Miracle-dz/UniFAR-FTUs huggingface dataset:
Buffer Management Mechanism Suitable for TCP Streaming in QoS-Aware IP Router

Video distributions using TCP (called "TCP streaming") like YouTube have been increased rapidly with thE improvement on the speed and capacity of the Internet. Thus, it is an important technical issue to guarantee QoS(Quality of Service) suitable for TCP streaming. However, it is difficult for the existing IP network to guarantee QoS of the TCP streaming. So far, in order to achieve the QoS enhancement per flow, we have proposed the class-based output buffer control mechanism in QoS-aware router by locally managing only active flows whose backlogged packets remain on the buffer. In this paper, we expand the function of this output buffer control mechanism so as to be suitable for TCP streaming. Specifically, we propose the mechanism which calculates transmission bandwidth and buffer volume preferentially available(called "basic volume") according to active flows' condition estimated by measuring transmitted packets per flow. Furthermore, through computer simulation, the effectiveness of our proposal is verified.
Cosette: An Automated Prover for SQL

Deciding query equivalence is an important problem in data management with many practical applications. Solving the problem, however, is not an easy task. While there has been a lot of work done in the database research community in reasoning about the semantic equivalence of SQL queries, prior work mainly focuses on theoretical limitations. In this paper, we present C OSETTE , a fully automated prover that can determine the equivalence of SQL queries. C OSETTE leverages recent advances in both automated constraint solving and interactive theorem proving, and returns a counterexample (in terms of input relations) if two queries are not equivalent, or a proof of equivalence otherwise. Although the problem of determining equivalence for arbitrary SQL queries is undecidable, our experiments show that C OSETTE can determine the equivalences of a wide range of queries that arise in practice, including conjunctive queries, correlated queries, queries with outer joins, and queries with aggregates. Using C OSETTE , we have also proved the validity of magic set rewrites, and conﬁrmed various real-world query rewrite errors, including the famous COUNT bug. We are unaware of any prior tool that can automatically determine the equivalences of a broad range of queries as C OSETTE , and believe that our tool represents a major step towards building provably-correct query optimizers for real-world database systems.
Application of a High-Density Oligonucleotide Microarray Approach To Study Bacterial Population Dynamics during Uranium Reduction and Reoxidation

ABSTRACT Reduction of soluble uranium U(VI) to less-soluble uranium U(IV) is a promising approach to minimize migration from contaminated aquifers. It is generally assumed that, under constant reducing conditions, U(IV) is stable and immobile; however, in a previous study, we documented reoxidation of U(IV) under continuous reducing conditions (Wan et al., Environ. Sci. Technol. 2005, 39:6162-6169). To determine if changes in microbial community composition were a factor in U(IV) reoxidation, we employed a high-density phylogenetic DNA microarray (16S microarray) containing 500,000 probes to monitor changes in bacterial populations during this remediation process. Comparison of the 16S microarray with clone libraries demonstrated successful detection and classification of most clone groups. Analysis of the most dynamic groups of 16S rRNA gene amplicons detected by the 16S microarray identified five clusters of bacterial subfamilies responding in a similar manner. This approach demonstrated that amplicons of known metal-reducing bacteria such as Geothrix fermentans (confirmed by quantitative PCR) and those within the Geobacteraceae were abundant during U(VI) reduction and did not decline during the U(IV) reoxidation phase. Significantly, it appears that the observed reoxidation of uranium under reducing conditions occurred despite elevated microbial activity and the consistent presence of metal-reducing bacteria. High-density phylogenetic microarrays constitute a powerful tool, enabling the detection and monitoring of a substantial portion of the microbial population in a routine, accurate, and reproducible manner.
OncoLnc: linking TCGA survival data to mRNAs, miRNAs, and lncRNAs

