Miracle-dz/UniFAR-FTUs huggingface dataset:
Experiences with a continuous network tracing infrastructure

One of the most pressing problems in network research is the lack of long-term trace data from ISPs. The Internet carries an enormous volume and variety of data; mining this data can provide valuable insight into the design and development of new protocols and applications. Although capture cards for high-speed links exist today, actually making the network traffic available for analysis involves more than just getting the packets off the wire, but also handling large and variable traffic loads, sanitizing and anonymizing the data, and coordinating access by multiple users. In this paper we discuss the requirements, challenges, and design of an effective traffic monitoring infrastructure for network research. We describe our experience in deploying and maintaining a multi-user system for continuous trace collection at a large regional ISP@. We evaluate the performance of our system and show that it can support sustained collection and processing rates of over 160--300Mbits/s.
An Empirical Comparison of Algorithms for Aggregating Expert Predictions

Predicting the outcomes of future events is a challenging problem for which a variety of solution methods have been explored and attempted. We present an empirical comparison of a variety of online and offline adaptive algorithms for aggregating experts' predictions of the outcomes of five years of US National Football League games (1319 games) using expert probability elicitations obtained from an Internet contest called ProbabilitySports. We find that it is difficult to improve over simple averaging of the predictions in terms of prediction accuracy, but that there is room for improvement in quadratic loss. Somewhat surprisingly, a Bayesian estimation algorithm which estimates the variance of each expert's prediction exhibits the most consistent superior performance over simple averaging among our collection of algorithms.
Subcutaneous Tissue Mechanical Behavior is Linear and Viscoelastic Under Uniaxial Tension

Subcutaneous tissue is part of a bodywide network of “loose” connective tissue including interstitial connective tissues separating muscles and surrounding all nerves and blood vessels. Despite its ubiquitous presence in the body and its potential importance in a variety of therapies utilizing mechanical stretch, as well as normal movement and exercise, very little is known about loose connective tissue's biomechanical behavior. This study aimed to determine elastic and viscoelastic mechanical properties of ex-vivo rat subcutaneous tissue in uniaxial tension with incremental stress relaxation experiments. The elastic response of the tissue was linear, with instantaneous and equilibrium tensile moduli of 4.77 kPa and 2.75 kPa, respectively. Using a 5 parameter Maxwell solid model, material parameters μ1 = 0.95 ± 0.24 Ns/m and μ2 = 8.49 ± 2.42 Ns/m defined coefficients of viscosity related to time constants τ1M = 3.83 ± 0.15 sec and τ2M = 30.15 ± 3.16 sec, respectively. Using a continuous relaxation function, parameters C = 0.25 ± 0.12, τ1C = 1.86 ± 0.34 sec, and τ2C = 110.40 ± 25.59 sec defined the magnitude and frequency limits of the relaxation spectrum. This study provides baseline information for the stress-strain behaviors of subcutaneous connective tissue. Our results underscore the differences in mechanical behaviors between loose and high-load bearing connective tissues and suggest that loose connective tissues may function to transmit mechanical signals to and from the abundant fibroblasts, immune, vascular, and neural cells present within these tissues.
Rev‐Erbα modulates retinal visual processing and behavioral responses to light

