Our findings suggest that rainfall anomalies and the degree of dependence on agriculture are crucial factors in assessing the negative impacts of climate change on the bottom of the income distribution. |
5,764 | NETWORK RELATIONS EFFECTS ON PERFORMANCE, FROM THE PERSPECTIVE OF ENVIRONMENT UNCERTAINTY: CASE ON CERAMIC ARTS PRODUCTS INDUSTRY | In the times of fast technology evolution, circumstances are ever-changing. Facing various influencing factors from a fast-pace world, environment uncertainty factors are hence made a top priority to companies. For them, utilising efficient strategies for a better performance and competitive edge is in fact a real necessity. Throughout history, business environment impacts greatly over the lifespans of companies. In this sense, impacts from network relations among network members are a significant topic in the research of interactive relations between network structure and environments. In this research, it mainly takes the staffs from Guangxi Ceramic Arts Products Industry as the study objects. A total of 300 questionnaires were sent out to them and 236 effective ones were collected back (a recovery rate of 79%). The research indicates: (1) Environment uncertainty has a positive and significant impact over network relations; (2) Network relations have a positive and significant impact over business performance; (3) Environment uncertainty has a positive and significant impact over business performance. In line with the results, certain advices are brought about to aid the Ceramic Arts Products Industry to upgrade itself, and to enhance competitiveness. |
5,765 | Two-Dimensional Tomographic Simultaneous Multispecies Visualization-Part II: Reconstruction Accuracy | Recently we demonstrated the simultaneous detection of the chemiluminescence of the radicals OH* (310 nm) and CH* (430 nm), as well as the thermal radiation of soot in laminar and turbulent methane/air diffusion flames. As expected, a strong spatial and temporal coupling of OH* and CH* in laminar and moderate turbulent flames was observed. Taking advantage of this coupling, multispecies tomography enables us to quantify the reconstruction quality completely independent of any phantom studies by simply utilizing the reconstructed distribution of both species. This is especially important in turbulent flames, where it is difficult to separate measurement noise from turbulent fluctuations. It is shown that reconstruction methods based on Tikhonov regularization should be preferred over the widely used algebraic reconstruction technique (ART) and multiplicative algebraic reconstruction techniques (MART), especially for high-speed imaging or generally in the limit of low signal-to-noise ratio. |
5,766 | Spatio-Temporal Multiscale Denoising of Fluoroscopic Sequence | In the past 20 years, a wide range of complex fluoroscopically guided procedures have shown considerable growth. Biologic effects of the exposure (radiation induced burn, cancer) lead to reduce the dose during the intervention, for the safety of patients and medical staff. However, when the dose is reduced, image quality decreases, with a high level of noise and a very low contrast. Efficient restoration and denoising algorithms should overcome this drawback. We propose a spatio-temporal filter operating in a multi-scales space. This filter relies on a first order, motion compensated, recursive temporal denoising. Temporal high frequency content is first detected and then matched over time to allow for a strong denoising in the temporal axis. We study this filter in the curvelet domain and in the dual-tree complex wavelet domain, and compare those results to state of the art methods. Quantitative and qualitative analysis on both synthetic and real fluoroscopic sequences demonstrate that the proposed filter allows a great dose reduction. |
5,767 | ResViT: Residual Vision Transformers for Multimodal Medical Image Synthesis | Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. However, CNNs are designed to perform local processing with compact filters, and this inductive bias compromises learning of contextual features. Here, we propose a novel generative adversarial approach for medical image synthesis, ResViT, that leverages the contextual sensitivity of vision transformers along with the precision of convolution operators and realism of adversarial learning. ResViT's generator employs a central bottleneck comprising novel aggregated residual transformer (ART) blocks that synergistically combine residual convolutional and transformer modules. Residual connections in ART blocks promote diversity in captured representations, while a channel compression module distills task-relevant information. A weight sharing strategy is introduced among ART blocks to mitigate computational burden. A unified implementation is introduced to avoid the need to rebuild separate synthesis models for varying source-target modality configurations. Comprehensive demonstrations are performed for synthesizing missing sequences in multi-contrast MRI, and CT images from MRI. Our results indicate superiority of ResViT against competing CNN- and transformer-based methods in terms of qualitative observations and quantitative metrics. |
5,768 | Overclocking Nitronyl Nitroxide Gold Derivatives in Cross-Coupling Reactions | Nitronyl nitroxides are functional building blocks in cutting-edge research fields, such as the design of molecular magnets, the development of redox and photoswitchable molecular systems and the creation of redox-active components for organic and hybrid batteries. The key importance of the nitronyl nitroxide function is to translate molecular-level-optimized structures into nano-scale devices and new technologies. In spite of great importance, efficient and versatile synthetic approaches to these compounds still represent a challenge. Particularly, methods for the direct introduction of a nitronyl nitroxide moiety into aromatic systems possess many limitations. Here, we report gold derivatives of nitronyl nitroxide that can enter Pd(0)-catalysed cross-coupling reactions with various aryl bromides, affording the corresponding functionalized nitronyl nitroxides. Based on the high thermal stability and enhanced reactivity in catalytic transformation, a new reagent is suggested for the synthesis of radical systems via a universal cross-coupling approach. |
