Miracle-dz/UniFAR-FTUs huggingface dataset:
Skewed Associativity Improves Program Performance and Enhances Predictability

Performance tuning becomes harder as computer technology advances. One of the factors is the increasing complexity of memory hierarchies. Most modern machines now use at least one level of cache memory. To reduce execution stalls, cache misses must be very low. Software techniques used to improve locality have been developed for numerical codes, such as loop blocking and copying. Unfortunately, the behavior of direct mapped and set associative caches is still erratic when large data arrays are accessed. Execution time can vary drastically for the same loop kernel depending on uncontrolled factors such as array leading size. The only software method available to improve execution time stability is the copying of frequently used data, which is costly in execution time. Users are not usually cache organization experts. They are not aware of such phenomena and have no control over it. In this paper, we show that the recently proposed four-way skewed associative cache yields very stable execution times and good average miss ratios on blocked algorithms. As a result, execution time is faster and much more predictable than with conventional caches. It is therefore possible to use larger block sizes in blocked algorithms, which will further reduce blocking overhead costs.
CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction

Bitrate adaptation is critical in ensuring good users’ quality-of-experience (QoE) in Internet video delivery system. Several efforts have argued that accurate throughput prediction can dramatically improve (1) initial bitrate selection for low startup delay and high initial resolution; (2) midstream bitrate adaptation for high QoE. However, prior ef- forts did not systematically quantify real-world throughput predictability or develop good prediction algorithms. To bridge this gap, this paper makes three key technical contributions: First, we analyze the throughput characteristics in a dataset with 20M+ sessions. We find: (a) Sessions sharing similar key features (e.g., ISP, region) present similar initial values and dynamical patterns; (b) There is a natural “stateful” dynamical behavior within a given session. Second, building on these insights, we develop CS2P, a better throughput prediction system. CS2P leverages data-driven approach to learn (a) clusters of similar sessions, (b) an initial throughput predictor, and (c) a Hidden-Markov-Model based midstream predictor modeling the stateful evolution of throughput. Third, we develop a prototype system and show by trace-driven simulation and real-world experiments that CS2P outperforms state-of-art by 40% and 50% median pre- diction error respectively for initial and midstream through- put and improves QoE by 14% over buffer-based adaptation algorithm.
Interleukin-13 Genetic Variants, Household Carpet Use and Childhood Asthma

Interleukin (IL)-13 genetic polymorphisms have shown adverse effects on respiratory health. However, few studies have explored the interactive effects between IL-13 haplotypes and environmental exposures on childhood asthma. The aims of our study are to evaluate the effects of IL-13 genetic variants on asthma phenotypes, and explore the potential interaction between IL-13 and household environmental exposures among Taiwanese children. We investigated 3,577 children in the Taiwan Children Health Study from 14 Taiwanese communities. Data regarding children's exposure and disease status were obtained from parents using a structured questionnaire. Four SNPs were tagged accounting for 100% of the variations in IL-13. Multiple logistic regression models with false-discovery rate (FDR) adjustments were fitted to estimate the effects of IL-13 variants on asthma phenotypes. SNP rs1800925, SNP rs20541 and SNP rs848 were significantly associated with increased risks on childhood wheeze with FDR of 0.03, 0.04 and 0.04, respectively. Children carrying two copies of h1011 haplotype showed increased susceptibility to wheeze. Compared to those without carpet use and h1011 haplotype, children carrying h1011 haplotype and using carpet at home had significantly synergistic risks of wheeze (OR, 2.5; 95% CI, 1.4–4.4; p for interaction, 0.01) and late-onset asthma (OR, 4.7; 95% CI, 2.0–10.9; p for interaction, 0.02). In conclusions, IL-13 genetic variants showed significant adverse effects on asthma phenotypes among children. The results also suggested that asthma pathogenesis might be mediated by household carpet use.
Review: Clock genes at the heart of depression

