nils-herrmann/clean_scirepeval huggingface dataset:
Deep JSLC: A Multimodal Corpus Collection for Data driven Generation of Japanese Sign Language Expressions
Venue: LREC
Year: 2018

The three dimensional visualization of spoken or written information in Sign Language (SL) is considered a potential tool for better inclusion of deaf or hard of hearing individuals with low literacy skills. However, conventional technologies for such CG supported data display are not able to depict all relevant features of a natural signing sequence such as facial expression, spatial references or inter sign movement, leading to poor acceptance amongst speakers of sign language. The deployment of fully data driven, deep sequence generation models that proved themselves powerful in speech and text applications might overcome this lack of naturalness. Therefore, we collected a corpus of continuous sentence utterances in Japanese Sign Language (JSL) applicable to the learning of deep neural network models. The presented corpus contains multimodal content information of high resolution motion capture data, video data and both visual and gloss like mark up annotations obtained with the support of fluent JSL signers. Furthermore, all annotations were encoded under three different encoding schemes with respect to directions, intonation and non manual information. Currently, the corpus is employed to learn first sequence to sequence networks where it shows the ability to train relevant language features.
Deep Cross Modal Projection Learning for Image Text Matching
Venue: ECCV
Year: 2018

The key point of image text matching is how to accurately measure the similarity between visual and textual inputs. Despite the great progress of associating the deep cross modal embeddings with the bi directional ranking loss, developing the strategies for mining useful triplets and selecting appropriate margins remains a challenge in real applications. In this paper, we propose a cross modal projection matching (CMPM) loss and a cross modal projection classification (CMPC) loss for learning discriminative image text embeddings. The CMPM loss minimizes the KL divergence between the projection compatibility distributions and the normalized matching distributions defined with all the positive and negative samples in a mini batch. The CMPC loss attempts to categorize the vector projection of representations from one modality onto another with the improved norm softmax loss, for further enhancing the feature compactness of each class. Extensive analysis and experiments on multiple datasets demonstrate the superiority of the proposed approach.
BinRec: dynamic binary lifting and recompilation
Venue: EuroSys
Year: 2020

Binary lifting and recompilation allow a wide range of install time program transformations, such as security hardening, deobfuscation, and reoptimization. Existing binary lifting tools are based on static disassembly and thus have to rely on heuristics to disassemble binaries. In this paper, we present BinRec, a new approach to heuristic free binary recompilation which lifts dynamic traces of a binary to a compiler level intermediate representation (IR) and lowers the IR back to a "recovered" binary. This enables BinRec to apply rich program transformations, such as compiler based optimization passes, on top of the recovered representation. We identify and address a number of challenges in binary lifting, including unique challenges posed by our dynamic approach. In contrast to existing frameworks, our dynamic frontend can accurately disassemble and lift binaries without heuristics, and we can successfully recover obfuscated code and all SPEC INT 2006 benchmarks including C+ applications. We evaluate BinRec in three application domains: i) binary reoptimization, ii) deobfuscation (by recovering partial program semantics from virtualization obfuscated code) and iii) binary hardening (by applying existing compiler level passes such as AddressSanitizer and SafeStack on binary code)
DeepConv DTI: Prediction of drug target interactions via deep learning with convolution on protein sequences
Venue: PLoS Comput. Biol.
Year: 2019

Identification of drug target interactions (DTIs) plays a key role in drug discovery. The high cost and labor intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico based DTI prediction approaches. In several computational models, conventional protein descriptors have been shown to not be sufficiently informative to predict accurate DTIs. Thus, in this study, we propose a deep learning based DTI prediction model capturing local residue patterns of proteins participating in DTIs. When we employ a convolutional neural network (CNN) on raw protein sequences, we perform convolution on various lengths of amino acids subsequences to capture local residue patterns of generalized protein classes. We train our model with large scale DTI information and demonstrate the performance of the proposed model using an independent dataset that is not seen during the training phase. As a result, our model performs better than previous protein descriptor based models. Also, our model performs better than the recently developed deep learning models for massive prediction of DTIs. By examining pooled convolution results, we confirmed that our model can detect binding sites of proteins for DTIs. In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than previous approaches. Our code is available at https:/github.com/GIST CSBL/DeepConv DTI.
An Animation Video Resource Conversion System Based on Supercomputers
Year: 2014

