Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of G EANT 4 Monte Carlo Models with a Highly Granular Scintillator-Steel Hadron Calorimeter The CALICE Collaboration

Calorimeters with a high granularity are a fundamental req uirement of the Particle Flow paradigm. This paper focuses on the prototype of a hadro n calorimeter with analog readout, consisting of thirty-eight scintillator layers alternati ng with steel absorber planes. The scintillator plates are finely segmented into tiles individually read out via Silicon Photomultipliers. The presented results are based on data collected with pion beam s in the energy range from 8 GeV to 100 GeV. The fine segmentation of the sensitive layers and the high sampling frequency allow for an excellent reconstruction of the spatial development of h adronic showers. A comparison between data and Monte Carlo simulations is presented, concerning b oth the longitudinal and lateral development of hadronic showers and the global response of the cal orimeter. The performance of several GEANT4 physics lists with respect to these observables is evaluat ed.
Matrix-Vector Multiplication via Erasure Decoding

The problem of fast evaluation of a matrix-vector product over GF (2) is considered. The problem is reduced to erasure decoding of a linear error-correcting code. A large set of redundant parity check equations for this code is generated. The multiplication algorithm is derived by tracking the execution of the message-passing algorithm on the obtained set of parity check equations. The obtained algorithms outperform the classical ones.
Duchenne Muscular Dystrophy: From Diagnosis to Therapy

Duchenne muscular dystrophy (DMD) is an X-linked inherited neuromuscular disorder due to mutations in the dystrophin gene. It is characterized by progressive muscle weakness and wasting due to the absence of dystrophin protein that causes degeneration of skeletal and cardiac muscle. The molecular diagnostic of DMD involves a deletions/duplications analysis performed by quantitative technique such as microarray-based comparative genomic hybridization (array-CGH), Multiple Ligation Probe Assay MLPA. Since traditional methods for detection of point mutations and other sequence variants require high cost and are time consuming, especially for a large gene like dystrophin, the use of next-generation sequencing (NGS) has become a useful tool available for clinical diagnosis. The dystrophin gene is large and finely regulated in terms of tissue expression, and RNA processing and editing includes a variety of fine tuned processes. At present, there are no effective treatments and the steroids are the only fully approved drugs used in DMD therapy able to slow disease progression. In the last years, an increasing variety of strategies have been studied as a possible therapeutic approach aimed to restore dystrophin production and to preserve muscle mass, ameliorating the DMD phenotype. RNA is the most studied target for the development of clinical strategies and Antisense Oligonucleotides (AONs) are the most used molecules for RNA modulation. The identification of delivery system to enhance the efficacy and to reduce the toxicity of AON is the main purpose in this area and nanomaterials are a very promising model as DNA/RNA molecules vectors. Dystrophinopathies therefore represent a pivotal field of investigation, which has opened novel avenues in molecular biology, medical genetics and novel therapeutic options.
Energy Efficient Heterogeneous Cellular Networks

With the exponential increase in mobile internet traffic driven by a new generation of wireless devices, future cellular networks face a great challenge to meet this overwhelming demand of network capacity. At the same time, the demand for higher data rates and the ever-increasing number of wireless users led to rapid increases in power consumption and operating cost of cellular networks. One potential solution to address these issues is to overlay small cell networks with macrocell networks as a means to provide higher network capacity and better coverage. However, the dense and random deployment of small cells and their uncoordinated operation raise important questions about the energy efficiency implications of such multi-tier networks. Another technique to improve energy efficiency in cellular networks is to introduce active/sleep (on/off) modes in macrocell base stations. In this paper, we investigate the design and the associated tradeoffs of energy efficient cellular networks through the deployment of sleeping strategies and small cells. Using a stochastic geometry based model, we derive the success probability and energy efficiency in homogeneous macrocell (single-tier) and heterogeneous K-tier wireless networks under different sleeping policies. In addition, we formulate the power consumption minimization and energy efficiency maximization problems, and determine the optimal operating regimes for macrocell base stations. Numerical results confirm the effectiveness of switching off base stations in homogeneous macrocell networks. Nevertheless, the gains in terms of energy efficiency depend on the type of sleeping strategy used. In addition, the deployment of small cells generally leads to higher energy efficiency but this gain saturates as the density of small cells increases. In a nutshell, our proposed framework provides an essential understanding on the deployment of future green heterogeneous networks.
An Effective Approach to Verbose Queries Using a Limited Dependencies Language Model