5,769 | A New Data Transfer Method via Signal-Rich-Art Code Images Captured by Mobile Devices | A new type of signal-rich-art image for applications of data transfer, called signal-rich-art code image, is proposed. The created code image is visually similar to a preselected target image and, with a given message embedded, achieves the effect of the so-called signal-rich art. With its function similar to that of a QR code, such a type of image is produced by encoding the message into a binary bit stream, representing the bits by binary code patterns of 2 x 2 blocks, and injecting the patterns into the target image by a novel image-block luminance modulation scheme. Each signal-rich-art code image may be printed or displayed and then is recaptured using a mobile-device camera. Skillful techniques for counting the number of pattern blocks and recognition of code patterns are also proposed for message extraction from the recaptured version of the signal-rich-art code image. Good experimental results and a comparison of them with those of an existing alternative method show the feasibility and superiority of the proposed new data transfer method. |
5,770 | Dynamic Background Subtraction Using Histograms Based on Fuzzy C-Means Clustering and Fuzzy Nearness Degree | Background subtraction has been widely used in the detection of a moving object from a still scene. Due to the uncertainty in the classification of the pixels in the foreground and background, we propose a novel fuzzy approach for background subtraction using fuzzy histograms based on fuzzy c-means clustering and the fuzzy nearness degree, called FCFN. In this method, the temporal characteristics of the pixels are described by a fuzzy histogram using the fuzzy c-means algorithm. The segmentation threshold is adaptively calculated according to the distribution of the fuzzy nearness degree of the individual pixel. Fuzzy adaptive background maintenance is adopted in the background update framework. The performance of the FCFN is evaluated against several state-of-the-art methods in the complex dynamic scenes. The experimental results demonstrate that the proposed method doubles the improvements in performance than the classic fuzzy background modeling methods and outperforms most state-of-the-art methods. |
5,771 | Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability | Successful development of wind farms relies on the optimal siting of wind turbines to maximize the power capacity under stochastic wind conditions and wake losses caused by neighboring turbines. This paper presents a novel method to quickly generate approximate optimal layouts to support infrastruc-ture design decisions. We model the quadratic integer formulation of the discretized layout design problem with an undirected graph that succinctly captures the spatial dependencies of the design pa-rameters caused by wake interactions. On the undirected graph, we apply probabilistic inference using sequential tree-reweighted message passing to approximate turbine siting. We assess the effectiveness of our method by benchmarking against a state-of-the-art branch and cut algorithm under varying wind regime complexities and wind farm discretization resolutions. For low resolutions, probabilistic infer-ence can produce optimal or nearly optimal turbine layouts that are within 3% of the power capacity of the optimal layouts achieved by state-of-the-art formulations, at a fraction of the computational cost. As the discretization resolution (and thus the problem size) increases, probabilistic inference produces optimal layouts with up to 9% more power capacity than the best state-of-the-art solutions at a much lower computational cost. (c) 2021 Elsevier Ltd. All rights reserved. |
5,772 | Fast MR Image Reconstruction for Partially Parallel Imaging With Arbitrary k-Space Trajectories | Both acquisition and reconstruction speed are crucial for magnetic resonance (MR) imaging in clinical applications. In this paper, we present a fast reconstruction algorithm for SENSE in partially parallel MR imaging with arbitrary k-space trajectories. The proposed method is a combination of variable splitting, the classical penalty technique and the optimal gradient method. Variable splitting and the penalty technique reformulate the SENSE model with sparsity regularization as an unconstrained minimization problem, which can be solved by alternating two simple minimizations: One is the total variation and wavelet based denoising that can be quickly solved by several recent numerical methods, whereas the other one involves a linear inversion which is solved by the optimal first order gradient method in our algorithm to significantly improve the performance. Comparisons with several recent parallel imaging algorithms indicate that the proposed method significantly improves the computation efficiency and achieves state-of-the-art reconstruction quality. |
