Miracle-dz/UniFAR-FTUs huggingface dataset:
COGEVIS: A New Scale to Evaluate Cognition in Patients with Visual Deficiency

We evaluated the cognitive status of visually impaired patients referred to low vision rehabilitation (LVR) based on a standard cognitive battery and a new evaluation tool, named the COGEVIS, which can be used to assess patients with severe visual deficits. We studied patients aged 60 and above, referred to the LVR Hospital in Paris. Neurological and cognitive evaluations were performed in an expert memory center. Thirty-eight individuals, 17 women and 21 men with a mean age of 70.3 ± 1.3 years and a mean visual acuity of 0.12 ± 0.02, were recruited over a one-year period. Sixty-three percent of participants had normal cognitive status. Cognitive impairment was diagnosed in 37.5% of participants. The COGEVIS score cutoff point to screen for cognitive impairment was 24 (maximum score of 30) with a sensitivity of 66.7% and a specificity of 95%. Evaluation following 4 months of visual rehabilitation showed an improvement of Instrumental Activities of Daily Living (p = 0.004), National Eye Institute Visual Functioning Questionnaire (p = 0.035), and Montgomery–Åsberg Depression Rating Scale (p = 0.037). This study introduces a new short test to screen for cognitive impairment in visually impaired patients.
Comprehension Effort and Programming Activities: Related? Or Not Related?

Researchers have observed programmers to allocate considerable amount of effort in program comprehension. But, how does program comprehension effort relate with programming activities? We answer this question by conducting an empirical study using the MSR 2018 Mining Challenge Dataset. We quantify programmers' comprehension effort, and investigate the relationship between program comprehension effort and four programming activities: navigating, editing, building projects, and debugging. We observe when programmers are involved in high comprehension effort they navigate and make edits at a significantly slower rate. However, we do not observe any significant differences in programmers' build and debugging behavior, when programmers are involved in high comprehension effort. Our findings suggest that the relationship between program comprehension effort and programming activities is nuanced, as not all programming activities associate with program comprehension effort.
An Empirical Evaluation of Sketching for Numerical Linear Algebra

Over the last ten years, tremendous speedups for problems in randomized numerical linear algebra such as low rank approximation and regression have been made possible via the technique of randomized data dimensionality reduction, also known as sketching. In theory, such algorithms have led to optimal input sparsity time algorithms for a wide array of problems. While several scattered implementations of such methods exist, the goal of this work is to provide a comprehensive comparison of such methods to alternative approaches. We investigate least squares regression, iteratively reweighted least squares, logistic regression, robust regression with Huber and Bisquare loss functions, leverage score computation, Frobenius norm low rank approximation, and entrywise $\ell_1$-low rank approximation. We give various implementation techniques to speed up several of these algorithms, and the resulting implementations demonstrate the tradeoffs of such techniques in practice.
Quantifying and Minimizing Risk of Conflict in Social Networks

Controversy, disagreement, conflict, polarization and opinion divergence in social networks have been the subject of much recent research. In particular, researchers have addressed the question of how such concepts can be quantified given people's prior opinions, and how they can be optimized by influencing the opinion of a small number of people or by editing the network's connectivity. Here, rather than optimizing such concepts given a specific set of prior opinions, we study whether they can be optimized in the average case and in the worst case over all sets of prior opinions. In particular, we derive the worst-case and average-case conflict risk of networks, and we propose algorithms for optimizing these. For some measures of conflict, these are non-convex optimization problems with many local minima. We provide a theoretical and empirical analysis of the nature of some of these local minima, and show how they are related to existing organizational structures. Empirical results show how a small number of edits quickly decreases its conflict risk, both average-case and worst-case. Furthermore, it shows that minimizing average-case conflict risk often does not reduce worst-case conflict risk. Minimizing worst-case conflict risk on the other hand, while computationally more challenging, is generally effective at minimizing both worst-case as well as average-case conflict risk.
Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data

Sports teams are nowadays collecting huge amounts of data from training sessions and matches. The teams are becoming increasingly interested in exploiting these data to gain a competitive advantage over their competitors. One of the most prevalent types of new data is event stream data from matches. These data enable more advanced descriptive analysis as well as the potential to investigate an opponent's tactics in greater depth. Due to the complexity of both the data and game strategy, most tactical analyses are currently performed by humans reviewing video and scouting matches in person. As a result, this is a time-consuming and tedious process. This paper explores the problem of automatic tactics detection from event-stream data collected from professional soccer matches. We highlight several important challenges that these data and this problem setting pose. We describe a data-driven approach for identifying patterns of movement that account for both spatial and temporal information which represent potential offensive tactics. We evaluate our approach on the 2015/2016 season of the English Premier League and are able to identify interesting strategies per team related to goal kicks, corners and set pieces.
StockAssIstant: A Stock AI Assistant for Reliability Modeling of Stock Comments

