Miracle-dz/UniFAR-FTUs huggingface dataset:
Time in SCCharts

Synchronous languages, such as the recently proposed SCCharts language, have been designed for the rigorous specification of real-time systems. Their sound semantics, which builds on an abstraction from physical execution time, make these languages appealing, in particular for safety-critical systems. However, they traditionally lack built-in support for physical time. This makes it rather cumbersome to express things like time-outs or periodic executions within the language. We here propose several mechanisms to reconcile the synchronous paradigm with physical time. Specifically, we propose extensions to the SCCharts language to express clocks and execution periods within the model. We draw on several sources, in particular timed automata, the Clock Constraint Specification Language, and the recently proposed concept of dynamic ticks. We illustrate how these extensions can be mapped to the SCChart language core, with minimal requirements on the run-time system, and we argue that the same concepts could be applied to other synchronous languages such as Esterel, Lustre or SCADE.
A model of cholinergic suppression of hippocampal ripples through disruption of balanced excitation/inhibition

Sharp wave‐ripples (140–220 Hz) are patterns of brain activity observed in the local field potential of the hippocampus which are present during memory consolidation. As rodents switch from memory consolidation to memory encoding behaviors, cholinergic inputs to the hippocampus from neurons in the medial septum‐diagonal band of Broca cause a marked reduction in ripple incidence. The mechanism for this disruption in ripple power is not fully understood. In isolated neurons, the major effect of cholinergic input on hippocampal neurons is depolarization of the membrane potential, which affects both hippocampal pyramidal neurons and inhibitory interneurons. Using an existing model of ripple‐frequency oscillations that includes both pyramidal neurons and interneurons, we investigated the mechanism whereby depolarizing inputs to these neurons can affect ripple power and frequency. We observed that ripple power and frequency are maintained, as long as inputs to pyramidal neurons and interneurons are balanced. Preferential drive to pyramidal neurons or interneurons, however, affects ripple power and can disrupt ripple oscillations by pushing ripple frequency higher or lower. Thus, an imbalance in drive to pyramidal neurons and interneurons provides a means whereby cholinergic input can suppress hippocampal ripples.
17‐Beta Hydroxysteroid Dehydrogenase 13 Is a Hepatic Retinol Dehydrogenase Associated With Histological Features of Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is a common cause of chronic liver disease. A single‐nucleotide polymorphism (SNP), rs6834314, was associated with serum liver enzymes in the general population, presumably reflecting liver fat or injury. We studied rs6834314 and its nearest gene, 17‐beta hydroxysteroid dehydrogenase 13 (HSD17B13), to identify associations with histological features of NAFLD and to characterize the functional role of HSD17B13 in NAFLD pathogenesis. The minor allele of rs6834314 was significantly associated with increased steatosis but decreased inflammation, ballooning, Mallory‐Denk bodies, and liver enzyme levels in 768 adult Caucasians with biopsy‐proven NAFLD and with cirrhosis in the general population. We found two plausible causative variants in the HSD17B13 gene. rs72613567, a splice‐site SNP in high linkage with rs6834314 (r2 = 0.94) generates splice variants and shows a similar pattern of association with NAFLD histology. Its minor allele generates simultaneous expression of exon 6‐skipping and G‐nucleotide insertion variants. Another SNP, rs62305723 (encoding a P260S mutation), is significantly associated with decreased ballooning and inflammation. Hepatic expression of HSD17B13 is 5.9‐fold higher (P = 0.003) in patients with NAFLD. HSD17B13 is targeted to lipid droplets, requiring the conserved amino acid 22‐28 sequence and amino acid 71‐106 region. The protein has retinol dehydrogenase (RDH) activity, with enzymatic activity dependent on lipid droplet targeting and cofactor binding site. The exon 6 deletion, G insertion, and naturally occurring P260S mutation all confer loss of enzymatic activity. Conclusion: We demonstrate the association of variants in HSD17B13 with specific features of NAFLD histology and identify the enzyme as a lipid droplet–associated RDH; our data suggest that HSD17B13 plays a role in NAFLD through its enzymatic activity.
CQASUMM: Building References for Community Question Answering Summarization Corpora

