Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid Training of Very Large Ensembles of Diverse Neural Networks

Ensembles of deep neural networks with diverse architectures significantly improve generalization accuracy. However, training such ensembles requires a large amount of computational resources and time as every network in the ensemble has to be separately trained. In practice, this restricts the number of different deep neural network architectures that can be included within an ensemble. We propose a new approach to address this problem. Our approach captures the structural similarity between members of a neural network ensemble and train it only once. Subsequently, this knowledge is transferred to all members of the ensemble using function-preserving transformations. Then, these ensemble networks converge significantly faster as compared to training from scratch. We show through experiments on CIFAR-10, CIFAR-100, and SVHN data sets that our approach can train large and diverse ensembles of deep neural networks achieving comparable accuracy to existing approaches in a fraction of their training time. In particular, our approach trains an ensemble of $100$ variants of deep neural networks with diverse architectures up to $6 \times$ faster as compared to existing approaches. This improvement in training cost grows linearly with the size of the ensemble.
Genetic Imbalances in Argentinean Patients with Congenital Conotruncal Heart Defects

Congenital conotruncal heart defects (CCHD) are a subset of serious congenital heart defects (CHD) of the cardiac outflow tracts or great arteries. Its frequency is estimated in 1/1000 live births, accounting for approximately 10–30% of all CHD cases. Chromosomal abnormalities and copy number variants (CNVs) contribute to the disease risk in patients with syndromic and/or non-syndromic forms. Although largely studied in several populations, their frequencies are barely reported for Latin American countries. The aim of this study was to analyze chromosomal abnormalities, 22q11 deletions, and other genomic imbalances in a group of Argentinean patients with CCHD of unknown etiology. A cohort of 219 patients with isolated CCHD or associated with other major anomalies were referred from different provinces of Argentina. Cytogenetic studies, Multiplex-Ligation-Probe-Amplification (MLPA) and fluorescent in situ hybridization (FISH) analysis were performed. No cytogenetic abnormalities were found. 22q11 deletion was found in 23.5% of the patients from our cohort, 66% only had CHD with no other major anomalies. None of the patients with transposition of the great vessels (TGV) carried the 22q11 deletion. Other 4 clinically relevant CNVs were also observed: a distal low copy repeat (LCR)D-E 22q11 duplication, and 17p13.3, 4q35 and TBX1 deletions. In summary, 25.8% of CCHD patients presented imbalances associated with the disease.
NNCP: A citation count prediction methodology based on deep neural network learning techniques

With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics and bibliometrics establish quantified analysis methods and measurements for scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citations prediction model, we employed artificial neural networks which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method out-performs state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations.
Rational Approaches to Correcting for Multiple Tests

The logic of the Bonferroni correction for multiple tests, or family-wise error, is to set the criterion to reduce the expected number of erroneous false positives, or Type I errors, below 1. This is a very stringent criterion for false positives in cases where the test may be applied millions of times, and will necessarily introduce a large proportion of false negatives (missed positives, or Type II errors). A proposed solution to this problem is to adjust the criterion for False Discovery Rate (Benjamini & Hochberg, 1995), which allows the number of false positives to increase proportionally to the number of true positives, though remaining at a small proportion, dramatically reducing the number of false negatives. This approach may be conceptualized as working with a relaxed confidence level that any one test is a true rather than a false positive, bringing the criterion more into line with our societal assessment of the validity of statements in general, and even in science, as having less than 100% certainty. The analytic strategy to the assessment of statistical significance provides a more intuitive approach to the identification of sparse signals in large datasets than the standard Bonferroni approach to correction for multiple tests. Introduction The role of statistical testing is to set meaningful limits on what may be regarded as the error range for statistical estimates. However, functional error ranges depend on the number of times a test is applied, which in the era of ‘big data’ may number in the millions of applications, making a criterion that reduces false alarms to 1 in 20 applications (symbolized as p < 0.05) subject to unacceptable numbers of false alarms. It is therefore important to provide an adequate approach to the assessment of large numbers of multiple tests when applying them to large datasets, such as those encountered in product quality control, identification of genetic substrates of diseases or individual characteristics, or high-resolution medical imaging such as brain connectivity analysis. The Era of Big Data The 21 century may be characterized as the beginning of the era of Big Data, spearheaded by the Human Genome Project whose initial deadline was the turn of the millennium in the year 2000. The term ‘Big Data’, which seems to have its origins in computer graphics developed by Silicon Graphics for Hollywood special effects (Mashey, 1998) and econometrics around the same time (Diebold, 2003), span many arenas of human endeavor. Climate data span the globe, with NASA satellites sending back more than ten million gigbytes of data per year. The human genome contains 3 billion base pairs, so it was a decade-long effort to sequence it for one individual, but techniques have rapidly improved and now it can be done for any of the 7 billion individuals on the planet in less than an hour. IS&T International Symposium on Electronic Imaging 2018 Human Vision and Electronic Imaging 2018 535-1 https://doi.org/10.2352/ISSN.2470-1173.2018. 14.HVEI-535 © 2018, Society for Imaging Science and Technology
Event Detection on Roads Using Perceptual Video Summarization

