Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive Parallel Algorithm for Computing Connected Components

We present an efficient distributed memory parallel algorithm for computing connected components in undirected graphs based on Shiloach-Vishkin’s PRAM approach. We discuss multiple optimization techniques that reduce communication volume as well as load-balance the algorithm. We also note that the efficiency of the parallel graph connectivity algorithm depends on the underlying graph topology. Particularly for short diameter graph components, we observe that parallel Breadth First Search (BFS) method offers better performance. However, running parallel BFS is not efficient for computing large diameter components or large number of small components. To address this challenge, we employ a heuristic that allows the algorithm to quickly predict the type of the network by computing the degree distribution and follow the optimal hybrid route. Using large graphs with diverse topologies from domains including metagenomics, web crawl, social graph and road networks, we show that our hybrid implementation is efficient and scalable for each of the graph types. Our approach achieves a runtime of 215 seconds using 32 K cores of Cray XC30 for a metagenomic graph with over 50 billion edges. When compared against the previous state-of-the-art method, we see performance improvements up to 24 <inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:href="aluru-ieq1-2672739.gif"/></alternatives></inline-formula>.
Bilinear time-frequency representations of signals: the shift-scale invariant class

The authors consider the class of bilinear time-frequency representations (BTFR's) that are invariant (or covariant) to time shifts, frequency shifts, and time-frequency scalings. This "shift-scale invariant" class is the intersection of the classical shift-invariant (Cohen) class and the recently defined affine class. The mathematical description of shift-scale invariant BTFR's is in terms of a 1-D kernel and is thus particularly simple. The paper concentrates on the time-frequency localization properties of shift-scale invariant BTFR's. Since any shift-scale invariant BTFR is a superposition of generalized Wigner distributions, the time-frequency localization of the family of generalized Wigner distributions is studied first. For those shift-scale invariant BTFR's that may be interpreted as smoothed versions of the Wigner distribution (e.g., the Choi-Williams distribution), an analysis in the Fourier transform domain shows interesting peculiarities regarding time-frequency concentration and interference geometry properties. >
HuSiDa—the human siRNA database: an open-access database for published functional siRNA sequences and technical details of efficient transfer into recipient cells

Small interfering RNAs (siRNAs) have become a standard tool in functional genomics. Once incorporated into the RNA-induced silencing complex (RISC), siRNAs mediate the specific recognition of corresponding target mRNAs and their cleavage. However, only a small fraction of randomly chosen siRNA sequences is able to induce efficient gene silencing. In common laboratory practice, successful RNA interference experiments typically require both, the labour and cost-intensive identification of an active siRNA sequence and the optimization of target cell line-specific procedures for optimal siRNA delivery. To optimize the design and performance of siRNA experiments, we have established the human siRNA database (HuSiDa). The database provides sequences of published functional siRNA molecules targeting human genes and important technical details of the corresponding gene silencing experiments, including the mode of siRNA generation, recipient cell lines, transfection reagents and procedures and direct links to published references (PubMed). The database can be accessed at http://www.human-siRNA-database.net. We used the siRNA sequence information stored in the database for scrutinizing published sequence selection parameters for efficient gene silencing.
IMGT/JunctionAnalysis: the first tool for the analysis of the immunoglobulin and T cell receptor complex V-J and V-D-J JUNCTIONs

MOTIVATION To create the enormous diversity of 10(12) immunoglobulins (IG) and T cell receptors (TR) per individual, very complex mechanisms occur at the DNA level: the combinatorial diversity results from the junction of the variable (V), diversity (D) and joining (J) genes; the N-diversity represents the addition at random of nucleotides not encoded in the genome; and somatic hypermutations occur in IG rearranged sequences. The accurate annotation of the junction between V, D, J genes in rearranged IG and TR sequences represents therefore a huge challenge by its uniqueness and complexity. We developed IMGT/JunctionAnalysis to analyse automatically in detail the IG and TR junctions, according to the IMGT Scientific chart rules, based on the IMGT-ONTOLOGY concepts. RESULTS IMGT/JunctionAnalysis is the first tool for the detailed analysis of the IG and TR complex V-J and V-D-J JUNCTION(s). It delimits, at the nucleotide level, the genes resulting from the combinatorial diversity. It identifies accurately the D genes in the junctions of IG heavy (IGH), TR beta (TRB) and delta (TRD) chains. It delimits the palindromic P-REGION(s) and the N-REGION(s) resulting from the N-diversity. It evaluates the number of somatic hypermutations for each gene, within the JUNCTION. IMGT/JunctionAnalysis is capable of analysing, in a single run, an unlimited number of junctions from the same species (currently human or mouse) and from the same locus. AVAILABILITY IMGT/JunctionAnalysis is available from the IMGT Home page at http://imgt.cines. fr
Tensions in teacher community: competing commitments in the teaching of US middle school physical education

