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5,900 | Gap in the conditioned stimulus: Differential impacts on temporal expectancy in appetitive and aversive conditions in rats | We analyzed, through a Pavlovian conditioning procedure in rats, the temporal pattern of behavior in appetitive and aversive conditions within subjects, and the difference in inferred temporal working memory functioning with the Gap paradigm. For both conditions, we paired a 60-s conditioned stimulus (CS: tone1 or tone2) with an unconditioned stimulus (US: shock or chocolate pellet) delivered 20s after CS onset. The analyses of mean response rate and individual-trial data were performed during Probe trials, consisting of CS alone, and trials in which gaps of different position or duration were inserted, to assess the effect of the temporal manipulation on behavior. The results showed: (1) An anticipatory peak time in the aversive condition but better accuracy in the appetitive condition, (2) constancy in the Weber fraction suggesting that the difference in peak time was under clock control, (3) a graded effect of gap parameters only in the aversive condition and (4) different gap effects between conditions when a gap was inserted early in the CS. These results highlight behavioral differences between aversive and appetitive conditions and suggest that the temporal working memory mechanism was not engaged in the same manner in each condition. |
5,901 | Nickel-Catalyzed Site-Selective Intermolecular C(sp3 )-H Amidation | A nickel-catalyzed site-selective intermolecular amidation of saturated C(sp3 )-H bonds is reported. This mild protocol exhibits a predictable reactivity pattern to incorporate amide functions at C(sp3 )-H sites adjacent to nitrogen and oxygen atoms in either cyclic or acyclic frameworks, thus offering a complementary reactivity profile to existing oxidative-type processes or metal-catalyzed C(sp3 )-N bond-forming reactions operating via two-electron manifolds. |
5,902 | An In-Bore Receiver for Magnetic Resonance Imaging | In magnetic resonance imaging, the use of array detection and the number of detector elements have seen a steady increase over the past two decades. As a result, per-channel analog connection via long coaxial cable, as commonly used, poses an increasing challenge in terms of handling, safety, and coupling among cables. This situation is exacerbated when complementary recording of radiofrequency transmission or NMR-based magnetic field sensing further add to channel counts. A generic way of addressing this trend is the transition to digital signal transmission, enabled by digitization and first-level digital processing close to detector coils and sensors in the magnet bore. The foremost challenge that comes with this approach is to achieve high dynamic range, linearity, and phase stability despite interference by strong static, audiofrequency, and radiofrequency fields. The present work reports implementation of a 16-channel in-bore receiver, performing signal digitization and processing with subsequent optical transmission over fiber. Along with descriptions of the system design and construction, performance evaluation is reported. The resulting device is fully MRI compatible providing practically equal performance and signal quality compared to state-of-the-art RF digitizers operating outside the magnet. Its use is demonstrated by examples of head imaging and magnetic field recording. |
5,903 | A Survey of Autonomous Driving: Common Practices and Emerging Technologies | Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development. |
5,904 | Cement Augmentation of Two-Level Lumbar Corpectomy Cage After Malposition: A Novel Salvage Procedure Technical Note | Intervertebral cage mispositioning is an uncommon complication of a posterior lumbar corpectomy. Most frequently, cages are placed obliquely, laterally, or protruding. However, there are few reports of implanted cages that fail to contact the adjacent vertebral endplate and thus no descriptions of successful revisions. The objective of this case report is to report a unique case of minimally invasive rescue vertebroplasty with cement augmentation following a lumbar corpectomy that resulted in graft-endplate noncontact in a medically complicated patient A 60-year-old male with a history of active intravenous (IV) drug use, untreated hepatitis C virus (HCV) infection, and chronic malnourishment presented with low back pain. He had a history of vertebral osteomyelitis managed with intravenous antibiotics, although he was noncompliant with infusions. The diagnosis of L2-L3 discitis-osteomyelitis with intradiscal abscess causing cord compression was made using inpatient lumbar imaging. The initial intervention was accomplished with L2 and L3 vertebral corpectomy with decompression and expandable cage placement as well as a T10-pelvis posterior fixation. Despite the resolution of presenting symptoms, routine postoperative radiographs identified noncontact between the inferior surface of the cage and the superior endplate of the L4 vertebral body. Salvage therapy was pursued via fluoroscopy-guided vertebroplasty with cement augmentation to correct cage malposition. Secondary surgical intervention was successful in bringing the intervertebral cage into contact with the adjacent vertebral body. Lower extremity strength improved, and back pain was resolved. The postoperative motor examination remained unchanged after the rescue procedure. Accurate intraoperative cage placement can be difficult in patients with poor bone quality, especially in the setting of ongoing infection and cachexia. For this reason, routine postoperative imaging is crucial to assessing graft complications. In patients who are poor candidates for revision surgery, we demonstrate that an interventional radiology-based approach may be successful in correcting cage mispositioning and preventing further changes during healing and fusion. |
5,905 | Deep Trident Decomposition Network for Single License Plate Image Glare Removal | Deep convolutional neural networks have achieved state-of-the-art performance for the removal of atmospheric obscuration. However, most relevant studies have focused on eliminating the effects of atmospheric obscuration but not on the glare in images caused by reflected sunlight. On the basis of a glare image formation model, we propose a deep trident decomposition network with a large-scale sun glare image dataset for glare removal from single images. Specifically, the proposed network is designed and implemented with a trident decomposition module for decomposing an input glare image into occlusion, foreground, and coarse glare-free images by exploring background features from spatial locations. Moreover, a residual refinement module is adopted to refine the coarse glare-free image into fine glare-free image by learning the residuals from features of multiscale receptive field. The experimental results indicated that the proposed network significantly outperforms state-of-the-art atmospheric obscuration removal networks on the built dataset. |
5,906 | Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems | Since their introduction, anti-lock braking systems (ABS) have mostly relied on heuristic, rule-based control strategies. ABS performance, however, can be significantly improved thanks to many recent technological developments. This work presents an extensive review of the state of the art to verify such a statement and quantify the benefits of a new generation of wheel slip control (WSC) systems. Motivated by the state of the art, as a case study, a nonlinear model predictive control (NMPC) design based on a new load-sensing technology was developed. The proposed ABS was tested on Toyota's high-end vehicle simulator and was benchmarked against currently applied industrial controller. Additionally, a comprehensive set of manoeuvres were deployed to assess the performance and robustness of the proposed NMPC design. The analysis showed substantial reduction of the braking distance and better steerability with the proposed approach. Furthermore, the proposed design showed comparable robustness against external factors to the industrial benchmark. |
5,907 | Attacks on state-of-the-art face recognition using attentional adversarial attack generative network | With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. Therefore, it is very important to study how face recognition networks are subject to attacks. Generating adversarial examples is an effective attack method, which misleads the face recognition system through obfuscation attack (rejecting a genuine subject) or impersonation attack (matching to an impostor). In this paper, we introduce a novel GAN, Attentional Adversarial Attack Generative Network (A(3)GN), to generate adversarial examples that mislead the network to identify someone as the target person not misclassify inconspicuously. For capturing the geometric and context information of the target person, this work adds a conditional variational autoencoder and attention modules to learn the instance-level correspondences between faces. Unlike traditional two-player GAN, this work introduces a face recognition network as the third player to participate in the competition between generator and discriminator which allows the attacker to impersonate the target person better. The generated faces which are hard to arouse the notice of onlookers can evade recognition by state-of-the-art networks and most of them are recognized as the target person. |
5,908 | Structure Tensor Riemannian Statistical Models for CBIR and Classification of Remote Sensing Images | This paper deals with parametric techniques for the description of texture on very high resolution (VHR) remote sensing images. These techniques focus on the property of anisotropy as described by the local structure tensor (LST). The novelty of this paper consists in proposing several comprehensive statistical frameworks to handle LST fields for rotation-invariant texture discrimination tasks. These frameworks are all based on probability models defined on the Riemannian manifold of positive definite matrices: a recent Riemannian Gaussian model on the affine-invariant metric space and a multivariate Gaussian distribution on the Log-Euclidean space. A thorough comparison of the proposed methods is performed with respect to some state-of-the-art texture analysis methods. Three experimental protocols are considered based on VHR remote sensing data. The first one consists of a content-based image retrieval (CBIR) protocol for browsing oyster field patches. The second one concerns a supervised classification protocol for grouping maritime pine forest stands in different age classes. The third one is, again, a CBIR protocol performed on the UC Merced land use/land cover patch collection. Tensor-based approaches show similar or even better results than the state-of-the-art texture analysis methods considered for comparison in all the experimental contexts. |
5,909 | Antiviral effects of natural small molecules on aquatic rhabdovirus by interfering with early viral replication | Spring viremia of carp virus (SVCV) is globally widespread and poses a serious threat to aquatic ecology and aquaculture due to its broad host range. To develop effective agents to control SVCV infection, we selected 16 naturally active small molecules to assess their anti-SVCV activity. Notably, dihydroartemisinin (DHA) (100 µmol/L) and (S, S)-(+)-tetrandrine (TET) (16 µmol/L) exhibited high antiviral effects in epithelioma papulosum cyprinid (EPC) cells, with inhibitory rates of 70.11% and 73.54%, respectively. The possible antiviral mechanisms were determined as follows: 1. Pre-incubation with DHA and TET decreased viral particle infectivity in fish cells, suggesting that horizontal transmission of SVCV in the aquatic environment was disrupted; 2. Although neither had an effect on viral adhesion, TET (but not DHA) interfered with SVCV entry into host cells (>80%), suggesting that TET may have an antiviral function in early viral replication. For in vivo study, both agents enhanced the survival rate of SVCV-infected zebrafish by 53.3%, significantly decreased viral load, and modulated the expression of antiviral-related genes, indicating that DHA and TET may stimulate the host innate immune response to prevent viral infection. Overall, our findings indicated that DHA and TET had positive effects on suppressing SVCV infection by affecting early-stage viral replication, thus holding great potential as immunostimulants to reduce the risk of aquatic rhabdovirus disease outbreaks. |
5,910 | Revisiting Linear Discriminant Techniques in Gender Recognition | Emerging applications of computer vision and pattern recognition in mobile devices and networked computing require the development of resource-limited algorithms. Linear classification techniques have an important role to play in this context, given their simplicity and low computational requirements. The paper reviews the state-of-the-art in gender classification, giving special attention to linear techniques and their relations. It discusses why linear techniques are not achieving competitive results and shows how to obtain state-of-the-art performances. Our work confirms previous results reporting very close classification accuracies for Support Vector Machines (SVMs) and boosting algorithms on single-database experiments. We have proven that Linear Discriminant Analysis on a linearly selected set of features also achieves similar accuracies. We perform cross-database experiments and prove that single database experiments were optimistically biased. If enough training data and computational resources are available, SVM's gender classifiers are superior to the rest. When computational resources are scarce but there is enough data, boosting or linear approaches are adequate. Finally, if training data and computational resources are very scarce, then the linear approach is the best choice. |
5,911 | Applying a Foucauldian lens to the Canadian code of ethics for registered nurses as a discursive mechanism for nurses professional identity | This study examines the Canadian Code of Ethics for Registered Nurses as a discursive mechanism for shaping nurses' professional identity using a Foucauldian lens. Nurses are considered essential in healthcare, yet the nursing profession has struggled to be recognized for its discipline-specific knowledge and expertise and, as such, has remained the subject of and subject to the dominant discourses within healthcare and society generally. Developing a professional identity in nursing begins after the necessary education and training are achieved and embodies the profession's history, values, code of ethics, and expectations of the profession that distinguish it from other professions. Since nurses' professional identity is shaped through discourse, it raises the question of whether there are spaces to reconceptualize nurses' subject position within health care. Since professional identity is considered the embodiment of knowledge and practice, the code of ethics bears examination both for its effect on nurses' professional identity and as a potential site from which to challenge hegemonic assumptions. This article discusses the concept of professional identity in nursing and its development through the discursive formations in the code of ethics. The sources of power/knowledge are examined as both mechanisms of control and as spaces for change. |