Stock comments from analysts contain important consulting information for investors to foresee stock volatility and market trends. Existing studies on stock comments usually focused on capturing coarse-grained opinion polarities or understanding market fundamentals. However, investors are often overwhelmed and confused by massive comments with huge noises and ambiguous opinions. Therefore, it is an emerging need to have a fine-grained stock comment analysis tool to identify more reliable stock comments. To this end, this paper provides a solution called StockAssIstant for modeling the reliability of stock comments by considering multiple factors, such as stock price trends, comment content, and the performances of analysts, in a holistic manner. Specifically, we first analyze the pattern of analysts' opinion dynamics from historical comments. Then, we extract key features from the time-series constructed by using the semantic information in comment text, stock prices and the historical behaviors of analysts. Based on these features, we propose an ensemble learning based approach for measuring the reliability of comments. Finally, we conduct extensive experiments and provide a trading simulation on real-world stock data. The experimental results and the profit achieved by the simulated trading in 12-month period clearly validate the effectiveness of our approach for modeling the reliability of stock comments.
Next-Step Suggestions for Modern Interactive Data Analysis Platforms

Modern Interactive Data Analysis (IDA) platforms, such as Kibana, Splunk, and Tableau, are gradually replacing traditional OLAP/SQL tools, as they allow for easy-to-use data exploration, visualization, and mining, even for users lacking SQL and programming skills. Nevertheless, data analysis is still a di cult task, especially for non-expert users. To that end we present REACT, a recommender system designed for modern IDA platforms. In these platforms, analysis sessions interweave high-level actions of multiple types and operate over diverse datasets . REACT identifies and generalizes relevant (previous) sessions to generate personalized next-action suggestions to the user. We model the user's analysis context using a generic tree based model, where the edges represent the user's recent actions, and the nodes represent their result "screens". A dedicated context-similarity metric is employed for efficient indexing and retrieval of relevant candidate next-actions. These are then generalized to abstract actions that convey common fragments, then adapted to the specific user context. To prove the utility of REACT we performed an extensive online and offline experimental evaluation over real-world analysis logs from the cyber security domain, which we also publish to serve as a benchmark dataset for future work.
Practical Experience: Methodologies for Measuring Route Origin Validation

Performing Route Origin Validation (ROV) to filter BGP announcements, which contradict Route Origin Authorizations (ROAs) is critical for protection against BGP prefix hijacks. Recent works quantified ROV enforcing Autonomous Systems (ASes) using control-plane experiments. In this work we show that control-plane experiments do not provide accurate information about ROV-enforcing ASes. We devise data-plane approaches for evaluating ROV in the Internet and perform both control and data-plane experiments using different data acquisition sources. We analyze and correlate the results of our study to identify the number of ASes enforcing ROV, and hence protected with RPKI. We perform simulations with the ROV-enforcing ASes that we identified, and find that their impact on the Internet security against prefix hijacks is negligible. As a countermeasure we provide recommendations how to cope with the main factor hindering wide adoption of ROV.
Characterization of Buffer-Related Current Collapse by Buffer Potential Simulation in AlGaN/GaN HEMTs

In this paper, the buffer-related current collapse (CC) in AlGaN/GaN HEMTs is investigated by using pulsed I–V measurements and 2-D drift-diffusion simulations. The simulation results indicate that the negative potential (NP), appearing in the Fe-doped buffer under the gate, is found to account for the CC. The NP will induce threshold voltage shifts, in which an inflection point (IP) of the threshold voltage is found. It could be revealed in pulsed I–V characteristics which are significant to depict trapping effect when building a compact large-signal model. The formation mechanism of the IP is detailed. In the end, a simple threshold voltage model is proposed for verification.
AccountTrade: Accountability Against Dishonest Big Data Buyers and Sellers

In this paper, a set of accountable protocols denoted as AccountTrade is proposed for big data trading among dishonest consumers. For achieving a secure big data trading environment, AccountTrade achieves book-keeping ability and accountability against dishonest consumers throughout the trading (i.e., buying and selling) of datasets. We investigate the consumers’ responsibilities in the dataset trading, then we design AccountTrade to achieve accountability against dishonest consumers that are likely to deviate from the responsibilities. Specifically, a uniqueness index is defined and proposed, which is a new rigorous measurement of the data uniqueness for this purpose. Furthermore, several accountable trading protocols are presented to enable data brokers to blame the misbehaving entities when misbehavior is detected. The accountability of AccountTrade is formally defined, proved, and evaluated by an automatic verification tool as well as extensive simulation with real-world datasets. Our evaluation shows that AccountTrade incurs at most 10-kB storage overhead per file, and it is capable of 8–1000 concurrent data upload requests per server.
Notification Volume Control and Optimization System at Pinterest