The rapid development of mobile internet has brought new opportunities to the development of the animation industry, but the network traffic bottlenecks animation video promotion. Based on the super computing resources, using the Python language, adopting FFMpeg, OpenCV, HEVC and PyGTK technologies, we designed and implemented an animation video resource conversion system to solve the traffic bottleneck in this paper. The conversion system provides HEVC parallel conversion, video serial conversion, video frames extraction and video playback functions, effectively promoted the promotion of animation products distribution.
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
Venue: IEEE Transactions on Image Processing
Year: 2020

Occlusion and pose variations, which can change facial appearance significantly, are two major obstacles for automatic Facial Expression Recognition (FER) Though automatic FER has made substantial progresses in the past few decades, occlusion robust and pose invariant issues of FER have received relatively less attention, especially in real world scenarios. This paper addresses the real world pose and occlusion robust FER problem in the following aspects. First, to stimulate the research of FER under real world occlusions and variant poses, we annotate several in the wild FER datasets with pose and occlusion attributes for the community. Second, we propose a novel Region Attention Network (RAN) to adaptively capture the importance of facial regions for occlusion and pose variant FER. The RAN aggregates and embeds varied number of region features produced by a backbone convolutional neural network into a compact fixed length representation. Last, inspired by the fact that facial expressions are mainly defined by facial action units, we propose a region biased loss to encourage high attention weights for the most important regions. We validate our RAN and region biased loss on both our built test datasets and four popular datasets: FERPlus, AffectNet, RAF DB, and SFEW. Extensive experiments show that our RAN and region biased loss largely improve the performance of FER with occlusion and variant pose. Our method also achieves state of the art results on FERPlus, AffectNet, RAF DB, and SFEW. Code and the collected test data will be publicly available.
Epidemiology of acute kidney injury in critically ill patients: the multinational AKI EPI study
Venue: Intensive Care Medicine
Year: 2015

PurposeCurrent reports on acute kidney injury (AKI) in the intensive care unit (ICU) show wide variation in occurrence rate and are limited by study biases such as use of incomplete AKI definition, selected cohorts, or retrospective design. Our aim was to prospectively investigate the occurrence and outcomes of AKI in ICU patients.MethodsThe Acute Kidney Injury Epidemiologic Prospective Investigation (AKI EPI) study was an international cross sectional study performed in 97 centers on patients during the first week of ICU admission. We measured AKI by Kidney Disease: Improving Global Outcomes (KDIGO) criteria, and outcomes at hospital discharge. total of 1032 ICU patients out of 1802 [57.3 95 confidence interval (CI) 55.0 59.6] had AKI. Increasing AKI severity was associated with hospital mortality when adjusted for other variables; odds ratio of stage 1 1.679 (95 CI 0.890 3.169; p 0.109) stage 2 2.945 (95 CI 1.382 6.276; p 0.005) and stage 3 6.884 (95 CI 3.876 12.228; p 0.001) Risk adjusted rates of AKI and mortality were similar across the world. Patients developing AKI had worse kidney function at hospital discharge with estimated glomerular filtration rate less than 60 mL/min/1.73 m2 in 47.7 (95 CI 43.6 51.7) versus 14.8 (95 CI 11.9 18.2) in those without AKI, p 0.001.ConclusionsThis is the first multinational cross sectional study on the epidemiology of AKI in ICU patients using the complete KDIGO criteria. We found that AKI occurred in more than half of ICU patients. Increasing AKI severity was associated with increased mortality, and AKI patients had worse renal function at the time of hospital discharge. Adjusted risks for AKI and mortality were similar across different continents and regions.
Broad temperature adaptability of vanadium redox flow battery Part 1: Electrolyte research
Year: 2016