The rhythms of life are ever pervasive, touching almost every aspect of our lives. We are finely tuned to the cycle of light and dark, so that we normally sleep during the night and are active during the day. Physiological rhythms are, however, not just slaves to the solar day, but are actually generated endogenously within the suprachiasmatic nuclei in the hypothalamus and are entrained via the retina. The circadian timing system is organized hierarchically with the suprachiasmatic nuclei providing neural and/or hormonal cues to the various organ systems, allowing them to express their own rhythmic physiological output. There is now a substantial body of evidence emerging that disruption of rhythmicity through altered sleep/wake patterns and exposure to light, or through endogenous disruption of key determinants of endogenous rhythms, can be detrimental to health. Circadian rhythm disturbances have long been associated with mood disorders, especially delayed sleep onset, and evidence is accumulating that alterations to the cellular timing system may underpin some aspects of the disorders. For example, mice carrying mutations in either Clock or per2 spend less time immobile in swim tests, which has been suggested as mimicking mania. In humans, single nucleotide polymorphisms in Clock and other clock genes have been associated with depression. With this increasing knowledge we may predict that new antidepressant drugs will emerge that, as a primary or secondary mechanism of action, target and correct abnormalities in the circadian timing system.
Multivariate Online Anomaly Detection Using Kernel Recursive Least Squares

High-speed backbones are regularly affected by various kinds of network anomalies, ranging from malicious attacks to harmless large data transfers. Different types of anomalies affect the network in different ways, and it is difficult to know a priori how a potential anomaly will exhibit itself in traffic statistics. In this paper we describe an online, sequential, anomaly detection algorithm, that is suitable for use with multivariate data. The proposed algorithm is based on the kernel version of the recursive least squares algorithm. It assumes no model for network traffic or anomalies, and constructs and adapts a dictionary of features that approximately spans the subspace of normal behaviour. The algorithm raises an alarm immediately upon encountering a deviation from the norm. Through comparison with existing block-based offline methods based upon Principal Component Analysis, we demonstrate that our online algorithm is equally effective but has much faster time-to-detection and lower computational complexity. We also explore minimum volume set approaches in identifying the region of normality.
A replicated assessment and comparison of common software cost modeling techniques

Delivering a software product on time, within budget, and to an agreed level of quality is a critical concern for many software organizations. Underestimating software costs can have detrimental effects on the quality of the delivered software and thus on a company's business reputation and competitiveness. On the other hand, overestimation of software cost can result in missed opportunities to funds in other projects. In response to industry demand, a myriad of estimation techniques has been proposed during the last three decades. In order to assess the suitability of a technique from a diverse selection, its performance and relative merits must be compared. The current study replicates a comprehensive comparison of common estimation techniques within different organizational contexts, using data from the European Space Agency. Our study is motivated by the challenge to assess the feasibility of using multi-organization data to build cost models and the benefits gained from company-specific data collection. Using the European Space Agency data set, we investigated a yet unexplored application domain, including military and space projects. The results showed that traditional techniques, namely, ordinary least-squares regression and analysis of variance outperformed analogy-based estimation and regression trees. Consistent with the results of the replicated study no significant difference was found in accuracy between estimates derived from company-specific data and estimates derived from multi-organizational data.
Electrophysiological and Morphological Analyses of Cortical Neurons Obtained from Children with Catastrophic Epilepsy: Dopamine Receptor Modulation of Glutamatergic Responses