OncoLnc is a tool for interactively exploring survival correlations, and for downloading clinical data coupled to expression data for mRNAs, miRNAs, or long noncoding RNAs (lncRNAs). OncoLnc contains survival data for 8,647 patients from 21 cancer studies performed by The Cancer Genome Atlas (TCGA), along with RNA-SEQ expression for mRNAs and miRNAs from TCGA, and lncRNA expression from MiTranscriptome beta. Storing this data gives users the ability to separate patients by gene expression, and then create publication-quality Kaplan-Meier plots or download the data for further analyses. OncoLnc also stores precomputed survival analyses, allowing users to quickly explore survival correlations for up to 21 cancers in a single click. This resource allows researchers studying a specific gene to quickly investigate if it may have a role in cancer, and the supporting data allows researchers studying a specific cancer to identify the mRNAs, miRNAs, and lncRNAs most correlated with survival, and researchers looking for a novel lncRNA involved with cancer lists of potential candidates. OncoLnc is available at http://www.oncolnc. org. Subjects Bioinformatics, Computational Biology, Databases
Simultaneous Dual-Isotope SPECT Imaging Using Independent Component Analysis Based on Wavelet Transformation: Wavelet Based Dual-Isotope SPECT Imaging Using ICA Analysis

Simultaneous dual-isotope imaging from two different energy gamma rays such as Tc-99m (140 keV) and I-123 (159 keV) is an unusual yet useful technology. Its advantages are less acquisition time, the same physiological condition, and better spatial registration. However, the disadvantages of dual-isotope technique are the scatter and cross-talk effect because of overlapping between two nearby photopeaks. We proposed a new method to correct cross-talk from primary and scattered photons through wavelet transformation followed by independent component analysis with multiple energy windows (W_eICA). We simulated dual-isotope digital images with five, six, and seven energy windows using SimSET software to generate scattered noise. We compared those images between W_eICA and standalone eICA method. According to our results, we verified the performance of W_eICA in separating Tc-99m and I-123 energy spectra generated from the SimSET Monte Carlo simulation. W_eICA method not only decreased the number of energy windows required but also separated dual-isotope photopeaks correctly. The results have demonstrated that the W_eICA method improved the quantitative accuracy and might be an effective tool for simultaneous dual-isotope SPECT imaging. Keywords-simultaneous; dual-isotope; SPECT; ICA; wavelet
Randomization Does Not Help Much, Comparability Does

According to R.A. Fisher, randomization “relieves the experimenter from the anxiety of considering innumerable causes by which the data may be disturbed. ” Since, in particular, it is said to control for known and unknown nuisance factors that may considerably challenge the validity of a result, it has become very popular. This contribution challenges the received view. First, looking for quantitative support, we study a number of straightforward, mathematically simple models. They all demonstrate that the optimism surrounding randomization is questionable: In small to medium-sized samples, random allocation of units to treatments typically yields a considerable imbalance between the groups, i.e., confounding due to randomization is the rule rather than the exception. In the second part of this contribution, the reasoning is extended to a number of traditional arguments in favour of randomization. This discussion is rather non-technical, and sometimes touches on the rather fundamental Frequentist/Bayesian debate. However, the result of this analysis turns out to be quite similar: While the contribution of randomization remains doubtful, comparability contributes much to a compelling conclusion. Summing up, classical experimentation based on sound background theory and the systematic construction of exchangeable groups seems to be advisable.
Molecular and Cellular Pathobiology Long Noncoding RNA GAPLINC Regulates CD 44-Dependent Cell Invasiveness and Associates with Poor Prognosis of Gastric Cancer