5,773 | DNA damage contributes to age-associated differences in SARS-CoV-2 infection | Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is known to disproportionately affect older individuals. How aging processes affect SARS-CoV-2 infection and disease progression remains largely unknown. Here, we found that DNA damage, one of the hallmarks of aging, promoted SARS-CoV-2 infection in vitro and in vivo. SARS-CoV-2 entry was facilitated by DNA damage caused by extrinsic genotoxic stress or telomere dysfunction and hampered by inhibition of the DNA damage response (DDR). Mechanistic analysis revealed that DDR increased expression of angiotensin-converting enzyme 2 (ACE2), the primary receptor of SARS-CoV-2, by activation of transcription factor c-Jun. Importantly, in vivo experiment using a mouse-adapted viral strain also verified the significant roles of DNA damage in viral entry and severity of infection. Expression of ACE2 was elevated in the older human and mice tissues and positively correlated with γH2AX, a DNA damage biomarker, and phosphorylated c-Jun (p-c-Jun). Finally, nicotinamide mononucleotide (NMN) and MDL-800, which promote DNA repair, alleviated SARS-CoV-2 infection and disease severity in vitro and in vivo. Taken together, our data provide insights into the age-associated differences in SARS-CoV-2 infection and a novel approach for antiviral intervention. |
5,774 | Maternal Delta-9-Tetrahydrocannabinol Exposure Induces Abnormalities of the Developing Heart in Mice | Introduction: Cannabis is increasingly being consumed by pregnant women for recreational purposes as well as for its antiemetic and anxiolytic effects despite limited studies on its safety during pregnancy. Importantly, phytocannabinoids found in cannabis can pass through the placenta and enter the fetal circulation. Recent reports suggest gestational cannabis use is associated with negative fetal outcomes, including fetal growth restriction and perinatal intensive care, however, the effects of delta-9-tetrahydrocannabinol (THC) on fetal heart development remains to be elucidated. Materials and Methods: We aimed to determine the outcomes of maternal THC exposure on fetal heart development in mice by administering 0, 5, or 10 mg/kg/day of THC orally to C57BL/6 dams starting at embryonic day (E)3.5. Offspring were collected at E12.5 for molecular analysis, at E17.5 to analyze cardiac morphology or at postnatal day (PND)21 to assess heart function. Results: Maternal THC exposure in E17.5 fetuses resulted in an array of cardiac abnormalities with an incidence of 44% and 55% in the 5 and 10 mg/kg treatment groups, respectively. Maternal THC exposure in offspring resulted in ventricular septal defect, higher semilunar valve volume relative to orifice ratio, and higher myocardial wall thickness. Notably, cell proliferation within the ventricular myocardium was increased, and expression of multiple cardiac transcription factors was downregulated in THC-exposed E12.5 fetuses. Furthermore, heart function was compromised with lower left ventricular ejection fraction, fractional shortening, and cardiac output in PND21 pups exposed to THC compared to controls. Discussion: The results show that maternal THC exposure during gestation induces myocardial hyperplasia and semilunar valve thickening in the fetal heart and postnatal cardiac dysfunction. Our study suggests that maternal cannabis consumption may induce abnormalities in the developing heart and cardiac dysfunction in postnatal life. |
5,775 | Condensation water in heritage touristic caves: Isotopic and hydrochemical data and a new approach for its quantification through image analysis | Condensation water is a major factor in the conservation of heritage caves. It can cause dissolution of the rock substrate (and the pigments of rock art drawn on it) or covering thereof with mineral components, depending on the chemical saturation degree of the condensation water. In show caves, visitors act as a source of CO2 and thus modify the microclimate, favouring negative processes that affect the conservation of the caves. In spite of their interest, studies of the chemical composition of this type of water are scarce and not very detailed. In this work we present research on the condensation water in the Nerja Cave, one of the main heritage and tourist caves in Europe. The joint analysis of isotopic, hydrochemical, mineralogical and microbiological data and the use of image analysis have allowed us to advance in the knowledge of this risk factor for the conservation of heritage caves, and to demonstrate the usefulness of image analysis to quantify the scope of the possible corrosion condensation process that the condensation water could be producing on the bedrock, speleothem and rock art. To our knowledge, this application of image analysis (relative to the condensation water in caves) is the first one of this type that has been documented. |
5,776 | A matrix inversion technique for the spherical modal decomposition field solution applied on the characterization of antennas in their environment | An efficient matrix-iversion technique is proposed for the spherical modal decomposition (SMD),field solution for radiating structures. The advantages of this approach compared to the classical dot-product technique are demonstrated through its application to the characterization of two radiating structures: the Yagi antenna and art antenna on the roof of a vehicle. (C) 2004 Wiley Periodicals, Inc. |
5,777 | A Tale of Two Sites: Neighboring Atomically Dispersed Pt Sites Cooperatively Remove Trace H2 in CO-Rich Stream | Single-atom catalysts (SACs) exhibit distinct catalytic behavior compared with nano-catalysts because of their unique atomic coordination environment without the direct bonding between identical metal centers. How these single atom sites interact with each other and influence the catalytic performance remains unveiled as designing densely populated but stable SACs is still an enormous challenge to date. Here, a fabrication strategy for embedding high areal density single-atom Pt sites via a defect engineering approach is demonstrated. Similar to the synergistic mechanism in binuclear homogeneous catalysts, from both experimental and theoretical results, it is proved that electrons would redistribute between the two oxo-bridged paired Pt sites after hydrogen adsorption on one site, which enables the other Pt site to have high CO oxidation activity at mild-temperature. The dynamic electronic interaction between neighboring Pt sites is found to be distance dependent. These new SACs with abundant Pt-O-Pt paired structures can improve the efficiency of CO chemical purification. |