Answers submitted to CQA forums are often elaborate, contain spam, are marred by slurs and business promotions. It is difficult for a reader to go through numerous such answers to gauge community opinion. As a result summarization becomes a prioritized task. However, there is a dearth of neural approaches for CQA summarization due to the lack of large scale annotated dataset. We create CQASUMM, the first annotated CQA summarization dataset by filtering the 4.4 million Yahoo! Answers L6 dataset. We sample threads where the best answer can double up as a reference and build hundred word summaries from them. We provide scripts1 to reconstruct the dataset and introduce the new task of Community Question Answering Summarization. Multi document summarization(MDS) has been widely studied using news corpora. However documents in CQA have higher variance and contradicting opinion. We compare the popular MDS techniques and evaluate their performance on our CQA corpora. We find that most MDS workflows are built for the entirely factual news corpora, whereas our corpus has a fair share of opinion based instances too. We therefore introduce OpinioSumm, a new MDS which outperforms the best baseline by 4.6% w.r. t ROUGE-1 score.
Multi-encoder multi-resolution framework for end-to-end speech recognition

Attention-based methods and Connectionist Temporal Classification (CTC) network have been promising research directions for end-to-end Automatic Speech Recognition (ASR). The joint CTC/Attention model has achieved great success by utilizing both architectures during multi-task training and joint decoding. In this work, we present a novel Multi-Encoder Multi-Resolution (MEMR) framework based on the joint CTC/Attention model. Two heterogeneous encoders with different architectures, temporal resolutions and separate CTC networks work in parallel to extract complimentary acoustic information. A hierarchical attention mechanism is then used to combine the encoder-level information. To demonstrate the effectiveness of the proposed model, experiments are conducted on Wall Street Journal (WSJ) and CHiME-4, resulting in relative Word Error Rate (WER) reduction of 18.0-32.1%. Moreover, the proposed MEMR model achieves 3.6% WER in the WSJ eval92 test set, which is the best WER reported for an end-to-end system on this benchmark.
Does Hypoxia Decrease the Metabolic Rate?

In some organisms and cells, oxygen availability influences oxygen consumption. In this review, we examine this phenomenon of hypoxic hypometabolism (HH), discussing its features, mechanisms, and implications. Small mammals and other vertebrate species exhibit “oxyconformism,” a downregulation of metabolic rate and body temperature during hypoxia which is sensed by the central nervous system. Smaller body mass and cooler ambient temperature contribute to a high metabolic rate in mammals. It is this hypermetabolic state that is suppressed by hypoxia leading to HH. Larger mammals including humans do not exhibit HH. Tissues and cells also exhibit reductions in respiration during hypoxia in vitro, even at oxygen levels ample for mitochondrial oxidative phosphorylation. The mechanisms of cellular HH involve intracellular oxygen sensors including hypoxia-inducible factors, AMP-activated protein kinase (AMPK), and mitochondrial reactive oxygen species (ROS) which downregulate mitochondrial activity and ATP utilization. HH has a profound impact on cardiovascular, respiratory, and metabolic physiology in rodents. Therefore, caution should be exercised when extrapolating the results of rodent hypoxia studies to human physiology.
On the practice of classification learning for clinical diagnosis and therapy advice in oncology

Artificial intelligence and medicine have a longstanding and proficuous relationship. In the present work we develop a brief assessment of this relationship with specific focus on machine learning, in which we highlight some critical points which may hinder the use of machine learning techniques for clinical diagnosis and therapy advice in practice. We then suggest a conceptual framework to build successful systems to aid clinical diagnosis and therapy advice, grounded on a novel concept we have coined drifting domains. We focus on oncology to build our arguments, as this area of medicine furnishes strong evidence for the critical points we take into account here.
Adaptive neural control for self-organized locomotion and obstacle negotiation of quadruped robots

