Miracle-dz/UniFAR-FTUs huggingface dataset:
The Tortoise and the Hare: Characterizing Synchrony in Distributed Environments (Practical Experience Report)

The design of distributed protocols that run in data centers and enterprise clusters is heavily dependent on synchrony assumptions regarding the timing behavior of the participating nodes and the network. However, little is known about the actual synchrony of real distributed systems, and how it varies across deployments. To better understand this timing behavior and how it impacts the design and implementation of distributed protocols, we conduct an extensive measurement study of the latency for transmitting and processing messages between nodes in four different environments. Our study determines how protocol characteristics affect the latency behavior. We also determine how different environmental factors can affect the measured latency and whether high latency events manifest globally or locally. Our results suggest several directions for reducing latency, and for leveraging recent distributed computing models in a more judicious way.
Longitudinal effects of developmental bisphenol A, variable diet, and physical activity on age-related methylation in blood

Abstract Research indicates that environmental factors can alter DNA methylation, but the specific effects of environmental exposures on epigenetic aging remain unclear. Here, using a mouse model of human-relevant exposures, we tested the hypothesis that early-life exposure to bisphenol A (BPA), variable diet, and/or changes in physical activity would modify rates of age-related methylation at several target regions, as measured from longitudinal blood samples (2, 4, and 10 months old). DNA methylation was quantified at two repetitive elements (LINE-1, IAP), two imprinted genes (Igf2, H19), and one non-imprinted gene (Esr1) in isogenic mice developmentally exposed to Control, Control + BPA (50 µg/kg diet), Western high-fat diet (WHFD), or Western + BPA diets. In blood samples, Esr1 DNA methylation increased significantly with age, but no other investigated loci showed significant age-related methylation. LINE-1 and IAP both showed significant negative environmental deflection by WHFD exposure (P < 0.05). Esr1also showed significant negative environmental deflection by WHFD exposure in female mice (P = 0.02), but not male mice. Physical activity had a non-significant positive effect on age-related Esr1 methylation in female blood, suggesting that it may partially abrogate the effects of WHFD on the aging epigenome. These results suggest that developmental nutritional exposures can modify age-related DNA methylation patterns at a gene related to growth and development. As such, environmental deflection of the aging epigenome may help to explain the growing prevalence of chronic diseases in human populations.
SEESAW: Using Superpages to Improve VIPT Caches

Hardware caches balance fast lookup, high hit rates, energy efficiency, and simplicity of implementation. For L1 caches however, achieving this balance is difficult because of constraints imposed by virtual memory. L1 caches are usually virtually-indexed and physically tagged (VIPT), but this means that they must be highly associative to achieve good capacity. Unfortunately, excessive associativity compromises performance by degrading access times without significantly boosting hit rates, and increases access energy. We propose SEESAW to overcome this problem. SEESAW leverages the increasing ubiquity of superpages1 – since super-pages have more page offset bits, they can accommodate VIPT caches with more sets than what is traditionally possible with only base page sizes. SEESAW dynamically reduces the number of ways that are looked up based on the page size, improving performance and energy. SEESAW requires modest hardware and no OS or application changes.
Quality of Video Oriented and Multi-Meeting Based Routing Algorithm for Video Data Offloading

The increasing popularity of video delivery among mobile users makes the problem of explosive traffic growth becoming more and more serious for traditional wireless networks, and video transmission-based on device-to-device communication through mobile opportunistic networks (MONs) is regarded as an ideal way to resolve this issue. However, data transmission in the MONs is mainly through the two ways: data replication and data forwarding. Thus, to achieve high delivery ratio and low delivery delay, data replication is usually excessively exploited, and the large number of redundant replicas will not only consume large amount of nodal resource but also greatly increase the overload of networks. For video transmission, this issue becomes more severe for its volume and continuity. Thus, in this paper, we propose a novel routing scheme for video data transmission in the MONs, namely, video routing-based multi-player cooperative game which can maximize the quality of reconstructed video data while minimizing the overhead of nodal and network resources. Specifically, we first construct the marginal gain model for video delivery quality, and then model the video data transmission among multiple encounters as a multi-player cooperative game. Under the guidance of the Nash equilibrium theory, the video data carried by these encountering nodes is adaptively and optimally re-assigned among them. Extensive simulations based on real-life mobility traces and synthetic traces have validated the effectiveness of the proposed routing algorithm.
Leveraging Historical Versions of Android Apps for Efficient and Precise Taint Analysis