5,778 | Overview of deep-learning based methods for salient object detection in videos | Video salient object detection is a challenging and important problem in computer vision domain. In recent years, deep-learning based methods have contributed to significant improvements in this domain. This paper provides an overview of recent developments in this domain and compares the corresponding methods up to date, including 1) Classification of the state-of-the-art methods and their frameworks; 2) summary of the benchmark datasets and commonly used evaluation metrics; 3) experimental comparison of the performances of the state-of-the-art methods; 4) suggestions of some promising future works for unsolved challenges. (C) 2020 Elsevier Ltd. All rights reserved. |
5,779 | Admittance matrices of power converters for distributed generators | To integrate distributed generators (DGs) with the power grid, interfacing power converters such as voltage source converters (VSCs) are required. However, due to the switching natures, these converters generate harmonics, which may have detrimental effects on the system. To find out how these harmonics may interact with the grid, it is essential to find the admittance matrices of the converters. In this paper, the state-of-the-art methods of modeling converters are thoroughly reviewed. Moreover, a new proposed method is also introduced, which can significantly increase the simulation efficiency. Finally, two numerical examples are presented. The results obtained by the proposed method are in great agreement with those of the state-of-the-art methods and brute-force time-domain simulation such as PSCAD/EMTDC, demonstrating the validity of the proposed method. (c) 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. |
5,780 | Lithium solid-state batteries: State-of-the-art and challenges for materials, interfaces and processing | Lithium solid-state batteries (SSBs) are considered as a promising solution to the safety issues and energy density limitations of state-of-the-art lithium-ion batteries. Recently, the possibility of developing practical SSBs has emerged thanks to striking advances at the level of materials; such as the discovery of new highly-conductive solid-state electrolytes. Consequently, the focus in research has progressively shifted towards the integration of the various components, the battery's functionality at full cell level, and the scalability of the fabrication processes. Considering these points, the development of SSBs still faces formidable challenges. This review covers the recent advances in SSB development, stressing the importance of full cell integration. The most relevant materials and fabrication processes are briefly summarized and their potential applications in SSBs are examined. The main challenges and strategies for full cell integration are then discussed highlighting the most promising materials and the best suited processing techniques. Particular attention is paid on the mutual compatibility of the cell components, the properties of the interfaces within the cell (anode-electrolyte, cathode-electrolyte, intraelectrolyte) and the strategies applied to stabilize and minimize the resistance of these interfaces via compatible processing. |
5,781 | Automated Phrase Mining from Massive Text Corpora | As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus and has various downstream applications including information extraction/retrieval, taxonomy construction, and topic modeling. Most existing methods rely on complex, trained linguistic analyzers, and thus likely have unsatisfactory performance on text corpora of new domains and genres without extra but expensive adaption. None of the state-of-the-art models, even data-driven models, is fully automated because they require human experts for designing rules or labeling phrases. In this paper, we propose a novel framework for automated phrase mining, AutoPhrase, which supports any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger. Compared to the state-of-the-art methods, AutoPhrase has shown significant improvements in both effectiveness and efficiency on five real-world datasets across different domains and languages. Besides, AutoPhrase can be extended to model single-word quality phrases. |
5,782 | Minimizing Retention Induced Refresh Through Exploiting Process Variation of Flash Memory | Refresh schemes have been the default approach in NAND flash memory to avoid data losses. The critical issue of the refresh schemes is that they introduce additional costs on lifetime and performance. Recent work proposed to minimize the refresh costs by using uniform refresh frequencies based on the number of program/erase (P/E) cycles. However, from our investigation, we find that the refresh costs still have a high burden on the lifetime performance. In this paper, a novel refresh minimization scheme is proposed by exploiting the process variation (PV) of flash memory. State-of-the-art flash memory always has significant PV, which introduces large variations on the retention time of flash blocks. In order to reduce the refresh costs, we first propose a new refresh frequency determination scheme by detecting the supported retention time of flash blocks. If the detected retention time is large, a low refresh frequency can be applied to minimize the refresh costs. Second, considering that the retention time requirements of data are varied with each others, we further propose a data hotness and refresh frequency matching scheme. The matching scheme is designed to allocate data to blocks with right higher supported retention time. Through simulation studies, the lifetime and performance are significantly improved compared with state-of-the-art refresh schemes. |
