Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient scheduling for the downlink of CDMA cellular networks using base station selection diversity

Efficient packet scheduling in CDMA cellular networks is a challenging problem due to the time variant and stochastic nature of the channel fading process. Selection diversity is one of the most effective techniques utilizing random and independent variations of diverse channels to improve the performance of communication over fading channels. Exploiting base station selection diversity, in this paper, we propose two scheduling schemes for the downlink of CDMA cellular networks. The proposed schemes rely on the limited instantaneous Channel State Information to transmit to the best user from the best serving base station in each time slot. This technique increases the system throughput by increasing multi-user diversity gain and reducing the effective interference among adjacent base stations. Results of Monte Carlo simulations are given to demonstrate the improvement of system throughput using the proposed scheduling schemes. We also investigate the issue of fairness analysis of wireless scheduling schemes. Due to the unique characteristics of wireless scheduling schemes, the existing fairness indexes fail to provide a proper comparison among different scheduling schemes. We propose a new fairness index to compare the overall satisfaction of the network users among different wireless scheduling schemes. This approach complies with the definition of max-min fairness which is a widely accepted notion of fairness for data communication networks.
A Novel Compound, NK150460, Exhibits Selective Antitumor Activity against Breast Cancer Cell Lines through Activation of Aryl Hydrocarbon Receptor

Antiestrogen agents are commonly used to treat patients with estrogen receptor (ER)–positive breast cancer. Tamoxifen has been the mainstay of endocrine treatment for patients with early and advanced breast cancer for many years. Following tamoxifen treatment failure, however, there are still limited options for subsequent hormonal therapy. We discovered a novel compound, NK150460, that inhibits 17β-estradiol (E2)–dependent transcription without affecting binding of E2 to ER. Against our expectations, NK150460 inhibited growth of not only most ER-positive, but also some ER-negative breast cancer cell lines, while never inhibiting growth of non–breast cancer cell lines. Cell-based screening using a random shRNA library, identified aryl hydrocarbon receptor nuclear translocator (ARNT) as a key gene involved in NK150460′s antitumor mechanism. siRNAs against not only ARNT but also its counterpart aryl hydrocarbon receptor (AhR) and their target protein, CYP1A1, dramatically abrogated NK150460′s growth-inhibitory activity. This suggests that the molecular cascade of AhR/ARNT plays an essential role in NK150460′s antitumor mechanism. Expression of ERα was decreased by NK150460 treatment, and this was inhibited by an AhR antagonist. Unlike two other AhR agonists now undergoing clinical developmental stage, NK150460 did not induce histone H2AX phosphorylation or p53 expression, suggesting that it did not induce a DNA damage response in treated cells. Cell lines expressing epithelial markers were more sensitive to NK150460 than mesenchymal marker-expressing cells. These data indicate that NK150460 is a novel AhR agonist with selective antitumor activity against breast cancer cell lines, and its features differ from those of the other two AhR agonists. Mol Cancer Ther; 14(2); 343–54. ©2014 AACR.
Learning latent structure for activity recognition

We present a novel latent discriminative model for human activity recognition. Unlike the approaches that require conditional independence assumptions, our model is very flexible in encoding the full connectivity among observations, latent states, and activity states. The model is able to capture richer class of contextual information in both state-state and observation-state pairs. Although loops are present in the model, we can consider the graphical model as a linear-chain structure, where the exact inference is tractable. Thereby the model is very efficient in both inference and learning. The parameters of the graphical model are learned with the Structured-Support Vector Machine (Structured-SVM). A data-driven approach is used to initialize the latent variables, thereby no hand labeling for the latent states is required. Experimental results on the CAD-120 benchmark dataset show that our model outperforms the state-of-the-art approach by over 5% in both precision and recall, while our model is more efficient in computation.
Aligning Verb Senses in Two Italian Lexical Semantic Resources

This work describes the evaluations of three different approaches, Lexical Match, Sense Similarity based on Personalized Page Rank, and Semantic Match based on Shallow Frame Structures, for word sense alignment of verbs between two Italian lexical-semantic resources, MultiWordNet and the Senso Comune Lexicon. The results obtained are quite satisfying with a final F1 score of 0.47 when merging together Lexical Match and Sense Similarity.
In-cell NMR: a topical review

In-cell NMR has emerged in the past decade as a unique approach to obtain structural and functional information on biological macromolecules within living cells at atomic resolution. In this review, the major advances of in-cell NMR are discussed, with a special focus on recent developments and applications in eukaryotic and mammalian cells.
Equalization technique utilizing series-parallel connected supercapacitors for energy storage system

