nils-herrmann/clean_scirepeval huggingface dataset:
Compiling Deep Learning Models for Custom Hardware Accelerators

Convolutional neural networks (CNNs) are the core of most state-of-the-art deep learning algorithms specialized for object detection and classification. CNNs are both computationally complex and embarrassingly parallel. Two properties that leave room for potential software and hardware optimizations for embedded systems. Given a programmable hardware accelerator with a CNN oriented custom instructions set, the compiler’s task is to exploit the hardware’s full potential, while abiding with the hardware constraints and maintaining generality to run different CNN models with varying workload properties. Snowflake is an efficient and scalable hardware accelerator implemented on programmable logic devices. It implements a control pipeline for a custom instruction set. The goal of this paper is to present Snowflake’s compiler that generates machine level instructions from Torch7 model description files. The main software design points explored in this work are: model structure parsing, CNN workload breakdown, loop rearrangement for memory bandwidth optimizations and memory access balancing. The performance achieved by compiler generated instructions matches against hand optimized code for convolution layers. Generated instructions also efficiently execute AlexNet and ResNet18 inference on Snowflake. Snowflake with 256 processing units was synthesized on Xilinx’s Zynq XC7Z045 FPGA. At 250 MHz, AlexNet achieved in 93.6 frames/s and 1.2 GB/s of off-chip memory bandwidth, and 21.4 frames/s and 2.2 GB/s for ResNet18. Total on-chip power is 5 W.
Surgical strategy for low rectal cancers.

The two goals of surgery for lower rectal cancer surgery are to obtain clear "curative" margins and to limit post-surgical functional disorders. The question of whether or not to preserve the anal sphincter lies at the center of the therapeutic choice. Histologically, tumor-free distal and circumferential margins of>1mm allow a favorable oncologic outcome. Whether such margins can be obtained depends of TNM staging, tumor location, response to chemoradiotherapy and type of surgical procedure. The technique of intersphincteric resection relies on these narrow margins to spare the sphincter. This procedure provides satisfactory oncologic outcome with a rate of circumferential margin involvement ranging from 5% to 11%, while good continence is maintained in half of the patients. The extralevator abdominoperineal resection provides good oncologic results, however this procedure requires a permanent colostomy. A permanent colostomy alters several domains of quality of life when located at the classical abdominal site but not when brought out at the perineal site as a perineal colostomy.
The Hegemony of Play

In this paper, we introduce the concept of a “Hegemony of Play,” to critique the way in which a complex layering of technological, commercial and cultural power structures have dominated the development of the digital game industry over the past 35 years, creating an entrenched status quo which ignores the needs and desires of “minority” players such as women and “non-gamers,” Who in fact represent the majority of the population. Drawing from the history of pre-digital games, we demonstrate that these practices have “narrowed the playing field,” and contrary to conventional wisdom, have actually hindered, rather than boosted, its commercial success. We reject the inevitability of these power structures, and urge those in game studies to “step up to the plate” and take a more proactive stance in questioning and critiquing the status of the Hegemony of Play.
LabCaS: labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields.

The calpain family of Ca(2+) -dependent cysteine proteases plays a vital role in many important biological processes which is closely related with a variety of pathological states. Activated calpains selectively cleave relevant substrates at specific cleavage sites, yielding multiple fragments that can have different functions from the intact substrate protein. Until now, our knowledge about the calpain functions and their substrate cleavage mechanisms are limited because the experimental determination and validation on calpain binding are usually laborious and expensive. In this work, we aim to develop a new computational approach (LabCaS) for accurate prediction of the calpain substrate cleavage sites from amino acid sequences. To overcome the imbalance of negative and positive samples in the machine-learning training which have been suffered by most of the former approaches when splitting sequences into short peptides, we designed a conditional random field algorithm that can label the potential cleavage sites directly from the entire sequences. By integrating the multiple amino acid features and those derived from sequences, LabCaS achieves an accurate recognition of the cleave sites for most calpain proteins. In a jackknife test on a set of 129 benchmark proteins, LabCaS generates an AUC score 0.862. The LabCaS program is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/LabCaS. Proteins 2013.
Unsupervised Induction of Contingent Event Pairs from Film Scenes

Human engagement in narrative is partially driven by reasoning about discourse relations between narrative events, and the expectations about what is likely to happen next that results from such reasoning. Researchers in NLP have tackled modeling such expectations from a range of perspectives, including treating it as the inference of the CONTINGENT discourse relation, or as a type of common-sense causal reasoning. Our approach is to model likelihood between events by drawing on several of these lines of previous work. We implement and evaluate different unsupervised methods for learning event pairs that are likely to be CONTINGENT on one another. We refine event pairs that we learn from a corpus of film scene descriptions utilizing web search counts, and evaluate our results by collecting human judgments of contingency. Our results indicate that the use of web search counts increases the average accuracy of our best method to 85.64% over a baseline of 50%, as compared to an average accuracy of 75.15% without web search.
On-demand language model interpolation for mobile speech input