5,783 | Functionalized Cellulose Nanocrystals as Active Reinforcements for Light-Actuated 3D-Printed Structures | Conventional manufacturing techniques allow the production of photoresponsive cellulose nanocrystals (CNC)-based composites that can reversibly modify their optical, mechanical, or chemical properties upon light irradiation. However, such materials are often limited to 2D films or simple shapes and do not benefit from spatial tailoring of mechanical properties resulting from CNC alignment. Herein, we propose the direct ink writing (DIW) of 3D complex structures that combine CNC reinforcement effects with photoinduced responses. After grafting azobenzene photochromes onto the CNC surfaces, up to 15 wt % of modified nanoparticles can be introduced into a polyurethane acrylate matrix. The influence of CNC on rheological properties allows DIW of self-standing 3D structures presenting local shear-induced alignment of the active reinforcements. The printed composites, with longitudinal elastic modulus of 30 MPa, react to visible-light irradiation with 30-50% reversible softening and present a shape memory behavior. The phototunable energy absorption of 3D complex structures is demonstrated by harnessing both geometrical and photoresponsive effects, enabling dynamic mechanical responses to environmental stimuli. Functionalized CNC in 3D printable inks have the potential to allow the rapid prototyping of several devices with tailored mechanical properties, suitable for applications requiring dynamic responses to environmental changes. |
5,784 | Yellow polyketide pigment suppresses premature hatching in social amoeba | Low-molecular-weight natural products from microbes are indispensable in the development of potent drugs. However, their biological roles within an ecological context often remain elusive. Here, we shed light on natural products from eukaryotic microorganisms that have the ability to transition from single cells to multicellular organisms: the social amoebae. These eukaryotes harbor a large number of polyketide biosynthetic genes in their genomes, yet virtually none of the corresponding products can be isolated or characterized. Using complementary molecular biology approaches, including CRISPR-Cas9, we generated polyketide synthase (pks5) inactivation and overproduction strains of the social amoeba Dictyostelium discoideum. Differential, untargeted metabolomics of wild-type versus mutant fruiting bodies allowed us to pinpoint candidate metabolites derived from the amoebal PKS5. Extrachromosomal expression of the respective gene led to the identification of a yellow polyunsaturated fatty acid. Analysis of the temporospatial production pattern of this compound in conjunction with detailed bioactivity studies revealed the polyketide to be a spore germination suppressor. |
5,785 | Perceiving Artistic Expression: A Formal Exploration of Performance Art Salsa | This paper studies artistic expression in human movement by exploring the performance art form salsa. The motions of a salsa performance are constructed as concatenations of motion primitives, each of which specifies the movement of the dance pair over the course of eight musical beats. To analyze the syntax of artistic expression, the choreography of dance performances is represented by a transition model that is based on humanoid robot representations of the dancers. In order to assess the quality of a performance, two distinct metrics are explored. By integrating the performance metrics into the proposed transition system, it is possible to create an algorithm that is capable of autonomously recognizing the dance moves and evaluating the quality of the performance with a score. To validate the model, a dance pair performed four distinct salsa dance sequences observed by an artificially intelligent (AI) judge. The video recordings of the performances are also shown to a dance audience for evaluation. By looking at the correlation between the dance audience and the AI judge's scores, we conclude that the proposed model performs well in evaluating the artistic merit of the dance. |
5,786 | Assisted Relaxation Therapy for Insomnia in Older Adults With Mild Cognitive Impairment: A Pilot Study | Insomnia symptoms are prevalent in older adults with mild cognitive impairment (MCI) and can pose treatment challenges. We tested the feasibility, acceptability, and preliminary efficacy of assisted relaxation therapy (ART) to improve insomnia symptoms in community-dwelling older adults with MCI. In this pilot RCT, 25 participants were assigned to intervention or control groups for 2 weeks. The final sample (n = 20) consisted of all Black, primarily female (70%) older adults (mean age 69.10; SD = 7.45) with mean Montreal Cognitive Assessment scores of 21.10 (SD = 2.49). Recruitment was timely; attrition was low (80%). Participants were able to use ART (average use 7.00; SD = 5.07 days). Participants in the ART group improved on Insomnia Severity Index (ISI) (- 7.10; 95% CI [-11.63, -2.55]; p = .004) compared to baseline. There were clinically meaningful mean change scores on ISI for the intervention group compared to the control (- 7.10 vs. - 4.33). Results provide justification for testing ART in a fully powered clinical trial. |
5,787 | Localization of Craniomaxillofacial Landmarks on CBCT Images Using 3D Mask R-CNN and Local Dependency Learning | Cephalometric analysis relies on accurate detection of craniomaxillofacial (CMF) landmarks from cone-beam computed tomography (CBCT) images. However, due to the complexity of CMF bony structures, it is difficult to localize landmarks efficiently and accurately. In this paper, we propose a deep learning framework to tackle this challenge by jointly digitalizing 105 CMF landmarks on CBCT images. By explicitly learning the local geometrical relationships between the landmarks, our approach extends Mask R-CNN for end-to-end prediction of landmark locations. Specifically, we first apply a detection network on a down-sampled 3D image to leverage global contextual information to predict the approximate locations of the landmarks. We subsequently leverage local information provided by higher-resolution image patches to refine the landmark locations. On patients with varying non-syndromic jaw deformities, our method achieves an average detection accuracy of 1.38 +/- 0.95mm, outperforming a related state-of-the-art method. |
