Miracle-dz/UniFAR-FTUs huggingface dataset:
From Missteps to Milestones: A Journey to Practical Fail-Slow Detection

The newly emerging “fail-slow” failures plague both software and hardware where the victim components are still functioning yet with degraded performance. To address this problem, this article presents Perseus, a practical fail-slow detection framework for storage devices. Perseus leverages a light regression-based model to quickly pinpoint and analyze fail-slow failures at the granularity of drives. Within a 10-month close monitoring on 248K drives, Perseus managed to find 304 fail-slow cases. Isolating them can reduce the (node-level) 99.99th tail latency by 48%. We assemble a large-scale fail-slow dataset (including 41K normal drives and 315 verified fail-slow drives) from our production traces, based on which we provide root cause analysis on fail-slow drives covering a variety of ill-implemented scheduling, hardware defects, and environmental factors. We have released the dataset to the public for fail-slow study.
Evaluating Distance Measures for Program Repair

Background and Context: Struggling with programming assignments while learning to program is a common phenomenon in programming courses around the world. Supporting struggling students is a common theme in Computing Education Research (CER), where a wide variety of support methods have been created and evaluated. An important stream of research here focuses on program repair, where methods for automatically fixing erroneous code are used for supporting students as they debug their code. Work in this area has so far assessed the performance of the methods by evaluating the closeness of the proposed fixes to the original erroneous code. The evaluations have mainly relied on the use of edit distance measures such as the sequence edit distance and there is a lack of research on which distance measure is the most appropriate. Objectives: Provide insight into measures for quantifying the distance between erroneous code written by a student and a proposed change. We conduct the evaluation in an introductory programming context, where insight into the distance measures can provide help in choosing a suitable metric that can inform which fixes should be suggested to novices. Method: A team of five experts annotated a subset of the Dublin dataset, creating solutions for over a thousand erroneous programs written by students. We evaluated how the prominent edit distance measures from the CER literature compare against measures used in Natural Language Processing (NLP) tasks for retrieving the experts’ solutions from a pool of proposed solutions. We also evaluated how the expert-generated solutions compare against the solutions proposed by common program repair algorithms. The annotated dataset and the evaluation code are published as part of the work. Findings: Our results highlight that the ROUGE score, classically used for evaluating the performance of machine summarization tasks, performs well as an evaluation and selection metric for program repair. We also highlight the practical utility of NLP metrics, which allow an easier interpretation and comparison of the performance of repair techniques when compared to the classic methods used in the CER literature. Implications: Our study highlights the variety of distance metrics used for comparing source codes. We find issues with the classically used distance measures that can be combated by using NLP metrics. Based on our findings, we recommend including NLP metrics, and in particular, the ROUGE metric, in evaluations when considering new program repair methodologies. We also suggest incorporating NLP metrics into other areas where source codes are compared, including plagiarism detection.
Evaluating Beacons, the Role of Variables, Tracing, and Abstract Tracing for Teaching Novices to Understand Program Intent

Background and context. “Explain in Plain English” (EiPE) questions ask students to explain the high-level purpose of code, requiring them to understand the macrostructure of the program’s intent. A lot is known about techniques that experts use to comprehend code, but less is known about how we should teach novices to develop this capability. Objective. Identify techniques that can be taught to students to assist them in developing their ability to comprehend code and contribute to the body of knowledge of how novices develop their code comprehension skills. Method. We developed interventions that could be taught to novices motivated by previous research about how experts comprehend code: prompting students to identify beacons, identify the role of variables, tracing, and abstract tracing. We conducted think-aloud interviews of introductory programming students solving EiPE questions, varying which interventions each student was taught. Some participants were interviewed multiple times throughout the semester to observe any changes in behavior over time. Findings. Identifying beacons and the name of variable roles were rarely helpful, as they did not encourage students to integrate their understanding of that piece in relation to other lines of code. However, prompting students to explain each variable’s purpose helped them focus on useful subsets of the code, which helped manage cognitive load. Tracing was helpful when students incorrectly recognized common programming patterns or made mistakes comprehending syntax (text-surface). Prompting students to pick inputs that potentially contradicted their current understanding of the code was found to be a simple approach to them effectively selecting inputs to trace. Abstract tracing helped students see high-level, functional relationships between variables. In addition, we observed student spontaneously sketching algorithmic visualizations that similarly helped them see relationships between variables. Implications. Because students can get stuck at many points in the process of code comprehension, there seems to be no silver bullet technique that helps in every circumstance. Instead, effective instruction for code comprehension will likely involve teaching a collection of techniques. In addition to these techniques, meta-knowledge about when to apply each technique will need to be learned, but that is left for future research. At present, we recommend teaching a bottom-up, concrete-to-abstract approach.
Employing a Research Community Network to Assess Centralized Computing Impact