Roads are the vital mode of transportation for people and goods around the globe and its use has grown dramatically over the years. There is one death every four minutes due to road accidents in the developing nations. This is of deep concern to the entire humanity. Road accident detection and vehicle behavior analysis is of great interest to the research community in intelligent transportation systems. It is very difficult from the state of the art techniques to provide the abstract form of salient parts of accidents from road surveillance videos. To resolve these issues, we present perceptual video summarization techniques to enrich the speed of visualizing the accident content from a stack of videos. The problem of vehicle analysis is formulated as an optimization problem. To the best of our knowledge, this is the first time we solve an accident detection as an optimization problem and filter the frames to be selected, through a single formulation. With the camera in a surrounding infrastructure and capturing a video, we exploited the properties of sub modularity to provide a relevant and condensed key frame summary. We have studied it for various real world traffic surveillance videos comprising of vehicular accidents and thus making it a promising approach.
A Verticillium dahliae Pectate Lyase Induces Plant Immune Responses and Contributes to Virulence

Verticillium dahliae is a wide-host-range fungal pathogen that causes soil-borne disease in hundreds of dicotyledonous hosts. In search of V. dahliae Vd991 cell death-inducing proteins, we identified a pectate lyase (VdPEL1) that exhibited pectin hydrolytic activity, which could induce strong cell death in several plants. Purified VdPEL1 triggered defense responses and conferred resistance to Botrytis cinerea and V. dahliae in tobacco and cotton plants. Our results demonstrated that the mutant VdPEL1rec lacking the enzymatic activity lacked functions to induce both cell death and plant resistance, implying that the enzymatic activity was necessary. In addition, VdPEL1 was strongly induced in V. dahliae infected Nicotiana benthamiana and cotton roots, and VdPEL1 deletion strains severely compromised the virulence of V. dahliae. Our data suggested that VdPEL1 contributed to V. dahliae virulence and induced plant defense responses. These findings provide a new insight for the function of pectate lyase in the host–pathogen interaction.
Compressive Channel Estimation and User Activity Detection in Distributed-Input Distributed-Output Systems

We address the cloud radio access network with wireless fronthaul links for massive machine-type communication as a distributed-input distributed-output (DIDO) system for simplicity. In this letter, the channel estimation and user activity detection problems in the DIDO system are studied. We notice that there are two types of sparsity in DIDO systems: The first is the sparsity of user equipment (UE) activities, and the second is the spatial sparsity of UE signals. In response, a two-stage compressed sensing process is proposed in which UE activities and the overall channel states from active UEs to the baseband unit pool are identified at the first stage, and channel states from active UEs to remote radio heads are estimated at the second stage. A low-complexity method is proposed to accelerate the process in the first stage. Simulation results are also presented to show the performance of the proposed approach.
Lyapunov-Optimized Two-Way Relay Networks With Stochastic Energy Harvesting

Energy harvesting wireless cooperative communications have become more and more popular in recent years. In this paper, we consider a two-way relay cooperative network, where the relay is an energy harvesting node and uses decode-and-forward (DF) or amplify-and-forward (AF) cooperation protocol. We formulate the energy management problem in such a network as an optimization problem, which minimizes the long-term average outages with a long-term average battery constraint. We then apply Lyapunov optimization to transform the long-term optimization problem into the drift-plus-penalty. We also conduct theoretic analysis of the proposed energy management strategy. Specifically, we prove that the long-term average battery constraint can be guaranteed with the proposed strategy and derive an upper bound of the average outages with the proposed strategy. Furthermore, we analyze theoretically the diversity order and energy harvesting gain of the proposed strategy with the DF and AF protocols. Finally, simulation results using real-solar irradiance data measured by the solar site in Elizabeth City University show that the outage performance and the diversity order of our algorithms are better than the existing MDP-based method.
Protein kinase A negatively regulates Ca2+ signalling in Toxoplasma gondii