5,788 | Synthesis and characterization of Black Au nanoparticles deposited over g-C3N4 nanosheets: enhanced photocatalytic degradation of methylene blue | Black AuNPs, prepared by a facile seeding growth method under ambient conditions, displayed efficient broadband absorption of the incident light over the entire visible and near-infrared regions of the solar spectrum. The spherical black AuNPs with the size of 2-4 nm were deposited over mesoporous g-C3N4 nanosheets. Novel black AuNPs/g-C3N4 plasmonic photocatalysts were used to remove methylene blue (MB) dye from an aqueous solution. The degradation efficiency for the optimal coupling of 1.3 wt.% black AuNPs with g-C3N4 (1.2 g) was found to be 85% within 60 min under visible light irradiation. The calculated kinetic constant was 0.0186 min-1 which was 6.4 and 2.9 times greater than those for g-C3N4 and AuNPs/g-C3N4 nanocomposite, respectively. The excellent potential in photocatalysis was attributed to the synergistic interactions of the g-C3N4 conduction band and the localized surface plasmon resonance effect of black AuNPs. These properties were responsible for the generation of high-energy electrons, a negative shift in the Fermi level of black AuNPs, and the migration of charge carriers. This work studied a new insight into black gold nanoparticles via the design of a visible-light-driven photocatalyst and provided a perspective on valuable photo-related applications such as water treatment. |
5,789 | 'This is the last episode': the association between problematic binge-watching and loneliness, emotion regulation, and sleep-related factors in poor sleepers | Evidence on the relation between binge-watching and sleep quality is still scarce and inconsistent and none has taken into account both the healthy and pathological dimensions of the phenomenon. This study aimed at filling this gap by investigating both aspects in healthy participants with high and low sleep quality. Further, we aimed at identifying sociodemographic, psychological and sleep-related determinants of problematic binge-watching in poor sleepers. We first conducted independent comparisons between good (n = 253) and poor sleepers (n = 209) on different binge-watching symptoms and motives, assessed through 'Binge-watching Engagement and Symptoms' and 'Watching TV Series Motives' questionnaires, respectively. Then, we focused on the problematic aspects of binge-watching in poor sleepers, investigating the role of emotion regulation, loneliness, and sleep-related factors using hierarchical multiple regressions. Comparisons between the two groups revealed a greater extent of binge-watching behaviour (t = -2.80, p = 0.005) and greater use of this practise to cope with negative emotions (t = -4.17, p < 0.001) in poor sleepers. In addition, hierarchical multiple regressions showed that gender (β = -0.166, p = 0.008), alcohol consumption (β = -0.135, p = 0.035), emotional dysregulation (β = 0.260, p = 0.001; β = 0.298, p < 0.001), feelings of loneliness (β = 0.159, p = 0.029; β = 0.199, p = 0.003), and daytime sleepiness (β = 0.149, p = 0.016) are significant determinants of problematic binge-watching in this population. In addition to showing for the first time the relationship between sleep quality and different aspects of binge-watching, our findings indicate that emotional dysregulation, feelings of loneliness, and daytime sleepiness play a key role in determining problematic binge-watching in poor sleepers, possibly due to the existence of a pathological vicious circle between these factors in poor sleepers. |
5,790 | Automated training of location-specific edge models for traffic counting | Deep neural networks are the state of the art for various machine learning problems dealing with large amounts of rich sensor data. It is often desirable to evaluate these models on edge devices instead of relying on cloud computing. In this paper, we perform traffic counting using surveillance cameras. Edge computing is required as only aggregated counts should leave the device and not the privacy sensitive video frames. Unfortunately, only small object detection models are suited for edge devices which results in sub-optimal performance. We introduce location specific models that are each trained for one specific camera. The model does not need to generalize to other locations. We show that smaller specialized models can outperform large general purpose models. We propose an automated way to train these small models without human intervention. We experimentally show that we can achieve a similar counting accuracy with 5x fewer parameters than state-of-the-art techniques. |
5,791 | A review of marine renewable energy storage | Marine renewable energies are promising enablers of a cleaner energy future. Some technologies, like wind, are maturing and have already achieved commercial success. Similar to their terrestrial counterparts, marine renewable energy systems require energy storage capabilities to achieve the flexibility of the 21st century grid demand. The unique difficulties imposed by a harsh marine environment challenge the unencumbered rise of marine renewable energy generation and storage systems. In this study, the fundamentals of marine renewable energy generation technologies are briefed. A comprehensive review and comparison of state-of-the-art novel marine renewable energy storage technologies, including pumped hydro storage (PHS), compressed air energy storage (CAES), battery energy storage (BES), hydrogen energy storage (HES), gravity energy storage (GES), and buoyancy energy storage (ByES), are conducted. The pros and cons, and potential applications, of various marine renewable energy storage technologies are also compiled. Finally, several future trends of marine renewable energy storage technologies are connoted. |