The broad temperature adaptability of vanadium redox flow battery (VRFB) is one of the key issues which affects the large scale and safety application of VRFB. Typically, five types of vanadium electrolytes, namely V2+ V3+ V3.5+ (V3+:VO2+ 1:1) V4+ (VO2+ and V5+ (VO2+ are the most common electrolytes' status existing in VRFB system. In this work, the physicochemical and electrochemical properties of these vanadium electrolytes are studied in detail at a broad temperature range 35 degC 50 degC) The results show that all types of vanadium electrolytes are stable between 25 degC 30 degC. The temperature fluctuation will largely influence the conductivity and viscosity of the electrolytes. Besides, the electrochemical properties of the positive (VO2+ and negative (V3+ electrolytes are greatly affected by the temperature; and the charge transfer process fluctuates more greatly with the temperature variation than the charge diffusion process does. These results enable us to better and more comprehensively evaluate the performance of the electrolyte changing with the temperature, which will be beneficial for the rational choice of electrolyte for VRFB operation under various conditions.
Nonlinear System Identification: A User Oriented Roadmap
Venue: ArXiv
Year: 2019

The goal of this article is twofold. Firstly, nonlinear system identification is introduced to a wide audience, guiding practicing engineers and newcomers in the field to a sound solution of their data driven modeling problems for nonlinear dynamic systems. In addition, the article also provides a broad perspective on the topic to researchers that are already familiar with the linear system identification theory, showing the similarities and differences between the linear and nonlinear problem. The reader will be referred to the existing literature for detailed mathematical explanations and formal proofs. Here the focus is on the basic philosophy, giving an intuitive understanding of the problems and the solutions, by making a guided tour along the wide range of user choices in nonlinear system identification. Guidelines will be given in addition to many examples, to reach that goal.
Structure Adaptive Lasso
Year: 2020

Lasso is of fundamental importance in high dimensional statistics and has been routinely used to regress a response on a high dimensional set of predictors. In many scientific applications, there exists external information that encodes the predictive power and sparsity structure of the predictors. In this article, we develop a new method, called the Structure Adaptive Lasso (SA Lasso) to incorporate these potentially useful side information into a penalized regression. The basic idea is to translate the external information into different penalization strengths for the regression coefficients. We study the risk properties of the resulting estimator. In particular, we generalize the state evolution framework recently introduced for the analysis of the approximate message passing algorithm to the SA Lasso setting. We show that the finite sample risk of the SA Lasso estimator is consistent with the theoretical risk predicted by the state evolution equation. Our theory suggests that the SA Lasso with an informative group or covariate structure can significantly outperform the Lasso, Adaptive Lasso, and Sparse Group Lasso. This evidence is further confirmed in our numerical studies. We also demonstrate the usefulness and the superiority of our method in a real data application.
A Stakeholder Relationship Framework for a Sustainable and Effective Business Strategy
Venue: International Journal of Business and Management
Year: 2019

Several studies have underlined the importance of including sustainability issues in business models, yet little is known on ways in which to embed stakeholders' influence in defining and executing firm strategies. In light of this gap, this study inductively develops a framework for stakeholder relationship assessment, which suggests methods to evaluate relationships between the firm and stakeholders, with a dual aim: to embed more sustainable strategies in business models and to make strategy execution more effective and less risky. In doing so, a case study is conducted on Science Lab owing to the multiple stakes of its stakeholders. It presents a unique example on how stakeholder relationships were evaluated for making a strategic decision that, in this case, was whether to close (or not) the organization. This study contributes to the existing literature stressing the role of stakeholders in influencing strategy realization and proposes a useful managerial tool to improve the effectiveness of strategy execution through a better understanding of the role played by each stakeholder.
Comprehensive assessment of patients with irritable bowel syndrome with constipation and chronic idiopathic constipation using deterministically linked administrative claims and patient reported data: the Chronic Constipation and IBS C Treatment and Outcomes Real World Research Platform (CONTOR)
Venue: Journal of medical economics
Year: 2020

