Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitigating Power Contention: A Scheduling Based Approach

Shared resource contention has been a major performance issue for CMPs. In this paper, we tackle the power contention problem in power constrained CMPs by considering and treating power as a first-class shared resource. Power contention occurs when multiple processes compete for power, and leads to degraded system performance. In order to solve this problem, we develop a shared resource contention-aware scheduling algorithm that mitigates the contention for power and the shared memory subsystem at the same time. The proposed scheduler improves system performance by balancing the shared resource usage among scheduling groups. Evaluation results across a variety of multiprogrammed workloads show performance improvements over a state-of-the-art scheduling policy which only considers memory subsystem contention.
Functions and Regulation of NF-κB RelA during Pneumococcal Pneumonia1

Eradication of bacteria in the lower respiratory tract depends on the coordinated expression of proinflammatory cytokines and consequent neutrophilic inflammation. To determine the roles of the NF-κB subunit RelA in facilitating these events, we infected RelA-deficient mice (generated on a TNFR1-deficient background) with Streptococcus pneumoniae. RelA deficiency decreased cytokine expression, alveolar neutrophil emigration, and lung bacterial killing. S. pneumoniae killing was also diminished in the lungs of mice expressing a dominant-negative form of IκBα in airway epithelial cells, implicating this cell type as an important locus of NF-κB activation during pneumonia. To study mechanisms of epithelial RelA activation, we stimulated a murine alveolar epithelial cell line (MLE-15) with bronchoalveolar lavage fluid (BALF) harvested from mice infected with S. pneumoniae. Pneumonic BALF, but not S. pneumoniae, induced degradation of IκBα and IκBβ and rapid nuclear accumulation of RelA. Moreover, BALF-induced RelA activity was completely abolished following combined but not individual neutralization of TNF and IL-1 signaling, suggesting either cytokine is sufficient and necessary for alveolar epithelial RelA activation during pneumonia. Our results demonstrate that RelA is essential for the host defense response to pneumococcus in the lungs and that RelA in airway epithelial cells is primarily activated by TNF and IL-1.
The emergence of gravitational wave science: 100 years of development of mathematical theory, detectors, numerical algorithms, and data analysis tools

On September 14, 2015, the newly upgraded Laser Interferometer Gravitational-wave Observatory (LIGO) recorded a loud gravitational-wave (GW) signal, emitted a billion light-years away by a coalescing binary of two stellar-mass black holes. The detection was announced in February 2016, in time for the hundredth anniversary of Einstein's prediction of GWs within the theory of general relativity (GR). The signal represents the first direct detection of GWs, the first observation of a black-hole binary, and the first test of GR in its strong-field, high-velocity, nonlinear regime. In the remainder of its first observing run, LIGO observed two more signals from black-hole binaries, one moderately loud, another at the boundary of statistical significance. The detections mark the end of a decades-long quest and the beginning of GW astronomy: finally, we are able to probe the unseen, electromagnetically dark Universe by listening to it. In this article, we present a short historical overview of GW science: this young discipline combines GR, arguably the crowning achievement of classical physics, with record-setting, ultra-low-noise laser interferometry, and with some of the most powerful developments in the theory of differential geometry, partial differential equations, high-performance computation, numerical analysis, signal processing, statistical inference, and data science. Our emphasis is on the synergy between these disciplines and how mathematics, broadly understood, has historically played, and continues to play, a crucial role in the development of GW science. We focus on black holes, which are very pure mathematical solutions of Einstein's gravitational-field equations that are nevertheless realized in Nature and that provided the first observed signals.
Codebook design for communication in spread and nonspread space-time block codes-based systems

The problem of space-time codebook design for communication in spread and nonspread multiple-antenna wireless systems is addressed and a new methodology for space-time codebook design is proposed. This optimizes the probability of error of the receiver's detector in the high signal-to-noise-ratio (SNR) regime, thus solving a nonlinear non-smooth optimization problem using an iterative method that exploits the Riemannian geometry imposed by the power constraints on the space-time codewords. Computer simulations demonstrate that, for the low SNR regime, our codebooks are marginally better than those provided by state-of-art known solutions. However, for the medium and high SNR regimes, our method provides codes that outperform other known codes.
The Effect of High-Altitude on Human Skeletal Muscle Energetics: 31P-MRS Results from the Caudwell Xtreme Everest Expedition