It is increasingly evident that long noncoding RNAs (lncRNA) have causative roles in carcinogenesis. In this study, we report findings implicating a novel lncRNA in gastric cancer, termed GAPLINC (gastric adenocarcinoma predictive long intergenic noncoding RNA), based on the use of global microarray and in situ hybridization (ISH) analyses to identify aberrantly expressed lncRNA in human gastric cancer specimens. GAPLINC is a 924-bp-long lncRNA that is highly expressed in gastric cancer tissues. GAPLINC suppression and with gene expression profiling in gastric cancer cells revealed alterations in cell migration pathways, with CD44 expression the most highly correlated. Manipulating GAPLINC expression altered CD44 mRNA abundance and the effects of GAPLINC on cell migration and proliferation were neutralized by suppressing CD44 expression. Mechanistic investigations revealed that GAPLINC regulates CD44 as a molecular decoy for miR211-3p, a microRNA that targets both CD44 and GAPLINC. Tissue ISH analysis suggested that GAPLINC overexpression defines a subgroup of patients with gastric cancer with very poor survival. Taken together, our results identify a noncoding regulatory pathway for the CD44 oncogene, shedding new light on the basis for gastric cancer cell invasiveness. Cancer Res; 74(23); 6890–902. 2014 AACR.
Learning Causal Models of Relational Domains

Methods for discovering causal knowledge from observational data have been a persistent topic of AI research for several decades. Essentially all of this work focuses on knowledge representations for propositional domains. In this paper, we present several key algorithmic and theoretical innovations that extend causal discovery to relational domains. We provide strong evidence that effective learning of causal models is enhanced by relational representations. We present an algorithm, relational PC, that learns causal dependencies in a state-of-the-art relational representation, and we identify the key representational and algorithmic innovations that make the algorithm possible. Finally, we prove the algorithm's theoretical correctness and demonstrate its effectiveness on synthetic and real data sets.
Comparative Effects of Acupressure at Local and Distal Acupuncture Points on Pain Conditions and Autonomic Function in Females with Chronic Neck Pain

Acupressure on local and distal acupuncture points might result in sedation and relaxation, thereby reducing chronic neck pain. The aim was to investigate the effect of acupressure at local (LP) and distal acupuncture points (DP) in females with chronic neck pain. Thirty-three females were assigned to three groups: the control group did not receive any stimuli, the LP group received acupressure at local acupuncture points, GB 21, SI 14 and SI 15, and the DP group received acupressure at distal acupuncture points, LI 4, LI 10 and LI 11. Verbal rating scale (VRS), Neck Disability Index (NDI), State-Trait Anxiety Inventory (STAI), muscle hardness (MH), salivary alpha-amylase (sAA) activity, heart rate (HR), heart rate variability (HRV) values and satisfaction due to acupressure were assessed. VRS, NDI, STAI and MH values decreased after acupressure in the LP and the DP group. HR decreased and the power of high frequency (HF) component of HRV increased after acupressure in only the LP group. Although acupressure on not only the LP but also the DP significantly improved pain conditions, acupressure on only the LP affected the autonomic nervous system while acupuncture points per se have different physical effects according to location.
Generation of the whole-body motion for humanoid robots with the complete dynamics. (Génération du mouvement du corps entier pour les robots humanoïdes avec la dynamique complets)

This thesis aims at providing a solution to the problem of motion generation for humanoid robots. The proposed framework generates whole-body motion using the complete robot dynamics in the task space satisfying contact constraints. This approach is known as operational-space inverse-dynamics control. The specification of the movements is done through objectives in the task space, and the high redundancy of the system is handled with a prioritized stack of tasks where lower priority tasks are only achieved if they do not interfere with higher priority ones. To this end, a hierarchical quadratic program is used, with the advantage of being able to specify tasks as equalities or inequalities at any level of the hierarchy. Motions where the robot sits down in an armchair and climbs a ladder show the capability to handle multiple non-coplanar contacts. The generic motion generation framework is then applied to some case studies using HRP-2 and Romeo. Complex and human-like movements are achieved using human motion imitation where the acquired motion passes through a kinematic and then dynamic retargeting processes. To deal with the instantaneous nature of inverse dynamics, a walking pattern generator is used as an input for the stack of tasks which makes a local correction of the feet position based on the contact points allowing to walk on non-planar surfaces. Visual feedback is also introduced to aid in the walking process. Alternatively, for a fast balance recovery, the capture point is introduced in the framework as a task and it is controlled within a desired region of space. Also, motion generation is presented for CHIMP which is a robot that needs a particular treatment.
Beta mixture models and the application to image classification