Notifications (including emails, mobile / desktop push notifications, SMS, etc.) are very effective channels for online services to engage with users and drive user engagement metrics and other business metrics. One of the most important and challenging problems in a production notification system is to decide the right frequency for each user. In this paper, we propose a novel machine learning approach to decide notification volume for each user such that long term user engagement is optimized. We will also discuss a few practical issues and design choices we have made. The new system has been deployed to production at Pinterest in mid 2017 and significantly reduced notification volume and improved CTR of notifications and site engagement metrics compared with the previous machine learning approach.
FOCES: Detecting Forwarding Anomalies in Software Defined Networks

A crucial requirement for Software Defined Network (SDN) is that data plane forwarding behaviors should always agree with control plane policies. Such requirement cannot be met when there are forwarding anomalies, where packets deviate from the paths specified by the controller. Most anomaly detection methods for SDN install dedicated rules to collect statistics of each flow, and check whether the statistics conform to the flow conservation principle. Such per-flow detection methods have a limited detection scope: they look at one flow each time, thus can only check a limited number of flows simultaneously. In addition, dedicated rules for statistics collection can impose a large overhead on flow tables of SDN switches. To this end, this paper presents FOCES, a network-wide forwarding anomaly detection method in SDN. Different from previous methods, FOCES applies a new kind of flow conservation principle at network wide, and can check forwarding behaviors of all flows in the network simultaneously, without installing any dedicated rules. Experiments show FOCES can achieve a detection precision higher than 90% for four network topologies, even when packet loss rates are as high as 10%.
The Anatomy of a Large-Scale Experimentation Platform

Online controlled experiments (e.g., A/B tests) are an integral part of successful data-driven companies. At Microsoft, supporting experimentation poses a unique challenge due to the wide variety of products being developed, along with the fact that experimentation capabilities had to be added to existing, mature products with codebases that go back decades. This paper describes the Microsoft ExP Platform (ExP for short) which enables trustworthy A/B experimentation at scale for products across Microsoft, from web properties (such as bing.com) to mobile apps to device drivers within the Windows operating system. The two core tenets of the platform are trustworthiness (an experiment is meaningful only if its results can be trusted) and scalability (we aspire to expose every single change in any product through an A/B experiment). Currently, over ten thousand experiments are run annually. In this paper, we describe the four core components of an A/B experimentation system: experimentation portal, experiment execution service, log processing service and analysis service, and explain the reasoning behind the design choices made. These four components work together to provide a system where ideas can turn into experiments within minutes and those experiments can provide initial trustworthy results within hours.
Self-Paced Learning with Adaptive Deep Visual Embeddings

Selecting the most appropriate data examples to present a deep neural network (DNN) at different stages of training is an unsolved challenge. Though practitioners typically ignore this problem, a non-trivial data scheduling method may result in a significant improvement in both convergence and generalization performance. In this paper, we introduce Self-Paced Learning with Adaptive Deep Visual Embeddings (SPL-ADVisE), a novel end-to-end training protocol that unites self-paced learning (SPL) and deep metric learning (DML). We leverage the Magnet Loss to train an embedding convolutional neural network (CNN) to learn a salient representation space. The student CNN classifier dynamically selects similar instance-level training examples to form a mini-batch, where the easiness from the cross-entropy loss and the true diverseness of examples from the learned metric space serve as sample importance priors. To demonstrate the effectiveness of SPL-ADVisE, we use deep CNN architectures for the task of supervised image classification on several coarse- and fine-grained visual recognition datasets. Results show that, across all datasets, the proposed method converges faster and reaches a higher final accuracy than other SPL variants, particularly on fine-grained classes.
Wren: Nonblocking Reads in a Partitioned Transactional Causally Consistent Data Store

Transactional Causal Consistency (TCC) extends causal consistency, the strongest consistency model compatible with availability, with interactive read-write transactions, and is therefore particularly appealing for geo-replicated platforms. This paper presents Wren, the first TCC system that at the same time i) implements nonblocking read operations, thereby achieving low latency, and ii) allows an application to efficiently scale out within a replication site by sharding. Wren introduces new protocols for transaction execution, dependency tracking and stabilization. The transaction protocol supports nonblocking reads by providing a transaction with a snapshot that is the union of a fresh causal snapshot S installed by every partition in the local data center and a client-side cache for writes that are not yet included in S. The dependency tracking and stabilization protocols require only two scalar timestamps, resulting in efficient resource utilization and providing scalability in terms of replication sites. In return for these benefits, Wren slightly increases the visibility latency of updates. We evaluate Wren on an AWS deployment using up to 5 replication sites and 16 partitions per site. We show that Wren delivers up to 1.4x higher throughput and up to 3.6x lower latency when compared to the state-of-the-art design. The choice of an older snapshot increases local update visibility latency by a few milliseconds. The use of only two timestamps to track causality increases remote update visibility latency by less than 15%.
Testicular versus ejaculated sperm should be used for intracytoplasmic sperm injection (ICSI) in cases of infertility associated with sperm DNA fragmentation | Opinion: Yes