Google offers several speech features on the Android mobile operating system: search by voice, voice input to any text field, and an API for application developers. As a result, our speech recognition service must support a wide range of usage scenarios and speaking styles: relatively short search queries, addresses, business names, dictated SMS and e-mail messages, and a long tail of spoken input to any of the applications users may install. We present a method of on-demand language model interpolation in which contextual information about each utterance determines interpolation weights among a number of n-gram language models. On-demand interpolation results in an 11.2% relative reduction in WER compared to using a single language model to handle all traffic.
Eryptosis: An Erythrocyte's Suicidal Type of Cell Death

Erythrocytes play an important role in oxygen and carbon dioxide transport. Although erythrocytes possess no nucleus or mitochondria, they fulfil several metabolic activities namely, the Embden-Meyerhof pathway, as well as the hexose monophosphate shunt. Metabolic processes within the erythrocyte contribute to the morphology/shape of the cell and important constituents are being kept in an active, reduced form. Erythrocytes undergo a form of suicidal cell death called eryptosis. Eryptosis results from a wide variety of contributors including hyperosmolarity, oxidative stress, and exposure to xenobiotics. Eryptosis occurs before the erythrocyte has had a chance to be naturally removed from the circulation after its 120-day lifespan and is characterised by the presence of membrane blebbing, cell shrinkage, and phosphatidylserine exposure that correspond to nucleated cell apoptotic characteristics. After eryptosis is triggered there is an increase in cytosolic calcium (Ca2+) ion levels. This increase causes activation of Ca2+-sensitive potassium (K+) channels which leads to a decrease in intracellular potassium chloride (KCl) and shrinkage of the erythrocyte. Ceramide, produced by sphingomyelinase from the cell membrane's sphingomyelin, contributes to the occurrence of eryptosis. Eryptosis ensures healthy erythrocyte quantity in circulation whereas excessive eryptosis may set an environment for the clinical presence of pathophysiological conditions including anaemia.
A new role for sphingosine: Up-regulation of Fam20C, the genuine casein kinase that phosphorylates secreted proteins.

Fam20C is an atypical kinase implicated in bio-mineralization and phosphate homeostasis disorders, and has recently been shown to account for the activity of an orphan enzyme ("genuine casein kinase", G-CK) previously characterized for its ability to phosphorylate casein and a plethora of secreted proteins at serine residues specified by the S-x-E/pS motif. CK activity is only appreciable in the presence of high Mn2+ concentration (>1 mM), and is negligible if Mn2+ is replaced by physiological Mg2+ concentrations. Here we show that sphingosine (but not its biological precursor ceramide) not only stimulates several-fold Fam20C activity in the presence of Mn2+, but also confers a comparable activity to Fam20C assayed with Mg2+. Activation by sphingosine is evident using a variety of substrates, and is accounted for by both higher Vmax and decreased KmATP, as judged from kinetics run with the β(28-40) substrate peptide and a physiological substrate, BMP-15. Sphingosine also protects Fam20C from thermal inactivation. Consistent with the in vitro results, by treating Fam20C expressing HEK293T cells with myriocin, a potent inhibitor of the sphingosine biosynthetic pathway, the activity of Fam20C released into the conditioned medium is substantially decreased corroborating the concept that sphingosine (or related metabolite(s)) is a co-factor required by Fam20C to optimally display its biological functions. None of the small molecule kinase inhibitors tested so far were able to inhibit Fam20C. Interestingly however fingolimod, an immunosuppressive drug structurally related to sphingosine, used for the treatment of multiple sclerosis, is a powerful activator of Fam20C, both wild type and its pathogenic, loss of function, T268M mutant. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.
A Mw 6 . 3 earthquake scenario in the city of Nice ( southeast France ) : ground motion simulations