5,912 | Video-Based Intervention for Improving Maternal Retention and Adherence to HIV Treatment: Patient Perspectives and Experiences | VITAL Start is a video-based intervention aimed to improve maternal retention in HIV care and adherence to antiretroviral therapy (ART) in Malawi. We explored the experiences of pregnant women living with HIV (PWLHIV) not yet on ART who received VITAL Start before ART initiation to assess the intervention's acceptability, feasibility, fidelity of delivery, and perceived impact. Between February and September 2019, we conducted semi-structured interviews with a convenience sample of 34 PWLHIV within one month of receiving VITAL Start. The participants reported that VITAL Start was acceptable and feasible and had good fidelity of delivery. They also reported that the video had a positive impact on their lives, encouraging them to disclose their HIV status to their sexual partners who, in turn, supported them to adhere to ART. The participants suggested using a similar intervention to provide health-related education/counseling to people with long term conditions. Our findings suggest that video-based interventions may be an acceptable, feasible approach to optimizing ART retention and adherence amongst PWLHIV, and they can be delivered with high fidelity. Further exploration of the utility of low cost, scalable, video-based interventions to address health counseling gaps in sub-Saharan Africa is warranted. |
5,913 | The Ideal Anticoagulation Strategy in ST-Elevation Myocardial Infarction | Heparin has been the principal anticoagulant in the management of ST-elevation myocardial infarction (STEMI) but has several limitations. Although glycoprotein IIb/IIIa inhibitors have been major adjuncts in previous years, in the era of novel P2Y12 receptor inhibitors they may have a greater role in bailout. Low molecular weight heparins have been extensively studied in fibrinolysis trials but data in primary percutaneous coronary intervention (PCI) are scarce. The direct thrombin inhibitor bivalirudin overcomes several shortcomings of heparins and has demonstrated a significant reduction in bleeding outcomes and net adverse cardiac events at the cost of increased acute stent thrombosis. This review discusses the pharmacology and clinical trial evidence for different anticoagulant treatment options in STEMI with a proposed selection strategy in contemporary primary PCI. |
5,914 | Correspondence on "Synergy and Antagonism between Allosteric and Active-Site Inhibitors of Abl Tyrosine Kinase" | Soellner published on the interplay between allosteric and adenosine triphosphate (ATP)-competitive inhibitors of ABL kinase, showing that the latter preferably binds to different conformational states of ABL compared to allosteric agents that specifically target the ABL myristate pocket (STAMP) and deducing that asciminib cannot bind to ABL simultaneously with ATP-competitive drugs. These results are to some extent in line with ours, although our analyses of dose-response matrices from combinations of asciminib with imatinib, nilotinib or dasatinib, show neither synergy nor antagonism, but suggest additive antiproliferative effects on BCR-ABL-dependent KCL22 cells. Furthermore, our X-ray crystallographic, solution nuclear magnetic resonance (NMR), and isothermal titration calorimetry studies show that asciminib can bind ABL concomitantly with type-1 or -2 ATP-competitive inhibitors to form ternary complexes. Concomitant binding of asciminib with imatinib, nilotinib, or dasatinib might translate to benefit some chronic myeloid leukaemia patients. |
5,915 | Weighted sparse representation for human ear recognition based on local descriptor | A two-stage ear recognition framework is presented where two local descriptors and a sparse representation algorithm are combined. In a first stage, the algorithm proceeds by deducing a subset of the closest training neighbors to the test ear sample. The selection is based on the K-nearest neighbors classifier in the pattern of oriented edge magnitude feature space. In a second phase, the co-occurrence of adjacent local binary pattern features are extracted from the preselected subset and combined to form a dictionary. Afterward, sparse representation classifier is employed on the developed dictionary in order to infer the closest element to the test sample. Thus, by splitting up the ear image into a number of segments and applying the described recognition routine on each of them, the algorithm finalizes by attributing a final class label based on majority voting over the individual labels pointed out by each segment. Experimental results demonstrate the effectiveness as well as the robustness of the proposed scheme over leading state-of-the-art methods. Especially when the ear image is occluded, the proposed algorithm exhibits a great robustness and reaches the recognition performances outlined in the state of the art. (C) 2016 SPIE and IS&T |
5,916 | Edge-Embedded Multi-Dropout Framework for Real-Time Face Alignment | We propose the Edge-Embedded Multi-Dropout (EEMD) framework for real-time face alignment. The EEMD framework extracts facial edge features and explores multiple dropout architecture for locating facial landmarks. It consists of two major component networks, namely the Contour Detection Network (CDN) and the Multi-Dropout Network (MDN); and two supplementary networks, one for face detection and the other for pose regression. When a face is detected by the face detector, its pose will be classified by the pose classifier, then the associated facial edges be detected by the CDN, and then the landmarks be located by the MDN. The embedding of the CDN into the EEMD framework describes the observation that most landmarks are located on the contours/edges of the facial components and of the whole face. We revise a state-of-the-art edge detector as part of the base network for the CDN. The MDN is proposed to better design the regression architecture with appropriate dropout settings for better preventing overfitting and enhancing regression accuracy. Unlike most of the 2D approaches unable to locate landmarks in extreme poses, the proposed framework can detect landmarks on profile faces, i.e., & x00B1;90 & x00B0; in yaw, <italic>in real time</italic>. Evaluated on benchmark databases, the EEMD demonstrates a competitive performance to other state-of-the-art approaches with a satisfying runtime speed. |
5,917 | Selective clustering for representative paintings selection | Selective classification (or rejection based classification) has been proved useful in many applications. In this paper we describe a selective clustering framework with reject option to carry out large-scale digital arts analysis. With the help of deep learning techniques, we extract content-style features from a pre-trained convolutional network for the paintings. By proposing a rejection mechanism under Bayesian framework, we focus on selecting style-oriented representative paintings of an artist, which is an interesting and challenging cultural heritage application. Two kinds of samples are rejected during the rejection based robust continuous clustering process. Representative paintings are selected during the selective clustering phase. Visual qualitative analysis on small painting set and large scale quantitative experiments on a subset of Wikiart show that the proposed rejection based selective clustering approach outperforms the standard clustering methods. |
5,918 | Anthranilic Acid Accumulation in Saccharomyces cerevisiae Induced by Expression of a Nonribosomal Peptide Synthetase Gene from Paecilomyces cinnamomeus BCC 9616 | Heterologous expression of nrps33, a nonribosomal peptide synthetase gene, from Paecilomyces cinnamomeus BCC 9616 in Saccharomyces cerevisiae unexpectedly resulted in the accumulation of anthranilic acid, an intermediate in tryptophan biosynthesis. Based on transcriptomic and real-time quantitative polymerase chain reaction (RT-qPCR) results, expression of nrps33 affected the transcription of tryptophan biosynthesis genes especially TRP1 which is also the selectable auxotrophic marker for the expression vector used in this work. The product of nrps33 could inhibit the activity of Trp4 involved in the conversion of anthranilate to N-(5'-phosphoribosyl)anthranilate and therefore caused the accumulation of anthranilic acid. This accumulation could in turn result in down-regulation of downstream tryptophan biosynthesis genes. Anthranilic acid is typically produced by chemical synthesis and has been used as a substrate for synthesising bioactive compounds including commercial drugs; our results could provide a new biological platform for production of this compound. |
5,919 | Surv-CRM-12: A Bayesian phase I/II survival CRM for right-censored toxicity endpoints with competing disease progression | The growing interest in new classes of anti-cancer agents, such as molecularly-targeted therapies and immunotherapies with modes of action different from those of cytotoxic chemotherapies, has changed the dose-finding paradigm. In this setting, the observation of late-onset toxicity endpoints may be precluded by treatment and trial discontinuation due to disease progression, defining a competing event to toxicity. Trial designs where dose-finding is modeled in the framework of a survival competing risks model appear particularly well-suited. We aim to provide a phase I/II dose-finding design that allows dose-limiting toxicity (DLT) outcomes to be delayed or unobserved due to competing progression within the possibly long observation window. The proposed design named the Survival-continual reassessment method-12, uses survival models for right-censored DLT and progression endpoints. In this competing risks framework, cause-specific hazards for DLT and progression-free of DLT were considered, with model parameters estimated using Bayesian inference. It aims to identify the optimal dose (OD), by minimizing the cumulative incidence of disease progression, given an acceptable toxicity threshold. In a simulation study, design operating characteristics were evaluated and compared to the TITE-BOIN-ET design and a nonparametric benchmark approach. The performance of the proposed method was consistent with the complexity of scenarios as assessed by the nonparametric benchmark. We found that the proposed design presents satisfying operating characteristics in selecting the OD and safety. |
5,920 | The complete chloroplast genome of Schrenkiella parvula (Brassicaceae) | Schrenkiella parvula is an Arabidopsis-related model species used here for studying plant stress tolerance. In this study, the complete chloroplast genome sequence of S. parvula has been reported for the first time. The total length of the chloroplast genome was 153 979 bp, which had a typical quadripartite structure. The annotated plastid genome includes 87 protein-coding genes, 39 tRNA genes and 8 ribosomal RNA genes. The evolutionary relationships revealed by our phylogenetic analysis indicated that S. parvula is closer to the Brassiceae species when compared with Eutrema salsugineum. |
5,921 | In Situ Activation of Superhydrophobic Surfaces with Triple Icephobicity at Low Temperatures | Superhydrophobic surfaces have been widely studied due to their potential applications in aerospace fields. However, superhydrophobic surfaces with excellent water-repellent, anti-icing, and icephobic performances at low temperatures have rarely been reported. Herein, superhydrophobic surfaces with heating capability were prepared by etching square micropillar arrays on the surface of multiwalled carbon nanotube (MWCNT)/poly(dimethylsiloxane) (PDMS) films. The fabricated superhydrophobic surface has triple icephobicity, which can be activated even at low temperatures. The triple icephobicity is triggered by an applied voltage to achieve excellent water-repellent and icephobic capabilities, even at -40 °C. Additionally, theoretical calculations reveal that a droplet on a superhydrophobic surface loses heat at a rate of 8.91 × 10-5 J/s, which is 2 orders of magnitude slower than a flat surface (2.15 × 10-3 J/s). Also, at -40 °C, the mechanical interlocking force formed between the superhydrophobic surface and ice can be released by the heating property of the superhydrophobic surface. This low-energy, multifunctional superhydrophobic surface opens up new possibilities for bionic smart multifunctional materials in icephobic applications. |
5,922 | A Novel Polar Space Random Field Model for the Detection of Glandular Structures | In this paper, we propose a novel method to detect glandular structures in microscopic images of human tissue. We first convert the image from Cartesian space to polar space and then introduce a novel random field model to locate the possible boundary of a gland. Next, we develop a visual feature-based support vector regressor to verify if the detected contour corresponds to a true gland. And finally, we combine the outputs of the random field and the regressor to form the GlandVision algorithm for the detection of glandular structures. Our approach can not only detect the existence of the gland, but also can accurately locate it with pixel accuracy. In the experiments, we treat the task of detecting glandular structures as object (gland) detection and segmentation problems respectively. The results indicate that our new technique outperforms state-of-the-art computer vision algorithms in respective fields. |
5,923 | Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud | Mobile device cloud (MDC) is a collaborative cloud computing platform over which neighboring smart devices form an alliance of shared resources to mitigate resource-scarcity of an individual user device for running compute-intensive applications. A major challenge of such a platform is maximizing user quality-of-experience (QoE) at minimum cost while providing attractive incentives to workers' mobile devices. In state-of-the-art works, either a voluntary task execution or merely resource-cost driven mechanism has been applied to minimize the task execution time while overlooking payment of any additional incentive to the worker devices for their quality services. In this paper, we develop a computational framework for MDC where the afore-mentioned challenging problem is formulated as a multi-objective linear programming (MOLP) optimization function that exploits reverse-auction bidding policy. Due to the NP-hardness of MOLP, we offer two greedy worker selection algorithms for maximizing user QoE or minimizing execution cost. In both algorithms, the amount of incentive awarded to a worker is determined following the QoE offered to a user. Theoretical proofs of desirable properties of the proposed incentive mechanisms are presented. Simulation results illustrate the effectiveness of our incentive algorithms compared to the state-of-the-art approaches. |
5,924 | SIFT Matching by Context Exposed | This paper investigates how to step up local image descriptor matching by exploiting matching context information. Two main contexts are identified, originated respectively from the descriptor space and from the keypoint space. The former is generally used to design the actual matching strategy while the latter to filter matches according to the local spatial consistency. On this basis, a new matching strategy and a novel local spatial filter, named respectively blob matching and Delaunay Triangulation Matching (DTM) are devised. Blob matching provides a general matching framework by merging together several strategies, including rank-based pre-filtering as well as many-to-many and symmetric matching, enabling to achieve a global improvement upon each individual strategy. DTM alternates between Delaunay triangulation contractions and expansions to figure out and adjust keypoint neighborhood consistency. Experimental evaluation shows that DTM is comparable or better than the state-of-the-art in terms of matching accuracy and robustness. Evaluation is carried out according to a new benchmark devised for analyzing the matching pipeline in terms of correct correspondences on both planar and non-planar scenes, including several state-of-the-art methods as well as the common SIFT matching approach for reference. This evaluation can be of assistance for future research in this field. |