Today, computing on various Android devices is pervasive. However, growing security vulnerabilities and attacks in the Android ecosystem constitute various threats through user apps. Taint analysis is a common technique for defending against these threats, yet it su? ers from challenges in attaining practical simultaneous scalability and e? ectiveness. This paper presents a novel approach to fast and precise taint checking, called incremental taint analysis, by exploiting the evolving nature of Android apps. The analysis narrows down the search space of taint checking from an entire app, as conventionally addressed, to the parts of the program that are di? erent from its previous versions. This technique improves the overall efciency of checking multiple versions of the app as it evolves. We have implemented the techniques as a tool prototype, EvoTaint, and evaluated our analysis by applying it to real-world evolving Android apps. Our preliminary results show that the incremental approach largely reduced the cost of taint analysis, by 78.6% on average, yet without sacrifcing the analysis e? ectiveness, relative to a representative precise taint analysis as the baseline.
Multikernel Adaptive Collaborative Representation for Hyperspectral Image Classification

To adequately represent the nonlinearities in the high-dimensional feature space for hyperspectral images (HSIs), we propose a multiple kernel collaborative representation-based classifier (CRC) in this paper. Extended morphological profiles are first extracted from the original HSIs, because they can efficiently capture the spatial and spectral information. In the proposed method, a novel multiple kernel learning (MKL) model is embedded into CRC. Multiple kernel patterns, e.g., Naive, Multimetric, and Multiscale are adopted for the optimal set of basic kernels, which are helpful to capture the useful information from different pixel distributions, kernel metric spaces, and kernel scales. To learn an optimal linear combination of the predefined basic kernels, we add an extra training stage to the typical CRC where kernel weights are jointly learned with the representation coefficients from the training samples by minimizing the representation error. Moreover, by considering different contributions of dictionary atoms, the adaptive representation strategy is applied to the MKL framework via a dissimilarity-weighted regularizer to obtain a more robust representation of test pixels in the fused kernel space. Experimental results on three real HSIs confirm that the proposed classifiers outperform the other state-of-the-art representation-based classifiers.
Alpha Entanglement Codes: Practical Erasure Codes to Archive Data in Unreliable Environments

Data centres that use consumer-grade disks drives and distributed peer-to-peer systems are unreliable environments to archive data without enough redundancy. Most redundancy schemes are not completely effective for providing high availability, durability and integrity in the long-term. We propose alpha entanglement codes, a mechanism that creates a virtual layer of highly interconnected storage devices to propagate redundant information across a large scale storage system. Our motivation is to design flexible and practical erasure codes with high fault-tolerance to improve data durability and availability even in catastrophic scenarios. By "flexible and practical", we mean code settings that can be adapted to future requirements and practical implementations with reasonable trade-offs between security, resource usage and performance. The codes have three parameters. Alpha increases storage overhead linearly but increases the possible paths to recover data exponentially. Two other parameters increase fault-tolerance even further without the need of additional storage. As a result, an entangled storage system can provide high availability, durability and offer additional integrity: it is more difficult to modify data undetectably. We evaluate how several redundancy schemes perform in unreliable environments and show that alpha entanglement codes are flexible and practical codes. Remarkably, they excel at code locality, hence, they reduce repair costs and become less dependent on storage locations with poor availability. Our solution outperforms Reed-Solomon codes in many disaster recovery scenarios.
A 22 V Compliant 56  ${\mu}$ W Twin-Track Active Charge Balancing Enabling 100% Charge Compensation Even in Monophasic and 36% Amplitude Correction in Biphasic Neural Stimulators