The southern Alps–Ligurian basin junction is one of the most seismically active zone of the western Europe. A constant microseismicity and moderate size events (3.5 < M < 5) are regularly recorded. The last reported historical event took place in February 1887 and reached an estimated magnitude between 6 and 6.5, causing human losses and extensive damages (intensity X, Medvedev–Sponheuer–Karnik). Such an event, occurring nowadays, could have critical consequences given the high density of population living on the French and Italian Riviera. We study the case of an offshore Mw 6.3 earthquake located at the place where two moderate size events (Mw 4.5) occurred recently and where a morphotectonic feature has been detected by a bathymetric survey. We used a stochastic empiriJ. Salichon · C. Kohrs-Sansorny · F. Courboulex Observatoire de la Côte d’Azur, Géoazur Nice-Sophia Antipolis University, CNRS, Nice, France E. Bertrand LCPC-CETE Méditerranée, Nice, France J. Salichon (B) · E. Bertrand (B) · F. Courboulex (B) Géoazur, 250 rue Albert Einstein, Sophia Antipolis, 06560 Valbonne, France e-mail: salichon@geoazur.unice.fr  Green’s functions (EGFs) summation method to produce a population of realistic accelerograms on rock and soil sites in the city of Nice. The ground motion simulations are calibrated on a rock site with a set of ground motion prediction equations (GMPEs) in order to estimate a reasonable stress-drop ratio between the February 25th, 2001, Mw 4.5, event taken as an EGF and the target earthquake. Our results show that the combination of the GMPEs and EGF techniques is an interesting tool for site-specific strong ground motion estimation.
Real-Time Vehicle Recognition and Improved Traffic Congestion Resolution

An intelligent traffic management system (E-Traffic Warden) is proposed, using image processing techniques along with smart traffic control algorithm. Traffic recognition was achieved using cascade classifier for vehicle recognition utilizing Open CV and Visual Studio C/C++. The classifier was trained on 700 positive samples and 1140 negative samples. The results show that the accuracy of vehicle detection is approximately 93 percent. The count of vehicles at all approaches of intersection is used to estimate traffic. Traffic build up is then avoided or resolved by passing the extracted data to traffic control algorithm. The control algorithm shows approximately 86% improvement over Fixed-Delay controller in worst case scenarios.
Affective mechanisms linking Internet use to learning performance in high school students: A moderated mediation study

Although previous studies have concluded that Internet use can help students in learning and research, a number of empirical investigations have confirmed that Internet addiction or excessive Internet use has negative effect on students. Thus, if the Internet does not always benefit students, under which conditions can Internet use have positive effects? Since students’ beliefs in their academic self-efficacy and their abilities to begin, continue, and complete their studies are as important as their academic successes and performances, this study hypothesizes that academic self-efficacy acts as a mediator for Internet use and academic performance. Based on Social cognitive theory, we argue that student academic performance will be mediated by academic self-efficacy with respect to Internet use. Two kinds of Internet use, general and professional, are considered to be antecedents of academic self-efficacy. Survey data from 212 twelfth-grade vocational high school students in Taiwan indicate that general Internet use has an indirect positive effect on student academic performance, which is also mediated through academic self-efficacy. In contrast, general Internet use has no significant direct impact on students learning performance. This study also shows that Internet anxiety moderates the relationship between academic self-efficacy and learning performance. In students with low Internet anxiety, the relationship is moderated, which results in enhanced learning performance.
Lipid mediators in the resolution of inflammation.

Mounting of the acute inflammatory response is crucial for host defense and pivotal to the development of chronic inflammation, fibrosis, or abscess formation versus the protective response and the need of the host tissues to return to homeostasis. Within self-limited acute inflammatory exudates, novel families of lipid mediators are identified, named resolvins (Rv), protectins, and maresins, which actively stimulate cardinal signs of resolution, namely, cessation of leukocytic infiltration, counterregulation of proinflammatory mediators, and the uptake of apoptotic neutrophils and cellular debris. The biosynthesis of these resolution-phase mediators in sensu stricto is initiated during lipid-mediator class switching, in which the classic initiators of acute inflammation, prostaglandins and leukotrienes (LTs), switch to produce specialized proresolving mediators (SPMs). In this work, we review recent evidence on the structure and functional roles of these novel lipid mediators of resolution. Together, these show that leukocyte trafficking and temporal spatial signals govern the resolution of self-limited inflammation and stimulate homeostasis.
Executive functions in extremely low birth weight and late-preterm preschoolers: effects on working memory and response inhibition.

Executive function (EF) refers to fundamental capacities that underlie more complex cognition and have ecological relevance across the individual's lifespan. However, emerging executive functions have rarely been studied in young preterm children (age 3) whose critical final stages of fetal development are interrupted by their early birth. We administered four novel touch-screen computerized measures of working memory and inhibition to 369 participants born between 2004 and 2006 (52 Extremely Low Birth Weight [ELBW]; 196 late preterm; 121 term-born). ELBW performed worse than term-born on simple and complex working memory and inhibition tasks and had the highest percentage of incomplete performance on a continuous performance test. The latter finding indicates developmental immaturity and the ELBW group's most at-risk preterm status. Additionally, late-preterm participants performed worse compared with term-born on measures of complex working memory but did not differ from those term-born on response inhibition measures. These results are consistent with a recent literature that identifies often subtle but detectable neurocognitive deficits in late-preterm children. Our results support the development and standardization of computerized touch-screen measures to assess EF subcomponent abilities during the formative preschool period. Such measures may be useful to monitor the developmental trajectory of critical executive function abilities in preterm children, and their use is necessary for timely recognition of deficit and application of appropriate interventional strategies.
A new hybrid global optimization approach for selecting clinical and biological features that are relevant to the effective diagnosis of ovarian cancer