The use of testicular in preference over ejaculated sperm for intracytoplasmic sperm injection (ICSI) has gained increased attention due to reports of better pregnancy outcomes using testicular sperm for cases of infertility associated with high sperm DNA fragmentation (SDF) (reviewed by Esteves et al. (1)). Indeed, it has been a common practice to perform testicular sperm retrieval for ICSI (Testi-ICSI) in selected groups of non-azoospermic men. In a recent survey study involving infertility experts from 19 countries, 67% responders admitted that an abnormal SDF test result would affect their decision to utilize testicular instead of ejaculated sperm for ICSI (2). Interestingly, identical numbers were reported by attendees of an interactive debate session held during the 2017 annual meeting of the American Society for Reproductive Medicine (unpublished data). The matter concerned has been subjected to opinionated debate as Testi-ICSI represents a paradigm shift in clinical practice (1, 3-5). I defend the argument that “Infertile couples undergoing ICSI, whose male partners have elevated SDF levels in the neat ejaculate, should be offered testicular sperm in the next ICSI cycle, provided SDF is persistent after treatment of the underlying condition, or the clinical scenario does not allow treatment”. There are three essential, evidence-based premises supporting this clinical approach, which I will discuss in the next paragraphs. First, SDF not only impacts in vitro fertilization (IVF) and ICSI pregnancy outcomes but also contributes to pregnancy loss. In fact, results from the most recent and largest systematic review and meta-analysis about the impact of SDF on assisted reproductive technology (ART), which pooled data from 70 studies and over 17,000 IVF and ICSI cycles, indicate that SDF reduces the probability of a successful pregnancy following ART (6). This observation holds true for both IVF and ICSI studies (IVF studies: odds ratio [OR] 1.15, 95% confidence interval [CI] 1.05-1.27; P=0.003; ICSI studies: inverse OR 1.12, 95% CI 1.01-1.25, P=0.02) and the four most common assays (TUNEL-terminal deoxynucleotidyl transferase dUTP nick end labeling, SCD-sperm chromatin dispersion, Comet-single cell gel electrophoresis, and SCSA-sperm chromatin structure assay) utilized for SDF assessment. Notably, the magnitude of effect size was amplified DIffErENCE Of OPINION Vol. 44 (4): 667-675, July August, 2018
Unlocking the Value of Privacy: Trading Aggregate Statistics over Private Correlated Data

With the commoditization of personal privacy, pricing private data has become an intriguing problem. In this paper, we study noisy aggregate statistics trading from the perspective of a data broker in data markets. We thus propose ERATO, which enables aggrEgate statistics pRicing over privATe cOrrelated data. On one hand, ERATO guarantees arbitrage freeness against cunning data consumers. On the other hand, ERATO compensates data owners for their privacy losses using both bottom-up and top-down designs. We further apply ERATO to three practical aggregate statistics, namely weighted sum, probability distribution fitting, and degree distribution, and extensively evaluate their performances on MovieLens dataset, 2009 RECS dataset, and two SNAP large social network datasets, respectively. Our analysis and evaluation results reveal that ERATO well balances utility and privacy, achieves arbitrage freeness, and compensates data owners more fairly than differential privacy based approaches.
Mechanobiological Feedback in Pulmonary Vascular Disease

Vascular stiffening in the pulmonary arterial bed is increasingly recognized as an early disease marker and contributor to right ventricular workload in pulmonary hypertension. Changes in pulmonary artery stiffness throughout the pulmonary vascular tree lead to physiologic alterations in pressure and flow characteristics that may contribute to disease progression. These findings have led to a greater focus on the potential contributions of extracellular matrix remodeling and mechanical signaling to pulmonary hypertension pathogenesis. Several recent studies have demonstrated that the cellular response to vascular stiffness includes upregulation of signaling pathways that precipitate further vascular remodeling, a process known as mechanobiological feedback. The extracellular matrix modifiers, mechanosensors, and mechanotransducers responsible for this process have become increasingly well-recognized. In this review, we discuss the impact of vascular stiffening on pulmonary hypertension morbidity and mortality, evidence in favor of mechanobiological feedback in pulmonary hypertension pathogenesis, and the major contributors to mechanical signaling in the pulmonary vasculature.