The circadian clock is thought to adjust retinal sensitivity to ambient light levels, yet the involvement of specific clock genes is poorly understood. We explored the potential role of the nuclear receptor subfamily 1, group D, member 1 (REV‐ERBα; or NR1D1) in this respect. In light‐evoked behavioral tests, comparedwith wildtype littermates, Rev‐Erbα−/− mice showed enhanced negative masking at low light levels (0.1 lx). Rev‐Erbα−/− mouse retinas displayed significantly higher numbers of intrinsically photosensitive retinal ganglion cells(ipRGCs;62% more compared with wild‐type) and more intense melanopsin immunostaining of individual ipRGCs. In agreement with a pivotal role for melanopsin, negative masking at low light intensities was abolished in Rev‐Erbα−/− Opn4−/− (melanopsin gene) double‐null mice. Rev‐Erbα−/− mice showed shortened latencies of both a and b electroretinogram waves, modified scotopic and photopic b‐wave and scotopic threshold responses, and increased pupillary constriction, all of which suggested increased light sensitivity. However, wild‐type and Rev‐Erbα−/− mice displayed no detectable differences by in vivo fundus imaging, retinal histology, or expression of cell type–specific markers for major retinal cell populations. We conclude that REV‐ERBα plays a major role in retinal information processing, and we speculate that REV‐ERBα and melanopsin set sensitivity levels of the rodmediated ipRGC pathway to coordinate activity with ambient light. —Ait‐Hmyed Hakkari, O. , Acar, N. , Savier, E. , Spinnhirny, P., Bennis, M. , Felder‐Schmittbuhl, M. ‐β. , Mendoza, J., Hicks, D. Rev‐Erbα modulates retinal visual processing and behavioral responses to light. FASEB J. 30, 3690–3701 (2016) www.fasebj. org
Trajectory-based visual analysis of large financial time series data

Visual Analytics seeks to combine automatic data analysis with visualization and human-computer interaction facilities to solve analysis problems in applications characterized by occurrence of large amounts of complex data. The financial data analysis domain is a promising field for research and application of Visual Analytics technology, as it prototypically involves the analysis of large data volumes in solving complex analysis tasks. We introduce a Visual Analytics system for supporting the analysis of large amounts of financial time-varying indicator data. A system, driven by the idea of extending standard technical chart analysis from one to two-dimensional indicator space, is developed. The system relies on an unsupervised clustering algorithm combined with an appropriately designed movement data visualization technique. Several analytical views on the full market and specific assets are offered for the user to navigate, to explore, and to analyze. The system includes automatic screening of the potentially large visualization space, preselecting possibly interesting candidate data views for presentation to the user. The system is applied to a large data set of time varying 2-D stock market data, demonstrating its effectiveness for visual analysis of financial data. We expect the proposed techniques to be beneficial in other application areas as well.
Parallel evaluation of dataflow programs for extreme-scale many-task computing

Developing programming solutions to help applications utilize the high concurrency of multi-petaflop computing systems is a challenge. Emerging languages such as Dryad, Swift, and Skywriting provide a promising direction. Their implicitly parallel dataflow semantics allow the high level logic of large-scale applications to be expressed in a manageable way while exposing massive parallelism through many-task programming. However, the implementations of these languages limit the evaluation of the overall program to a single-node computer, relying on threads and the multiple SMP cores of the evaluation node to generate the parallel tasks which are in turn executed on many nodes. In this work, we provide a model for distributed parallel evaluation of dataflow programs in a manner that spreads the overhead of program evaluation and parallel task generation throughout an extreme-scale computing system. This execution model enables function and expression evaluation to take place on any node of a computing system. It breaks parallel loops into fragments for distributed execution, and can more readily achieve the task generation rates needed to efficiently utilize future exascale systems. This paper describes the design and preliminary implementation of a distributed evaluation model for implicitly parallel dataflow programs, and motivates it with requirements projected from scientific applications. The preliminary implementation shows promising scalability curves and processes 500K task/sec on 1024 lowspeed compute nodes.
Moving to the RADARSAT Constellation Mission: Comparing Synthesized Compact Polarimetry and Dual Polarimetry Data with Fully Polarimetric RADARSAT-2 Data for Image Classification of Peatlands

For this research, the Random Forest (RF) classifier was used to evaluate the potential of simulated RADARSAT Constellation Mission (RCM) data for mapping landcover within peatlands. Alfred Bog, a large peatland complex in Southern Ontario, was used as a test case. The goal of this research was to prepare for the launch of the upcoming RCM by evaluating three simulated RCM polarizations for mapping landcover within peatlands. We examined (1) if a lower RCM noise equivalent sigma zero (NESZ) affects classification accuracy, (2) which variables are most important for classification, and (3) whether classification accuracy is affected by the use of simulated RCM data in place of the fully polarimetric RADARSAT-2. Results showed that the two RCM NESZs (−25 dB and −19 dB) and three polarizations (compact polarimetry, HH+HV, and VV+VH) that were evaluated were all able to achieve acceptable classification accuracies when combined with optical data and a digital elevation model (DEM). Optical variables were consistently ranked to be the most important for mapping landcover within peatlands, but the inclusion of SAR variables did increase overall accuracy, indicating that a multi-sensor approach is preferred. There was no significant difference between the RF classifications which included RADARSAT-2 and simulated RCM data. Both medium- and high-resolution compact polarimetry and dual polarimetric RCM data appear to be suitable for mapping landcover within peatlands when combined with optical data and a DEM.
Comprehensive Association Study of Type 2 Diabetes and Related Quantitative Traits With 222 Candidate Genes