Many quadruped robots have been developed to imitate their biological counterparts, several of which show excellent performance. However, the biological neural control mechanisms responsible for self-organized adaptive quadruped locomotion remain elusive. By drawing lessons from biological findings and using an artificial neural approach, we simulated a mammal-like quadruped robot and used it as our simulation platform to investigate and develop neural control mechanisms. In this study, we proposed an adaptive neural control network that can autonomously generate self-organized emergent locomotion with adaptability for the robot. The control network consists of three main components: Decoupled neural central pattern generator circuits (one for each leg), sensory feedback adaptation with dual-rate learning, and multiple neural reflex mechanisms. Simulation results show that the robot can perform quadruped-like gaits in a self-organized manner and adapt its gait to negotiate an obstacle. In addition, this work also suggests that the tight combination of the body-environment interaction and adaptive neural control, guided by sensory feedback adaptation and neural reflexes, is a powerful approach to better understand and solve self-organized adaptive coordination problems in quadruped locomotion.
Efficient Intrusion Detection With Bloom Filtering in Controller Area Networks

Due to its cost efficiency, the controller area network (CAN) is still the most wide-spread in-vehicle bus, and the numerous reported attacks demonstrate the urgency in designing new security solutions for CAN. In this paper, we propose an intrusion detection mechanism that takes advantage of Bloom filtering to test frame periodicity based on message identifiers and parts of the data-field which facilitates detection of potential replay or modification attacks. This proves to be an effective approach since most of the traffic from in-vehicle buses is cyclic in nature and the format of the data-field is fixed due to rigid signal allocation. Bloom filters provide an efficient time-memory tradeoff which is beneficial for the constrained resources of automotive grade controllers. We test the correctness of our approach and obtain good results on an industry-standard CANoe-based simulation for a J1939 commercial-vehicle bus and also on CAN with flexible data-rate traces obtained from a real-world high-end vehicle. The proposed filtering mechanism is straightforward to adapt for any other time-triggered in-vehicle bus, e.g., FlexRay, since it is built on time-driven characteristics.
Millimeter-Wave Slotted Waveguide Array With Unequal Beamwidths and Low Sidelobe Levels for Vehicle Radars and Communications

A low profile, high gain 32 × 64-slot array antenna with unequal beamwidths in the E- and H-planes and low sidelobes is proposed for vehicular applications in the 71–81 GHz band. The antenna is composed of 512 (16 × 32) 2 × 2-slot subarrays arranged with equal spacing and excited by a nonuniform corporate-feed-network. A two dimensional amplitude-tapering (complying with Taylor distribution) is used to achieve the low sidelobe levels (SLLs). Combined with the different dimensions of the array, the sidelobes and the beamwidths in the E- and H-planes are independently controlled. A prototype slotted waveguide array is fabricated to verify the array properties. Measurements show that the matching of the array is better than −14 dB over the entire bandwidth. The first SLL is lower than −18.9 dB/−24 dB with the maximum 3-dB beamwidth of 2.3°/1.3° in the E-/H-plane, respectively. The peak gain of over 39.4 dBi and the cross-polarization discrimination (XPD) of below −36.2 dB are also achieved. The demonstrated features of high gain, low SLL, and independent controllability of the beam in the E- and H-planes are most desirable for vehicular communications and radars.
Strengthening Megrelishvili Protocol Against Man-in-the-Middle Attack

In this paper we study the security aspect of Megrelishvili protocol—a linear algebra-based variant of the Diffie-Hellman key agreement. We demonstrate that the conventional version of this protocol is vulnerable to the man-in-the-middle attack. Hence, to avert such attack, we propose an authenticated version of this protocol using an embedded digital signature scheme. The scheme is constructed using the hardness assumption of the Megrelishvili vector-matrix problem (MVMP)—the underlying computational problem for the security of the conventional Megrelishvili protocol. We prove the correctness of the signature scheme and argue that our proposed protocol is secure against the man-in-the-middle attack provided that the MVMP is intractable.
Initial experience with minimally invasive extended pancreatectomies for locally advanced pancreatic malignancies: Report of six cases