To characterize a US population of patients with irritable bowel syndrome with constipation (IBS C) or chronic idiopathic constipation (CIC) using CONTOR, a real world longitudinal research platform that deterministically linked administrative claims data with patient reported outcomes data among patients with these conditions.  Patients with IBS C or CIC were identified using diagnosis and treatment codes from administrative claims. Potential respondents received a mailed survey followed by 12 monthly online follow up surveys and 2 mailed diaries. Surveys collected symptom severity, treatment use, quality of life, productivity, and condition/treatment history. Comorbidities and healthcare costs/utilization were captured from claims data. Diaries collected symptoms, treatments, and clinical outcomes at baseline and 12 months. Data were linked to create a patient centric research platform.  Baseline surveys were returned by 2,052 respondents (16.8% response rate) and retention rates throughout the study were high (64.8% 70.8% Most participants reported burdensome symptoms despite having complex treatment histories that included multiple treatments over many years. More than half (55.3% were dissatisfied with their treatment regimen; however, a higher proportion of those treated with prescription medications were satisfied.  The study sample may have been biased by patients with difficult to treat symptoms as a result of prior authorization processes for IBS C/CIC prescriptions. Results may not be generalizable to uninsured or older populations because all participants had commercial insurance coverage.  By combining administrative claims and patient reported data over time, CONTOR afforded a deeper understanding of the IBS C/CIC patient experience than could be achieved with 1 data source alone; for example, participants self reported burdensome symptoms and treatment dissatisfaction despite making few treatment changes, highlighting an opportunity to improve patient management. This patient centric approach to understanding real world experience and management of a chronic condition could be leveraged for other conditions in which the patient experience is not adequately captured by standardized data sources.
Vibration behavior of metallic sandwich panels with Hourglass truss cores
Year: 2019

Exploring lightweight sandwich structures with excellent load bearing and vibration damping performances is one of the important topics in structural and functional applications. The aim of the present work is to design, fabricate novel metallic sandwich structures with Hourglass truss cores and investigate their modal characteristics and vibration isolation performances by comparing with the traditional pyramidal sandwich structure experimentally and numerically. It is shown that the natural frequencies of the Hourglass sandwich structures are much higher than that of the pyramidal sandwich structures under free free boundary condition and equal relative density of the truss cores. The torsional modes and transverse bending modes of the Hourglass sandwich structures separately play a dominant role in lower and higher modes of vibration, which are opposite to the pyramidal sandwich structures. From the results of the acceleration frequency responses and vibration level difference (VLD) it is indicated that the Hourglass sandwich structures exhibit better vibration isolation performance than the pyramidal sandwich structures. Furthermore, experimentally validated finite element analysis (FEA) models are developed to study the influences of truss inclination angles and boundary conditions on the modal characteristics of the present sandwich structures. Some conclusions are summarized, which may be useful for understanding the vibration behavior of such kinds of lattice sandwich structures.
Qualitative Changes in the SARS CoV 2 Antibody Response in the Post Infection Phase Impact the estimates of infections in Population Based Seroprevalence Studies
Venue: medRxiv
Year: 2020