The present study examined the electrophysiological effects produced by activation of specific dopamine (DA) receptors and the distribution of DA receptor subtypes and glutamate receptor subunits [N-methyl-D-aspartate (NMDAR1) and GluR1] in cortical tissue samples obtained from children (ages 3 months to 16 years) undergoing epilepsy surgery. DA receptor activation produced differential effects depending on the receptor subtype that was activated. D1 receptor family agonists generally enhanced cortical excitability and favored the emergence of epileptogenic activity. In contrast, D2 receptor family agonists had more variable effects on cortical excitability and the expression of epileptiform discharges. Activation of D1 or D2 receptors decreased the amplitude of non-NMDA-mediated excitatory postsynaptic potentials. In contrast, DA and D1 agonists increased the amplitude of NMDA-mediated potentials. Immunohistochemical analysis showed that the DA receptor subtypes and glutamate receptor subunits examined were present in all cortical layers and areas throughout development. Whole-cell voltage clamp recordings of pyramidal neurons visualized with differential interference contrast optics and infrared videomicroscopy indicated that these neurons displayed a persistent Na+ current, followed by an outward current. DA reduced the outward current but had little effect on the persistent Na+ current. These results suggest a dual role for DA’s actions in the human cerebral cortex. Activation of D2 receptors or antagonism of D1 receptors may help control seizures in children.
Reliability of data in structured peer-to-peer systems

Peer-to-peer (P2P) systems are very useful for managing large amounts of widely distributed data. For this purpose distributed hash tables (DHT) offer a highly scalable and self-organizing paradigm for efficient distribution and retrieval of data. Thereby a common assumption of P2P systems is, that the participating nodes are unreliable and may fail at any time. Since many of research goes into the design of DHT lookup services, these systems aim to provide a stable global addressing structure. But to storage data reliable in a DHT only few techniques were already developed. However since data has to be stored persistent in the network, it should be retrieved anytime, even if nodes fail. In this work we discuss possibilities to store data fault tolerant in a structured peer-to-peer system.
SEMI-automated liver CT segmentation using Laplacian meshes

Liver volumetry is considered to be an accurate indicator of hepatic function and a prognostic indicator in hepatic surgery planning. Despite many years of research, automated liver segmentation remains an open challenge and manual segmentation is still widely used clinically although it is time-consuming and tedious. In this paper we propose a novel semi-automated segmentation method based on deformable models independent of training data. First, an initial shape of the liver is generated by variational interpolation from a few user-generated contours. A template-matching method then identifies target points corresponding to the liver boundary. Using a Laplacian mesh optimization framework, the geometric model is iteratively deformed until it converges to the liver boundary. This liver segmentation method was tested against 20 publicly available datasets and is shown to be fast and robust to pathological cases with a mean volumetric overlap error of 6.8% and an average runtime under 6 minutes.
Peekaboo: A gray hole attack on encrypted SCADA communication using traffic analysis

We consider a potential gray hole attack against SCADA substation to control center communications using DNP3. We propose a support vector machine-based traffic analysis algorithm that relies on message direction and timing information only, and we use trace-based simulations to show that even if SCADA traffic is sent through an encrypted tunnel, as often done in practice, the gray hole attack can be effectively performed based on the timing and direction of three consecutive messages. Our results show that the attacker does not need accurate system information to be successful, and could affect monitoring accuracy by up to 20%. We discuss possible mitigation schemes at different layers of the communication protocol stack, and show that a minor modification of message timing could help mitigate the attack.
E-broidery: Design and fabrication of textile-based computing

Highly durable, flexible, and even washable multilayer electronic circuitry can be constructed on textile substrates, using conductive yarns and suitably packaged components. In this paper we describe the development of e-broidery (electronic embroidery, i.e., the patterning of conductive textiles by numerically controlled sewing or weaving processes) as a means of creating computationally active textiles. We compare textiles to existing flexible circuit substrates with regard to durability, conformability, and wearability. We also report on: some unique applications enabled by our work; the construction of sensors and user interface elements in textiles; and a complete process for creating flexible multilayer circuits on fabric substrates. This process maintains close compatibility with existing electronic components and design tools, while optimizing design techniques and component packages for use in textiles.
Approaches to black box MT evaluation