The phylum Apicomplexa comprises a group of obligate intracellular parasites that alternate between intracellular replicating forms and actively motile extracellular forms that move through tissue. Parasite cytosolic Ca2+ signalling activates motility, but how this is switched off after invasion is not understood. Here we show that the cAMP-dependent Protein Kinase A catalytic subunit 1 (PKAc1) of Toxoplasma is responsible for suppression of Ca2+ signalling upon host cell invasion. We demonstrate that that PKAc1 is sequestered to the parasite periphery by dual acylation of its regulatory subunit PKAr1. Newly invaded PKAc1-deficient parasites exit host cells shortly thereafter in a perforin-like protein 1 (PLP-1)-dependent fashion. We demonstrate that loss of PKAc1 results in an inability to rapidly downregulate cytosolic Ca2+ levels shortly after invasion. Furthermore, we demonstrate that PKAc1 also specifically negatively regulates resting cytosolic Ca2+ in conditions that mimic intracellularity. We also show that cAMP and cGMP have opposing role in microneme secretion, further supporting evidence that cAMP signalling has a suppressive role during motility. Together, this work provides a new paradigm in understanding how Toxoplasma and related apicomplexan parasites regulate infectivity.
The neural basis of hostility-related dimensions in schizophrenia

Abstract Hostility and related dimensions like anger, urgency, impulsivity and aggressiveness have been described in non-clinical populations and various serious mental illnesses including schizophrenia. Although representing a mental healthcare challenge, the investigation of such constructs is often limited by the presence of complex and multi-factorial causes and lack of agreement in their conceptualisation and measurement. In this review, we aim to clarify the anatomical basis of hostility-related dimensions in schizophrenia. Imaging studies suggest malfunctioning of a neural circuitry including amygdala, striatum, prefrontal cortex, anterior cingulate cortex, insula and hippocampus to modulate hostile thoughts and behaviours, at least in the subgroup of patients with schizophrenia who exhibit high levels of urgency, impulsivity and aggressiveness.
Co-whitening of I-vectors for Short and Long Duration Speaker Verification

An i-vector is a ﬁxed-length and low-rank representation of a speech utterance. It has been used extensively in text-independent speaker veriﬁcation. Ideally, speech utterances from the same speaker would map to an unique i-vector. However, this is not the case due to some intrinsic and extrinsic factors like physical condition of the speaker, channel difference, noise and notably the duration of speech utterances. In particular, we found that i-vectors extracted from short utterances exhibit larger variance than that of long utterances. To address the problem, we propose a co-whitening approach, taking into account the duration, while maximizing the correlation between the i-vectors of short and long duration. The proposed co-whitening method was derived based on canonical correlation analysis (CCA). Experimental results on NIST SRE 2010 show that co-whitening method is effective in compensating the duration mismatch, leading to a reduction of up to 13.07% in equal error rate (EER).
COREMatters: A Bullying Intervention Pilot Study

Abstract While bullying is a serious concern for students and educators alike, empirically tested interventions are needed. This study examined the impact of a bullying intervention curriculum at a public middle school in the Midwest. This intervention, COREMatters, was designed to reduce bullying behaviors, foster a greater sense of community cohesion and trust in the school, improve school climate, and increase student self-esteem. As such, the intervention required participation of teachers, administrators, students, and outside experts. COREMatters was modeled after theoretical frameworks of socioemotional learning and Bloom’s taxonomy. Additionally, components of This We Believe outlining successful schools were considered. Uniquely contributing to this intervention is the inclusion of martial arts instruction. The intervention was taught as a whole, integrated model. The t-tests indicated significant differences between the control and intervention groups on measures of school climate, student self-esteem, and school cohesion and trust. Students in the intervention group scored higher in measures of self-esteem and rated their schools more positively on measures of cohesion and trust and climate.
Handling Co-Resident Attacks: A Case for Cost-Efficient Dedicated Resource Provisioning

Co-resident attacks on public clouds could extract sensitive information like encryption keys, memory content or workload patterns from cloud-based applications. Most existing defense mechanisms have been targeting cloud providers. They usually require substantial changes to the underlying hardware, cloud infrastructure or VM placement strategies, therefore making their deployment difficult. In this work, we investigate secure VM provisioning from the user perspective. Our approach leverages existing secure resource allocation methods provided by public cloud providers such as EC2 Dedicated Instances. We consider several different VM provisioning algorithms which are secure, cost-effective and at the same time immediately deployable for cloud users without any changes required from cloud providers' part. Extensive experiments using real workload traces and EC2 resource pricing confirm the effectiveness of our approach. To the best of our knowledge, this work is the first to consider secure and cost-effective VM provisioning from the user perspective.
Autophagy regulates glucose-mediated root meristem activity by modulating ROS production in Arabidopsis