We present a study of a university research collaboration network constructed from publication co-authorship and grant co-investigator data. Subsequent analysis of the network’s structural characteristics confirms a small-world network topology. In the aim of providing a quantitative means for centralized computing centers to assess impact, degree and centrality metrics for researcher node cohorts with and without compute cluster accounts are contrasted. Additional assessment of the centralized computing researcher cohort confirms an impact exceeding its proportion of the researcher population. As an input to an agent-based model, the network is then employed in a simulation to measure idea propagation, once again contrasting the two researcher node cohorts as idea initiators. This analysis is extended to peer institutions to see if similar patterns are observed. Finally, the analysis is applied to researchers at all XSEDE participating universities in the United States, divided into cohorts with and without XSEDE allocations.
Deep unsupervised learning methods for the identification and characterization of TCR specificity to Sars-Cov-2

The T-cell receptor (TCR) is one of the key players in the immune response to the Sars-Cov-2 virus. In this study, we used deep unsu-pervised learning methods to identify and characterize TCR speci-ficity. Our research focused on developing and applying state-of-the-art modelling techniques, including AutoEncoders, Variational Au-to Encoders and transfer learning with Transformers, to analyze TCR data. Through our experiments and analyses, we have achieved promis-ing results in identifying TCR patterns and understanding TCR speci-ficity for Sars-Cov-2. The insights gained from our research provide valuable tools and knowledge for interpreting the immunological re-sponse to the virus, ultimately contributing to the development of effective vaccines and treatments against the viral infection.
CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making

Here we introduce CBGTPy, a virtual environment for designing and testing goal-directed agents with internal dynamics that are modeled off of the cortico-basal-ganglia-thalamic (CBGT) pathways in the mammalian brain. CBGTPy enables researchers to investigate the internal dynamics of the CBGT system during a variety of tasks, allowing for the formation of testable predictions about animal behavior and neural activity. The framework has been designed around the principle of flexibility, such that many experimental parameters in a decision making paradigm can be easily defined and modified. Here we demonstrate the capabilities of CBGTPy across a range of single and multi-choice tasks, highlighting the ease of set up and the biologically realistic behavior that it produces. We show that CBGTPy is extensible enough to apply to a wide range of experimental protocols and to allow for the implementation of model extensions with minimal developmental effort. Author summary We introduce a toolbox for producing biologically realistic simulations of the cortico-basal ganglia-thalamic pathways during a variety of experimental tasks. The purpose is to foster the theory-experiment cycle, offering a tool for generating testable predictions of behavioral and neural responses that can be validated experimentally, in a framework that allows for simple updating as new experimental evidence emerges. We outline how our toolbox works and demonstrate its performance on a set of normative cognitive tasks.
Scanning Electron Microscopy Study of Ca0.5Nd0.5MnO3 and Ca0.5Pr0.5MnO3 Crystals

Abstract Polycrystals of perovskite manganites Ca0.5Nd0. 5MnO3 and Ca0.5Pr0. 5MnO3 have been synthesized, their surface structure and elemental analysis have been studied using a Scanning Electron Microscope. X-ray phase analysis revealed that these compounds have an orthorhombic crystal structure with space group Pbnm. These compounds were synthesized as polycrystals in the solid phase. It was determined that Ca − 12.25%, Mn − 31.55%, Nd − 35.58%, O − 20.62% in the compound Ca0.5Nd0. 5MnO3. In Ca0.5Pr0. 5MnO3 compound Ca − 12.11%, Mn − 30.62%, Pr − 36.81%, O − 20.46%. From the surface structure obtained on a micron scale, it was determined that the size of the crystallites is in the range of 20-30 microns.
Inhibition of the serine protease HtrA1 by SerpinE2 suggests an extracellular proteolytic pathway in the control of neural crest migration