In the course of four evaluations in the Advanced Research Projects Agency Machine Translation series, evaluation methods have evolved for measuring the core components of a diverse set of systems. This paper describes the methodologies in terms of the most recent evaluation of research and production MT systems, and discusses indications of ways to improve the focus and portability of the evaluation. 0. Introduction. Over the past four years, a set of evaluation methodologies have evolved within the MT initiative of the U.S. Advanced Research Projects Agency (ARPA). The ARPA program has faced unique challenges for evaluation, because the systems participating differ radically in the linguistic approach, their level of maturity, and the languages translated. The differences among these systems have made a black-box orientation to evaluation inevitable. While such an orientation differs from the methods that might be employed in the evaluation of a particular system by that system's developers, there are nevertheless certain advantages to the black box approaches in determining the focus and metrics of evaluation. This paper describes the methodologies of the ARPA program, in terms of their objectives, results, and evolution, and discusses analyses of the methods themselves to continue to improve the process. 1.0. Background. The ARPA initiative in machine translation began in 1991 as part of the Human Language Technologies Program. Three projects in MT research were sponsored under the initiative, with voluntary participation by several commercial and institutional MT organizations. The sponsored projects were: Candide (IBM Watson Research Center), a statistical modeling approach, translating French to English (Brown et al., 1993); Pangloss (Center for Machine Translation, Computing Research Laboratory, and Institute for Information Science), using knowledgebased approaches, translating Spanish and Japanese to English (Frederking et al., 1993); and Lingstat (Dragon Systems, Inc.), using a combination of modeling and rule-based approaches, translating Japanese and Spanish to English (Yamron et al., 1994). Many organizations have provided production MT systems for these evaluations, principally to assist ARPA's mission by helping to determine industry/discipline benchmarks. A significant goal of the ARPA MT Evaluation program, in return, is to provide a useful set of evaluation processes for a general standard. In the most recent test-evaluation cycle of August November 1994 (the "3Q94" evaluation), the following production systems participated: • the Sietec METAL system (French English); • the Nippon Electric PIVOT system (Japanese English); • Globalink Power Translator (French and
Structured Message Passing

In this paper, we present structured message passing (SMP), a unifying framework for approximate inference algorithms that take advantage of structured representations such as algebraic decision diagrams and sparse hash tables. These representations can yield significant time and space savings over the conventional tabular representation when the message has several identical values (context-specific independence) or zeros (determinism) or both in its range. Therefore, in order to fully exploit the power of structured representations, we propose to artificially introduce context-specific independence and determinism in the messages. This yields a new class of powerful approximate inference algorithms which includes popular algorithms such as cluster-graph Belief propagation (BP), expectation propagation and particle BP as special cases. We show that our new algorithms introduce several interesting bias-variance trade-offs. We evaluate these trade-offs empirically and demonstrate that our new algorithms are more accurate and scalable than state-of-the-art techniques.
Principal component analysis of water quality monitoring data in XiaSha region

Principal component analytic method is a statistical analysis method, which transforms the multi objectives into a minority of overall targets according to dimension reduction thought. This article applied this method to conduct the analytical study of water quality monitoring data in XiaSha region. The result indicated: The latter three principal components have already synthesized above 90% factor information, then can definitely substitute for eight index factors to evaluate water quality in XiaSha region synthetically. The result explained that the principal component analytic method is a quite ideal analytic method because it has overcome the defect of multi-dimensional target with difficulty in synthesis analysis.
On second-order optimality conditions in nonlinear optimization

In this work we present new weak conditions that ensure the validity of necessary second-order optimality conditions (SOC) for nonlinear optimization. We are able to prove that weak and strong SOCs hold for all Lagrange multipliers using Abadie-type assumptions. We also prove weak and strong SOCs for at least one Lagrange multiplier imposing the Mangasarian–Fromovitz constraint qualification and a weak constant rank assumption.
Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis

OBJECTIVE To conduct a systematic review of cross-sectional and prospective human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on prediabetes and type 2 diabetes. RESEARCH DESIGN AND METHODS We searched MEDLINE and EMBASE databases through August 2015. We conducted a qualitative review of cross-sectional and prospective studies. Additionally, meta-analyses of metabolite markers, with data estimates from at least three prospective studies, and type 2 diabetes risk were conducted, and multivariable-adjusted relative risks of type 2 diabetes were calculated per study-specific SD difference in a given metabolite. RESULTS We identified 27 cross-sectional and 19 prospective publications reporting associations of metabolites and prediabetes and/or type 2 diabetes. Carbohydrate (glucose and fructose), lipid (phospholipids, sphingomyelins, and triglycerides), and amino acid (branched-chain amino acids, aromatic amino acids, glycine, and glutamine) metabolites were higher in individuals with type 2 diabetes compared with control subjects. Prospective studies provided evidence that blood concentrations of several metabolites, including hexoses, branched-chain amino acids, aromatic amino acids, phospholipids, and triglycerides, were associated with the incidence of prediabetes and type 2 diabetes. We meta-analyzed results from eight prospective studies that reported risk estimates for metabolites and type 2 diabetes, including 8,000 individuals of whom 1,940 had type 2 diabetes. We found 36% higher risk of type 2 diabetes per study-specific SD difference for isoleucine (pooled relative risk 1.36 [1.24–1.48]; I2 = 9.5%), 36% for leucine (1.36 [1.17–1.58]; I2 = 37.4%), 35% for valine (1.35 [1.19–1.53]; I2 = 45.8%), 36% for tyrosine (1.36 [1.19–1.55]; I2 = 51.6%), and 26% for phenylalanine (1.26 [1.10–1.44]; I2 = 56%). Glycine and glutamine were inversely associated with type 2 diabetes risk (0.89 [0.81–0.96] and 0.85 [0.82–0.89], respectively; both I2 = 0.0%). CONCLUSIONS In studies using high-throughput metabolomics, several blood amino acids appear to be consistently associated with the risk of developing type 2 diabetes.
Expression and subcellular localization of Ror tyrosine kinase receptors are developmentally regulated in cultured hippocampal neurons

Ror1 and Ror2 are two novel receptor tyrosine kinases that have been implicated in neuronal differentiation in Caenorhabditis elegans. As a first step toward elucidating their role in the mammalian brain, we analyzed their expression and localization patterns in hippocampal neurons. Our results showed that both receptors are expressed from early stages of development and that their protein levels peak during periods of active synapse formation. Immunocytochemical analysis indicated that Ror1 and Ror2 are highly concentrated in the growth cones of immature neurons and are present throughout the somatodendritic compartment of mature hippocampal cells. Further analysis indicated that they are present not only in the cell membrane but also in Triton‐ and saponin‐insoluble fractions, suggesting that they may be associated with both the cytoskeleton and membrane‐bound organelles. Taken collectively, our results suggest that Ror1 and Ror2 might play a role during early stages of development in mammalian central neurons. © 2003 Wiley‐Liss, Inc.
A Moderate Zinc Deficiency Does Not Impair Gene Expression of PPARα, PPARγ, and Mitochondrial Enoyl-CoA Delta Isomerase in the Liver of Growing Rats

The aim of the study was to investigate the impact of a moderate zinc deficiency and a high intake of polyunsaturated fat on the mRNA expression of peroxisome-proliferator-activated receptor alpha (PPARα), peroxisome-proliferator-activated receptor gamma (PPARγ), and mitochondrial Δ3Δ2-enoyl-CoA isomerase (ECI) in the liver. Weanling rats were assigned to five groups (eight animals each) and fed semi-synthetic, low-carbohydrate diets containing 7 or 50 mg Zn/kg (low-Zn (LZ) or high-Zn (HZ)) and 22% cocoa butter (CB) or 22% safflower (SF) oil for four weeks. One group each was fed the LZ-CB, LZ-SF, or HZ-SF diet free choice, and one group each was fed the HZ-CB and HZ-SF diets in restricted amounts according to intake of the respective LZ diets. The LZ diets markedly lowered growth and zinc concentrations in plasma and femur. Hepatic mRNA levels of PPARα, PPARγ, and ECI were not reduced by the moderate zinc deficiency. Overall, ECI-mRNA abundance was marginally higher in the SF-fed than in the CB-fed animals.