5,925 | Neural Architecture Search for LF-MMI Trained Time Delay Neural Networks | State-of-the-art automatic speech recognition (ASR) system development is data and computation intensive. The optimal design of deep neural networks (DNNs) for these systems often require expert knowledge and empirical evaluation. In this paper, a range of neural architecture search (NAS) techniques are used to automatically learn two types of hyper-parameters of factored time delay neural networks (TDNN-Fs): i) the left and right splicing context offsets; and ii) the dimensionality of the bottleneck linear projection at each hidden layer. These techniques include the differentiable neural architecture search (DARTS) method integrating architecture learning with lattice-free MMI training; Gumbel-Softmax and pipelined DARTS methods reducing the confusion over candidate architectures and improving the generalization of architecture selection; and Penalized DARTS incorporating resource constraints to balance the trade-off between performance and system complexity. Parameter sharing among TDNN-F architectures allows an efficient search over up to 7(28) different systems. Statistically significant word error rate (WER) reductions of up to 1.2% absolute and relative model size reduction of 31% were obtained over a state-of-the-art 300-hour Switchboard corpus trained baseline LF-MMI TDNN-F system featuring speed perturbation, i-Vector and learning hidden unit contribution (LHUC) based speaker adaptation as well as RNNLM restoring. Performance contrasts on the same task against recent end-to-end systems reported in the literature suggest the best NAS auto-configured system achieves state-of-the-art WERs of 9.9% and 11.1% on the NIST HubS' 00 and Rt03 s test sets respectively with up to 96% model size reduction. Further analysis using Bayesian learning shows that the proposed NAS approaches can effectively minimize the structural redundancy in the TDNN-F systems and reduce their model parameter uncertainty. Consistent performance improvements were also obtained on a UASpeech dysarthric speech recognition task. |
5,926 | Grounded Vocabulary for Image Retrieval Using a Modified Multi-Generator Generative Adversarial Network | With the recent increase in requirement of both natural-language and visual information, the demand for research on seamless multi-modal processing for effective retrieval of these types of information has increased. However, because of the unstructured nature of images, it is difficult to retrieve images that accurately represent the input text. In this study, we utilized an augmented version of a multi-generator generative adversarial network that uses BERT embeddings and attention maps as input to enable grounded vocabulary for visual representations. We compared the performance of our proposed model with those of other state-of-the-art text input-based image retrieval methods on the MSCOCO and Flikr30K datasets, and the results showed the potential of our proposed method. Even with limited vocabulary, our proposed model was comparable to other state-of-the-art performances on R@10 or even exceed them in R@1. Moreover, we revealed the unique properties of our method by demonstrating how it could perform successfully even when using more descriptive text or short sentences as input. |
5,927 | Living in Contaminated Areas-Consideration of Different Perspectives | Following large-scale nuclear power plant accidents such as those that occurred at Chernobyl (Ukraine) in 1986 and Fukushima Daiichi (Japan) in 2011, large populations are living in areas containing residual amounts of radioactivity. As a key session of the ConRad conference, experts were invited from different disciplines to provide state-of-the-art information on the topic of "living in contaminated areas." These experts provided their different perspectives on a range of topics including radiation protection principles and dose criteria, environmental measurements and dose estimation, maintaining decent living and working conditions, evidence of health risks, and social impact and risk communication. A short summary of these different perspectives is provided in this paper. |
5,928 | HTLV-1-associated adult T cell leukemia is highly susceptible to Navitoclax due to enhanced Bax expression | Over-expression of Bcl-2, Bcl-xL and Bcl-w is frequently associated with cancer resistance to chemotherapy. Navitoclax (ABT-263), an orally bio-available small-molecule mimetic of the Bcl-2 homology domain 3, specifically inhibits Bcl-2, Bcl-xL and Bcl-w. Despite promising results obtained from the clinical trials, the use of Navitoclax in patients is dose-limited due to induction of death of platelets via inhibition of Bcl-xL and subsequent thrombocytopenia. This side effect limits the use of Navitoclax in low doses and to very sensitive tumors. In this study, we show that HTLV-1-associated adult T-cell leukemia/lymphoma (ATL) cells, which over-express Bcl-2, Bcl-xL and Bcl-w, show a 10- to 20-fold higher sensitivity (EC50 = ∼ 25-50 nM) to Navitoclax compared to non-HTLV-1-associated leukemic cells (EC50 = ∼ 1 μM). Investigation of the molecular mechanisms revealed that the HTLV-1 oncogenic protein Tax up-regulates expression of the pro-apoptotic protein Bax which enhances the therapeutic efficacy of Navitoclax. In addition, we show that agents that inhibit the transcription elongation or translation initiation such as Wogonin and Roc-A can further decrease the effective dose of Navitoclax. Our study suggests that HTLV-1 ATL may be a good candidate disease for low dose Navitoclax therapy and probably with less risk of thrombocytopenia. |
5,929 | STI-BT: A Scalable Transactional Index | Distributed Key-Value (DKV) stores have been intensively used to manage online transaction processing on large data-sets. DKV stores provide simplistic primitives to access data based on the primary key of the stored objects. To help programmers to efficiently retrieve data, some DKV stores provide distributed indexes. Besides that, and also to simplify programming such applications, several proposals have provided strong consistency abstractions via distributed transactions. In this paper we present STI-BT, a highly scalable, transactional index for Distributed Key-Value stores. STI-BT is organized as a distributed B(+)Tree and adopts an innovative design that allows to achieve high efficiency in large-scale, elastic DKV stores. As such, it provides both the desirable properties identified above, and does so in a far more efficient and scalable way than the few existing state of the art proposals that also enable programmers to have strongly consistent distributed transactional indexes. We have implemented STI-BTon top of an open-source DKV store and deployed it on a public cloud infrastructure. Our extensive study demonstrates scalability in a cluster of 100 machines, and speed ups with respect to state of the art up to 5.4x. |
5,930 | Haladaptatus halobius sp. nov. and Haladaptatus salinisoli sp. nov., two extremely halophilic archaea isolated from Gobi saline soil | Two extremely halophilic archaeal strains, PSR5T and PSR8T, were isolated from a saline soil sample collected from the Tarim Basin, Xinjiang, PR China. Both strains had two copies of the 16S rRNA genes rrn1 and rrn2, showing 2.6 and 3.9% divergence, respectively. The rrn1 gene of PSR5T showed 98.4 and 95.3% similarity to the rrn1 and rrn2 genes of strain PSR8T; the rrn2 gene of PSR5T displayed 97.4 and 96.7% similarity to those of strain PSR8T, respectively. Phylogenetic analyses based on the 16S rRNA and rpoB' genes revealed that strains PSR5T and PSR8T formed a single cluster, and then tightly clustered with the current four Haladaptatus species (93.5-97.1% similarities for the 16S rRNA gene and 89.3-90.9% similarities for the rpoB' gene, respectively). Several phenotypic characteristics differentiate strains PSR5T and PSR8T from current Haladaptatus members. The polar lipids of the two strains are phosphatidic acid, phosphatidylglycerol, phosphatidylglycerol phosphate methyl ester phosphatidylglycerol sulphate and three glycolipids. One of the glycolipids is sulphated mannosyl glucosyl diether, and the remaining two glycolipids are unidentified. The average nucleotide identity, in silico DNA-DNA hybridization, amino acid identity and percentage of conserved proteins values between the two strains were 88.5, 39.1, 89.3 and 72.8 %, respectively, much lower than the threshold values proposed as a species boundary. These values among the two strains and Haladaptatus members were 77.9-79.2, 22.0-23.5, 75.1-78.2 and 56.8-69.9 %, respectively, much lower than the recommended threshold values for species delimitation. These results suggested that strains PSR5T and PSR8T represent two novel species of Haladaptatus. Based on phenotypic, chemotaxonomic, genomic and phylogenetic properties, strains PSR5T (=CGMCC 1.16851T=JCM 34141T) and PSR8T (=CGMCC 1.17025T=JCM 34142T) represent two novel species of the genus Haladaptatus, for which the names Haladaptatus halobius sp. nov. and Haladaptatus salinisoli sp. nov. are proposed. |
5,931 | Studying the genotoxic effects of high intensity terahertz radiation on fibroblasts and CNS tumor cells | The data is obtained on the effect of high-intensity pulses of terahertz (THz) radiation with a broad spectrum (0.2-3 THz) on cell cultures. We have evaluated the threshold exposure parameters of THz radiation causing genotoxic effects in fibroblasts. Phosphorylation of histone H2AX at Ser 139 (γH2AX) was chosen as a marker for genotoxicity and a quantitative estimation of γH2AX foci number in fibroblasts was performed after cell irradiation with THz pulses for 30 min. No genotoxic effects of THz radiation were observed in fibroblasts unless peak intensity and electric field strength exceeded 21 GW cm-2 and 2.8 MV cm-1 , respectively. In tumor cell lines (neuroblastoma (SK-N-BE (2)) and glioblastoma (U87)), exposure to THz pulses with peak intensity of 21 GW cm-2 for 30 min caused no morphological changes as well as no statistically significant increase in histone phosphorylation foci number. |
5,932 | Visualization of the microscopic flow profile of state-of-the-art absorption heat pump working pairs under operational conditions | The research and development of novel working pairs and highly efficient heat exchangers for absorption heat pump applications require a detailed analysis of the microscopic flow profile at the absorber heat exchanger during the absorption process. A recently developed Single Aperture Defocussing Micro Particle Tracking method, based on a conventional Particle Image Velocimetry system, demonstrates the possibility of simultaneous measurements of velocity profiles and film thicknesses in falling film absorbers at steady-state flows. We show first results of velocity profile measurements of the state-of-the-art working pair LiBr/water, with and without a 2-Ethyl-1-Hexanol additive, during operation in a lab-scale absorption heat pump. Based on the results of the measurements, the advantages and disadvantages of the developed measurement technique and their potential application in the characterization of novel working pairs is discussed. (C) 2014 Elsevier Ltd. All rights reserved. |
5,933 | IEC 62697-2012: State of the Art Methods for Quantification of DBDS and Other Corrosive Sulfur Compounds in Unused and Used Insulating Liquids | This article describes state of the art methods developed by IEC TC-10 WG-37 for quantitative determination of a highly corrosive sulfur compound dibenzodisulfide (DBDS) and other corrosive sulfur compounds in unused and used insulating liquids. The methods permit an objective approach for assessing corrosiveness of insulating liquids rather than subjective assessment based on color perception that is prevalent in the current standard test methods for corrosive or potentially corrosive sulfur in insulating liquids. In addition, quantification of total corrosive sulfur TCS) in insulating liquids permits an objective ranking of sulfur compounds according to their corrosivity towards copper. |
5,934 | Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories | In this paper, we study the problem of segmenting tracked feature point trajectories of multiple moving objects in an image sequence. Using the affine camera model, this problem can be cast as the problem of segmenting samples drawn from multiple linear subspaces. In practice, due to limitations of the tracker, occlusions, and the presence of nonrigid objects in the scene, the obtained motion trajectories may contain grossly mistracked features, missing entries, or corrupted entries. In this paper, we develop a robust subspace separation scheme that deals with these practical issues in a unified mathematical framework. Our methods draw strong connections between lossy compression, rank minimization, and sparse representation. We test our methods extensively on the Hopkins155 motion segmentation database and other motion sequences with outliers and missing data. We compare the performance of our methods to state-of-the-art motion segmentation methods based on expectation-maximization and spectral clustering. For data without outliers or missing information, the results of our methods are on par with the state-of-the-art results and, in many cases, exceed them. In addition, our methods give surprisingly good performance in the presence of the three types of pathological trajectories mentioned above. All code and results are publicly available at http://perception.csl.uiuc.edu/coding/motion/. |
5,935 | Standard Plenoptic Cameras Mapping to Camera Arrays and Calibration Based on DLT | First prototypes of standard plenoptic cameras (SPCs) were based on arrays of pinhole cameras. Despite the array nature, viewpoint pinhole arrays are not intrinsically provided by current SPC calibration tools. In this work, we start by detailing the mapping between the SPC model and a camera array of viewpoints. Then, the mapping is used to propose a calibration procedure for the SPC based on a grid of corners. Calibration involves two steps, first a linear solution and then a nonlinear optimization minimizing the ray re-projection error. The proposed calibration methodology compares favourably with state of the art calibrations and the linear solution proposed for the initial stage of the calibration outperforms the state of the art. |
5,936 | Deep feature extraction for document forgery detection with convolutional autoencoders | Context: Document forgery is a significant problem for ages due to paper-based documents' pervasive use. Classical destructive approaches for this problem, such as chromatography and electrophoresis, cannot be implemented as they flaw the document under analysis. Hyperspectral imaging - non-destructive approach that assists in finding the unique features of an image under investigation through their unique spectral signatures. It captures multiple narrow-band images at the electromagnetic spectrum, which is difficult through conventional imaging. Deep learning approaches for hyperspectral images have attained state-of-the-art results for solving many complex and challenging problems. Supervised classification of hyperspectral images is a tedious task since obtaining image labels and labeling the training data is a time-consuming and expensive process. In this paper, an unsupervised approach for classification of hyperspectral document images is proposed. Objective: To propose an unsupervised deep learning approach for ink mismatch detection in hyperspectral document images using spectral features. Approach: CAE-LR approach is proposed that uses Convolutional Autoencoder (CAE) for feature extraction and utilizing them for ink mismatch detection through Logistic Regression (LR). Results: We evaluated the performance of CAE-LR on UWA writing ink hyperspectral images dataset for blue and black inks. Artificially similar color inks of different types (2 similar to 5) were mixed in varying proportions to detect ink mismatch. Additionally, results are compared with three machine learning algorithms with variants of each, CNN, and five state-of-art methods used by the researchers. Experimental results illustrated that the CAE-LR outperforms all the above - mentioned approaches by achieving the state of art results, which depicts the efficacy of unsupervised deep learning approach for ink mismatch detection in hyperspectral document images. |