Many disease states are associated with regional or systemic hypoxia. The study of healthy individuals exposed to high-altitude hypoxia offers a way to explore hypoxic adaptation without the confounding effects of disease and therapeutic interventions. Using 31P magnetic resonance spectroscopy and imaging, we investigated skeletal muscle energetics and morphology after exposure to hypobaric hypoxia in seven altitude-naïve subjects (trekkers) and seven experienced climbers. The trekkers ascended to 5300 m while the climbers ascended above 7950 m. Before the study, climbers had better mitochondrial function (evidenced by shorter phosphocreatine recovery halftime) than trekkers: 16±1 vs. 22±2 s (mean ± SE, p<0.01). Climbers had higher resting [Pi] than trekkers before the expedition and resting [Pi] was raised across both groups on their return (PRE: 2.6±0.2 vs. POST: 3.0±0.2 mM, p<0.05). There was significant muscle atrophy post-CXE (PRE: 4.7±0.2 vs. POST: 4.5±0.2 cm2, p<0.05), yet exercising metabolites were unchanged. These results suggest that, in response to high altitude hypoxia, skeletal muscle function is maintained in humans, despite significant atrophy.
Combining Monte Carlo tree search and apprenticeship learning for capture the flag

In this paper we introduce a novel approach to agent control in competitive video games which combines Monte Carlo Tree Search (MCTS) and Apprenticeship Learning (AL). More specifically, an opponent model created through AL is used during the expansion phase of the Upper Confidence Bounds for Trees (UCT) variant of MCTS. We show how this approach can be applied to a game of Capture the Flag (CTF), an environment which is both non-deterministic and partially observable. The performance gain of a controller utilizing an opponent model learned via AL when compared to a controller using just UCT is shown both with win/loss ratios and True Skill rankings. Additionally, we build on previous findings by providing evidence of a bias towards a particular style of play in the AI Sandbox CTF environment. We believe that the approach highlighted here can be extended to a wider range of games other than just CTF.
Low-latency visual odometry using event-based feature tracks

New vision sensors, such as the Dynamic and Active-pixel Vision sensor (DAVIS), incorporate a conventional camera and an event-based sensor in the same pixel array. These sensors have great potential for robotics because they allow us to combine the benefits of conventional cameras with those of event-based sensors: low latency, high temporal resolution, and high dynamic range. However, new algorithms are required to exploit the sensor characteristics and cope with its unconventional output, which consists of a stream of asynchronous brightness changes (called “events”) and synchronous grayscale frames. In this paper, we present a low-latency visual odometry algorithm for the DAVIS sensor using event-based feature tracks. Features are first detected in the grayscale frames and then tracked asynchronously using the stream of events. The features are then fed to an event-based visual odometry algorithm that tightly interleaves robust pose optimization and probabilistic mapping. We show that our method successfully tracks the 6-DOF motion of the sensor in natural scenes. This is the first work on event-based visual odometry with the DAVIS sensor using feature tracks.
The sensitization of sodium appetite: Plasticity in neural networks governing body fluid homeostasis and motivated behavior

The sensitization of sodium appetite: Plasticity in neural networks governing body fluid homeostasis and motivated behavior. Acknowledgements This work could not have been conducted without the help of a handful of individuals. My mentor, Kim Johnson, deserves the most credit for providing the conceptual and experimental basis to many of these experiments. Our lab technician, Terry Beltz, helped with organizing and conducting many of the experiments. Ryan LaLumiere also provided conceptual and literature background for studies examining motivation and reward systems. Jason Radley offered invaluable advice and feedback on immunohistochemistry and labeling experiments. Abstract When most omnivores and herbivores become sodium depleted they engage in the motivated behavior of sodium appetite (AKA salt appetite), or the seeking out and ingestion of salty substances. Sodium appetite is associated with psychological processes that serve to enhance the incentive and rewarding value of salty substances. These processes attract animals to salty substances and reinforce the ingestion of salty substances. The experience of sodium depletion also produces long-lasting changes in behavior; one of the most apparent changes being a seemingly lifelong increase in hypertonic salt intake which indicates sodium appetite is sensitized. Two neural circuits have been implicated in the sensitization of sodium appetite: 1) a forebrain neural circuit that regulates body fluid homeostasis, and 2) the mesolimbic dopamine system which mediates motivated behaviors. This dissertation has three aims that serve the overall purpose of providing a better understanding of the neurobiological mechanisms that mediate sodium appetite and the sensitization of sodium appetite. The first aim is to develop a model of sodium depletion that is amenable to pharmacological manipulation in order to determine whether the blockade of N-methyl-d-aspartate receptors, which are critical for neural plasticity, will prevent the sensitization of sodium appetite. The second aim is to determine whether sensitization is associated with relatively long-term molecular changes in forebrain areas that regulate body fluid homeostasis. The third aim is to identify how forebrain areas involved in body fluid homeostasis connect to and influence activity in the mesolimbic dopamine system. Taken together, these experiments provide support for a neural circuit that exhibits plasticity after sodium depletion that is comprised of brain structures that sense and process body fluid balance, integrate homeostatic state with motivation and reward systems, and motivation and reward systems themselves. iv Public abstract People in Westernized societies tend to eat too much salt (also known as sodium). This " salt gluttony …
Consumer Policy Remedies and Consumer Segment Interactions