Series connection of supercapacitors (SCs) leads to cell voltage imbalance due to individual differences in cell properties. Cell voltage imbalance should be minimized to prolong cycle lives and maximize the available energies of SCs. In this paper, an equalization technique utilizing series-parallel connected SCs, which is considered to be suitable for energy storage systems using SCs instead of secondary batteries as main energy sources, is proposed. The proposed equalization circuit, which requires only SCs and switches as its main elements, utilizes SCs not only for energy storage but also for charge shuttling between adjacent SCs for equalization. The operating principle and experimental charge/discharge performance of the SCs using the proposed equalization technique are presented.
Grainy head controls apical membrane growth and tube elongation in response to Branchless/FGF signalling

Epithelial organogenesis involves concerted movements and growth of distinct subcellular compartments. We show that apical membrane enlargement is critical for lumenal elongation of the Drosophila airways, and is independently controlled by the transcription factor Grainy head. Apical membrane overgrowth in grainy head mutants generates branches that are too long and tortuous without affecting epithelial integrity, whereas Grainy head overexpression limits lumenal growth. The chemoattractant Branchless/FGF induces tube outgrowth, and we find that it upregulates Grainy head activity post-translationally, thereby controlling apical membrane expansion to attain its key role in branching. We favour a two-step model for FGF in branching: first, induction of cell movement and apical membrane growth, and second, activation of Grainy head to limit lumen elongation, ensuring that branches reach and attain their characteristic lengths.
Datamonkey: rapid detection of selective pressure on individual sites of codon alignments

UNLABELLED Datamonkey is a web interface to a suite of cutting edge maximum likelihood-based tools for identification of sites subject to positive or negative selection. The methods range from very fast data exploration to the some of the most complex models available in public domain software, and are implemented to run in parallel on a cluster of computers. AVAILABILITY http://www.datamonkey. org. In the future, we plan to expand the collection of available analytic tools, and provide a package for installation on other systems.
Bridging Social Circles: Need for Cognition, Prejudicial Judgments, and Personal Social Network Characteristics

Various factors pertaining to the social context (availability of plausible social contacts) as well as personality traits influence the emergence of social ties that ultimately compose one’s personal social network. We build on a situational selection model to argue that personality traits influence the cognitive processing of social cues that in turn influences the preference for particular social ties. More specifically, we use a cross-lagged design to test a mediation model explaining the effects of need for cognition (NFC) on egocentric network characteristics. We used the data available in the LISS panel, in which a probabilistic sample of Dutch participants were asked to fill in surveys annually. We tested our model on data collected in three successive years and our results show that people scoring high in NFC tend to revolve in information-rich egocentric networks, characterized by high demographic diversity, high interpersonal dissimilarity, and high average education. The results also show that the effect of NFC on social network characteristics is mediated by non-prejudicial judgments.
Challenges in Statistical Machine Learning

A surge of research in machine learning during the past decade has led to powerful learning methods that are successfully being applied to a wide range of application domains, from search engines to computational biology and robotics. These advances have in part been achieved by refining the art and engineering practice of machine learning, paralleled by a confluence of machine learning and statistics. But an understanding of the scientific foundations and fundamental limits to learning from data can also be effectively leveraged in practice. In this overview of recent work we present some of the current technical challenges in the field of machine learning, focusing on high dimensional data and minimax rates of convergence. These challenges include understanding the role of sparsity in statistical learning, semi-supervised learning, the tradeoff between computation and risk, and structured prediction problems.
Involvement of the Pyrilamine Transporter, a Putative Organic Cation Transporter, in Blood-Brain Barrier Transport of Oxycodone

The purpose of this study was to characterize blood-brain barrier (BBB) transport of oxycodone, a cationic opioid agonist, via the pyrilamine transporter, a putative organic cation transporter, using conditionally immortalized rat brain capillary endothelial cells (TR-BBB13). Oxycodone and [3H]pyrilamine were both transported into TR-BBB13 cells in a temperature- and concentration-dependent manner with Km values of 89 and 28 μM, respectively. The initial uptake of oxycodone was significantly enhanced by preloading with pyrilamine and vice versa. Furthermore, mutual uptake inhibition by oxycodone and pyrilamine suggests that a common mechanism is involved in their transport. Transport of both substrates was inhibited by type II cations (quinidine, verapamil, and amantadine), but not by classic organic cation transporter (OCT) substrates and/or inhibitors (tetraethylammonium, 1-methyl-4-phenylpyridinium, and corticosterone), substrates of OCTN1 (ergothioneine) and OCTN2 (l-carnitine), or organic anions. The transport was inhibited by metabolic inhibitors (rotenone and sodium azide) but was insensitive to extracellular sodium and membrane potential for both substrates. Furthermore, the transport of both substrates was increased at alkaline extracellular pH and decreased in the presence of a protonophore (carbonyl cyanide-p-trifluoromethoxyphenylhydrazone). Intracellular acidification induced with ammonium chloride enhanced the uptakes, suggesting that the transport is driven by an oppositely directed proton gradient. The brain uptake of oxycodone measured by in situ rat brain perfusion was increased in alkaline perfusate and was significantly inhibited by pyrilamine. These results suggest that blood-brain barrier transport of oxycodone is at least partly mediated by a common transporter with pyrilamine, and this transporter is an energy-dependent, proton-coupled antiporter.
Rough Set Semantics for Identity on the Web