Closed-loop charge balancers make a considerable contribution to a safe and secure electrical stimulation and additionally present an alternative to bulky blocking capacitors. This CMOS-integrated, 22 V compliant Twin-Track active charge balancer accomplishes both instantaneous and long-term balanced conditions. Long-term charge balancing is achieved by a cause-based PI-controlled offset compensation of the remaining electrode voltage. A <inline-formula> <tex-math notation="LaTeX">$G_{\mathrm{ m}}$ </tex-math></inline-formula> of only 1.5 nS offers a large (8 ms) time constant with only a 12 pF capacitor and leads to the first integrated PI-controller for offset compensation in neural implants. Instantaneous autonomous balancing is realized by a second concept, the consequence-based Inter-Pulse Charge Control. It uses a class-B architecture to supply the compensation currents. Thus, no additional references are required to define the safety window of the remaining electrode voltage. It consumes 31.8 <inline-formula> <tex-math notation="LaTeX">${\mu }\text{W}$ </tex-math></inline-formula> in the idle state, while the delivered output power can be as high as 11 mW. The two complementary approaches can be combined to one system, providing the opportunity to achieve a better performance. The system consumes 56 <inline-formula> <tex-math notation="LaTeX">${\mu }\text{W}$ </tex-math></inline-formula> and features a 36% mismatch correction in biphasic stimuli amplitude, as well as an autonomous 100% charge removal at maximum compensation amplitudes of 500 <inline-formula> <tex-math notation="LaTeX">${\mu }\text{A}$ </tex-math></inline-formula>, which makes it a suitable complement to monophasic stimulators.
Exploring the neural correlates of dream phenomenology and altered states of consciousness during sleep

Abstract The science of dreaming constitutes a relevant topic in modern-day neuroscientific research and provides major insights into the study of human consciousness. Linking specific, universal, and regularly occurring stages of sleep with dreaming encourages the direct and systematic investigation of a topic that has fascinated humankind for centuries. In this review, we explore to what extent individuals dream during periods of rapid eye movement and non-rapid eye movement sleep, and we introduce research on lucid dreaming. We then discuss how dreaming during different stages of sleep varies in terms of phenomenological characteristics, and to what extent individuals are conscious throughout the sleep cycle. Finally, we provide a synopsis of the previous literature on brain activity during sleep, and we aim to clarify how the neurofunctional changes observed throughout sleep may lead to changes in phenomenological aspects of dreams, and in the domain of consciousness.
Residual Network based Aggregation Model for Skin Lesion Classification

We recognize that the skin lesion diagnosis is an essential and challenging sub-task in Image classification, in which the Fisher vector (FV) encoding algorithm and deep convolutional neural network (DCNN) are two of the most successful techniques. Since the joint use of FV and DCNN has demonstrated proven success, the joint techniques could have discriminatory power on skin lesion diagnosis as well. To this hypothesis, we propose the aggregation algorithm for skin lesion diagnosis that utilize the residual network to extract the local features and the Fisher vector method to aggregate the local features to image-level representation. We applied our algorithm on the International Skin Imaging Collaboration 2018 (ISIC2018) challenge and only focus on the third task, i.e., the disease classification.
Zinc, Copper, and Iron in Oral Submucous Fibrosis: A Meta-Analysis