In this article, tensions in teacher community arose when the school’s ‘rainy day’ policy was invoked in the middle of a class period, disturbing instruction on the athletic field and subsequently in the gymnasium. The narrative inquiry takes a multiperspectival stance towards competing commitments to educational policy, on one hand, and high quality instruction, on the other hand, through threading in viewpoints of six, diverse teachers comprising the physical education department in a US middle school. The narrative exemplar shows how the teachers and the administrator shifted from absolute fidelity to the school’s ‘rainy day’ policy to a reflective fidelity stance. The international significance of this research study is that it demonstrates why tensions in teacher community occur around commonplace matters in schools, the complexities and emotions that enter into decision-making situations and the ways tensions are addressed in pedagogical contexts, regardless of country of origin and subject matter focus.
Keyframe animation of articulated figures using autocollision-free interpolation

Keyframing is a popular method for animating articulated figures because it allows artistic expressiveness by providing control to the animator. The animator defines a movement by providing a set of poses. The motion is then obtained by simply interpolating between these keyframes, typically with cubic splines. The drawback of this process is that it requires significant effort from the animator. Defining a movement often requires a high level of detail to ensure that the interpolation curves induce the desired motion. In particular, the animator has to focus on avoiding collisions between the limbs of his or her articulated figure. The paper describes a new interpolation method producing autocollision-free paths. A first interpolation is computed using any classical inbetweening method. Autocollisions are then identified and corrected if necessary. At some collision times, autocollision-free sub-keyframes are automatically generated using geometric properties. They are then used for obtaining an autocollision-free interpolation. Experimental results using a human model show that the animator can reduce the level of detail needed for describing a movement and still get realistic results at interactive speeds.
Optimal Strategies for Risk-Sensitive Portfolio Optimization Problems for General Factor Models

We consider constructing optimal strategies for risk-sensitive portfolio optimization problems on an infinite time horizon for general factor models, where the mean returns and the volatilities of individual securities or asset categories are explicitly affected by economic factors. The factors are assumed to be general diffusion processes. In studying the ergodic type Bellman equations of the risk-sensitive portfolio optimization problems, we introduce some auxiliary classical stochastic control problems with the same Bellman equations as the original ones. We show that the optimal diffusion processes of the problem are ergodic and that under some condition related to integrability by the invariant measures of the diffusion processes we can construct optimal strategies for the original problems by using the solution of the Bellman equations.
Oligonucleotide Probes That Detect Quantitatively Significant Groups of Butyrate-Producing Bacteria in Human Feces

ABSTRACT 16S rRNA-targeted oligonucleotide probes were designed for butyrate-producing bacteria from human feces. Three new cluster-specific probes detected bacteria related to Roseburia intestinalis, Faecalibacterium prausnitzii, and Eubacterium hallii at mean populations of 2.3, 3.8, and 0.6%, respectively, in samples from 10 individuals. Additional species-level probes accounted for no more than 1%, with a mean of 7.7%, of the total human fecal microbiota identified as butyrate producers in this study. Bacteria related to E. hallii and the genera Roseburia and Faecalibacterium are therefore among the most abundant known butyrate-producing bacteria in human feces.
Delta-operator based consensus analysis of multi-agent networks with link failures

In this paper, a discrete-time multi-agent system is presented which is formulated in terms of the delta operator. The proposed multi-agent system can unify discrete-time and continuous-time multi-agent systems. In a multi-agent network, in practice, the communication among agents is acted upon by various factors. The communication network among faulty agents may cause link failures, which is modeled by randomly switching graphs. First, we show that the delta representation of discrete-time multi-agent system reaches consensus in mean (in probability and almost surely) if the expected graph is strongly connected. The results induce that the continuous-time multi-agent system with random networks can also reach consensus in the same sense. Second, the influence of faulty agents on consensus value is quantified under original network. By using matrix perturbation theory, the error bound is also presented in this paper. Finally, a simulation example is provided to demonstrate the effectiveness of our theoretical results.
LAV Based Robust State Estimation for Systems Measured by PMUs