OBJECTIVE—Type 2 diabetes is a common complex disorder with environmental and genetic components. We used a candidate gene–based approach to identify single nucleotide polymorphism (SNP) variants in 222 candidate genes that influence susceptibility to type 2 diabetes. RESEARCH DESIGN AND METHODS—In a case-control study of 1,161 type 2 diabetic subjects and 1,174 control Finns who are normal glucose tolerant, we genotyped 3,531 tagSNPs and annotation-based SNPs and imputed an additional 7,498 SNPs, providing 99.9% coverage of common HapMap variants in the 222 candidate genes. Selected SNPs were genotyped in an additional 1,211 type 2 diabetic case subjects and 1,259 control subjects who are normal glucose tolerant, also from Finland. RESULTS—Using SNP- and gene-based analysis methods, we replicated previously reported SNP-type 2 diabetes associations in PPARG, KCNJ11, and SLC2A2; identified significant SNPs in genes with previously reported associations (ENPP1 [rs2021966, P = 0.00026] and NRF1 [rs1882095, P = 0.00096]); and implicated novel genes, including RAPGEF1 (rs4740283, P = 0.00013) and TP53 (rs1042522, Arg72Pro, P = 0.00086), in type 2 diabetes susceptibility. CONCLUSIONS—Our study provides an effective gene-based approach to association study design and analysis. One or more of the newly implicated genes may contribute to type 2 diabetes pathogenesis. Analysis of additional samples will be necessary to determine their effect on susceptibility.
Notice of Violation of IEEE Publication Principles Nuclear Localization of HIV-1 Tat Functionalized Gold Nanoparticles

The impermeable nature of the cell plasma membrane limits the therapeutic uses of many macromolecules and there is therefore a growing effort to circumvent this problem by designing strategies for targeted intracellular delivery. During the last decade several cell penetrating peptides, such as the HIV-1 tat peptide, have been shown to traverse the cell membrane, where integral protein transduction domains (PTDs) are responsible for their cellular uptake, and to reach the nucleus while retaining biological activity. It has since been discovered that PTDs can enable the cellular delivery of conjugated biomolecules and even nanoparticles, but nuclear delivery has remained problematic. This present study focuses on the development of water soluble, biocompatible gold nanoparticles of differing size functionalized with the HIV-1 tat PTD with the aim of producing nuclear targeting agents. The particles were subsequently tested in vitro with a human fibroblast cell line, with results demonstrating successful nanoparticle transfer across the plasma membrane, with 5 nm particles achieving nuclear entry while larger 30 nm particles are retained in the cytoplasm, suggesting entry is blocked via nuclear pores dimensions.
04-13 Sequence Classification with Input-Output Hidden Markov Models

e present a training and testing method for Input-Output Hidden Markov Model that is particularly suited for classification of sequences in which class information accumulates over time. We discuss two such cases: the discrimination of mental tasks from sequences of EEG features, common in Brain Computer Interface research, and phoneme classification from sequences of acoustic features for speech recognition. The objective function is modified so that training focuses on the improvement of classification accuracy. For both tasks the algorithm performs significantly better than the alternative solution proposed in the literature, specifically designed for other types of sequences.
Non-Invasive Monitoring of Temporal and Spatial Blood Flow during Bone Graft Healing Using Diffuse Correlation Spectroscopy