We previously showed that SerpinE2 and the serine protease HtrA1 modulate fibroblast growth factor (FGF) signaling in germ layer specification and head-to-tail development of Xenopus embryos. Here we present an extracellular proteolytic mechanism involving this serpin-protease system in the developing neural crest (NC). Knockdown of SerpinE2 by injected antisense morpholino oligonucleotides did not affect the specification of NC progenitors but instead inhibited the migration of NC cells, causing defects in dorsal fin, melanocyte and craniofacial cartilage formation. Similarly, overexpression of the HtrA1 protease impaired NC cell migration and the formation of NC-derived structures. The phenotype of SerpinE2 knockdown was overcome by concomitant downregulation of HtrA1, indicating that SerpinE2 stimulates NC migration by inhibiting endogenous HtrA1 activity. SerpinE2 binds to HtrA1, and the HtrA1 protease triggers degradation of the cell surface proteoglycan Syndecan-4 (Sdc4). Microinjection of Sdc4 mRNA partially rescued NC migration defects induced both by HtrA1 upregulation and SerpinE2 downregulation. These epistatic experiments suggest a proteolytic pathway by a double inhibition mechanism: SerpinE2 ┤HtrA1 protease ┤Syndecan-4 → NC cell migration
cs . L G ] 1 1 M ay 2 00 5 A linear memory algorithm for Baum-Welch training

Background: Baum-Welch training is an expectation-maximisation algorithm for training the emission and transition probabilities of hidden Markov models. Until know, no memory-efficient algorithm was available. Methods and results: We introduce a linear space algorithm for Baum-Welch training. For a hidden Markov model with M states, an input sequence of length L and |T | transition and |E| emission parameters, it requires O(M) memory and O(L(|T | + |E|)|T |) time rather than O(LM) memory and O(L|T | + L(|T |+ |E|)) time. For a pair hidden Markov model with input sequences Lx and Ly, the requirement of O(LxLyM) memory and O(LxLy|T |+ LxLy(|T | + |E|)) time is reduced to O(min{Lx, Ly}M) memory and O(LxLy(|T | + |E|)|T |) time. For hidden Markov models used on long sequences, for example in gene finding, this means a huge memory saving compared to the best available current algorithms which need O( √ LM) (hidden Markov models) or O( √ LxLyM) (pair hidden Markov models) memory. Additionally, our new algorithm is very simple in the sense that its implementation does not require sophisticated programming techniques like checkpoints or recursive functions. Conclusions: Using our algorithm, Baum-Welch training can now be performed on standard personal computers, even for sophisticated pair hidden Markov models and very long biological sequences.
Taking a Look into the Cookie Jar: A Comprehensive Study towards the Security of Web Cookies

Cookies play a vital role in our Internet browsing experience, enabling various functions on websites. However, their significance also extends to potential vulnerabilities, especially in the face of cyber-attacks like cross-site scripting. As we interact with websites daily, it remains uncertain how well these platforms protect user data from such attacks or how efficiently they address vulnerabilities. To address these concerns, our research endeavors to conduct a comprehensive measurement study across the entire Internet landscape. Our goal is to shed light on the potential security risks and the extent to which protective measures are deployed on websites. We developed a customized toolkit to scrape a multitude of websites on the Internet, this objective can be assessed by analyzing the security flags of cookies. Through this study on web cookies, we obtained a better understanding of the current state of web security and identified potential areas of improvement.
Short communication Anti-hepatitis C virus activity of novel β-d-2'-C-methyl-4'-azido pyrimidine nucleoside phosphoramidate prodrugs