Oral submucous fibrosis (OSF) is a potentially malignant disorder which causes fibrosis and inflammation of the oral mucosa. Studies have reported altered levels of trace elements in oral submucous fibrosis subjects, but findings have been inconsistent. The objective of this research is to perform a meta-analysis to summarize studies that report zinc (Zn), copper (Cu), and iron (Fe) in patients, with and without OSF. A literature search of Embase, PubMed, Cochrane Library, and Web of Science electronic databases was conducted for studies up to January 2017. A total of 34 reports met the inclusion criteria. The standardized mean difference was utilized as the effect size. The robust variance estimation method was chosen to handle dependency of multiple related outcomes in meta-analysis. There was a significant increase in the levels of Cu (effect size = 1.17, p value < 0.05, 95% confidence interval (CI): 0.164–2.171) and a significant decrease in levels of Zn (effect size = −1.95, p value < 0.05, 95% CI: −3.524 to −0.367) and Fe (effect size = −2.77, p value < 0.01, 95% CI: −4.126 to −1.406) in OSF patients. The estimation of Zn, Cu, and Fe levels may serve as additional biomarkers in the diagnosis and prognosis of OSF along with the clinical features.
Improved Oracle Complexity for Stochastic Compositional Variance Reduced Gradient

Convex composition optimization is an emerging topic that covers a wide range of applications arising from stochastic optimal control, reinforcement learning and multi-stage stochastic programming. Existing algorithms suffer from unsatisfactory sample complexity and practical issues since they ignore the convexity structure in the algorithmic design. In this paper, we develop a new stochastic compositional variance-reduced gradient algorithm with the sample complexity of $O((m+n)\log(1/\epsilon)+1/\epsilon^3)$ where $m+n$ is the total number of samples. Our algorithm is near-optimal as the dependence on $m+n$ is optimal up to a logarithmic factor. Experimental results on real-world datasets demonstrate the effectiveness and efficiency of the new algorithm.
Near Real-time Optimization of Activity-based Notifications

In recent years, social media applications (e.g., Facebook, LinkedIn) have created mobile applications (apps) to give their members instant and real-time access from anywhere. To keep members informed and drive timely engagement, these mobile apps send event notifications. However, sending notifications for every possible event would result in too many notifications which would in turn annoy members and create a poor member experience. In this paper, we present our strategy of optimizing notifications to balance various utilities (e.g., engagement, send volume) by formulating the problem using constrained optimization. To guarantee freshness of notifications, we implement the solution in a stream computing system in which we make multi-channel send decisions in near real-time. Through online A/B test results, we show the effectiveness of our proposed approach on tens of millions of members.
A Simple Probabilistic Model for Uncertainty Estimation

The article focuses on determining the predictive uncertainty of a model on the example of atrial fibrillation detection problem by a single-lead ECG signal. To this end, the model predicts parameters of the beta distribution over class probabilities instead of these probabilities themselves. It was shown that the described approach allows to detect atypical recordings and significantly improve the quality of the algorithm on confident predictions.
An Initial Investigation of the Charlson Comorbidity Index Regression Based on Clinical Notes

The Charlson comorbidity index (CCI) is widely used to predict mortality for patients who may have many comorbid conditions. Such index is also used as an indicator of the patients' complexity inside a hospital. In this paper, we evaluate a variety of feature extraction and regression methods to predict the CCI from clinical notes. We used a tertiary hospital dataset with 48 thousand hospitalizations featuring the CCI annotated by physicians. In our experiments, Dense Neural Networks with Word Embeddings proved to be the best regression method, with a mean absolute error of 0.51.
Leveraging the Agile Development Process for Selecting Invoking/Excluding Tests to Support Feature Location

A practical approach to feature location using agile unit tests is presented. The approach employs a modified software reconnaissance method for feature location, but in the context of an agile development methodology. Whereas a major drawback to software reconnaissance is the identification or development of invoking and excluding tests, the approach allows for the automatic identification of invoking and excluding tests by partially ordering existing agile unit tests via iteration information from the agile development process. The approach is validated in a comparison study with industry professionals, where the approach is shown to improve feature location speed, accuracy, and developer confidence over purely manual feature location.
Samul-Tang Regulates Cell Cycle and Migration of Vascular Smooth Muscle Cells against TNF-α Stimulation