The weighted least squares (WLS) estimator is commonly employed to solve the state estimation problem in today's power systems, which are primarily measured by SCADA measurements, including bus power injection, branch power flow and bus voltage magnitude measurements. Despite being widely used, WLS estimator remains to be non-robust, i.e., it fails in the presence of bad measurements. The so called least absolute value (LAV) estimator is more robust, but is not widely used due to its higher computational cost. LAV estimator has the desirable property of automatic bad-data rejection provided that the measurement set does not include any “leverage measurements”. There will be two important advantages to using LAV estimator when systems are observed by phasor measurements: 1) if the measurement set consists of only phasors, the leveraging effect of measurements can be easily eliminated by strategic scaling; 2) computational performance of LAV estimator will become competitive with that of WLS due to the linearity of the estimation problem for phasor measurements. This paper demonstrates these advantages and argues that the LAV estimator will be a statistically robust and computationally competitive estimator for those power systems that will be measured entirely by PMUs.
Community Service by Offenders: How Much Does the Community Benefit?

Community service orders enable convicted offenders to perform unpaid work of benefit to their local community. In Scotland, community service schemes are operated by the social work departments, who have responsibility for the provision of statutory social work services in the criminal justice system. The present article describes the experiences and views of 567 members of the public who had work, such as painting and decorating or gardening, carried out by offenders in community service work teams during 1989. The results of the survey indicated that such work was highly valued by the recipients who would, with few exceptions, be willing to make use of the service in the future. The importance of community service schemes providing work that both benefits the community and offers the offender a constructive experience is emphasized.
Grand tour and projection pursuit

Abstract The grand tour and projection pursuit are two methods for exploring multivariate data. We show how to combine them into a dynamic graphical tool for exploratory data analysis, called a projection pursuit guided tour. This tool assists in clustering data when clusters are oddly shaped and in finding general low-dimensional structure in high-dimensional, and in particular, sparse data. An example shows that the method, which is projection-based, can be quite powerful in situations that may cause grief for methods based on kernel smoothing. The projection pursuit guided tour is also useful for comparing and developing projection pursuit indexes and illustrating some types of asymptotic results.
Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX

Open Online Courses (MOOCs) are an increasingly pervasive newcomer to the virtual landscape of higher-education, delivering a wide variety of topics in science, engineering, and the humanities. However, while technological innovation is enabling unprecedented open access to high quality educational material, these systems generally inherit similar homework, exams, and instructional resources to that of their classroom counterparts and currently lack an underlying model with which to talk about learning. In this paper we will show how existing learner modeling techniques based on Bayesian Knowledge Tracing can be adapted to the inaugural course, 6.002x: circuit design, on the edX MOOC platform. We identify three distinct challenges to modeling MOOC data and provide predictive evaluations of the respective modeling approach to each challenge. The challenges identified are; lack of an explicit knowledge component model, allowance for unpenalized multiple problem attempts, and multiple pathways through the system that allow for learning influences outside of the current assessment.
Transformative social marketing: co‐creating the social marketing discipline and brand

Purpose – The purpose of this paper is to explore the field of social marketing. The field needs to evaluate what works, and more importantly for it to prosper and remain relevant, it must discover and incorporate concepts and techniques from other disciplines that are aligned around core ideas of people‐centered and socially oriented.Design/methodology/approach – The paper reviews new insights and understandings from modern social marketing practice, social innovation, design thinking and service design, social media, transformative consumer research, marketing theory and advertising practice and develops a model for transforming social marketing thought, research and practice.Findings – A three dimensional model is presented that includes: scope – co‐creation, conversations, communities and markets; design – honoring people, radiating value, engaging service and enhancing experiences; value space – dignity, hope, love and trust.Originality/value – The presentation weaves together a set of ideas from dif.
Revisiting the activity of two poly(vinyl chloride)- and polyethylene-degrading enzymes

Biocatalytic degradation of non-hydrolyzable plastics is a rapidly growing field of research, driven by the global accumulation of waste. Enzymes capable of cleaving the carbon-carbon bonds in synthetic polymers are highly sought-after as they may provide tools for environmentally friendly plastic recycling. Despite some reports of oxidative enzymes acting on non-hydrolyzable plastics, including polyethylene or poly(vinyl chloride), the notion that these materials are susceptible to efficient enzymatic degradation remains controversial, partly driven by a general lack of studies independently reproducing previous observations. We attempted to replicate two recent studies reporting that deconstruction of polyethylene and poly(vinyl chloride) can be achieved using an insect hexamerin from Galleria mellonella (so-called “Ceres”) or a bacterial catalase-peroxidase from Klebsiella sp. , respectively. Reproducing previously described experiments with the recombinant proteins, we did not observe any activity on plastics using multiple reaction conditions and multiple substrate types. Digging deeper into the discrepancies between the previous data and our observations, we show how and why the original experimental results may have been misinterpreted, leading to the erroneous claim that enzymatic deconstruction of polyethylene and poly(vinyl chloride) had occurred. Our results should lead to caution when interpreting the growing amount of literature claiming enzymatic degradation of non-hydrolyzable plastics.
Facilitated tucking as a non‐pharmacological intervention for neonatal pain relief: is it clinically feasible?