Background: 2′-C-methyl and 4′-azido nucleosides have previously demonstrated inhibition of hepatitis C virus (HCV) replication by targeting the RNA-dependent RNA polymerase NS5B. In an effort to discover new and more potent anti-HCV agents, we envisioned synthesizing nucleoside analogues by combining the 2′-C-methylmoiety with the 4′-azido-moiety into one molecule. Methods: 2′-C-methyl-4′-azido pyrimidine nucleosides were synthesized by first converting 2′-C-methyl ribonucleosides to the corresponding 4′-exocyclic methylene nucleosides. Treatment with iodine azide, benzoylation of the 2′- and 3′-hydroxy groups, oxidative displacement of the 5′-iodo group with meta-chloroperoxybenzoic acid, and debenzoylation gave the desired 2′-C-methyl4′-azido uridine and thymidine analogues in good yield. Standard conversion of uridine to cytidine via the 4-triazole yielded 2′-C-methyl-4′-azido cytidine. In addition, 5′-phosphoramidate derivatives of 2′-C-methyl-4′-azido uridine and cytidine were synthesized to bypass the initial phosphorylation step. Results: The prepared nucleosides and their 5′monophosphate prodrugs were evaluated for their ability to inhibit replication of the hepatitis C virus in a subgenomic replicon cell based assay. Cytotoxicity in Huh7 cells was determined simultaneously with anti-HCV activity by extraction and amplification of both HCV RNA and ribosomal RNA. Among the newly synthesized com
Assessment of Skeletal Maturity and Prediction of Adult Height (TW2 Method)

This book describes the Tanner-Whitehouse (TW) approach to bone age determination and presents the new TW2 standards. Drs Tanner, Whitehouse and co-workers are at the Department of Growth and Development, Institute of Child Health, University of London, and the Hospital for Sick Children in London. This new book includes a much clearer description of their method for the evaluation of bone age than in the previously published TW1 standards. Several other significant improvements have been made since the previous study. First of all, the publication gives both line and radiographic illustrations of the various stages of development for each of the bones. It also presents separate standards for boys and girls that recognize systematic differences between the sexes and the fact that girls are not simply more mature boys. Probably the most useful addition to the book is the separate standards for the carpals and the tubular bones. The tubular
ESAIM: Control, Optimisation and Calculus of Variations

This paper is devoted to the computation of distance to set, called S, defined by polynomial equations. First we consider the case of quadratic systems. Then, application of results stated for quadratic systems to the quadratic equivalent of polynomial systems (see [5]), allows us to compute distance to semi-algebraic sets. Problem of computing distance can be viewed as non convex minimization problem: d(u, S) = infx∈S ‖x−u‖, where u is in R . To have, at least, lower approximation of distance, we consider the dual bound m(u) associated with the dual problem and give sufficient conditions to guarantee m(u) = d(u, S). The second part deal with numerical computation of m(u) using an interior point method in semidefinite programming. Last, various examples, namely from chemistry and robotic, are given. Résumé. Dans cet article nous nous intéressons au calcul de la distance à un ensemble S défini par des équations polynomiales. Nous considérons d’abord le cas quadratique. Le passage au cas polynomial se fait ensuite grâce aux équivalents quadratiques des systèmes polynomiaux développés dans [5]. Le calcul de la distance peut être vu comme un problème de minimisation non convexe d(u, S) = /infx∈S‖x − u‖ où u ∈ R . Pour obtenir, au moins un minorant de cette distance, nous considérons la borne duale m(u) issue de la résolution du problème dual. De plus, nous donnons des conditions suffisantes pour avoir m(u) = d(u, S). La seconde partie de cet article est consacrée au calcul de m(u) en utilisant une méthode de points intérieurs en programmation semi-définie positive. Pour finir, nous donnons des exemples d’applications issus notamment de la chimie et de la robotique. AMS Subject Classification. 90C46. Received September 23, 1998. Revised October 1, 1999.
UvA-DARE (Digital Academic Repository) Deep Learning with Order-invariant Operator for Multi-instance Histopathology Classification