Intuitively, any ‘bag of words’ approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distributions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document’s initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur’s search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.
Political Parties, Party Communication and New Information and Communication Technologies

The article provides a theoretical overview of how parties in modern democracies are using the Internet to perform a range of key functions, such as opinion formation, interest mediation and party organization. Drawing on the party goals’ literature and classic party typologies, the central argument of the article is that new information and communication technologies (ICTs) are utilized in different ways by different types of political parties. While some parties stress the downward dissemination of information via new ICTs, others emphasize their interactive and targeting possibilities. The findings of the articles included in this Special Issue are profiled and assessed for the extent to which they provide empirical support for the strategies outlined.
ENHANCED LENSING RATE BY CLUSTERING OF MASSIVE GALAXIES: NEWLY DISCOVERED SYSTEMS IN THE SLACS FIELDS

Galaxy-scale strong gravitational lens systems are useful for a variety of astrophysical applications. However, their use is limited by the relatively small samples of lenses known to date. It is thus important to develop efficient ways to discover new systems both in present and forthcoming data sets. For future large high-resolution imaging surveys we anticipate an ever-growing need for efficiency and for independence from spectroscopic data. In this paper, we exploit the clustering of massive galaxies to perform a high-efficiency imaging search for gravitational lenses. Our data set comprises 44 fields imaged by the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS), each of which is centered on a lens discovered by the Strong Lens ACS Survey (SLACS). We compare four different search methods: (1) automated detection with the HST Archive Galaxy-scale Gravitational Lens Survey (HAGGLeS) robot, (2) examining cutout images of bright galaxies (BGs) after subtraction of a smooth galaxy light distribution, (3) examining the unsubtracted BG cutouts, and (4) performing a full-frame visual inspection of the ACS images. We compute purity and completeness and consider investigator time for the four algorithms, using the main SLACS lenses as a testbed. The first and second algorithms perform the best. We present the four new lens systems discovered during this comprehensive search, as well as one other likely candidate. For each new lens we use the fundamental plane to estimate the lens velocity dispersion and predict, from the resulting lens geometry, the redshifts of the lensed sources. Two of these new systems are found in galaxy clusters, which include the SLACS lenses in the two respective fields. Overall we find that the enhanced lens abundance (30+24−8 lenses deg−2) is higher than expected for random fields (12+4−2 lenses deg−2 for the COSMOS survey). Additionally, we find that the gravitational lenses we detect are qualitatively different from those in the parent SLACS sample: this imaging survey is largely probing higher redshift, and lower mass, early-type galaxies.
Neuronal Growth Cone Retraction Relies on Proneurotrophin Receptor Signaling Through Rac

Whereas NGF promotes neurite outgrowth, the precursor form proNGF triggers neurite retraction. Promoting Neurite Retraction with ProNGF Nerve growth factor (NGF), which was identified because of its ability to stimulate neurite outgrowth, promotes neuronal survival and differentiation during development. Like other neurotrophins, NGF is synthesized through cleavage of a precursor form and, here, Deinhardt et al. show that this precursor form (proNGF) induced the acute collapse and retraction of growth cones in cultured hippocampal neurons. ProNGF bound to a complex of the p75 neurotrophin receptor (p75NTR, which also acts as a co-receptor for NGF and other neurotrophins) and SorCS2 to stimulate neurite collapse through two distinct mechanisms. It promoted dissociation of the guanine nucleotide exchange factor Trio from the p75NTR-SorCS2 complex, decreasing activity of the Rac guanosine triphosphatase, and, in parallel, stimulated the phosphorylation and inactivation of the actin-bundling protein fascin. Thus, whereas NGF promotes neurite outgrowth, proNGF signals through a distinct pathway to elicit neurite retraction. Growth of axons and dendrites is a dynamic process that involves guidance molecules, adhesion proteins, and neurotrophic factors. Although neurite extension is stimulated by the neurotrophin nerve growth factor (NGF), we found that the precursor of NGF, proNGF, induced acute collapse of growth cones of cultured hippocampal neurons. This retraction was initiated by an interaction between the p75 neurotrophin receptor (p75NTR) and the sortilin family member SorCS2 (sortilin-related VPS10 domain–containing receptor 2). Binding of proNGF to the p75NTR-SorCS2 complex induced growth cone retraction by initiating the dissociation of the guanine nucleotide exchange factor Trio from the p75NTR-SorCS2 complex, resulting in decreased Rac activity and, consequently, growth cone collapse. The actin-bundling protein fascin was also inactivated, contributing to the destabilization and collapse of actin filaments. These results identify a bifunctional signaling mechanism by which proNGF regulates actin dynamics to acutely modulate neuronal morphology.
District magnitude, electoral formula, and the number of parties