5,937 | FANCD2 promotes the malignant behavior of endometrial cancer cells and its prognostic value | Defective DNA damage repair is a key mechanism affecting tumor susceptibility, treatment response, and survival outcome of endometrial cancer (EC). Fanconi anemia complementation group D2 (FANCD2) is the core component of the Fanconi anemia repair pathway. To explore the function of FANCD2 in EC, we examined the expression of FANCD2 in human specimens and databases, and discussed the possible mechanism of carcinogenesis by in vitro assays. Immunohistochemistry results showed overexpression of FANCD2 was detected in EC tissues compared to normal and atypical hyperplasia endometrium. Higher FANCD2 expression was correlated with deeper myometrial invasion (MI) and proficient mismatch repair status. The Cancer Genome Atlas (TCGA) database analysis showed FANCD2 was upregulated in EC compared with normal tissue. The high expression of FANCD2 was associated with poor overall survival in EC. Knockdown of FANCD2 expression in EC cell lines inhibited malignant proliferation and migration ability. We demonstrated that decreased FANCD2 expression results in increased DNA damage and decreased S-phase cells, leading to a decrease in proliferative capacity in EC cells. Down-regulated FANCD2 confers sensitivity of EC cells to interstrand crosslinking agents. This study provides evidence for the malignant progression and prognostic value of FANCD2 in EC. |
5,938 | New polyacetylenes from Bidens procera | A phytochemical investigation of Bidens procera L.C.Xu ex X.W.Zheng afforded two novel polyacetylenes, tridecane-2E-monoene-4,6,8-triyntylen-1,13-diol-12-O-β-glucoside (1) and tetradecane-2E,8E-diene-4,6-diyne-1,14-diol-13-O-β-glucoside (2), together with ten known compounds (3 - 12). Their chemical structures were elucidated by NMR and MS spectrums as well as the comparison of the published data. Furthermore, the chemotaxonomy of the yielded compounds was also discussed. |
5,939 | Intracellular infection by symbiotic bacteria requires the mitotic kinase AURORA1 | The subcellular events occurring in cells of legume plants as they form transcellular symbiotic-infection structures have been compared with those occurring in premitotic cells. Here, we demonstrate that Aurora kinase 1 (AUR1), a highly conserved mitotic regulator, is required for intracellular infection by rhizobia in Medicago truncatula. AUR1 interacts with microtubule-associated proteins of the TPXL and MAP65 families, which, respectively, activate and are phosphorylated by AUR1, and localizes with them within preinfection structures. MYB3R1, a rhizobia-induced mitotic transcription factor, directly regulates AUR1 through two closely spaced, mitosis-specific activator cis elements. Our data are consistent with a model in which the MYB3R1-AUR1 regulatory module serves to properly orient preinfection structures to direct the transcellular deposition of cell wall material for the growing infection thread, analogous to its role in cell plate formation. Our findings indicate that the eukaryotically conserved MYB3R1-TPXL-AUR1-MAP65 mitotic module was conscripted to support endosymbiotic infection in legumes. |
5,940 | Toward energy-efficient online Complete Coverage Path Planning of a ship hull maintenance robot based on Glasius Bio-inspired Neural Network | Regular Ship hull maintenance is an essential for sustainability. The maintenance work of ship hulls that involve human labor suffers from many shortcomings. Maintenance robots have been introduced for drydocks to eliminate these shortcomings. An energy-efficient Complete Coverage Path Planning (CCPP) is a crucial requirement from a ship hull maintenance robot. This paper proposes a novel energy-efficient CCPP method based on Glasius Bioinspired Neural Network (GBNN) for a ship hull inspection robot. The proposed method accounts for a comprehensive energy model for path planning. This energy model reflects the energy usage of a ship hull maintenance robot due to changes in direction, distance, and vertical position. Furthermore, the proposed method is effective for dynamic workspaces since it performs online path planning. These are the major contributions made to state of the art by the work proposed in this paper. The behavior and the performance of the proposed method have been compared against state of the art through simulations considering Hornbill, a multipurpose ship hull maintenance robot. The validation confirms the ability of the proposed in realizing a complete coverage of a given dynamic workspace. According to the statistical outcomes of the comparison, the performance of the proposed method significantly surpasses that of the state-of-the-art methods in terms of energy usage. Therefore, the proposed method contributes to the development of energy-efficient CCPP methods for a ship hull maintenance robot. |
5,941 | Asymmetric hashing based on generative adversarial network | In the era of big data, social media, large-scale video, image, and text data is produced every day. The approximate nearest neighbor (ANN) search has drawn significant attention for content-based image retrieval applications to ensure retrieval quality and computational efficiency. Hashing has become a cutting-edge technology for image retrieval and big data applications due to its low-storage and high-computational efficiency. Hashing algorithms are useful for mapping images into short binary codes and generating a similar binary code for similar data points from the database. Many supervised/unsupervised hashing methods have been deployed for retrieving the query points from the database images, and many recently developed methods can achieve a higher accuracy regarding image retrieval performance. However, the current state-of-the-art algorithms can only improve binary code hashing, and the retrieval performance of binary representation is not good. To overcome this issue, we propose an asymmetric learning method that generates the hash codes. This work proposes a novel asymmetric learning-based generative adversarial network (AGAN) for image retrieval, which integrates the feature learning with hashing to an end-to-end learning framework. Moreover, to equip with the binary representation of image retrieval; we propose three loss functions, i.e., encoder loss, generator loss, and discriminator loss, which significantly improve retrieval performance. The extensive experiments show that our proposed method outperformed several state-of-the-art methods. |
5,942 | Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images | Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifierand/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D high-resolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-the-art landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in amulti-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy. |
5,943 | A survey on indoor RGB-D semantic segmentation: from hand-crafted features to deep convolutional neural networks | Semantic segmentation is one of the most important tasks in the field of computer vision. It is the main step towards scene understanding. With the advent of RGB-Depth sensors, such as Microsoft Kinect, nowadays RGB-Depth images are easily available. This has changed the landscape of some tasks such as semantic segmentation. As the depth images are independent of illumination, the combination of depth and RGB images can improve the quality of semantic labeling. The related research has been divided into two main categories, based on the usage of hand-crafted features and deep learning. Although the state-of-the-art results are mainly achieved by deep learning methods, traditional methods have also been at the center of attention for some years and lots of valuable work have been done in that category. As the field of semantic segmentation is very broad, in this survey, a comprehensive analysis has been carried out on RGB-Depth semantic segmentation methods, their challenges and contributions, available RGB-Depth datasets, metrics of evaluation, state-of-the-art results, and promising directions of the field. |
5,944 | Nucleic Acid Delivery to the Vascular Endothelium | This Review examines the state-of-the-art in the delivery of nucleic acid therapies that are directed to the vascular endothelium. First, we review the most important homeostatic functions and properties of the vascular endothelium and summarize the nucleic acid tools that are currently available for gene therapy and nucleic acid delivery. Second, we consider the opportunities available with the endothelium as a therapeutic target and the experimental models that exist to evaluate the potential of those opportunities. Finally, we review the progress to date from investigations that are directly targeting the vascular endothelium: for vascular disease, for peri-transplant therapy, for angiogenic therapies, for pulmonary endothelial disease, and for the blood-brain barrier, ending with a summary of the future outlook in this field. |
5,945 | A Comparison of Three Uniquely Different State of the Art and Two Classical Multiobjective Optimization Algorithms as Applied to Electromagnetics | This paper compares three modern and two classical multiobjective optimizers (MOOs) as applied to real-world problems in electromagnetics. The behavior of sophisticated optimizers on simple test functions has been studied exhaustively. In contrast, the algorithms here are tested on practical applications, where the function evaluations are computationally expensive, making the convergence rate a crucial factor. The examples considered include the optimization of a narrowband slot antenna, a mushroom-type electromagnetic bandgap structure, and an ultrawideband Vivaldi antenna. Another popular topic in the literature is in comparing classical MOOs on electromagnetics problems. The modern optimizers chosen in this paper are state of the art and each has a distinct design philosophy. This paper introduces two unique MOOs to the electromagnetics community: BORG, an auto-adaptive genetic algorithm and the Multi-Objective Covariance Matrix Adaptation Evolutionary Strategy (MO-CMA-ES), an extension of the popular single-objective CMA-ES. These algorithms are compared to the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), a Chebysheff scalarization algorithm, and two classical MOOs. This paper will study the behavior of these algorithms on problems in electromagnetics with a limited number of function evaluations using five distinct metrics and will provide useful guidelines and recommended optimizer settings. |
5,946 | Biomedical Magnetic Induction Tomography: An Inhomogeneous Green's Function Approach | Magnetic induction tomography aims to reconstruct the passive electric properties of an object by measuring its scattered magnetic field. Current state-of-the-art numerical techniques are based on differential formulations such as the finite element method. A formulation based on volume integral equations has not yet been applied to its biomedical field and could improve the reconstruction speed by reducing the number of unknowns. This paper investigates salient characteristics of the approach and offers a solution based on inhomogeneous Green's functions. |
5,947 | Robust action recognition using local motion and group sparsity | Recognizing actions in a video is a critical step for making many vision-based applications possible and has attracted much attention recently. However, action recognition in a video is a challenging task due to wide variations within an action, camera motion, cluttered background, and occlusions, to name a few. While dense sampling based approaches are currently achieving the state-of-the-art performance in action recognition, they do not perform well for many realistic video sequences since, by considering every motion found in a video equally, the discriminative power of these approaches is often reduced due to clutter motions, such as background changes and camera motions. In this paper, we robustly identify local motions of interest in an unsupervised manner by taking advantage of group sparsity. In order to robustly classify action types, we emphasize local motion by combining local motion descriptors and full motion descriptors and apply group sparsity to the emphasized motion features using the multiple kernel method. In experiments, we show that different types of actions can be well recognized using a small number of selected local motion descriptors and the proposed algorithm achieves the state-of-the-art performance on popular benchmark datasets, outperforming existing methods. We also demonstrate that the group sparse representation with the multiple kernel method can dramatically improve the action recognition performance. (C) 2013 Elsevier Ltd. All rights reserved. |
5,948 | Assessment of HDAC Inhibitor-Induced Endoplasmic Reticulum (ER) Stress | The endoplasmic reticulum (ER) is a multifunctional cell organelle which is important for the folding and processing of proteins. Different endogenous and exogenous factors can disturb the ER homeostasis, causing ER stress and activating the unfolded protein response (UPR) to remove misfolded proteins and aggregates. ER stress and the UPR are associated with several human diseases, such as diabetes, Alzheimer's or Parkinson's disease, and cancer. Histone deacetylase inhibitors (HDACi) are used to treat cancer and were shown to induce ER stress/to modulate the UPR, although the exact mechanism is not fully understood and needs further research. Several approaches to monitoring ER stress exist. Here we describe methods including qPCR, Western blot, transmission electron microscopy, and fluorescence microscopy to analyze changes in mRNA and protein expression levels as well as defects in ER structures after HDAC inhibitor-induced ER stress. |
5,949 | Computationally Efficient Pricing and Benefit Distribution Mechanisms for Incentivizing Stable Peer-to-Peer Energy Trading | Peer-to-peer (P2P) energy trading has emerged as a promising market paradigm toward maximizing the value of distributed energy resources (DERs) for electricity prosumers, by enabling direct energy trading among them. However, state-of-the-art P2P mechanisms either fail to adequately incentivize prosumers to participate, prevent prosumers from accessing the highest achievable monetary benefits, or suffer severely from the curse of dimensionality. This article proposes two computationally efficient mechanisms to construct a stable grand coalition of prosumers participating in P2P trading, founded on cooperative game-theoretic principles. The first one involves a benefit distribution scheme inspired by the core ttonnement process while the second involves a novel pricing mechanism based on the solution of a single linear program. The performance of the proposed mechanisms is validated against state-of-the-art mechanisms through numerous case studies using real-world data. The results demonstrate that the proposed mechanisms exhibit superior computational performance than the nucleolus and are superior to the rest of the examined mechanisms in incentivizing prosumers to remain in the grand coalition. |