This paper introduces a framework for examining consumer and market problems as a function of consumer segment interaction patterns. Three patterns of interactions are described as consisting of positive, negative, or no spillovers among consumer segments. The efficacy of regulatory remedies is shown to be affected by the type and extent of these interaction patterns. The paper complements and extends conventional aggregate cost-benefit approaches in three ways: 1) by treating consumer interactions as a central feature of the market system; 2) by focusing on how various interaction patterns affect the distribution and intensity of spillovers among consumers; and 3) by describing how policy remedies can be used to facilitate or overcome these interaction patterns.
Assessment of accuracy, reliability and force measurement errors for a tethered swimming apparatus

Researchers frequently use purpose-built devices to calculate propulsive forces during tethered swimming. Although such devices are subject to force measurement errors, no specific methods have been suggested in the swimming literature for the estimation of these errors. The purpose of this study was to access the accuracy and reliability of a portable device that is used to measure propulsive forces in tethered swimming, and to estimate the errors caused by hysteresis, sensitivity and linearity. The force values recorded during a maximum front crawl test of an international level swimmer were used to provide an example of the extent to which measurement errors could affect the values collected during a tethered swimming study. The calculations revealed small and acceptable errors. When summing the errors from all sources, the total errors affecting the minimum, average and peak forces recorded during the case study were 1.15%, 0.94% and 0.86% respectively. It is recommended that investigators always calculate and report such errors for tethered swimming studies. The methods used in the present study are reasonably simple and not time-consuming, and could be used when assessing errors for similar tethered swimming devices.
Online Music Ranking Service: Ranking Mechanism Based on Popularity and Slot Effect

This paper analyzes music charts of an online music distributor. In music charts, the digital music provider displays a daily ranking of 1st ~ 100th and a weekly ranking of 1st ~ 1,000th songs on its website. And the ranking of each song is assigned based on streaming volumes and download volumes. This paper studies how the online music distributor should set its ranking policy to maximize the value of online music ranking service. Compared to the current ranking mechanism which is being used by music sites and only considers streaming and download volumes, a new ranking mechanism is proposed in this paper. A key improvement of the new ranking mechanism is to reflect a more accurate preference pertinent to popularity, pricing policy and slot effect based on exponential decay model for online users. A ranking model is built to verify correlations between two service volumes and popularity, pricing policy, and slot effect. An empirical analysis is followed to illustrate some of the general features of online music charts and to validate the assumptions used in the new ranking model. The results from the empirical work show that the new ranking mechanism proposed will be more effective than the former one in several aspects.
SpyStorage: A Highly Reliable Multi-Cloud Storage with Secure and Anonymous Data Sharing

Users outsourcing their critical data to cloud services has been increasingly popular recently. System reliability and data security have become the top priority to be concerned because of the possible service failures, unauthorized data access and privacy leaks in the cloud storage system. However, traditional single cloud storage service is far from meeting the demands because of the inevitable problems of data loss, vendor-lock in and privacy leaks during data storage and share. In this paper, a novel design is to be proposed and named by SpyStorage that offers users a highly reliable storage service and a secure anonymous data sharing mechanism based on Multi-Cloud by implementing the quorum protocol and utilizing the ABE/ABS cryptographic model. To the best of our knowledge, this is the first work using ABE and ABS to completely solve the issues in secret storing and sharing with anonymity simultaneously in the Multi- Cloud field. Extensive measuring experiments have proved that SpyStorage can achieve a relative high reliability with a low tradeoff of file transfer speed.
iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition

Meiotic recombination is an important biological process. As a main driving force of evolution, recombination provides natural new combinations of genetic variations. Rather than randomly occurring across a genome, meiotic recombination takes place in some genomic regions (the so-called ‘hotspots’) with higher frequencies, and in the other regions (the so-called ‘coldspots’) with lower frequencies. Therefore, the information of the hotspots and coldspots would provide useful insights for in-depth studying of the mechanism of recombination and the genome evolution process as well. So far, the recombination regions have been mainly determined by experiments, which are both expensive and time-consuming. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the recombination regions. In this study, a predictor, called ‘iRSpot-PseDNC’, was developed for identifying the recombination hotspots and coldspots. In the new predictor, the samples of DNA sequences are formulated by a novel feature vector, the so-called ‘pseudo dinucleotide composition’ (PseDNC), into which six local DNA structural properties, i.e. three angular parameters (twist, tilt and roll) and three translational parameters (shift, slide and rise), are incorporated. It was observed by the rigorous jackknife test that the overall success rate achieved by iRSpot-PseDNC was >82% in identifying recombination spots in Saccharomyces cerevisiae, indicating the new predictor is promising or at least may become a complementary tool to the existing methods in this area. Although the benchmark data set used to train and test the current method was from S. cerevisiae, the basic approaches can also be extended to deal with all the other genomes. Particularly, it has not escaped our notice that the PseDNC approach can be also used to study many other DNA-related problems. As a user-friendly web-server, iRSpot-PseDNC is freely accessible at http://lin.uestc. edu.cn/server/iRSpot-PseDNC.
Core-competitive Auctions

One of the major drawbacks of the celebrated VCG auction is its low (or zero) revenue even when the agents have high value for the goods and a competitive outcome would have generated a significant revenue. A competitive outcome is one for which it is impossible for the seller and a subset of buyers to 'block' the auction by defecting and negotiating an outcome with higher payoffs for themselves. This corresponds to the well-known concept of core in cooperative game theory. In particular, VCG revenue is known to be not competitive when the goods being sold have complementarities. Complementary goods are present in many application domains including spectrum, procurement, and ad auctions. The absence of good revenue from VCG auction poses a real hurdle when trying to design auctions for these settings. Given the importance of these application domains, researchers have looked for alternate auction designs. One important research direction that has come from this line of thinking is that of the design of core-selecting auctions (See Ausubel and Milgrom, Day and Milgrom, Day and Cramton, Ausubel and Baranov). Core-selecting auctions are combinatorial auctions whose outcome implements competitive prices even when the goods are complements. While these auction designs have been implemented in practice in various scenarios and are known for having good revenue properties, they lack the desired incentive-compatibility property of the VCG auction. A bottleneck here is an impossibility result showing that there is no auction that simultaneously achieves competitive prices (a core outcome) and incentive-compatibility. In this paper we try to overcome the above impossibility result by asking the following natural question: is it possible to design an incentive-compatible auction whose revenue is comparable (even if less) to a competitive outcome? Towards this, we define a notion of core-competitive auctions. We say that an incentive-compatible auction is α-core-competitive if its revenue is at least 1/α fraction of the minimum revenue of a core-outcome. We study one of the most commonly occurring setting in Internet advertisement with complementary goods, namely that of the Text-and-Image setting. In this setting, there is an ad slot which can be filled with either a single image ad or k text ads. We design an O(ln ln k) core-competitive randomized auction and an O(√ln k) competitive deterministic auction for the Text-and-Image setting. We also show that both factors are tight.
Controlling for Continuous Confounders in Epidemiologic Research

Multiple regression models are commonly used to control for confounding in epidemiologic research. Parametric regression models, such as multiple logistic regression, are powerful tools to control for multiple covariates provided that the covariate‐risk associations are correctly specified. Residual confounding may result, however, from inappropriate specification of the confounder‐risk association. In this paper, we illustrate the order of magnitude of residual confounding that may occur with traditional approaches to control for continuous confounders in multiple logistic regression, such as inclusion of a single linear term or categorization of the confounder, under a variety of assumptions on the confounder‐risk association. We show that inclusion of the confounder as a single linear term often provides satisfactory control for confounding even in situations in which the model assumptions are clearly violated. In contrast, categorization of the confounder may often lead to serious residual confounding if the number of categories is small. Alternative strategies to control for confounding, such as polynomial regression or linear spline regression, are a useful supplement to the more traditional approaches.
Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification

Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, Bayesian network etc can be applied on the educational data for predicting the student's performance in examination. This prediction will help to identify the weak students and help them to score better marks. The C4.5, ID3 and CART decision tree algorithms are applied on engineering student's data to predict their performance in the final exam. The outcome of the decision tree predicted the number of students who are likely to pass, fail or promoted to next year. The results provide steps to improve the performance of the students who were predicted to fail or promoted. After the declaration of the results in the final examination the marks obtained by the students are fed into the system and the results were analyzed for the next session. The comparative analysis of the results states that the prediction has helped the weaker students to improve and brought out betterment in the result.