Statistical pattern recognition is one of the most studied and applied approaches in the area of pattern recognition. Mixture modelling of densities is an efficient statistical pattern recognition method for continuous data. We propose a classifier based on the beta mixture models for strictly bounded and asymmetrically distributed data. Due to the property of the mixture modelling, the statistical dependence in a multi-dimensional variable is captured, even with the conditional independence assumption in each mixture component. A synthetic example and the USPS handwriting digit data was used to verify the effectiveness of this approach. Compared to the conventional Gaussian mixture models (GMM), the beta mixture models has a better performance on data which has strictly bounded value and asymmetric distribution. The performance of beta mixture models is about equivalent to that of GMM applied to data transformed via a strictly increasing link function.
Learning exploration strategies in model-based reinforcement learning

Reinforcement learning (RL) is a paradigm for learning sequential decision making tasks. However, typically the user must hand-tune exploration parameters for each different domain and/or algorithm that they are using. In this work, we present an algorithm called LEO for learning these exploration strategies on-line. This algorithm makes use of bandit-type algorithms to adaptively select exploration strategies based on the rewards received when following them. We show empirically that this method performs well across a set of five domains. In contrast, for a given algorithm, no set of parameters is best across all domains. Our results demonstrate that the LEO algorithm successfully learns the best exploration strategies on-line, increasing the received reward over static parameterizations of exploration and reducing the need for hand-tuning exploration parameters.
The Anti-Tumor Histone Deacetylase Inhibitor SAHA and the Natural Flavonoid Curcumin Exhibit Synergistic Neuroprotection against Amyloid-Beta Toxicity

With the trend of an increasing aged population worldwide, Alzheimer's disease (AD), an age-related neurodegenerative disorder, as one of the major causes of dementia in elderly people is of growing concern. Despite the many hard efforts attempted during the past several decades in trying to elucidate the pathological mechanisms underlying AD and putting forward potential therapeutic strategies, there is still a lack of effective treatments for AD. The efficacy of many potential therapeutic drugs for AD is of main concern in clinical practice. For example, large bodies of evidence show that the anti-tumor histone deacetylase (HDAC) inhibitor, suberoylanilidehydroxamic acid (SAHA), may be of benefit for the treatment of AD; however, its extensive inhibition of HDACs makes it a poor therapeutic. Moreover, the natural flavonoid, curcumin, may also have a potential therapeutic benefit against AD; however, it is plagued by low bioavailability. Therefore, the integrative effects of SAHA and curcumin were investigated as a protection against amyloid-beta neurotoxicity in vitro. We hypothesized that at low doses their synergistic effect would improve therapeutic selectivity, based on experiments that showed that at low concentrations SAHA and curcumin could provide comprehensive protection against Aβ25–35-induced neuronal damage in PC12 cells, strongly implying potent synergism. Furthermore, network analysis suggested that the possible mechanism underlying their synergistic action might be derived from restoration of the damaged functional link between Akt and the CBP/p300 pathway, which plays a crucial role in the pathological development of AD. Thus, our findings provided a feasible avenue for the application of a synergistic drug combination, SAHA and curcumin, in the treatment of AD.
Thalamic projections to areas 5a and 5b of the parietal cortex in the cat: a retrograde horseradish peroxidase study