5,950 | Integrating electrical impedance tomography and transpulmonary pressure monitoring to personalize PEEP in hypoxemic patients undergoing pressure support ventilation | Monitoring with electrical impedance tomography (EIT) during a decremental PEEP trial has been used to identify the PEEP that yields the optimal balance of pulmonary overdistension and collapse. This method is based on pixel-level changes in respiratory system compliance and depends on fixed or measured airway driving pressure. We developed a novel approach to quantify overdistension and collapse during pressure support ventilation (PSV) by integrating transpulmonary pressure and EIT monitoring and performed pilot tests in three hypoxemic patients. We report that our experimental approach is feasible and capable of identifying a PEEP that balances overdistension and collapse in intubated hypoxemic patients undergoing PSV. |
5,951 | Synthetic image super resolution using FeatureMatch | In this paper, we propose a super resolution (SR) method for synthetic images using FeatureMatch. Existing state-of-the-art super resolution methods are learning based methods, where a pair of low-resolution and high-resolution dictionary pair are trained, and this trained pair is used to replace patches in low-resolution image with appropriate matching patches from the high-resolution dictionary. In this paper, we show that by using Approximate Nearest Neighbour Fields (ANNF), and a common source image, we can by-pass the learning phase, and use a single image for dictionary. Thus, reducing the dictionary from a collection obtained from hundreds of training images, to a single image. We show that by modifying the latest developments in ANNF computation, to suit super resolution, we can perform much faster and more accurate SR than existing techniques. To establish this claim we will compare our algorithm against various state-of-the-art algorithms, and show that we are able to achieve better and faster reconstruction without any training phase. |
5,952 | Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform | In free-viewpoint video, there is a recent trend to represent scene objects as solids rather than using multiple depth maps. Point clouds have been used in computer graphics for a long time, and with the recent possibility of real-time capturing and rendering, point clouds have been favored over meshes in order to save computation. Each point in the cloud is associated with its 3D position and its color. We devise a method to compress the colors in point clouds, which is based on a hierarchical transform and arithmetic coding. The transform is a hierarchical sub-band transform that resembles an adaptive variation of a Haar wavelet. The arithmetic encoding of the coefficients assumes Laplace distributions, one per sub-band. The Laplace parameter for each distribution is transmitted to the decoder using a custom method. The geometry of the point cloud is encoded using the well-established octtree scanning. Results show that the proposed solution performs comparably with the current state-of-the-art, in many occasions outperforming it, while being much more computationally efficient. We believe this paper represents the state of the art in intra-frame compression of point clouds for real-time 3D video. |
5,953 | TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network | In this paper, a trust framework is proposed for misbehavior detection in software defined vehicular networks (TFMD-SDVN) to detect the correct events in the network reported by the trusted or untrusted nodes. The trust value of a node is calculated based on rating, recommendation, and similarity. If the trust value is greater than a threshold, then the event reported by the event reporting node (ERN) is assumed to be correct. The performance of the proposed work is evaluated using OMNeT++ network simulator and SUMO traffic simulator in Veins hybrid framework. The performance parameters taken are True Positive Rate (TPR), False Positive Rate (FPR), Detection Time (DT), and Packet Delivery Ratio (PDR). Simulation results show that the proposed approach performs better than ART scheme, RPRep scheme, and BYOR scheme. |
5,954 | Perceived benefits and challenges of repeated exposure to high fidelity simulation experiences of first degree accelerated bachelor nursing students | This study explored perceptions of first-degree entry-level accelerated bachelor nursing students regarding benefits and challenges of exposure to multiple high fidelity simulation (HFS) scenarios, which has not been studied to date. These perceptions conformed to some research findings among Associate Degree, traditional non-accelerated, and second-degree accelerated Bachelor of Science in Nursing (BSN) students faced with one to two simulations. However, first-degree accelerated BSN students faced with multiple complex simulations perceived improvements on all outcomes, including critical thinking, confidence, competence, and theory-practice integration. On the negative side, some reported feeling overwhelmed by the multiple HFS scenarios. Evidence from this study supports HFS as an effective teaching and learning method for nursing students, along with valuable implications for many other fields. |
5,955 | Deep submodular network: An application to multi-document summarization | Employing deep learning makes it possible to learn high-level features from raw data, resulting in more precise models. On the other hand, submodularity makes the solution scalable and provides the means to guarantee a lower bound for its performance. In this paper, a deep submodular network (DSN) is introduced, which is a deep network meeting submodularity characteristics. DSN lets modular and submodular features to participate in constructing a tailored model that fits the best with a problem. Various properties of DSN are examined and its learning method is presented. By proving that cost function used for learning process is a convex function, it is concluded that minimization can be done in polynomial time and also, by choosing a suitable learning rate and performing enough iterations, a lower empirical error can be ensured. Finally, in order to demonstrate the applicability of DSN for real-world problems, automatic multi-document summarization is considered and a summarizer called DSNSum is introduced. Then, the performance of DSNSum is compared with the state-of-the-art summarizers based on DUC 2004 and CNN/DailyMail corpora. The experimental results show that the performance of the proposed summarizer is comparable with the state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved. |
5,956 | Siamese-Based Architecture for Cross-Lingual Plagiarism Detection in English-Hindi Language Pairs | The cross-lingual plagiarism detection (CLPD) is a challenging problem in natural language processing. Cross-lingual plagiarism is when a text is translated from any other language and used as it is without proper acknowledgment. Most of the existing methods provide good results for monolingual plagiarism detection, whereas the performances of existing methods for the CLPD are very limited. The reason for this is that it is difficult to represent the text from two different languages in a common semantic space. In this article, a novel Siamese architecture-based model is proposed to detect the cross-lingual plagiarism in English-Hindi language pairs. The proposed model combines the convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) network to learn the semantic similarity among the cross-lingual sentences for the English-Hindi language pairs. In the proposed model, the CNN model learns the local context of words, whereas the Bi-LSTM model learns the global context of sentences in forward and backward directions. The performances of the proposed models are evaluated on the benchmark data set, that is, Microsoft paraphrase corpus, which is converted in the English-Hindi language pairs. The proposed model outperforms other models giving 67%, 72%, and 67% weighted average precision, recall, and F1-measure scores. The experimental results show the effectiveness of the proposed models over the baseline models because the proposed model is very efficient in representing the cross-lingual text very efficiently. |
5,957 | Single-Stage Buck-Boost Inverters: A State-of-the-Art Survey | Single-stage buck-boost inverters have attracted the attention of many researchers, due to their ability to increase/decrease the output voltage in one power conversion stage. One of the most important uses of these inverters is in photovoltaic applications, where the voltage of the solar panels varies in a wide range. In recent years, many new inverters have been proposed to improve the performance of existing structures. In this paper, the state of the art of these single-stage buck-boost inverters is discussed. The advantages and disadvantages of each structure are examined from different perspectives, such as the number of components, losses, and performance. Finally, in a general comparison, the properties of all structures are discussed and summarized in a table. |
5,958 | High-Resolution Self-Gated Dynamic Abdominal MRI Using Manifold Alignment | We present a novel retrospective self-gating method based on manifold alignment (MA), which enables reconstruction of free breathing, high spatial, and temporal resolution abdominal magnetic resonance imaging sequences. Based on a radial golden- angle acquisition trajectory, our method enables a multidimensional self-gating signal to be extracted from the k-space data for more accurate motion representation. The k-space radial profiles are evenly divided into a number of overlapping groups based on their radial angles. MA is then used to simultaneously learn and align the low dimensional manifolds of all groups, and embed them into a common manifold. In the manifold, k-space profiles that represent similar respiratory positions are close to each other. Image reconstruction is performed by combining radial profiles with evenly distributed angles that are close in the manifold. Our method was evaluated on both 2-D and 3-D synthetic and in vivo data sets. On the synthetic data sets, our method achieved high correlation with the ground truth in terms of image intensity and virtual navigator values. Using the in vivo data, compared with a state-of-the-art approach based on the center of k-space gating, our method was able to make use of much richer profile data for self-gating, resulting in statistically significantly better quantitative measurements in terms of organ sharpness and image gradient entropy. |
5,959 | Association between semen collection time and semen parameters: an observational study | The process of semen collection plays a key role in the quality of semen specimens. However, the association between semen collection time and semen quality is still unclear. In this study, ejaculates by masturbation from 746 subfertile men or healthy men who underwent semen analysis were examined. The median (interquartile range) semen collection time for all participants was 7.0 (5.0-11.0) min, and the median time taken for semen collection was lower in healthy men than that in subfertile men (6.0 min vs 7.0 min). An increase in the time required to produce semen samples was associated with poorer semen quality. Among those undergoing assisted reproductive technology (ART), the miscarriage rate was positively correlated with the semen collection time. After adjusting for confounders, the highest quartile (Q4) of collection time was negatively associated with semen volume and sperm concentration. A longer time to produce semen samples (Q3 and Q4) was negatively correlated with progressive and total sperm motility. In addition, there was a significant negative linear association between the semen collection time and the sperm morphology. Higher risks of asthenozoospermia (adjusted odds ratio [OR] = 2.06, 95% confidence interval [CI]: 1.31-3.25, P = 0.002) and teratozoospermia (adjusted OR = 1.98, 95% CI: 1.10-3.55, P = 0.02) were observed in Q3 than those in Q1. Our results indicate that a higher risk of abnormal semen parameter values was associated with an increase in time for semen collection, which may be related to male fertility through its association with semen quality. |
5,960 | Madhubani Art Classification using transfer learning with deep feature fusion and decision fusion based | Some traditional Indian art forms enjoy widespread popularity across the world. One of the most prominent among these is the Madhubani style. This art form's rich heritage and beauty enthrall the connoisseurs and continue to inspire new designs catering to the changing tastes of prevalent fashion. Preservation of these traditional art forms is the need of the hour. Modern technological advances can be utilized with great advantage for this purpose. Since a database of Madhubani art forms was hitherto unavailable, an attempt is made in this work to create one from scratch. Five different classes of Madhubani art, i.e., Bharni, Godna, Kachni, Kohbar, and Tantrik, are identified, and the collected images are annotated with these classes. Classification of the art images is attempted using the handcrafted Local Binary Pattern (LBP) texture descriptors and state-of-the-art Convolutional Neural Networks (CNNs). The Transfer Learning approach with CNNs is employed to classify the designs. An attempt is made to obtain a better classification accuracy than the one provided by standard CNNs. Towards this end, the current work proposes a fusion of features extracted from several deep CNNs, decision fusion-based classification based on averaging prediction score (FAVG), and maximum vote score (FMAX). The proposed method's performance is tested on our Madhubani art dataset and compared against several standard pre-trained CNNs available in the literature. The proposed approaches provide significantly higher classification accuracy for Madhubani art patterns, with decision fusion based on averaging prediction score (FAVG) approach being the best. The maximum accuracy, specificity, and error rate scores are 98.82%, 99.72%, and 1.18%, respectively. This is the first such attempt, and the excellent results motivate further work to develop content-based image retrieval tools and evolutionary design-based tools for automating the development of new designs. These endeavors are expected to go a long way in preserving precious art heritage and fostering its rapid growth in the world market. The dataset will be made publically available for further experimentation. |
5,961 | Rapid Weight Loss and Dietary Inadequacies among Martial Arts Practitioners from Poland | Healthy nutrition and maintaining a normal body weight are very important for reducing the risk of various diseases not only among the general population, but also among athletes, especially combat sport athletes. The aim of the study was to evaluate the prevalence of rapid weight loss (RWL) and to indicate eventual dietary inadequacies among professional martial arts practitioners. Sixty-two male athletes (aged: 23 +/- 4) completed a questionnaire (i.e., frequency of food group consumption, questions about training and RWL) and single 24-h dietary recall. This study confirmed the high prevalence of RWL in athletes (58%) for two to three days before the competition, which allowed for reduction of 3.4 +/- 1.0 kg (4.3 +/- 1.5%) of their body weight. Many dietary inaccuracies were found such as: lower than recommended by nutrition experts, level of consumption of dairy products, grain products, fruit, and vegetables, and insufficient intake of energy, carbohydrates, minerals (especially iodine, potassium, calcium) and vitamins (especially D, folate, C, E) during the training day. Adequate nutrition is a natural support for the whole training process, and may allow for regulation of body weight in a longer period and in a safer manner; thus, there is a need for nutrition education dedicated to athletes and their trainers. |