Reducing the number of features whilst maintaining an acceptable classification accuracy is a fundamental step in the process of constructing cancer predictive models. In this work, we introduce a novel hybrid (MI-LDA) feature selection approach for the diagnosis of ovarian cancer. This hybrid approach is embedded within a global optimization framework and offers a promising improvement on feature selection and classification accuracy processes. Global Mutual Information (MI) based feature selection optimizes the search process of finding best feature subsets in order to select the highly correlated predictors for ovarian cancer diagnosis. The maximal discriminative cancer predictors are then passed to a Linear Discriminant Analysis (LDA) classifier, and a Genetic Algorithm (GA) is applied to optimise the search process with respect to the estimated error rate of the LDA classifier (MI-LDA). Experiments were performed using an ovarian cancer dataset obtained from the FDA-NCI Clinical Proteomics Program Databank. The performance of the hybrid feature selection approach was evaluated using the Support Vector Machine (SVM) classifier and the LDA classifier. A comparison of the results revealed that the proposed (MI-LDA)-LDA model outperformed the (MI-LDA)-SVM model on selecting the maximal discriminative feature subset and achieved the highest predictive accuracy. The proposed system can therefore be used as an efficient tool for finding predictors and patterns in serum (blood)-derived proteomic data for the detection of ovarian cancer.
Noise Adaptive Speech Enhancement using Domain Adversarial Training

In this study, we propose a novel noise adaptive speech enhancement (SE) system, which employs a domain adversarial training (DAT) approach to tackle the issue of noise type mismatch between training and testing conditions. Such a mismatch is a critical problem in deep-learning-based SE systems. A large mismatch may cause serious performance degradation to the SE performance. Since we generally use a well trained SE system to handle various unseen noise types, the noise type mismatch commonly happens in realworld scenarios. The proposed noise adaptive SE system contains an encoder-decoder-based enhancement model and a domain discriminator model. During adaptation, the DAT approach encourages the encoder to produce noise invariant features based on the information from the discriminator model and consequentially increases the robustness of the enhancement model to unseen noise types. Here we regard stationary noises as the source domain (with ground-truth clean speech) and non-stationary noises as the target domain (without ground truth). We evaluated the proposed system on the TMHINT sentences. Experimental results show that the proposed noise adaptive SE system successfully provide notable PESQ (55.9%) and SSNR (26.1%) relative improvements over the SE system without performing noise adaptation.
An Architectural Model-Based Approach to Quality-Aware DevOps in Cloud Applicationsc

Cloud-based software applications are designed to change often and rapidly during operations to provide constant quality of service. As a result the boundary between development and operations is becoming increasingly blurred. DevOps provides a set of practices for the integrated consideration of developing and operating software. Software architecture is a central artifact in DevOps practices. Existing architectural models used in the development phase differ from those used in the operation phase in terms of purpose, abstraction, and content. In this chapter, we present the iObserve approach to address these differences and allow for phase-spanning usage of architectural models.
The flipped voltage follower: a useful cell for low-voltage low-power circuit design

In this paper, a basic cell for low-power and/or low-voltage operation is identified. It is evidenced how different versions of this cell, coined as "flipped voltage follower (FVF)" have been used in the past for many applications. A detailed classification of basic topologies derived from the FVF is given. In addition, a comprehensive list of recently proposed low-voltage/low-power CMOS circuits based on the FVF is given. Although the paper has a tutorial taste, some new applications of the FVF are also presented and supported by a set of simulated and experimental results. Finally, a design example showing the application of the FVF to build systems based on translinear loops is described which shows the potential of this cell for the design of high-performance low-power/low-voltage analog and mixed-signal circuits.
Soft transition from probabilistic to possibilistic fuzzy clustering

In the fuzzy clustering literature, two main types of membership are usually considered: A relative type, termed probabilistic, and an absolute or possibilistic type, indicating the strength of the attribution to any cluster independent from the rest. There are works addressing the unification of the two schemes. Here, we focus on providing a model for the transition from one schema to the other, to exploit the dual information given by the two schemes, and to add flexibility for the interpretation of results. We apply an uncertainty model based on interval values to memberships in the clustering framework, obtaining a framework that we term graded possibility. We outline a basic example of graded possibilistic clustering algorithm and add some practical remarks about its implementation. The experimental demonstrations presented highlight the different properties attainable through appropriate implementation of a suitable graded possibilistic model. An interesting application is found in automated segmentation of diagnostic medical images, where the model provides an interactive visualization tool for this task