Background: Recently, there have been several reports on minimally-invasive surgery for extended pancreatectomy (MIEP) in the literature. However, to date, only a limited number of studies reporting on the outcomes of MIEP have been published. In the present study, we report our initial experience with MIEP defined according to the latest the International Study Group for Pancreatic Surgery (ISPGS) guidelines. Methods: Over a 14-month period, a total of 6 consecutive MIEP performed by a single surgeon at a tertiary institution were identified from a prospectively maintained surgical database. EP was defined as per the 2014 ISPGS consensus. Hybrid pancreatoduodenectomy (PD) was defined as when the entire resection was completed through minimally-invasive surgery, and the reconstruction was performed open through a mini-laparotomy incision. Results: Six cases were performed including 2 robotic extended subtotal pancreatosplenectomies with gastric resection, 1 laparoscopic-assisted (hybrid) extended PD with superior mesenteric vein wedge resection, 2 robotic-assisted (hybrid) PD with portal vein resection (1 interposition Polytetrafluoroethylene graft reconstruction and 1 wedge resection) and 1 totally robotic PD with wedge resection of portal vein. Median estimated blood loss was 400 (250–1500) ml and median operative time was 713 (400-930) min. Median post-operative stay was 9 (6–36) days. There was 1 major morbidity (Grade 3b) in a patient who developed early post-operative intestinal obstruction secondary to port site herniation necessitating repeat laparoscopic surgery. There were no open conversions and no in-hospital mortalities. Conclusion: Based on our initial experience, MIEP although technically challenging and associated with long operative times, is feasible and safe in highly selected cases.
Criminal Investigation Image Classification Based on Spatial CNN Features and ELM

With the advent of the era of big data, the image of the criminal investigation has been explosively growing. To effectively help police officers classify suspect targets, a new classification method of criminal investigation image that combines spatial convolutional neural network (SCNN) and extreme learning machine (ELM) is proposed. Firstly, the image sub-blocks of different space are obtained by a five-block preprocessing on the criminal investigation image, which is part of the original image. Secondly, different image blocks are sent to a fine-tuned convolutional neural network to extract the features of the criminal investigation image. Finally, a criminal investigation image classification algorithm called SCNN-ELM is proposed in combination with extreme learning machine. The experimental results based on 2800 real criminal investigation image database and the standard Corel 1K image database show that the proposed method has an average accuracy improvement of 7.98% compared with the image classification method based on non-blocking CNN features, and the average classification accuracy is also superior than other similar methods.
Improving Lost/Won Classification in CRM Systems Using Sentiment Analysis

In this work, we are proposing several approaches to enhance lost/won classification of complex deals using sentiment analysis. The analysis of sentiments is done by text mining the activity notes recorded in CRM Systems used to manage complex sales. Using a baseline SVM model, we extended the baseline features with opinion predictors gathered using various techniques that included different preprocessing approaches of the CRM notes, scoring and counting of opinion sentences and inference of sentiment level features. We analyzed and compared the accuracy and f1-measure gained in comparison to the baseline and we discovered that, among the approaches analyzed, counting the polarity sentences gives the highest gain.
Content Based Image Retrieval Using Improved Gabor Wavelet Transform and Linear Discriminant Analysis

Content based image retrieval (CBIR) system performs an important role in the retrieval of desired images from larger image database efficiently. It finds applications in all areas including government, hospitals, surveillance, architecture, journalism and many more. Improvement in the precision and recall of the CBIR systems is desired and challenging problem for all the researchers. CBIR system using collective color and texture feature extraction with linear discriminant analysis is proposed in this paper. Gabor filter and color histogram has proved to be very effective in describing visual content of the image. Images in database are described using concatenated feature vectors of texture and color. The computational complexity of obtaining the texture features through Gabor wavelet transform has been improved through down sampling of the query and database images. Linear discriminant analysis is a powerful and popular method to extract the exact information for retrieving the images from the database. It also assist in increasing recognition rate of the content based image retrieval even despite the fact that training set belongs to unsupervised nature. thus the combined features of texture and color obtained through Gabor wavelet transform and color histogram respectively with linear discriminant analysis assist in reducing retrieval time.
IRES-dependent ribosome repositioning directs translation of a +1 overlapping ORF that enhances viral infection