The computer-aided analysis of medical scans is a longstanding goal in the medical imaging ﬁeld. Currently, deep learning has became a dominant methodology for supporting pathologists and radiologist. Deep learning algorithms have been successfully applied to digital pathology and radiology, nevertheless, there are still practical issues that prevent these tools to be widely used in practice. The main obstacles are low number of available cases and large size of images (a.k.a. the small n, large p problem in machine learning), and a very limited access to annotation at a pixel level that can lead to severe overﬁtting and large computational requirements. We propose to handle these issues by introducing a framework that processes a medical image as a collection of small patches using a single, shared neural network. The ﬁnal diagnosis is provided by combining scores of individual patches using a permutation-invariant operator (combination). In machine learning community such approach is called a multi-instance learning (MIL).
Towards a new task model for merging control theory and real-time scheduling problems

—Existing task models for cyber-physical systems have been proposed to merge the requirements associated to the stability of control tasks managing physical components, and to the schedulability of these tasks. Nevertheless, these models stay to a high level without covering concerns like the data and/or precedence constraints between different control tasks as well as non-control tasks, the impact of period variation between dependent control tasks and the impact of data constraints on the execution times of control tasks. Our current work concentrates on discussing properties of a new task model in order to cover these concerns.
Gradient-based reinforcement learning techniques for underwater robotics behavior learning

Darrerament, l'interes pel desenvolupament d'aplicacions amb robots submarins autonoms (AUV) ha crescut de forma considerable. Els AUVs son atractius gracies al seu tamany i el fet que no necessiten un operador huma per pilotar-los. Tot i aixo, es impossible comparar, en termes d'eficiencia i flexibilitat, l'habilitat d'un pilot huma amb les escasses capacitats operatives que ofereixen els AUVs actuals. L'utilitzacio de AUVs per cobrir grans arees implica resoldre problemes complexos, especialment si es desitja que el nostre robot reaccioni en temps real a canvis sobtats en les condicions de treball. Per aquestes raons, el desenvolupament de sistemes de control autonom amb l'objectiu de millorar aquestes capacitats ha esdevingut una prioritat. Aquesta tesi tracta sobre el problema de la presa de decisions utilizant AUVs. El treball presentat es centra en l'estudi, disseny i aplicacio de comportaments per a AUVs utilitzant tecniques d'aprenentatge per reforc (RL). La contribucio principal d'aquesta tesi consisteix en l'aplicacio de diverses tecniques de RL per tal de millorar l'autonomia dels robots submarins, amb l'objectiu final de demostrar la viabilitat d'aquests algoritmes per aprendre tasques submarines autonomes en temps real. En RL, el robot intenta maximitzar un reforc escalar obtingut com a consequencia de la seva interaccio amb l'entorn. L'objectiu es trobar una politica optima que relaciona tots els estats possibles amb les accions a executar per a cada estat que maximitzen la suma de reforcos totals. Aixi, aquesta tesi investiga principalment dues tipologies d'algoritmes basats en RL: metodes basats en funcions de valor (VF) i metodes basats en el gradient (PG). Els resultats experimentals finals mostren el robot submari Ictineu en una tasca autonoma real de seguiment de cables submarins. Per portar-la a terme, s'ha dissenyat un algoritme anomenat metode d'Actor i Critic (AC), fruit de la fusio de metodes VF amb tecniques de PG.
BOUNDARY EFFECTS AND WEAK (cid:2) LOWER SEMICONTINUITY FOR SIGNED INTEGRAL FUNCTIONALS ON BV ∗

We characterize lower semicontinuity of integral functionals with respect to weak (cid:2) convergence in BV, including integrands whose negative part has linear growth. In addition, we allow for sequences without a ﬁxed trace at the boundary. In this case, both the integrand and the shape of the boundary play a key role. This is made precise in our newly found condition – quasi-sublinear growth from below at points of the boundary – which compensates for possible concentration eﬀects generated by the sequence. Our work extends some recent results by Kristensen and Rindler [J. Kristensen and F. Rindler, Arch. Rat. Mech. Anal. 197 (2010) 539–598; J. Kristensen and F. Rindler, Calc. Var. 37 (2010) 29–62].