Duverger's propositions concerning the psychologicaland mechanical consequences of electoral rules have previously beenexamined mainly through the lens of district magnitude,comparing the properties of single-member district pluralityelections with those of multimember proportional representationelections. The empirical consequences of multi-member plurality(MMP) rules, on the other hand, have received scant attention.Theory suggests that the effect of district magnitude on the numberand concentration of parties will differ with regard to whether theallocation rules are plurality-based or proportional. I test thistheory by drawing on a uniquely large-sample dataset where districtmagnitude and electoral formula vary but the basic universe ofpolitical parties is held constant, applying regression analysis todata from several thousand Hungarian local bodies elected in 1994consisting of municipal councils, county councils, and mayors. Theresults indicate that omitting the variable of electoral formula hasthe potential to cause significant bias in estimates of Duvergerianconsequences of district magnitude. In addition, the analysis ofmulti-member plurality elections from the local election datasetreveals counter-intuitively that candidate and party entry mayincrease with district magnitude under MMP, suggesting importantdirections for future investigation of MMP rules.
Human centric object detection in highly crowded scenes

In this paper, we propose to detect human centric objects, including face, head shoulder, upper body, left body, right body and whole body, which can provide essential information to locate humans in highly crowed scenes. In the literature, the approaches to detect multi-class objects are either taking each class independently to learn and apply its classifier successively or taking all classes as a whole to learn individual classifier based on sharing features and to detect by step-by-step dividing. Different from these works, we consider two issues, one is the similarities and discriminations of different classes and the other is the semantic relations among them. Our main idea is to predict class labels quickly using a Salient Patch Model (SPM) first, and then do detection accurately using detectors of predicted classes in which a Semantic Relation Model (SRM) is proposed to capture relations among classes for efficient inferences. SPM and SRM are designed for these two issues respectively. Experiments on challenging real-world datasets demonstrate that our proposed approach can achieve significant performance improvements.
Subcubic Equivalences between Graph Centrality Problems, APSP, and Diameter

Measuring the importance of a node in a network is a major goal in the analysis of social networks, biological systems, transportation networks, and so forth. Different centrality measures have been proposed to capture the notion of node importance. For example, the center of a graph is a node that minimizes the maximum distance to any other node (the latter distance is the radius of the graph). The median of a graph is a node that minimizes the sum of the distances to all other nodes. Informally, the betweenness centrality of a node w measures the fraction of shortest paths that have w as an intermediate node. Finally, the reach centrality of a node w is the smallest distance r such that any s-t shortest path passing through w has either s or t in the ball of radius r around w. The fastest known algorithms to compute the center and the median of a graph and to compute the betweenness or reach centrality even of a single node take roughly cubic time in the number n of nodes in the input graph. It is open whether these problems admit truly subcubic algorithms, i.e., algorithms with running time Õ(n3-δ) for some constant δ > 0.1 We relate the complexity of the mentioned centrality problems to two classical problems for which no truly subcubic algorithm is known, namely All Pairs Shortest Paths (APSP) and Diameter. We show that Radius, Median, and Betweenness Centrality are equivalent under subcubic reductions to APSP, i.e., that a truly subcubic algorithm for any of these problems implies a truly subcubic algorithm for all of them. We then show that Reach Centrality is equivalent to Diameter under subcubic reductions. The same holds for the problem of approximating Betweenness Centrality within any finite factor. Thus, the latter two centrality problems could potentially be solved in truly subcubic time, even if APSP required essentially cubic time. On the positive side, our reductions for Reach Centrality imply an improved Õ(Mnω)-time algorithm for this problem in case of non-negative integer weights upper bounded by M, where ω is a fast matrix multiplication exponent.
DS-MMAC: dynamic schedule based MAC for mobile wireless sensor network