Vascular infiltration and associated alterations in microvascular blood flow are critical for complete bone graft healing. Therefore, real-time, longitudinal measurement of blood flow has the potential to successfully predict graft healing outcomes. Herein, we non-invasively measure longitudinal blood flow changes in bone autografts and allografts using diffuse correlation spectroscopy in a murine femoral segmental defect model. Blood flow was measured at several positions proximal and distal to the graft site before implantation and every week post-implantation for a total of 9 weeks (autograft n = 7 and allograft n = 10). Measurements of the ipsilateral leg with the graft were compared with those of the intact contralateral control leg. Both autografts and allografts exhibited an initial increase in blood flow followed by a gradual return to baseline levels. Blood flow elevation lasted up to 2 weeks in autografts, but this duration varied from 2 to 6 weeks in allografts depending on the spatial location of the measurement. Intact contralateral control leg blood flow remained at baseline levels throughout the 9 weeks in the autograft group; however, in the allograft group, blood flow followed a similar trend to the graft leg. Blood flow difference between the graft and contralateral legs (ΔrBF), a parameter defined to estimate graft-specific changes, was elevated at 1–2 weeks for the autograft group, and at 2–4 weeks for the allograft group at the proximal and the central locations. However, distal to the graft, the allograft group exhibited significantly greater ΔrBF than the autograft group at 3 weeks post-surgery (p < 0.05). These spatial and temporal differences in blood flow supports established trends of delayed healing in allografts versus autografts.
Expression of inducible nitric oxide synthase and interleukin-12 in experimental necrotizing enterocolitis.

BACKGROUND Previous investigators have relied on administration of pro-inflammatory cytokines or invasive surgical procedures to reproduce the morphologic changes of necrotizing enterocolitis (NEC) in rats. However, these artificial insults do not mimic the human disease. We developed a reproducible model of NEC in rats that more closely resembles human NEC and determined the pattern of inflammatory cytokine expression in this model. MATERIALS AND METHODS Newborn rats were randomized into four groups. Groups 1 and 2 were breast-fed, while Groups 3 and 4 were gavaged with formula thrice daily. In addition, Groups 2 and 4 were subjected to 3 min of hypoxia thrice daily, prior to each feeding. The rats were killed on day 4 and the distal 2 cm of terminal ileum was harvested for morphological studies and analysis of inflammatory cytokine mRNA expression. RESULTS Nearly 70% of formula-fed neonatal rats displayed moderate or severe morphological abnormalities resembling human NEC. Breast-fed pups had normal histology. The terminal ileum from rats with abnormal histology demonstrated increased inducible nitric oxide synthase (iNOS) expression, decreased interleukin-12 (IL-12) mRNA expression, and enterocyte apoptosis. There was a trend toward upregulation of IFN-gamma mRNA, but no difference in expression of TNF-alpha mRNA. Hypoxia did not significantly alter intestinal morphology or mRNA expression. CONCLUSIONS Formula-fed neonatal rats, with or without hypoxia, exhibit morphological changes in the intestinal epithelium similar to those seen in patients with acute NEC. The mechanism likely involves upregulation of iNOS mRNA, enterocyte apoptosis, and decreased IL-12 production in the intestinal epithelium. This model may offer a simple reproducible method for inducing experimental NEC.
Boosting election prediction accuracy by crowd wisdom on social forums

Recent years, with the rapid development of the online social services, people are able to publish and share their opinions, activities, and feelings of their everyday lives as well as public issues on the Internet. By the increasingly large amount of data among online social services, researchers explore approaches to understand how human beings demonstrate their political tendency from the data. In this paper, we focus on boosting election prediction accuracy by leveraging the power of crowd wisdom among social media. In addition to previous proposed approaches, we conduct Chinese sentiment analysis and use peer-to-peer rating records among the social network to establish a better prediction model. To verify our proposal, we validate our methodology by the 2014 Taiwanese local elections. We use the data collected from the largest forum in Taiwan during the last three months election campaign. From the results, our proposal outperforms previous approaches in predicting the final vote winners of the elections as well as the final vote shares.
Syllable detection in read and spontaneous speech