Identity relations are at the foundation of many logic-based knowledge representations. We argue that the traditional notion of equality, is unsuited for many realistic knowledge representation settings. The classical interpretation of equality is too strong when the equality statements are re-used outside their original context. On the Semantic Web, equality statements are used to interlink multiple descriptions of the same object, using owl:sameAs assertions. And indeed, many practical uses of owl:sameAs are known to violate the formal Leibniz-style semantics. We provide a more flexible semantics to identity by assigning meaning to the subrelations of an identity relation in terms of the predicates that are used in a knowledge-base. Using those indiscernability-predicates, we define upper and lower approximations of equality in the style of rought-set theory, resulting in a quality-measure for identity relations.
The Design and Verification of the Rio File Cache

Today's file systems are limited in speed and reliability by memory's vulnerability to operating system crashes. Because memory is viewed as unsafe, systems periodically write modified file data back to disk. These extra disk writes lower system performance and the delay period before data is safe lowers reliability. The goal of the Rio (RAM I/O) file cache is to make ordinary main memory safe for persistent storage by enabling memory to survive operating system crashes. Reliable main memory enables the Rio file cache to be as reliable as a write-through file cache, where every write is safe instantly, and as fast as a pure write-back file cache, with no reliability-induced writes to disk. This paper describes the systematic, quantitative process we used to design and verify the Rio file cache on Intel PCs running FreeBSD and the reliability and performance of the resulting system.
Discrimination of normal and at-risk populations from fetal heart rate variability

Using clinically measured intrapartum cardiotocography (CTG) data, the objective of this study was to compare the discrimination of fetal heart rate variability in the low frequency (LF, 30-150 mHz) movement frequency (MF, 150-500 mHz) and high frequency (HF, 500-1000 mHz) bands for two at-risk groups: fetuses experiencing either neonatal depression or metabolic acidosis. These parameters had statistically significant differences when comparing normal fetus and these indexed groups.
Agreement among Teachers of Secondary Students in Using the Revised Behavior Problem Checklist to Identify Deviant Behavior

Behavior of 120 secondary school students grades 9 through 12 was rated by two groups of teachers using the Revised Behavior Problem Checklist. Pearson correlation coefficients indicated a low degree of relationship between raw score ratings provided by the two groups. Ratings were then recoded and placed into one of three categories: normal (recoded rating = 1), mildly deviant (recoded rating = 2), or highly deviant (recoded rating = 3). The degree of agreement between the two rating groups in identifying mildly deviant and highly deviant students was determined. Results indicated little agreement between the two groups. The significance of these results and their relationship to results obtained by previous investigators is discussed. Conclusions and recommendations are also provided.
Retrieving Occurrences of Grammatical Constructions

Finding authentic examples of grammatical constructions is central in constructionist approaches to linguistics, language processing, and second language learning. In this paper, we address this problem as an information retrieval (IR) task. To facilitate research in this area, we built a benchmark collection by annotating the occurrences of six constructions in a Swedish corpus. Furthermore, we implemented a simple and flexible retrieval system for finding construction occurrences, in which the user specifies a ranking function using lexical-semantic similarities (lexicon-based or distributional). The system was evaluated using standard IR metrics on the new benchmark, and we saw that lexical-semantical rerankers improve significantly over a purely surface-oriented system, but must be carefully tailored for each individual construction.
Olfactory bulb drives respiration‐coupled beta oscillations in the rat hippocampus

The synchronization of neuronal oscillations has been suggested as a mechanism to coordinate information flow between distant brain regions. In particular, the olfactory bulb (OB) and the hippocampus (HPC) have been shown to exhibit oscillations in the beta frequency range (10–20 Hz) that are likely to support communication between these structures. Here, we further characterize features of beta oscillations in OB and HPC of rats anesthetized with urethane. We find that beta oscillations simultaneously appear in HPC and OB and phase‐lock across structures. Moreover, Granger causality analysis reveals that OB beta activity drives HPC beta. The laminar voltage profile of beta in HPC shows the maximum amplitude in the dentate gyrus (DG), spatially coinciding with olfactory inputs to this region. Finally, we also find that the respiratory cycle and respiration‐coupled field potential rhythms (1–2 Hz)—but not theta oscillations (3–5 Hz)—modulate beta amplitude in OB and HPC. In all, our results support the hypothesis that beta activity mediates the communication between olfactory and hippocampal circuits in the rodent brain.
A Body-centric Design Space for Multi-surface Interaction