Abstract RNA structures can interact with the ribosome to alter translational reading frame maintenance and promote recoding that result in alternative protein products. Here, we show that the internal ribosome entry site (IRES) from the dicistrovirus Cricket paralysis virus drives translation of the 0-frame viral polyprotein and an overlapping +1 open reading frame, called ORFx, via a novel mechanism whereby a subset of ribosomes recruited to the IRES bypasses 37 nucleotides downstream to resume translation at the +1-frame 13th non-AUG codon. A mutant of CrPV containing a stop codon in the +1 frame ORFx sequence, yet synonymous in the 0-frame, is attenuated compared to wild-type virus in a Drosophila infection model, indicating the importance of +1 ORFx expression in promoting viral pathogenesis. This work demonstrates a novel programmed IRES-mediated recoding strategy to increase viral coding capacity and impact virus infection, highlighting the diversity of RNA-driven translation initiation mechanisms in eukaryotes.
A Stacked Technique for Gender Recognition Through Voice

Detecting the gender of a person (male or female) through their voice seems to be a very trivial task for humans. Our minds are trained over the course of time to detect the differences in voices of males and females. Our ears work as the front end, receiving the audio signals which our brain processes and makes the decision. But it is a challenging problem for computers. Gender classification has applications like, it is able to improve the intelligence of a surveillance system, analyze the customer's demands for store management, and allow the robots to perceive gender etc. This paper proposes a stacked machine learning algorithm to determine gender using the acoustic parameters of voice sample and compares its performance with existing classifiers as CART, Random forest and neural network.
MiRNAs and inflammatory bowel disease: An interesting new story

Inflammatory bowel disease (IBD), as a chronic and recurrent inflammatory disorder, is caused by a dysregulated and aberrant immune response to exposed environmental factors in genetically susceptible individuals. Despite huge efforts in determining the molecular pathogenesis of IBD, an increasing worldwide incidence of IBD has been reported. MicroRNAs (miRNAs) are a set of noncoding RNA molecules that are about 22 nucleotides long, and these molecules are involved in the regulation of the gene expression. By clarifying the important role of miRNAs in a number of diseases, their role was also considered in IBD; numerous studies have been performed on this topic. In this review, we attempt to summarize a number of studies and discuss some of the recent developments in the roles of miRNAs in the pathophysiology, diagnosis, and treatment of IBD.
Intervention effect estimates in cluster randomized versus individually randomized trials: a meta-epidemiological study

Abstract Background Cluster randomized trials (CRTs) and individually randomized trials (IRTs) are often pooled together in meta-analyses (MAs) of randomized trials. However, the potential systematic differences in intervention effect estimates between these two trial types has never been investigated. Therefore, we conducted a meta-epidemiological study comparing intervention effect estimates between CRTs and IRTs. Methods All Cochrane MAs including at least one CRT and one IRT, published between 1 January 2010 and 31 December 2014, were included. For each MA, we estimated a ratio of odds ratios (ROR) for binary outcomes or a difference of standardized differences (DSMD) for continuous outcomes, where less than 1 (or 0, respectively) indicated a greater intervention effect estimate with CRTs. Results Among 1301 screened reviews, we selected 121 MAs, of which 76 had a binary outcome and 45 had a continuous outcome. For binary outcomes, intervention effect estimates did not differ between CRTs and IRTs [ROR 1.00, 95% confidence interval (0.93 to 1.08)]. Subgroup and adjusted analyses led to consistent results. For continuous outcomes, the DSMD was 0.13 (0.06 to 0.19). It was lower for MAs with a pharmacological intervention [-0.03, (-0.12 to 0.07)], an objective outcome [0.05, (-0.08 to 0.17)] or after adjusting for trial size [0.06, (-0.01 to 0.15)]. Conclusion For binary outcomes, CRTs and IRTs can safely be pooled in MAs because of an absence of systematic differences between effect estimates. For continuous outcomes, the results were less clear although accounting for trial sample sizes led to a non-significant difference. More research is needed for continuous outcomes and, meanwhile, MAs should be completed with subgroup analyses (CRTs vs IRTs).