Automatic syllable detection is an important task when analysing very large speech corpora in order to answer questions concerning prosody, rhythm, speech rate, speech recognition and synthesis. A new method for automatic detection of syllable nuclei is presented. Two large spoken language corpora (PhonDatII, Verbmobil) were labelled by three phoneticians and then used to adjust the key parameters of the algorithm and to evaluate its error rate. Additionally, parts of the corpora were used to test the inter- and intra-individual consistency of the transcribers. The evaluation of the algorithm currently shows an error rate of 12.87% for read speech and 21.03% for spontaneous speech. The inter-individual consistency of 95.8% might be considered as an upper limit for any automatic detection method.
A Potential Role of Crystallin in the Vitreous Bodies of Rats after Ischemia-reperfusion Injury

Purpose Ischemia-reperfusion injury (I/R injury) is known not only to induce hypoxic and oxidative stress, but also to cause retinal degeneration in rats. Crystallins, known to inhibit the formation of reactive oxygen species, reduce apoptotic cell death. Our goal was to clarify not only the role of I/R injury-mediated crystallins, but also to evaluate the correlation of these compounds to anti-inflammation in the vitreous body. Methods Twenty-four Sprague-Dawley rats were used in this study. We induced I/R injury by clamping the optic nerve for 30 minutes and then releasing it. The vitreous bodies were obtained from the experimental and control subjects 24, 48, and 72 hours after I/R injury. Two-dimensional electrophoresis was performed, and the targeted spots were further investigated using matrix-assisted laser desorption-ionization time-of-flight mass spectrometry, spectrophotometry, Western blotting, and histological examination. Results After I/R injury, 23 spots were identified as crystallins. The βB2 crystallins were transcriptionally and post-translationally regulated, whereas the αB crystallins were controlled by post-translational modifications in the vitreous bodies of the rats. The total amounts of αA and β crystallins (including isotypes of β crystalline) had increased 48 hours after injury. The phosphorylation of αB crystallin (at serine residues 19, 45, and 59) was significantly increased 48 hours later, whereas phosphorylation of ERK1/2 showed the greatest decrease. Conclusions During hypoxic and oxidation stress, our results suggest that phosphorylated αB crystalline inhibits RAS, resulting in the inactivation of ERK1/2. The phosphorylation of αB crystallin may be associated with the inflammatory suppression in the vitreous body via the I/R injury model system.
Risk in a Large Claims Insurance Market with Bipartite Graph Structure

We model business relationships exemplified for a (re)insurance market by a bipartite graph which determines the sharing of severe losses. Using Pareto-tailed claims and multivariate regular variation we obtain asymptotic results for the Value-at-Risk and the Conditional Tail Expectation. We show that the dependence on the network structure plays a fundamental role in their asymptotic behaviour. As is well-known, if the Pareto exponent is larger than 1, then for the individual agent (re-insurance company) diversification is beneficial, whereas when it is less than 1, concentration on a few objects is the better strategy. The situation changes, however, when systemic risk comes into play. The random network structure has a strong influence on diversification effects, which destroys this simple individual agent's diversification rule. It turns out that diversification is always beneficial from a macro-prudential point of view creating a conflicting situation between the incentives of individual agents and the interest of some superior entity to keep overall risk small. We explain the influence of the network structure on diversification effects in different network scenarios.
Formulas for intersection numbers in q-Hamiltonian reduced spaces

Jeffrey and Kirwan [19] gave expressions for intersection pairings on the reduced space μ(0)/G of a particular Hamiltonian G-space M in terms of iterated residues. The definition of quasi-Hamiltonian spaces was introduced in [1]. In [3] a localization formula for equivariant de Rham cohomology of a compact q-Hamiltonian G-space was proved. In this paper we prove a residue formula for intersection pairings of reduced spaces of certain quasi-Hamiltonian G-spaces, by constructing the corresponding Hamiltonian G-space. We show that in an important class of special cases our result agrees with that in [3]. In this article we rely heavily on the methods of [19]; for the more general class of compact Lie groups G treated in [3], we rely on results of Szenes and Brion-Vergne concerning diagonal bases. Date: April 16, 2008. 1 2 LISA JEFFREY AND JOON-HYEOK SONG