In this paper, we propose a medium access protocol for Mobile Wireless Sensor Network which uses dynamic scheduling of time slot for channel access. We have considered a mixed deployment of both mobile and static wireless sensor nodes. The static nodes, which act as cluster head, maintain a local schedule to service the mobile neighbors and also route data to the base station. The design goals of our MAC are energy efficiency, increased packet delivery and low control overhead. The request-reply mechanism for data transfer, used in our approach, plays a vital role in the handover process. Simulation results indicate that our approach performs better in terms of data rate, energy efficiency and control overhead when compared with Hybrid MAC protocol. Testbed results indicate that the proposed approach can be effectively used in WSN where topology change is frequent.
Developing a causal model of game addiction and stealing behavior among undergraduate students

The objectives of this study were to examine game addiction and stealing behavior, factors affecting game addiction, and factors affecting stealing behavior among undergraduate students. The samples of this study were 340 undergraduate students from 3 universities, Burapha University, Rajamangala University of Technology Tawan-ok, and Kasetsart University Sriracha Campus. The questionnaire was employed to collect the data. The collected data were analyzed using structural equation modeling technique. The results indicated that game addiction had a positive effect on stealing behavior. The most important factor affecting game addiction was friend influence followed by family relationship, and social environment respectively. Then, discussion and implication for related stakeholders were discussed.
Mouse Ability to Perceive Subjective Contours

In contrast to the previously held notion that mice have a weak visual system, it is now generally accepted that mice can perceive rather complicated figures in various contexts such as in cognitive experiments and in social settings. Here, we show that mice could even be capable of perceiving a visual illusion—subjective contours. This illusion requires the visual system to compensate for a lack of visual information in compressed 2D images on the retina. In this experiment, we trained mice to respond appropriately to a rectangle-shaped rewarded figure of specific orientation in a two-choice visual discrimination task with a touchscreen monitor. In Transfer Test 1, mice could discriminate illusory rectangle-shaped figures significantly as compared with a figure, which did not induce illusory figures. In Transfer Test 2, the choice rate of targets decreased with imperfect illusory figures, which produced weak perception of rotated or deficient inducers. Moreover, in Transfer Test 3, mice could not discriminate the low-resolution illusory figure, which also induced weak perception. These results demonstrated the possibility that mice might be useful for investigating fundamental properties of the neural visual system.
Dynamics of $(2+1)$-dimensional SOS surfaces above a wall: Slow mixing induced by entropic repulsion

We study the Glauber dynamics for the $(2+1)\mathrm{D}$ Solid-On-Solid model above a hard wall and below a far away ceiling, on an $L\times L$ box of $\mathbb{Z}^2$ with zero boundary conditions, at large inverse-temperature $\beta$. It was shown by Bricmont, El Mellouki and Fr\"{o}hlich [J. Stat. Phys. 42 (1986) 743-798] that the floor constraint induces an entropic repulsion effect which lifts the surface to an average height $H\asymp(1/\beta)\log L$. As an essential step in understanding the effect of entropic repulsion on the Glauber dynamics we determine the equilibrium height $H$ to within an additive constant: $H=(1/4\beta)\log L+O(1)$. We then show that starting from zero initial conditions the surface rises to its final height $H$ through a sequence of metastable transitions between consecutive levels. The time for a transition from height $h=aH$, $a\in(0,1)$, to height $h+1$ is roughly $\exp(cL^a)$ for some constant $c>0$. In particular, the mixing time of the dynamics is exponentially large in $L$, that is, $T_{\mathrm{MIX}}\geq e^{cL}$. We also provide the matching upper bound $T_{\mathrm{MIX}}\leq e^{c'L}$, requiring a challenging analysis of the statistics of height contours at low temperature and new coupling ideas and techniques. Finally, to emphasize the role of entropic repulsion we show that without a floor constraint at height zero the mixing time is no longer exponentially large in $L$.
Can the Content of Public News Be Used to Forecast Abnormal Stock Market Behaviour?

A popular theory of markets is that they are efficient: all available information is deemed to provide an accurate valuation of an asset at any time. In this paper, we consider how the content of market- related news articles contributes to such information. Specifically, we mine news articles for terms of interest, and quantify this degree of interest. We then incorporate this measure into traditional models for market index volatility with a view to forecasting whether the incidence of interesting news is correlated with a shock in the index, and thus if the information can be captured to value the underlying asset. We illustrate the methodology on stock market indices for the USA, the UK, and Australia.