We introduce BodyScape , a body-centric design space that allows us to describe, classify and systematically compare multi-surface interaction techniques, both individually and in combination. BodyScape reﬂects the relationship be-tween users and their environment, speciﬁcally how different body parts enhance or restrict movement within particular interaction techniques and can be used to analyze existing techniques or suggest new ones. We illustrate the use of BodyScape by comparing two free-hand techniques, on-body touch and mid-air pointing , ﬁrst separately, then combined. We found that touching the torso is faster than touching the lower legs, since it affects the user’s balance; and touching targets on the dominant arm is slower than targets on the torso because the user must compensate for the applied force.
A new efficient mixture screening design for optimization of media

Screening ingredients for the optimization of media is an important first step to reduce the many potential ingredients down to the vital few components. In this study, we propose a new method of screening for mixture experiments called the centroid screening design. Comparison of the proposed design with Plackett‐Burman, fractional factorial, simplex lattice design, and modified mixture design shows that the centroid screening design is the most efficient of all the designs in terms of the small number of experimental runs needed and for detecting high‐order interaction among ingredients. © 2009 American Institute of Chemical Engineers Biotechnol. Prog. , 2009
Asiatic acid protects against hepatic ischemia/reperfusion injury by inactivation of Kupffer cells via PPARγ/NLRP3 inflammasome signaling pathway

Hepatic ischemia/reperfusion (I/R) contributes to major complications in clinical practice affecting perioperative morbidity and mortality. Recent evidence suggests the key role of nucleotide-binding oligomerization domain-like receptor (NLR) family pyrin domain-containing 3 (NLRP3) inflammaosme activation on the pathogenesis of I/R injury. Asiatic acid (AA) is a pentacyclic triterpene derivative presented with versatile activities, including antioxidant, anti-inflammation and hepatoprotective effects. This study was designed to determine whether AA had potential hepatoprotective benefits against hepatic I/R injury, as well as to unveil the underlying mechanisms involved in the putative effects. Mice subjected to warm hepatic I/R, and Kupffer cells (KCs) or RAW264.7 cells challenged with lipopolysaccharide (LPS)/H2O2, were pretreated with AA. Administration of AA significantly attenuated hepatic histopathological damage, global inflammatory level, apoptotic signaling level, as well as NLRP3 inflammasome activation. These effects were correlated with increased expression of peroxisome proliferator-activated receptor gamma (PPARγ). Conversely, pharmacological inhibition of PPARγ by GW9662 abolished the protective effects of AA on hepatic I/R injury and in turn aggravated NLRP3 inflammasome activation. Activation of NLRP3 inflammasome was most significant in nonparenchymal cells (NPCs). Depletion of KCs by gadolinium chloride (GdCl3) further attenuated the detrimental effects of GW9662 on hepatic I/R as well as NLRP3 activation. In vitro, AA concentration-dependently inhibited LPS/H2O2-induced NLRP3 inflammaosome activation in KCs and RAW264.7 cells. Either GW9662 or genetic knockdown of PPARγ abolished the AA-mediated inactivation of NLRP3 inflammasome. Mechanistically, AA attenuated I/R or LPS/H2O2-induced ROS production and phosphorylation level of JNK, p38 MAPK and IκBα but not ERK, a mechanism dependent on PPARγ. Finally, AA blocked the deleterious effects of LPS/H2O2-induced macrophage activation on hepatocyte viability in vitro, and improved survival in a lethal hepatic I/R injury model in vivo. Collectively, these data suggest that AA is effective in mitigating hepatic I/R injury through attenuation of KCs activation via PPARγ/NLRP3 inflammasome signaling pathway.
OptEx: A Deadline-Aware Cost Optimization Model for Spark

We present OptEx, a closed-form model of job execution on Apache Spark, a popular parallel processing engine. To the best of our knowledge, OptEx is the first work that analytically models job completion time on Spark. The model can be used to estimate the completion time of a given Spark job on a cloud, with respect to the size of the input dataset, the number of iterations, the number of nodes comprising the underlying cluster. Experimental results demonstrate that OptEx yields a mean relative error of 6% in estimating the job completion time. Furthermore, the model can be applied for estimating the cost optimal cluster composition for running a given Spark job on a cloud under a completion deadline specified in the SLO (i.e.,Service Level Objective). We show experimentally that OptEx is able to correctly estimate the cost optimal cluster composition for running a given Spark job under an SLO deadline with an accuracy of 98%.
Identification of speech source coupling between sensors in reverberant noisy environments

An important component of a multichannel hands-free communication system is the identification of the coupling between sensors in response to a desired speech signal. In this letter, a system identification approach adapted to speech signals is proposed. A weighted least-squares optimization criterion is introduced, which incorporates an indicator function for the presence of the desired signal in the observed signals. We show that compared to a competing nonstationarity-based method, a significantly smaller error variance is achievable.