5,962 | Factors influencing nursing professional identity development: A qualitative study | This research focuses on approaches that best support nursing professional identity formation, particularly by providing the insights of nursing students in their own words. This report reflects qualitative phenomenological research on nursing professional development from the perspective of both associate degree and baccalaureate degree student nurses in their final semester of study and describes factors that support or detract from the experience of nursing professional identity development. Participants were guided through individual interviews using semi-structured interview questions and later invited to facilitated focus groups with other students to clarify and elaborate on previous comments. The approach participants most often described as helping them develop into a professional nurse was clinical experience and the ability to practice independently. It was mentioned far more than the next most common response: role models and mentors. Other methods include reflection, critical thinking, and confidence. This research adds to a limited body of literature on the factors that influence nursing professional identity. |
5,963 | Residual Networks of Residual Networks: Multilevel Residual Networks | A residual networks family with hundreds or even thousands of layers dominates major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper proposes a novel residual network architecture, residual networks of residual networks (RoR), to dig the optimization ability of residual networks. RoR substitutes optimizing residual mapping of residual mapping for optimizing original residual mapping. In particular, RoR adds levelwise shortcut connections upon original residual networks to promote the learning capability of residual networks. More importantly, RoR can be applied to various kinds of residual networks (ResNets, Pre-ResNets, and WRN) and significantly boost their performance. Our experiments demonstrate the effectiveness and versatility of RoR, where it achieves the best performance in all residual-networklike structures. Our RoR-3-WRN58-4 + SD models achieve new state-of-the-art results on CIFAR-10, CIFAR-100, and SVHN, with the test errors of 3.77%, 19.73%, and 1.59%, respectively. RoR-3 models also achieve state-of-the-art results compared with ResNets on the ImageNet data set. |
5,964 | Ten years of art imaging research | This paper describes a decade of work on digital imaging for museums. From 1989 to 1992, the visual arts system for archiving and retrieval of images (VASARI) project produced a digital-imaging system that made color-calibrated images of up to 20000 x 20000 pixels directly from paintings. It used seven color-separation bands in the visible region, resulting in an average color error of around I DeltaE(ab)* unit. These images have since been used to monitor the condition of paintings, document paintings during conservation treatment, including predicting appearance after cleaning, reconstruct the original appearance of paintings in which pigments have faded, and assess whether paintings have been damaged during transportation, in estimations of the surface reflectance spectra and in the printing of high-quality reproductions. We have applied similar techniques to museum infrared and X-ray imaging. To manage the images produced by the VASARI system, an image-processing package has been developed that is tailored for very large colorimetric images. This package has since been used in several other projects. including a remote image viewer designed to provide internet access to high-resolution images. The paper explores these developments and gives details of the current generation of VASARI-derived systems. set in the context of the state of the art for museum imaging. |
5,965 | Copredication and Complexity Revisited: A Reply to Löhr and Michel | Human language affords the ability to attribute semantically distinct concepts to a single nominal, a process now commonly termed 'copredication'. If we describe a lunch as being delayed but also filling , senses of distinct semantic categories (event, physical object) are simultaneously being accessed. Copredication is relevant to major debates in cognitive science, since it cuts to the core of how the lexicon is formatted, and how distinct lexico-semantic representations relate to each other. The apparent scope and limits of copredication licensing can be explored via acceptability judgment and processing experiments, exposing certain replicable and generalizable patterns that apply across lexical types, syntactic structures, and different languages (Murphy 2021a, 2021b). As such, laying out the psycholinguistic terrain in which to address this phenomenon is crucial - and accounts that lack a valid psycholinguistic and empirical basis should be highlighted as problematic if they are to be accommodated and refined. Löhr and Michel (2022) claim that copredication acceptability is determined by a "set of expectations that are influenced by higher-order priors associated with discourse context and world knowledge". I will show that their model encounters a number of obstacles, and ends up unintentionally supporting an alternative model in Murphy (2019, 2021a, 2021b, 2021c), which they attempt to critique. |
5,966 | Towards Human-centric Digital Twins: Leveraging Computer Vision and Graph Models to Predict Outdoor Comfort | Conventional sidewalk studies focused on quantitative analysis of sidewalk walkability at a large scale which cannot capture the dynamic interactions between the environment and individual factors. Embracing the idea of Tech for Social Good, Urban Digital Twins seek AI-empowered approaches to bridge humans with digitally-mediated technologies to enhance their prediction ability. We employ GraphSAGE-LSTM, a geo-spatial artificial intelligence (GeoAI) framework on crowdsourced data and computer vision to predict human comfort on the sidewalks. Conceptualising the pedestrians and their interactions with surrounding built and unbuilt environments as human-centric dynamic graphs, our model captures such spatio-temporal variations given by the sequential movements of human walking, enabling the GraphSAGE-LSTM to be spatio-temporal-explicit. Our experiments suggest that the proposed model provides higher accuracy by more than 20% than a traditional machine learning model and two state-of-art deep learning frameworks, thus, enhancing the prediction power of Urban Digital Twin. The source code for the model is shared openly on GitHub. |
5,967 | Development of biomimetic triazine-based affinity ligands for efficient immunoglobulin G purification from human and rabbit plasma | Immunoglobulin purification from different biological fluids is considered one of the most critical steps in antibody production for diagnostic, therapeutic, and research purposes. The current study aimed to elucidate the role of the different aryl substituents in triazine-based affinity ligands on the performance of an affinity chromatography purification media to separate immunoglobulin G (IgG). The biomimetic triazine-based affinity ligand was chosen as a varied containing fix spacer and support. The sepharose beads were activated by epichlorohydrin, and five types of aryl substituents were replaced in the triazine ring and covalently immobilized to the resin surface by 1, 4-diaminobutane spacer. All affinity resins with various ligands were characterized and validated using FTIR, SEM, EDX, and microscopic images. The findings revealed that using R1=3-aminophenol and R2=3-aminophenol substituents in the triazine ring, as affinity ligands attached to the sepharose surface with a 10-atom linker CAES-6B-Cl@R1= MAF, R2= MAF (No. 4), leads to better purification of IgG from human and rabbit plasma with 22.8 mg/mL resin binding capacity in 73±5% yield and 95% of purity. All results confirmed that the designed triazine-based affinity ligands could effectively purify IgG compatible with a fast and low-cost approach. |
5,968 | A new diterpenoid from the leaves and twigs of Croton lachnocarpus Benth | A new clerodane diterpenoid, crotolanin A (1), along with three known clerodane diterpenoids, crotoeurin B (2), teucvidin (3) and teucvin (4), was isolated from the ethanol extract of the leaves and twigs of Croton lachnocarpus Benth. Their structures were identified by extensive NMR spectroscopic and HRESIMS analyses. The dopaminergic neuroprotective activity of compounds 1-4 was tested by using transgenic Caenorhabditis elegans pathological model. Compound 2 alleviated dopaminergic neuron degeneration of worms induced by 6-hydroxydopamine (6-OHDA) that represented a potential therapy for Parkinson's disease (PD). |
5,969 | Decentralized Active Demand Response (DADR) system for improvement of frequency stability in distribution network | The concepts of decentralized demand response systems are present in the state of the art literature, however, the material is limited to general ideas and possible system services. In this paper the original and application ready proposition of Decentralized Active Demand Response (DADR) system realization is presented in the form of a stochastic control algorithm. Such an approach to the system description enables simulation investigations of the DADR system as an element of dynamic stability improvement of an electrical energy distribution network. The results obtained from simulations have confirmed that the proposed DADR solution, because of its high dynamic response in dealing with disturbance phenomena, might be used as part of both primary and secondary Load Frequency Control in electrical power systems. (C) 2016 Elsevier B.V. All rights reserved. |
5,970 | Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access Networks | Enhanced with wireless power transfer capability, cloud radio access network (C-RAN) enables energy-restrained mobile devices to function uninterruptedly. Beamforming of C-RAN has potential to improve the efficiency of wireless power transfer, in addition to transmission data rates. In this paper, we design the beamforming jointly for data transmission and energy transfer, under finite fronthaul capacity of C-RAN. A non-convex problem is formulated to balance the fronthaul requirements of different remote radio heads (RRHs). Norm approximations and relaxations are carried out to convexify the problem to second order cone programming (SOCP). To improve the scalability of the design to large networks, we further decentralize the SOCP problem using the alternating direction multiplier method (ADMM). A series of reformulations and transformations are conducted, such that the resultant problem conforms to the state-of-the-art ADMM solver and can be efficiently solved in real time. Simulation results show that the distributed algorithm can remarkably reduce the time complexity without compromising the fronthaul load balancing of its centralized counterpart. The proposed algorithms can also reduce the fronthaul bandwidth requirements by 25% to 50%, compared with the prior art. |
5,971 | Multi-Part Modeling and Segmentation of Left Atrium in C-Arm CT for Image-Guided Ablation of Atrial Fibrillation | As a minimally invasive surgery to treat atrial fibrillation (AF), catheter based ablation uses high radio-frequency energy to eliminate potential sources of abnormal electrical events, especially around the ostia of pulmonary veins (PV). Fusing a patient- specific left atrium (LA) model (including LA chamber, appendage, and PVs) with electro-anatomical maps or overlaying the model onto 2-D real-time fluoroscopic images provides valuable visual guidance during the intervention. In this work, we present a fully automatic LA segmentation system on nongated C-arm computed tomography (C-arm CT) data, where thin boundaries between the LA and surrounding tissues are often blurred due to the cardiac motion artifacts. To avoid segmentation leakage, the shape prior should be exploited to guide the segmentation. A single holistic shape model is often not accurate enough to represent the whole LA shape population under anatomical variations, e. g., the left common PVs vs. separate left PVs. Instead, a part based LA model is proposed, which includes the chamber, appendage, four major PVs, and right middle PVs. Each part is a much simpler anatomical structure compared to the holistic one and can be segmented using a model-based approach (except the right middle PVs). After segmenting the LA parts, the gaps and overlaps among the parts are resolved and segmentation of the ostia region is further refined. As a common anatomical variation, some patients may contain extra right middle PVs, which are segmented using a graph cuts algorithm under the constraints from the already extracted major right PVs. Our approach is computationally efficient, taking about 2.6 s to process a volume with 256 x 256 x 245 voxels. Experiments on 687 C-arm CT datasets demonstrate its robustness and state-of-the-art segmentation accuracy. |
5,972 | Innovative Upscaling of Architectural Elements for Strengthening Building Structures | For conservation of heritages or life prolongation of aged buildings that contributes to environmental sustainability, there is a global need of structural strengthening or upgrading so as to restore their original functions or fulfil more stringent performance requirements stipulated in modern design codes of practice. However, the actual implementation is usually met with resistance from the property owner; hence, it is desirable to adopt an effective, economical and less invasive technique. In order to provide a further incentive, this article explores an innovative idea of upscaling decorative architectural elements, such as brackets, knee braces and corbels, in order that they also possess adequate strength capacity to resist extreme loadings such as earthquake actions. The required dimensions of architectural brackets for seismic retrofitting of concrete beam-column joints are calculated for different levels of seismicity through a parametric study. It is demonstrated that the proposed design can enhance both the aesthetics and structural performance of a building. This exemplifies how art can be integrated into engineering design for solving real-world problems. |
5,973 | Structural Analysis of Oligosaccharides and Glycoconjugates Using NMR | Carbohydrate chains play critical roles in cellular recognition and subsequent signal transduction in the nervous system. Furthermore, gangliosides are targets for various amyloidogenic proteins associated with neurodegenerative disorders. To better understand the molecular mechanisms underlying these biological phenomena, atomic views are essential to delineate dynamic biomolecular interactions. Nuclear magnetic resonance (NMR) spectroscopy provides powerful tools for studying structures, dynamics, and interactions of biomolecules at the atomic level. This chapter describes the basics of solution NMR techniques and their applications to the analysis of 3D structures and interactions of glycoconjugates in the nervous system. |
5,974 | Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework | Mobile payment systems are becoming more popular due to the increase in the number of smartphones, which, in turn, attracts the interest of fraudsters. Extant research has therefore developed various fraud detection methods using supervised machine learning. However, sufficient labeled data are rarely available and their detection performance is negatively affected by the extreme class imbalance in financial fraud data. The purpose of this study is to propose an XGBoost-based fraud detection framework while considering the financial consequences of fraud detection systems. The framework was empirically validated on a large dataset of more than 6 million mobile transactions. To demonstrate the effectiveness of the proposed framework, we conducted a comparative evaluation of existing machine learning methods designed for modeling imbalanced data and outlier detection. The results suggest that in terms of standard classification measures, the proposed semi-supervised ensemble model integrating multiple unsupervised outlier detection algorithms and an XGBoost classifier achieves the best results, while the highest cost savings can be achieved by combining random under-sampling and XGBoost methods. This study has therefore financial implications for organizations to make appropriate decisions regarding the implementation of effective fraud detection systems. |