There is an impressive body of knowledge on pain management in infants hospitalized in neonatal intensive care units. However, deficits in the clinical management of pain in these infants remain. One reason is the gap between research evidence and translation of this knowledge into the clinical setting. This is particularly true for non‐pharmacological pain‐relieving methods. Effective performance of some of these methods requires additional staffing and time. This viewpoint articles describes the clinical challenges associated with implementing ‘facilitated tucking’. Although ‘facilitated tucking’ is described as an efficient method for acute pain relief, the clinical facilitators required to successfully implement such a resource consuming‐intervention remain unclear.
Three-phase Cascaded multilevel inverter using power cells with two inverter legs in series

In this paper the study of a modular three-phase multilevel inverter for application in electrical machines is proposed. Unlike the Cascaded H-bridge inverter, this topology uses power cells connected in cascade using two inverter legs in series. A detailed analysis of the structure and the development of an equation in the load voltage for n levels is carried out using PWM phase-shifted multicarrier modulation. The simulations and experimental results for a 15kW three-phase system, with nine voltage levels, validate the study presented.
Brain shape characterization from deformation

The characterization of shape in the brain is of great importance for understanding differences in structure and the relationship to function. Structural differences have been associated with, for example, age, sex, handedness, cognitive abilities and many neurologic and psychiatric conditions. Nonrigid registration methods enable the characterization of shape differences between images based on the transformation that relates them. Unlike methods which characterize shape in terms of geometric features computed from individual structures, transformation-based deformation description characterizes the entire space and therefore may better reflect the interrelationships between structures, as well as changes within and near structure. The transformation, as characterized by the local Jacobian, can yield an expressive description of local shape differences
Empathy and burnout in private practice Physical Therapists.

Resumo A empatia representa um papel de relevo na relacao entre o fisioterapeuta e os seus pacientes. Os fisioterapeutas estao sujeitos a um contexto de trabalho e carreira profissional particulares e expostos a exigencias elevadas, o que os torna mais susceptiveis ao burnout. A empatia e burnout podem representar polos opostos no contexto da relacao clinica, sofrendo influencias de genero, condicoes profissionais e estabelecerem entre si uma relacao circular, influenciando-se mutuamente.
Efficient Fully Homomorphic Encryption from (Standard) $\mathsf{LWE}$

A fully homomorphic encryption (FHE) scheme allows anyone to transform an encryption of a message, $m$, into an encryption of any (efficient) function of that message, $f(m)$, without knowing the secret key. We present a leveled FHE scheme that is based solely on the (standard) learning with errors ($\mathsf{LWE}$) assumption. (Leveled FHE schemes are initialized with a bound on the maximal evaluation depth. However, this restriction can be removed by assuming “weak circular security.'') Applying known results on $\mathsf{LWE}$, the security of our scheme is based on the worst-case hardness of “short vector problems” on arbitrary lattices. Our construction improves on previous works in two aspects: 1. We show that “somewhat homomorphic” encryption can be based on $\mathsf{LWE}$, using a new relinearization technique. In contrast, all previous schemes relied on complexity assumptions related to ideals in various rings. 2. We deviate from the “squashing paradigm” used in all previous works. We introduce a n.
Efficient Approximation of Correlated Sums on Data Streams

In many applications such as IP network management, data arrives in streams and queries over those streams need to be processed online using limited storage. Correlated-sum (CS) aggregates are a natural class of queries formed by composing basic aggregates on (x, y) pairs and are of the form SUM{g(y) : x /spl les/ f(AGG(x))}, where AGG(x) can be any basic aggregate and f(), g() are user-specified functions. CS-aggregates cannot be computed exactly in one pass through a data stream using limited storage; hence, we study the problem of computing approximate CS-aggregates. We guarantee a priori error bounds when AGG(x) can be computed in limited space (e.g., MIN, MAX, AVG), using two variants of Greenwald and Khanna's summary structure for the approximate computation of quantiles. Using real data sets, we experimentally demonstrate that an adaptation of the quantile summary structure uses much less space, and is significantly faster, than a more direct use of the quantile summary structure, for the same a posteriori error bounds. Finally, we prove that, when AGG(x) is a quantile (which cannot be computed over a data stream in limited space), the error of a CS-aggregate can be arbitrarily large.