Samul-Tang (SMT), consisting of four medicinal herbs, is a well-known herbal prescription treating hematological disorders related symptoms. Our previous study demonstrated that SMT attenuated inflammation of vascular endothelial cells. In condition of retained vascular dysfunction, vascular inflammation is initiated and results in activation of smooth muscle cells (SMCs). Activated SMCs lose control of cell cycle regulation and migrate into intima, resulting in formation of atheroma. Here, we further investigated whether SMT suppresses proliferation and migration of SMCs. SMT showed antiproliferative effects on SMCs by suppressing [3H]-thymidine incorporation against TNF-α stimulation. Underlying mechanisms of antiproliferative effects were found to be resulting from cell cycle regulation. SMT downregulated expression of cyclin D1-CDK4 and cyclin E-CDK2 complexes and upregulated p21waf1/cip1 and p27kip1. SMT also suppressed migration of SMCs against TNF-α stimulation. This is thought to have resulted from suppressing MMP2 and MMP9 expressions and ROS production. In summary, SMT attenuates abnormal migration of vascular smooth muscle cells via regulating cell cycle and suppressing MMPs expression and ROS production. Our study suggests that SMT, a traditionally used herbal formula, protects vascular smooth muscle cells and might be used as an antiatherosclerotic drug.
Active Sensing for Measuring Contact of Thin Film Gecko-Inspired Adhesives

Active sensing provides a way to assess whether a thin film of gecko-inspired adhesive has made good contact with a surface. This knowledge is useful for applications like gripping objects in space where a failed grasp could lead to loss of the object. Our active sensing approach uses Lamb waves in thin bilayers, excited, and detected by piezoelectric strips. From the theory, we describe how attenuation increases with contact boundary condition changes. We validated the theory using frustrated total internal reflection imaging, showing that attenuation increases as the contact area grows. Pull tests on different textures of acrylic plate show that the slope change of the signal can predict the maximum adhesion limit with a 10 N window and predict impending failure with a detection rate <inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>80%. Lifting a cylindrical object shows that the sensor can signal different types of failures with a detection rate <inline-formula> <tex-math notation="LaTeX">$>$</tex-math></inline-formula>85%, associated with unstable grasping.
High-Gain Transimpedance Amplifier for Flexible Radiation Dosimetry Using InGaZnO TFTs

This paper presents a novel high-gain transimpedance amplifier for flexible radiation sensing systems that can be used as large-area dosimeters. The circuit is implemented with indium-gallium-zinc-oxide thin-film-transistors and uses two stages for the amplification of the sensor signal (current). The first stage consists of cascode current mirrors with a diode connected load that performs current amplification and voltage conversion. Then, the first stage is followed by a voltage amplifier based on a positive feedback topology for gain enhancement. The proposed circuit converts nano-ampere (10 nA) currents into hundreds of millivolts (280 mV), showing a gain around 149 dB and a power consumption of 0.45 mW. The sensed radiation dose level, in voltage terms, can drive the next stages in the radiation sensing system, such as analog to digital converters. These radiation sensing devices can find potential applications in real-time, large area, flexible health, and security systems.
Bugs.jar: A Large-Scale, Diverse Dataset of Real-World Java Bugs

We present Bugs.jar, a large-scale dataset for research in automated debugging, patching, and testing of Java programs. Bugs.jar is comprised of 1,158 bugs and patches, drawn from 8 large, popular opensource Java projects, spanning 8 diverse and prominent application categories. It is an order of magnitude larger than Defects4J, the only other dataset in its class. We discuss the methodology used for constructing Bugs.jar, the representation of the dataset, several use-cases, and an illustration of three of the use-cases through the application of 3 specific tools on Bugs.jar, namely our own tool, Elixir, and two third-party tools, Ekstazi and JaCoCo.