In the present study, we have determined SARS CoV 2 specific antibody responses in a cohort of 96 individuals during the acute phase of infection and in a cohort of 578 individuals enrolled in a seroprevalence population study in Switzerland including three groups, i.e. subjects with previous RT PCR confirmed SARS CoV 2 infections (n=90) positive patient contact (n=177) and random selected subjects (n=311) Six serological assays detecting predominantly IgG antibodies targeting either the Spike (S) and/or the nucleocapsid (N) proteins were used including also a Luminex based assay using an S protein in its native trimeric form. Antibody responses against the S and/or the N proteins were equally sensitive in the acute infection phase although differences in sensitivity (range 83 to 97% 16 33 days post initial symptoms) were observed between the different assays and the Luminex S protein trimer assay was the most sensitive. Interestingly, antibody responses against the N protein appear to wane in the post infection phase of the infection while those against the S protein persist over time, as indicated by the drop in sensitivity of the assays targeting the N protein (sensitivity range 71 77% Assays detecting anti N IgG antibodies may substantially underestimate the proportion of SARS CoV 2 infections in the groups of patient positive contacts, i.e. 10.9 to 32.2% reduction (P<0.05 <0.0001) and in the random selected general population, i.e. up to 45% reduction (P<0.05) The overall reduction in seroprevalence for the total cohort ranged from 9.4 to 31% (P<0.0009 <0.0001) Of note, the assay using the S protein in its native trimer form was more sensitive as compared to those using monomeric S proteins. These results indicate that assays targeting the S protein, ideally the trimeric form, should be implemented as reference test to estimate SARS CoV 2 infections in seroprevalence population studies.
Empirical Auto Evaluation of Python Code for Performance Analysis of Transformer Network Using T5 Architecture
Venue: 2021 8th International Conference on Smart Computing and Communications (ICSCC)
Year: 2021

The immense real time applicability of Python coding makes the task of evaluating the code highly intriguing, in the Natural Language Processing (NLP) domain. Evaluation of computer programs induces a challenge of logical and arithmetic understanding. Therefore, it is indeed very relevant to analyze the empirical ability of current state of the art sequence based neural architectures in evaluating small computer programs. One of the possible applications of such analysis is the auto evaluation of erroneous Python code. In this context, we focused our work on evaluating small python code blocks with or without error and examined the efficiency of the latest T5 Transformer network model in this task. In terms of accuracy, different Rouge scores, and BLEU scores, the performance measurements has been calculated. Observations reveal that T5 Transformer is able to compute the output for both correct and erroneous python code blocks with more than 65% accuracy.
Reduced annexin A6 expression promotes the degradation of activated epidermal growth factor receptor and sensitizes invasive breast cancer cells to EGFR targeted tyrosine kinase inhibitors
Venue: Molecular Cancer
Year: 2013

BackgroundThe expression of annexin A6 (AnxA6) in AnxA6 deficient non invasive tumor cells has been shown to terminate epidermal growth factor receptor (EGFR) activation and downstream signaling. However, as a scaffolding protein, AnxA6 may stabilize activated cell surface receptors to promote cellular processes such as tumor cell motility and invasiveness. In this study, we investigated the contribution of AnxA6 in the activity of EGFR in invasive breast cancer cells and examined whether the expression status of AnxA6 influences the response of these cells to EGFR targeted tyrosine kinase inhibitors (TKIs) and/or patient survival.ResultsWe demonstrate that in invasive BT 549 breast cancer cells AnxA6 expression is required for sustained membrane localization of activated (phosho Y1068) EGFR and consequently, persistent activation of MAP kinase ERK1/2 and phosphoinositide 3 kinase/Akt pathways. Depletion of AnxA6 in these cells was accompanied by rapid degradation of activated EGFR, attenuated downstream signaling and as expected enhanced anchorage independent growth. Besides inhibition of cell motility and invasiveness, AnxA6 depleted cells were also more sensitive to the EGFR targeted TKIs lapatinib and PD153035. We also provide evidence suggesting that reduced AnxA6 expression is associated with a better relapse free survival but poorer distant metastasis free and overall survival of basal like breast cancer patients.ConclusionsTogether this demonstrates that the rapid degradation of activated EGFR in AnxA6 depleted invasive tumor cells underlies their sensitivity to EGFR targeted TKIs and reduced motility. These data also suggest that AnxA6 expression status may be useful for the prediction of the survival and likelihood of basal like breast cancer patients to respond to EGFR targeted therapies.