ABSTRACT Glucose produced from photosynthesis is a key nutrient signal regulating root meristem activity in plants; however, the underlying mechanisms remain poorly understood. Here, we show that, by modulating reactive oxygen species (ROS) levels, the conserved macroautophagy/autophagy degradation pathway contributes to glucose-regulated root meristem maintenance. In Arabidopsis thaliana roots, a short exposure to elevated glucose temporarily suppresses constitutive autophagosome formation. The autophagy-defective autophagy-related gene (atg) mutants have enhanced tolerance to glucose, established downstream of the glucose sensors, and accumulate less glucose-induced ROS in the root tips. Moreover, the enhanced root meristem activities in the atg mutants are associated with improved auxin gradients and auxin responses. By acting with AT4G39850/ABCD1 (ATP-binding cassette D1; Formerly PXA1/peroxisomal ABC transporter 1), autophagy plays an indispensable role in the glucose-promoted degradation of root peroxisomes, and the atg mutant phenotype is partially rescued by the overexpression of ABCD1. Together, our findings suggest that autophagy is an essential mechanism for glucose-mediated maintenance of the root meristem. Abbreviation: ABA: abscisic acid; ABCD1: ATP-binding cassette D1; ABO: ABA overly sensitive; AsA: ascorbic acid; ATG: autophagy related; CFP: cyan fluorescent protein; Co-IP: co-immunoprecipitation; DAB: 3’,3’-diaininobenzidine; DCFH-DA: 2’,7’-dichlorodihydrofluorescin diacetate; DR5: a synthetic auxin response element consists of tandem direct repeats of 11 bp that included the auxin-responsive TGTCTC element; DZ: differentiation zone; EZ, elongation zone; GFP, green fluorescent protein; GSH, glutathione; GUS: β-glucuronidase; HXK1: hexokinase 1; H2O2: hydrogen peroxide; IAA: indole-3-acetic acid; IBA: indole-3-butyric acid; KIN10/11: SNF1 kinase homolog 10/11; MDC: monodansylcadaverine; MS: Murashige and Skoog; MZ: meristem zone; NBT: nitroblue tetrazolium; NPA: 1-N-naphtylphthalamic acid; OxIAA: 2-oxindole-3-acetic acid; PIN: PIN-FORMED; PLT: PLETHORA; QC: quiescent center; RGS1: Regulator of G-protein signaling 1; ROS: reactive oxygen species; SCR: SCARECROW; SHR, SHORT-ROOT; SKL: Ser-Lys-Leu; SnRK1: SNF1-related kinase 1; TOR: target of rapamycin; UPB1: UPBEAT1; WOX5: WUSCHEL related homeobox 5; Y2H: yeast two-hybrid; YFP: yellow fluorescent protein
Life Cycle and Genetic Diversity of Symplocarpus nipponicus (Araceae), an Endangered Species in Japan

Symplocarpus nipponicus, a member of the Araceae family, is an endangered plant in several prefectures in Japan. For the conservation of this wild species, we investigated the morphology, life cycle, and genetic diversity of three wild populations. By fixed-point observation over several years, we found that it takes at least four years for the plant to set the inflorescences consisting of spadices and spathes, and another two years for it to set mature seeds. To examine the genetic diversity in the wild population, we developed 11 novel microsatellite markers and investigated the genetic variation in three populations in Kyoto Prefecture: Ayabe, Hanase, and Momoi. The Ayabe population carried less genetic variation than the other two areas, suggesting the isolation of the habitat and thus a higher risk of extinction. Our results provide basic knowledge of the ecological aspects of S. nipponicus, as well as molecular techniques for the assessment of its genetic diversity, and thus are useful for the conservation of this endangered species.
Hand Gesture Recognition Using a Radar Echo IQ Plot and a Convolutional Neural Network

We propose a hand gesture recognition technique using a convolutional neural network applied to radar echo in-phase/quadrature (I/Q) plot trajectories. The proposed technique is demonstrated to accurately recognize six types of hand gestures for ten participants. The system consists of a low-cost 2.4-GHz continuous-wave monostatic radar with a single antenna. The radar echo trajectories are converted to low-resolution images and are used for the training and evaluation of the proposed technique. Results indicate that the proposed technique can recognize hand gestures with average accuracy exceeding 90.
Synergetic Learning Control Paradigm for Redundant Robot to Enhance Error-Energy Index

In order to perform energetically efficient motion as in human control, so-called optimization-based approach is commonly used in both robotics and neuroscience. Such an optimization approach can provide optimal solution when the prior dynamics information of the manipulator and the environment is explicitly given. However, the environment, where the robot faces with in a real world rarely has such a situation. The dynamics conditions change by the contact situation or the hand load for the manipulation task. Simple computational paradigm to realize both adaptability and learning is essential to bridge the gap between learning and control process in redundancy. We verify a novel synergetic learning control paradigm in reaching task of redundant manipulator. The performance in handling different dynamics conditions is evaluated in dual criteria of error-energy (E-E) coupling without prior knowledge of the given environmental dynamics and with model-optimization-free approach. This paper aims at investigating the ability of phenomenological optimization with the proposed human-inspired learning control paradigm for environmental dynamics recognition and adaptation, which is different from conventional model optimization approach. E-E index is introduced to evaluate not only the tracking performance, but also the error reduction rate per the energy consumption.