5,792 | Detailed Feature Guided Generative Adversarial Pose Reconstruction Network | Face frontalization is a critical and difficult task on face pose reconstruction. Previous researches use simple posture information as guidance, such as pose coding and facial landmarks. To explore the guidance effect of profile faces, we propose detailed features that provide much detailed information. In this paper, a Detailed Feature Guided Generative Adversarial Pose Reconstruction Network (DGPR) is proposed. Firstly, frontal pose coding and profile detailed features are fed into DGPR to generate detailed features of front face. Then, the second generator combines frontal detailed features and profile face to reconstruct front face. Besides, we propose a conditional enhancement loss to strengthen the guiding role of detailed features, and a smoothing loss to reduce edge sharpness in generated faces. Experimental results show that our method generates photorealistic front faces and outperforms state-of-the-art methods on M(2)FPA and CAS-PEAL. Specifically, DGPR improves the face recognition accuracy under pose angles of +/- 60 degrees, +/- 75 degrees, +/- 90 degrees by 2%, 1%, and 6% respectively over the state-of-the-art methods on M(2)FPA, achieves the average rank-1 recognition rate to 99.95% and improves it by 0.05% on CAS-PEAL. These results demonstrate the effects of detailed features and corresponding modules. |
5,793 | Automatic Detection of Discrimination Actions from Social Images | In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first work to create a dataset for discrimination action recognition and relationship identification. Secondly, a practical approach is developed to achieve automatic detection and identification of discrimination actions and relationships from social images. Thirdly, the task of relationship identification is seamlessly integrated with the task of discrimination action recognition into one single network called the Co-operative Visual Translation Embedding++ network (CVTransE++). We also compared our proposed method with numerous state-of-the-art methods, and our experimental results demonstrated that our proposed methods can significantly outperform state-of-the-art approaches. |
5,794 | Functional Impairment of the Nervous System with Glycolipid Deficiencies | Patients with nervous system disorders suffer from impaired cognitive, sensory and motor functions that greatly inconvenience their daily life and usually burdens their family and society. It is difficult to achieve functional recovery for the damaged central nervous system (CNS) because of its limited ability to regenerate. Glycosphingolipids (GSLs) are abundant in the CNS and are known to play essential roles in cell-cell recognition, adhesion, signal transduction, and cellular migration, that are crucial in all phases of neurogenesis. Despite intense investigation of CNS regeneration, the roles of GSLs in neural regeneration remain unclear. Here we focus on the respective potentials of glycolipids to promote regeneration and repair of the CNS. Mice lacking glucosylceramide, lactosylceramide or gangliosides show lethal phenotypes. More importantly, patients with ganglioside deficiencies exhibit severe clinical phenotypes. Further, neurodegenerative diseases and mental health disorders are associated with altered GSL expression. Accumulating studies demonstrate that GSLs not only delimit physical regions but also play central roles in the maintenance of the biological functions of neurons and glia. We anticipate that the ability of GSLs to modulate behavior of a variety of molecules will enable them to ameliorate biochemical and neurobiological defects in patients. The use of GSLs to treat such defects in the human CNS will be a paradigm-shift in approach since GSL-replacement therapy has not yet been achieved in this manner clinically. |
5,795 | Evolution and Diversity of the TopoVI and TopoVI-like Subunits With Extensive Divergence of the TOPOVIBL subunit | Type II DNA topoisomerases regulate topology by double-stranded DNA cleavage and ligation. The TopoVI family of DNA topoisomerase, first identified and biochemically characterized in Archaea, represents, with TopoVIII and mini-A, the type IIB family. TopoVI has several intriguing features in terms of function and evolution. TopoVI has been identified in some eukaryotes, and a global view is lacking to understand its evolutionary pattern. In addition, in eukaryotes, the two TopoVI subunits (TopoVIA and TopoVIB) have been duplicated and have evolved to give rise to Spo11 and TopoVIBL, forming TopoVI-like (TopoVIL), a complex essential for generating DNA breaks that initiate homologous recombination during meiosis. TopoVIL is essential for sexual reproduction. How the TopoVI subunits have evolved to ensure this meiotic function is unclear. Here, we investigated the phylogenetic conservation of TopoVI and TopoVIL. We demonstrate that BIN4 and RHL1, potentially interacting with TopoVIB, have co-evolved with TopoVI. Based on model structures, this observation supports the hypothesis for a role of TopoVI in decatenation of replicated chromatids and predicts that in eukaryotes the TopoVI catalytic complex includes BIN4 and RHL1. For TopoVIL, the phylogenetic analysis of Spo11, which is highly conserved among Eukarya, highlighted a eukaryal-specific N-terminal domain that may be important for its regulation. Conversely, TopoVIBL was poorly conserved, giving rise to ATP hydrolysis-mutated or -truncated protein variants, or was undetected in some species. This remarkable plasticity of TopoVIBL provides important information for the activity and function of TopoVIL during meiosis. |