The cytoarchitecture of areas 5a and 5b of the cat's parietal cortex was re-examined and the afferent connections from the thalamus were investigated using the horseradish peroxidase (HRP) retrograde transport technique. Single or multiple small injections of the enzyme were made in different points of these areas in the rostral sectors of the lateral and middle suprasylvian gyri. The cytoarchitecture of the cortical region affected by the injections was carefully assessed in each case, and the labeled neurons found in the thalamus were plotted on projection drawings of each histological section. A prominent projection to area 5a arises from the posterior (Po) and ventral lateral (VL) complexes; less substantial projections originate in the ventral anterior nucleus (VA), the lateral intermediate complex (LI), and the central lateral nucleus (CL). Projections to area 5b (and to the laterally adjacent area suprasylviana anterior) mainly arise from LI, the dorsal part of VL, and the caudodorsal part of VA and CL; a moderate projection was also found from Po, the pulvinar, and the lateral dorsal complex. The main conclusions of this study are as follows. The shape and extent of areas 5a and 5b show notable variations when only their projection on the convoluted cortical surface is considered; however, they are relatively constant when plotted on unfolded cortical maps. The thalamic neurons projecting to areas 5a and 5b are organized according to a loose topographic plan, particularly noticeable in Po, VL and LI. In general, the rostral portion of this cortex (5a) receives projections from more ventral regions of the thalamus (mainly Po and VL), whereas the caudal part (5b) has connections from more dorsal regions (mainly LI and VA-VL). Moreover, the medial portions of these areas receive projections from lateral and ventral parts of the thalamic nuclei, whereas more dorsal and medial sectors of the thalamus project to the lateral portions of areas 5a and 5b. When labeled thalamic cell populations resulting from cases with single injections in neighboring cortical loci were compared, no abrupt changes of labeling were observed; rather, we generally observed gradual transitions and overlaps, even across nuclear boundaries. When only layers I and II of the cortex received the HRP, the number of labeled neurons and the intensity of their labeling decreased, their location in the thalamus was more restricted, and the mean size of the labeled cells was significantly smaller than that of the neurons labeled in the same regions after deep HRP injections.
Assessment and management of respiratory function in patients with Duchenne muscular dystrophy: current and emerging options

Duchenne muscular dystrophy (DMD) is an X-linked myopathy resulting in progressive weakness and wasting of all the striated muscles including the respiratory muscles. The consequences are loss of ambulation before teen ages, cardiac involvement and breathing difficulties, the main cause of death. A cure for DMD is not currently available. In the last decades the survival of patients with DMD has improved because the natural history of the disease can be changed thanks to a more comprehensive therapeutic approach. This comprises interventions targeted to the manifestations and complications of the disease, particularly in the respiratory care. These include: 1) pharmacological intervention, namely corticosteroids and idebenone that significantly reduce the decline of spirometric parameters; 2) rehabilitative intervention, namely lung volume recruitment techniques that help prevent atelectasis and slows the rate of decline of pulmonary function; 3) scoliosis treatment, namely steroid therapy that is used to reduce muscle inflammation/degeneration and prolong ambulation in order to delay the onset of scoliosis, being an additional contribution to the restrictive lung pattern; 4) cough assisted devices that improve airway clearance thus reducing the risk of pulmonary infections; and 5) non-invasive mechanical ventilation that is essential to treat nocturnal hypoventilation, sleep disordered breathing, and ultimately respiratory failure. Without any intervention death occurs within the first 2 decades, however, thanks to this multidisciplinary therapeutic approach life expectancy of a newborn with DMD nowadays can be significantly prolonged up to his fourth decade. This review is aimed at providing state-of-the-art methods and techniques for the assessment and management of respiratory function in DMD patients.
Resource-Aware Task Scheduling

Dependency-aware task-based parallel programming models have proven to be successful for developing efficient application software for multicore-based computer architectures. The programming model is amenable to programmers, thereby supporting productivity, whereas hardware performance is achieved through a runtime system that dynamically schedules tasks onto cores in such a way that all dependencies are respected. However, even if the scheduling is completely successful with respect to load balancing, the scaling with the number of cores may be suboptimal due to resource contention. Here we consider the problem of scheduling tasks not only with respect to their interdependencies but also with respect to their usage of resources, such as memory and bandwidth. At the software level, this is achieved by user annotations of the task resource consumption. In the runtime system, the annotations are translated into scheduling constraints. Experimental results for different hardware, demonstrating performance gains both for model examples and real applications, are presented. Furthermore, we provide a set of tools to detect resource sensitivity and predict the performance improvements that can be achieved by resource-aware scheduling. These tools are solely based on parallel execution traces and require no instrumentation or modification of the application code.