5,975 | Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks | Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN) model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs) is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification. |
5,976 | PolSAR Coherency Matrix Optimization Through Selective Unitary Rotations for Model-Based Decomposition Scheme | In this letter, a special unitary SU(3) matrix group is exploited for coherency matrix transformations to decouple the energy between orthogonal states of polarization. This decoupling results in the minimization of the cross-polarization power along with the removal of some off-diagonal terms of coherency matrix. The proposed unitary transformations are utilized on the basis of the underlying dominant scattering mechanism. By doing so, the reduced power from the cross-polarization channel is always concentrated on the underlying dominant copolar scattering component. This makes it unique in comparison to state-of-the-art techniques. The proposed methodology can be adopted to optimize the coherency matrix to be used for the model-based decomposition methods. To verify this, pioneer three-component decomposition model is implemented using the proposed optimized coherency matrix of two different test sites. The comparative studies are analyzed to show the improvements over state-of-the-art techniques. |
5,977 | Self-Similarity and Spectral Correlation Adaptive Algorithm for Color Demosaicking | Most common cameras use a CCD sensor device measuring a single color per pixel. The other two color values of each pixel must be interpolated from the neighboring pixels in the so-called demosaicking process. State-of-the-art demosaicking algorithms take advantage of interchannel correlation locally selecting the best interpolation direction. These methods give impressive results except when local geometry cannot be inferred from neighboring pixels or channel correlation is low. In these cases, they create interpolation artifacts. We introduce a new algorithm involving nonlocal image self-similarity in order to reduce interpolation artifacts when local geometry is ambiguous. The proposed algorithm introduces a clear and intuitive manner of balancing how much channel-correlation must be taken advantage of. Comparison shows that the proposed algorithm gives state-of-the-art methods in several image bases. |
5,978 | Supervised Evaluation of Image Segmentation and Object Proposal Techniques | This paper tackles the supervised evaluation of image segmentation and object proposal algorithms. It surveys, structures, and deduplicates the measures used to compare both segmentation results and object proposals with a ground truth database; and proposes a new measure: the precision-recall for objects and parts. To compare the quality of these measures, eight state-of-the-art object proposal techniques are analyzed and two quantitative meta-measures involving nine state of the art segmentation methods are presented. The meta-measures consist in assuming some plausible hypotheses about the results and assessing how well each measure reflects these hypotheses. As a conclusion of the performed experiments, this paper proposes the tandem of precision-recall curves for boundaries and for objects-and-parts as the tool of choice for the supervised evaluation of image segmentation. We make the datasets and code of all the measures publicly available. |
5,979 | Scale-Aware Visual-Inertial Depth Estimation and Odometry Using Monocular Self-Supervised Learning | For real-world applications with a single monocular camera, scale ambiguity is an important issue. Because self-supervised data-driven approaches that do not require additional data containing scale information cannot avoid the scale ambiguity, state-of-the-art deep-learning-based methods address this issue by learning the scale information from additional sensor measurements. In that regard, inertial measurement unit (IMU) is a popular sensor for various mobile platforms due to its lightweight and inexpensiveness. However, unlike supervised learning that can learn the scale from the ground-truth information, learning the scale from IMU is challenging in a self-supervised setting. We propose a scale-aware monocular visual-inertial depth estimation and odometry method with end-to-end training. To learn the scale from the IMU measurements with end-to-end training in the monocular self-supervised setup, we propose a new loss function named as preintegration loss function, which trains scale-aware ego-motion by comparing the ego-motion integrated from IMU measurement and predicted ego-motion. Since the gravity and the bias should be compensated to obtain the ego-motion by integrating IMU measurements, we design a network to predict the gravity and the bias in addition to the ego-motion and the depth map. The overall performance of the proposed method is compared to state-of-the-art methods in the popular outdoor driving dataset, i.e., KITTI dataset, and the author-collected indoor driving dataset. In the KITTI dataset, the proposed method shows competitive performance compared with state-of-the-art monocular depth estimation and odometry methods, i.e., root-mean-square error of 5.435 m in the KITTI Eigen split and absolute trajectory error of 22.46 m and 0.2975 degrees in the KITTI odometry 09 sequence. Different from other up-to-scale monocular methods, the proposed method can estimate the metric-scaled depth and camera poses. Additional experiments on the author-collected indoor driving dataset qualitatively confirm the accurate performance of metric-depth and metric pose estimations. |
5,980 | Multimodal MR Synthesis via Modality-Invariant Latent Representation | We propose a multi-input multi-output fully convolutional neural network model for MRI synthesis. The model is robust to missing data, as it benefits from, but does not require, additional input modalities. The model is trained end-to-end, and learns to embed all input modalities into a shared modality-invariant latent space. These latent representations are then combined into a single fused representation, which is transformed into the target output modality with a learnt decoder. We avoid the need for curriculum learning by exploiting the fact that the various input modalities are highly correlated. We also show that by incorporating information from segmentation masks the model can both decrease its error and generate data with synthetic lesions. We evaluate our model on the ISLES and BRATS data sets and demonstrate statistically significant improvements over state-of-the-art methods for single input tasks. This improvement increases further when multiple input modalities are used, demonstrating the benefits of learning a common latent space, again resulting in a statistically significant improvement over the current best method. Finally, we demonstrate our approach on non skull-stripped brain images, producing a statistically significant improvement over the previous best method. Code is made publicly available at https://github.com/agis85/multimodal_brain_synthesis. |
5,981 | Graded Applications of NQS Theory for Modeling Correlated Noise in SiGe HBTs | In this paper, we develop a correlated noise model for bipolar transistors from an accurate nonquasi-static model. The proposed noise model includes the signal delay through base-collector space-charge region and is implemented using four extra nodes. We also present a simplified version of the same model that requires only two extra nodes. A further simplified version that uses only one extra node is found to be identical with a state-of-the-art correlated noise model. When compared with the device simulation data, our proposed models show improved accuracy compared with the existing state-of-the-art noise models. |
5,982 | A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification | Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced-SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way. |
5,983 | [Effects of microencapsulated and heme iron supplementation on the recovery of hemoglobin levels in iron-depleted rats] | Introduction: one of the causes of nutritional anemia is that the amount of iron absorbed is insufficient to meet the body's needs. Objective: to determine the effects of microencapsulated and heminic iron supplementation to increase hemoglobin levels and body weight in iron-depleted rats. Methods: a randomized experimental study was designed. Four study groups were formed. Control group (GC), experimental group (GE1) (MC microencapsulated iron supplementation), experimental group 2 (GE2) (heminic iron supplementation) and experimental group 3 (GE3) (MC + heminic iron supplementation). A dry powdered diet containing all normal nutrients except iron was fed to the four groups for 15 days (three times a day). Weight, length and hemoglobin (Hb) were evaluated at pre- and post-test under similar conditions. Results: in Hb, no significant differences were observed in the CG (p = 0.225), despite a -9.6 % decrease in the post-test. GE1 significantly increased hemoglobin (14.3 %, Hb 2.1 g/dl) and body weight (21.6 %, 25.8 g) (p < 0.05) in the post-test. Similarly, GE2 significantly increased hemoglobin (14.5 %, Hb 2.1 g/dl) and body weight (44.5 %, 52.3 g) (p < 0.05). However, in GE3, despite significantly increasing weight (30.2 %, 35.2 g), the increase in hemoglobin levels was similar to what occurred in GE1 and GE2 groups (increasing by 14.5 % and Hb 2.2 g/dl). There were no significant differences in rat length between pre- and post-test in the four groups. Conclusion: these results suggest that heme iron together with quinoa and cañihua flour could be exploited as a new safe and efficient iron supplement compared to microencapsulated iron, given its higher iron bioavailability and its ability to increase body weight. |
5,984 | Cross-View Person Identification Based on Confidence-Weighted Human Pose Matching | Cross-view person identification (CVPI) from multiple temporally synchronized videos taken by multiple wearable cameras from different, varying views is a very challenging but important problem, which has attracted more interest recently. Current state-of-the-art performance of CVPI is achieved by matching appearance and motion features across videos, while the matching of pose features does not work effectively given the high inaccuracy of the 3D pose estimation on videos/images collected in the wild. To address this problem, we first introduce a new metric of confidence to the estimated location of each human-body joint in 3D human pose estimation. Then, a mapping function, which can be hand-crafted or learned directly from the datasets, is proposed to combine the inaccurately estimated human pose and the inferred confidence metric to accomplish CVPI. Specifically, the joints with higher confidence are weighted more in the pose matching for CVPI. Finally, the estimated pose information is integrated into the appearance and motion features to boost the CVPI performance. In the experiments, we evaluate the proposed method on three wearable-camera video datasets and compare the performance against several other existing CVPI methods. The experimental results show the effectiveness of the proposed confidence metric, and the integration of pose, appearance, and motion produces a new state-of-the-art CVPI performance. |
5,985 | Echocardiography Segmentation With Enforced Temporal Consistency | Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, CNNs still struggle to leverage temporal information to provide accurate and temporally consistent segmentation maps across the whole cycle. Such consistency is required to accurately describe the cardiac function, a necessary step in diagnosing many cardiovascular diseases. In this paper, we propose a framework to learn the 2D+time apical long-axis cardiac shape such that the segmented sequences can benefit from temporal and anatomical consistency constraints. Our method is a post-processing that takes as input segmented echocardiographic sequences produced by any state-of-the-art method and processes it in two steps to (i) identify spatio-temporal inconsistencies according to the overall dynamics of the cardiac sequence and (ii) correct the inconsistencies. The identification and correction of cardiac inconsistencies relies on a constrained autoencoder trained to learn a physiologically interpretable embedding of cardiac shapes, where we can both detect and fix anomalies. We tested our framework on 98 full-cycle sequences from the CAMUS dataset, which are available alongside this paper. Our temporal regularization method not only improves the accuracy of the segmentation across the whole sequences, but also enforces temporal and anatomical consistency. |
5,986 | The Trend of Metaverse and Augmented & Virtual Reality Extending to the Healthcare System | There is no escaping Internet's favorite buzzword for 2022: The Metaverse. Everyone is talking about it, but only a few know what it is or how it works. One can look at the Metaverse as a 3D model of the Internet where it is possible to spend your reality parallel to the virtual world. In broad terms, Metaverse can be explained as a virtual space, graphically rich, leaning towards verisimilitude where people can do all sorts of things they do in real-life such as shop, play, socialize, and party. The pandemic has accelerated innovations in the digital age. Looking beyond revolutions in telehealth, payments, remote monitoring, and secure data-sharing are other essential innovations in the fields of artificial intelligence (AI), virtual reality (VR), augmented reality (AR), and blockchain technology. The Metaverse is still in its nascent stage and evolving continuously, having a huge potential in health care to combine the technologies of AI, AR/VR, web 3.0, Internet of medical devices, and quantum computing, along with robotics to give a new direction to healthcare systems. From improving surgical precision to therapeutic usage and more, the Metaverse can bring significant changes to the industry. |
5,987 | Similarity-based Demosaicing Algorithm using Unified High-Frequency Map | This paper introduces an innovative demosaicing algorithm for color filter array (CFA). Conventional demosaicing algorithms usually detect edges in horizontal-, vertical- or omni-direction, and apply a directional filtering along the edge direction. It is found that these algorithms do not properly work for diagonal edges. We, then, invent a similarity based filtering using unified high-frequency (UHF) map. This new technique enables flexible filtering masks so as to be able to deal with edges of any direction. It is confirmed through experiments that the proposed algorithm outperforms state-of-the-art algorithms in terms of PSNR and subjective quality. In addition, the proposed algorithm requires fewer resources than the state-of-the-art algorithms. |