5,796 | Attenuating sugar spot and retaining quality of banana fruits by combined use of hot water and calcium lactate during storage | Combined use of hot water (HW) treatment and calcium lactate (CL) is a promising postharvest approach to preserve the food value and prolong the shelf life of fruits. The present experiment aims to determine the physiological loss in weight, firmness, respiration rate, ethylene and biochemical attributes of banana fruits treated with hot water (50°C for 7 min) and aqueous CL dipping (1, 2, and 3% for 2 min). Treated fruits were stored under ambient conditions (22-25°C temperature and 60-65% of relative humidity) for up to 9 days. The study showed that combined use of HW and CL (3%) maintained higher hue angle, peel firmness (4.4 N), reduced decay loss (10.63%), respiration and ethylene evolution rate of stored fruits. Also, CL treatments (3%) with HW proved the best which reduced 6-fold sugar spot and 1.5-fold decay loss over untreated fruits. At the end of storage sensory parameters such as mouthfeel, peel colour and overall acceptability (score 6.9) were recorded higher in HW and CL 3% treated fruits. The findings indicated that pre-storage combined use of HW and CL has a great potential to preserve quality, delay ripening, and reduce sugar spots, and postharvest decay loss in banana fruit without any adverse effect on consumer appeal. |
5,797 | Ring-fused 2-pyridones effective against multidrug-resistant Gram-positive pathogens and synergistic with standard-of-care antibiotics | The alarming rise of multidrug-resistant Gram-positive bacteria has precipitated a healthcare crisis, necessitating the development of new antimicrobial therapies. Here we describe a new class of antibiotics based on a ring-fused 2-pyridone backbone, which are active against vancomycin-resistant enterococci (VRE), a serious threat as classified by the Centers for Disease Control and Prevention, and other multidrug-resistant Gram-positive bacteria. Ring-fused 2-pyridone antibiotics have bacteriostatic activity against actively dividing exponential phase enterococcal cells and bactericidal activity against nondividing stationary phase enterococcal cells. The molecular mechanism of drug-induced killing of stationary phase cells mimics aspects of fratricide observed in enterococcal biofilms, where both are mediated by the Atn autolysin and the GelE protease. In addition, combinations of sublethal concentrations of ring-fused 2-pyridones and standard-of-care antibiotics, such as vancomycin, were found to synergize to kill clinical strains of VRE. Furthermore, a broad range of antibiotic resistant Gram-positive pathogens, including those responsible for the increasing incidence of antibiotic resistant healthcare-associated infections, are susceptible to this new class of 2-pyridone antibiotics. Given the broad antibacterial activities of ring-fused 2-pyridone compounds against Gram-positive (GmP) bacteria we term these compounds GmPcides, which hold promise in combating the rising tide of antibiotic resistant Gram-positive pathogens. |
5,798 | Detectability-Based JPEG Steganography Modeling the Processing Pipeline: The Noise-Content Trade-off | The current art of steganography shows that schemes using a deflection criterion (such as MiPOD) for JPEG steganography are usually subpar with respect to distortion-based schemes. We link this lack of performance to a poor estimation of the variance of the model of the noise on the cover image. However, this statistically-based method provides a better assessment of the detectability of hidden data as well as theoretical guarantees under a given model. In this paper, we propose a method to obtain better estimates of the variances of DCT coefficients by taking into account the dependencies introduced by development pipeline on pixels. A second method, which is a side-informed extension of Gaussian Embedding in the JPEG domain using quantization error as side-information, is also formulated and shown to achieve state-of-the-art performances. Eventually, the trade-off between noise and content complexity in steganography is thoroughly analyzed through the lenses of these two new methods using a wide range of numerical experiments. |
5,799 | Learning an Attention Model for Robust 2-D/3-D Registration Using Point-To-Plane Correspondences | Minimally invasive procedures rely on image guidance for navigation at the operation site to avoid large surgical incisions. X-ray images are often used for guidance, but important structures may be not well visible. These structures can be overlaid from pre-operative 3-D images and accurate alignment can be established using 2-D/3-D registration. Registration based on the point-to-plane correspondence model was recently proposed and shown to achieve state-of-the-art performance. However, registration may still fail in challenging cases due to a large portion of outliers. In this paper, we describe a learning-based correspondence weighting scheme to improve the registration performance. By learning an attention model, inlier correspondences get higher attention in the motion estimation while the outlier correspondences are suppressed. Instead of using per-correspondence labels, our objective function allows to train the model directly by minimizing the registration error. We demonstrate a highly increased robustness, e.g. increasing the success rate from 84.9% to 97.0% for spine registration. In contrast to previously proposed learning-based methods, we also achieve a high accuracy of around 0.5mm mean re-projection distance. In addition, our method requires a relatively small amount of training data, is able to learn from simulated data, and generalizes to images with additional structures which are not present during training. Furthermore, a single model can be trained for both, different views and different anatomical structures. |
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