5,988 | A Comparison Between Direct Telehealth and In-Person Methods of Teaching Expressive Labels to Children Diagnosed With Autism Spectrum Disorder | Recent behavior analytic research has demonstrated that the provision of applied behavior analytic services via direct telehealth can be an effective teaching modality for some learners with autism spectrum disorder (ASD). Historically, teaching procedures based on applied behavior analysis (ABA), including discrete trial teaching (DTT), have been provided and evaluated via in-person delivery. This study sought to compare the implementation of DTT via direct telehealth to DTT implemented in-person within and across participants. Specifically, this study evaluated the two delivery modalities in terms of skill acquisition, maintenance, efficiency, and learner responding during teaching sessions. Results of an adapted alternating treatments design nested into a multiple baseline design demonstrated that all three participants diagnosed with ASD met the mastery criteria for the expressive labels taught. Areas of future research, participant prerequisite skills, and clinical implications will be discussed in the context of these results. |
5,989 | Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault | Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However, the performance of the diagnostic systems depends on the quantity and quality of the data. The training database can be augmented utilizing data augmentation techniques that generate synthetic data to improve diagnostic performance. However, existing data augmentation techniques are generic methods that do not include additional information in the synthetic data. In this paper, we develop a model-based data augmentation technique integrating computer-implementable electromechanical models. Synthetic normal- and abnormal-condition data are generated with an electromechanical model and a stochastic parameter value sampling method. The model-based data augmentation is showcased to detect an abnormal condition of a distribution transformer. First, the synthetic data are compared with the measurements to verify the synthetic data. Then, ML-based diagnostic systems are created using model-based data augmentation and are compared with state-of-the-art diagnostic systems. It is shown that using the model-based data augmentation results in an improved accuracy compared to state-of-the-art diagnostic systems. This holds especially true when only a small abnormal-condition database is available. |
5,990 | Updated insight into the role of Th2-associated immunity in systemic lupus erythematosus | Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple organs involvement, abundant autoantibodies, complement activation, and immune complexes depositions. By regulating inflammation and immune homeostasis, cytokines have been well documented to participate in the pathogenesis of SLE. A number of studies have shown that T helper 2 (Th2)-associated immunity plays an important role in autoimmune diseases, including SLE. Key molecules underlying Th2-related immunity are expected to serve as promising targets for the diagnosis and targeted treatment of SLE. Current progress in SLE pathogenesis and biological treatment strategies has been reviewed, focusing on the latest development in Th2-associated immunity. |
5,991 | Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization | Multiplicative noise (also known as speckle noise) models are central to the study of coherent imaging systems, such as synthetic aperture radar and sonar, and ultrasound and laser imaging. These models introduce two additional layers of difficulties with respect to the standard Gaussian additive noise scenario: 1) the noise is multiplied by (rather than added to) the original image; 2) the noise is not Gaussian, with Rayleigh and Gamma being commonly used densities. These two features of multiplicative noise models preclude the direct application of most state-of-the-art algorithms, which are designed for solving unconstrained optimization problems where the objective has two terms: a quadratic data term (log-likelihood), reflecting the additive and Gaussian nature of the noise, plus a convex (possibly nonsmooth) regularizer (e.g., a total variation or wavelet-based regularizer/prior). In this paper, we address these difficulties by: 1) converting the multiplicative model into an additive one by taking logarithms, as proposed by some other authors; 2) using variable splitting to obtain an equivalent constrained problem; and 3) dealing with this optimization problem using the augmented Lagrangian framework. A set of experiments shows that the proposed method, which we name MIDAL (multiplicative image denoising by augmented Lagrangian), yields state-of-the-art results both in terms of speed and denoising performance. |
5,992 | Statistical Shape Model-Based Femur Kinematics From Biplane Fluoroscopy | Studying joint kinematics is of interest to improve prosthesis design and to characterize postoperative motion. State of the art techniques register bones segmented from prior computed tomography or magnetic resonance scans with X-ray fluoroscopic sequences. Elimination of the prior 3D acquisition could potentially lower costs and radiation dose. Therefore, we propose to substitute the segmented bone surface with a statistical shape model based estimate. A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame. The algorithm minimizes the difference between the projected bone contour and image edges. To increase robustness, we employ a dynamic prior, image features, and prior knowledge about bone edge appearances. This enables tracking and reconstruction from a single initial pose per sequence. We evaluated our method on the distal femur using eight biplane fluoroscopic drop-landing sequences. The proposed dynamic prior and features increased the convergence rate of the reconstruction from 71% to 91%, using a convergence limit of 3 mm. The achieved root mean square point-to-surface accuracy at the converged frames was 1.48 +/- 0.41 mm. The resulting tracking precision was 1-1.5 mm, with the largest errors occurring in the rotation around the femoral shaft (about 2.5 degrees precision). |
5,993 | Sufu- and Spop-mediated regulation of Gli2 is essential for the control of mammalian cochlear hair cell differentiation | Development of mammalian auditory epithelium, the organ of Corti, requires precise control of both cell cycle withdrawal and differentiation. Sensory progenitors (prosensory cells) in the cochlear apex exit the cell cycle first but differentiate last. Sonic hedgehog (Shh) signaling is required for the spatiotemporal regulation of prosensory cell differentiation, but the underlying mechanisms remain unclear. Here, we show that suppressor of fused (Sufu), a negative regulator of Shh signaling, is essential for controlling the timing and progression of hair cell (HC) differentiation. Removal of Sufu leads to abnormal Atoh1 expression and a severe delay of HC differentiation due to elevated Gli2 mRNA expression. Later in development, HC differentiation defects are restored in the Sufu mutant by the action of speckle-type PDZ protein (Spop), which promotes Gli2 protein degradation. Deletion of both Sufu and Spop results in robust Gli2 activation, exacerbating HC differentiation defects. We further demonstrate that Gli2 inhibits HC differentiation through maintaining the progenitor state of Sox2+ prosensory cells. Along the basal-apical axis of the developing cochlea, the Sox2 expression level is higher in the progenitor cells than in differentiating cells and is down-regulated from base to apex as differentiation proceeds. The dynamic spatiotemporal change of Sox2 expression levels is controlled by Shh signaling through Gli2. Together, our results reveal key functions of Gli2 in sustaining the progenitor state, thereby preventing HC differentiation and in turn governing the basal-apical progression of HC differentiation in the cochlea. |
5,994 | Dual-level attention-aware network for temporal emotion segmentation | Human emotions are known to always have four phases in the temporal domain: neutral, onset, apex, and offset. This has been demonstrated to be of great benefit for emotion recognition. Therefore, temporal segmentation has attracted considerable research interest. Although state-of-the-art techniques use recurrent neural networks to highly increase the performance, they ignore the relevance of each frame (time step) of a video, and they do not consider the changing contribution of different features when fusing them. We propose a framework called dual-level attention-aware bidirectional grated recurrent unit, which integrates ideas from attention models to discover the most important frames and features for improving temporal segmentation. Specifically, it applies attention mechanisms at two levels: frame and feature. A significant advantage is that the two-level attention weights provide a meaningful value to depict the importance of each frame and feature. The experiments demonstrated that the proposed framework outperforms state-of-the-art methods. (C) 2018 SPIE and IS&T |
5,995 | ADAPT: A Write Disturbance-Aware Programming Technique for Scaled Phase Change Memory | Phase change memory (PCM) is an emerging, resistance-based, nonvolatile memory. With a promising scaling potential, PCM can replace the existing charge-based memory technologies. A highly scaled PCM is prone to write disturbance (WD) because of a high-current RESET programming pulse. Exploiting the data-dependent nature of WD, encoding techniques have been proposed to reduce the frequency of WD-vulnerable data patterns. These techniques work along with a verify and correct (VnC) method to ensure memory reliability. However, the effectiveness of these techniques varies depending on the data patterns. Unlike the conventional methods, this article introduces a WD-aware programming technique to mitigate WD in PCM. The proposed method encodes the data based on the number of WD-vulnerable cells and the bit flips. By reducing the number of WD-vulnerable cells as well as the bit flips, the proposed method is more effective than the existing encoding techniques, in mitigating WD as well as improving the memory lifetime. Evaluation using various realistic workloads shows that the proposed method can reduce the average word-line WD errors by 62%, compared to the existing state of the art. With a reduced number of WD errors, the frequency of a VnC operation is also reduced. This leads to a reduction of 44% in the number of extra writes and 20% in the average write time. With reduction in the number of writes and the write time, instructions-per-cycle is improved by 9% and the write energy by 11% over the existing art. By reducing the number of bit flips compared to the previous state of the art, the proposed method improves the PCM memory lifetime by 13% to 33%, considering the asymmetry of SET and RESET operations in impacting the cell endurance. |
5,996 | Oxy-fuel combustion technology for cement production - State of the art research and technology development | Oxy-fuel combustion stands as a promising carbon capture technology to significantly reduce CO2 emissions from industrial combustion processes. Due to a different process layout compared to power industry as well as different boundary conditions further investigations and demonstration activities are required to develop the oxy-fuel cement process to maturity. This paper presents an overview on research activities and current state-of-the-art on the development of oxy-fuel combustion applied to the cement process. Oxy-firing concepts for cement plants are introduced under two process configurations. Modifications regarding plant lay-out and key components as well as operational implications are discussed. Relevant research projects focusing on the application of the oxy-fuel technology in the cement industry are presented and finally fields, in which further research is required, are identified. (C) 2015 Elsevier Ltd. All rights reserved. |
5,997 | Ultrasonic‑assisted molten salt hydrates pretreated Eucheuma cottonii residues as a greener precursor for third-generation L-lactic acid production | This study aims to establish an efficient pretreatment method that facilitates the conversion of sugars from macroalgae wastes, Eucheuma cottonii residues (ECRs) during hydrolysis and subsequently enhances L-lactic acid (L-LA) production. Hence, ultrasonic-assisted molten salt hydrates (UMSHs) pretreatment was proposed to enhance the accessibility of ECRs to hydrolyze into glucose through dilute acid hydrolysis (DAH). The obtained hydrolysates were employed as the substrate in producing L-LA by separate hydrolysis and fermentation (SHF). The maximum glucose yield (97.75%) was achieved using UMSHs pretreated ECRs with 40wt% ZnCl2 at 80 °C for 2 h and followed with DAH. The optimum glucose to L-LA yield obtained for SHF was 90.08% using 5% (w/w) inoculum cell densities of B. coagulans ATCC 7050 with yeast extract (YE). A comparable performance (89.65%) was obtained using a nutrient combination (lipid-extracted Chlorella vulgaris residues (CVRs), vitamin B3, and vitamin B5) as a partial alternative for YE. |
5,998 | Transcriptional Profiling of Pseudomonas aeruginosa Infections | Pseudomonas aeruginosa is an opportunistic pathogen that causes life-devastating acute as well as chronic biofilm-associated infections with limited treatment options. Its success is largely due to its remarkable adaptability. P. aeruginosa uses different long- and short-term adaptive mechanisms to increase its fitness, both at the population level through genetic diversification and at the individual cell level by adapting gene expression. These adapted gene expression profiles can be fixed by the accumulation of patho-adaptive mutations. The latter are often found in transcriptional regulators and lead to rewiring of the regulatory network to promote survival at the infected host site. In this chapter, we review recent developments in transcriptional profiling and explain how these provide new insights into the establishment and maintenance of P. aeruginosa infections. We illustrate what can be learned from the application of advanced RNA-seq technology, such as ex vivo RNA-seq, host-pathogen crosstalk (dual RNA-seq), or recording of transcriptional heterogeneity within a bacterial population (single-cell RNA-seq). In addition, we discuss how large transcriptome datasets from a variety of clinical isolates can be used to gain an expanded understanding of bacterial adaptation during the infection process. Global genotype-phenotype correlation studies provide a unique opportunity to discover new evolutionary pathways of infection-related phenotypes and led to the discovery of different strategies of the pathogen P. aeruginosa to build a biofilm. Insights gained from large-scale, multi-layered functional -omics approaches will continue to contribute to a more comprehensive understanding of P. aeruginosa adaptation to the host habitat and promises to pave the way for novel strategies to combat recalcitrant infections. |
5,999 | Deep Learning Architectures for Navigation Using Forward Looking Sonar Images | This paper investigates the use of supervised Deep Learning (DL) networks to process sonar images for underwater navigation. State-of-the-art DL techniques for micro-navigation using sequences of optical images have been adapted to work with sonar images. Specifically, the DL networks estimate the Forward-Looking Sonar (FLS) motion in three degrees of freedom corresponding to x- and y-translation and rotation around z-axis. The state-of-the-art DL architectures and a proposed new architecture are investigated for motion estimation. They are trained using images generated by a FLS simulator. The data sets are made using pairs of consecutive images associated with labels that represent the motion of the sonar platform between images. The results show the effectiveness of using the DL architectures, which can provide millimeter accuracy for translation motion and below 0.1 degrees for rotation motion between two consecutive sonar images. Examples of trajectory estimation and mosaic building using simulated and real sonar images are also presented. |
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