Miracle-dz/UniFAR-FTUs huggingface dataset:
An exactly solvable phase transition model: generalized statistics and generalized Bose–Einstein condensation

In this paper, we present an exactly solvable phase transition model in which the phase transition is purely statistically derived. The phase transition in this model is a generalized Bose–Einstein condensation. The exact expression for the thermodynamic quantity, which can be used to simultaneously describe both the gas phase and the condensed phase, is solved with the help of the homogeneous Riemann–Hilbert problem, so one can judge whether there exists a phase transition and determine the phase transition point mathematically rigorously. A generalized statistics in which the maximum occupation numbers of different quantum states can take on different values is introduced, as a generalization of Bose–Einstein and Fermi–Dirac statistics.
Power Reduction Technique for Successive-Approximation Analog-to-Digital Converters

This paper addresses the problem of reducing power consumption in successive approximation ADC architecture as a building block of power-aware electronic devices. Two binary search control algorithms, intended to decrease the total ADC's consumed energy during the conversion cycle are proposed in this paper. Both algorithms take advantage of sensor input signal properties and dynamically reduce the number of conversion steps in order to save energy. Metrics for evaluating energy efficiency of the conversion process, called energy reduction factor, Fer is involved. Simulation results show that energy saving per conversion cycle of up to 25 % can be obtained.
Fractal analysis of a grocery store shopping path

The concept of a fractal has been applied to analyze the complex structure of various phenomena, including scale hierarchy in the natural sciences. In this study, we focus on the scale of customer shopping paths in a store and introduce fractal dimension as a quantified index to assess the complexity and nonlinearity of customer shopping paths. By applying box-counting analysis to the extracted shopping paths, we quantify the complexity of individual consumer behavior and delineate a relationship between customer movement and corresponding sales. We show that the average fractal dimension of the customer shopping path, which is related to the average stay-time in a store, is approximately 1.3. This is close to the fractal dimension of some natural phenomena (e.g., turbulence), including complex scale structures.
Understanding Small Family Business Succession in a Knowledge Management Perspective

Business succession in family firms has been one of the most investigated topics through theoretical, empirical and anecdotal studies (Brockhaus, 2004; Sharma, 2004; and Ward, 2004). However, we argue that this bulk of literature seems to neglect the role of the most intangible source of competitive advantage of firms: knowledge. At the same time, small family firms, although representing most part of the firms and facing higher risks of failure due to succession, did not receive much interest (Venter et al., 2005). The present study attempts to contribute to the literature shedding some lights on the business succession in small family firms informed by an Intellectual Capital (IC) perspective. The paper develops a theoretical model drawing from Intellectual Capital (IC) literature (Rastogi, 2003; and Mouritsen, 2004) and knowledge management concepts of knowledge repository (McGrath and Argote, 2000) and knowledge transfer (Szulansky, 2000). We assume that business succession is a process during which the intellectual capital evolves, with the risk of a slump loss due to the incumbent retirement, and so the knowledge flow needs to be managed. In particular, knowledge transfers through the interaction among the incumbent, the successor and the organization. This theoretical result illuminates how small family firms can maintain and increase their competitive advantage during the succession process, reducing the risk of failure.
Social network analysis of uncertain networks

Analysing networks (primarily social, but also computer networks or networks relating events to each other) is an important part of the intelligence analysis process. Currently, computer tools for network analysis lack functionality for adequately representing and reasoning with uncertain information. In this extended abstract, we outline some approaches to adding uncertainty-handling capabilities to network analysis.
Role of Mitochondrial RNA Polymerase in the Toxicity of Nucleotide Inhibitors of Hepatitis C Virus

ABSTRACT Toxicity has emerged during the clinical development of many but not all nucleotide inhibitors (NI) of hepatitis C virus (HCV). To better understand the mechanism for adverse events, clinically relevant HCV NI were characterized in biochemical and cellular assays, including assays of decreased viability in multiple cell lines and primary cells, interaction with human DNA and RNA polymerases, and inhibition of mitochondrial protein synthesis and respiration. NI that were incorporated by the mitochondrial RNA polymerase (PolRMT) inhibited mitochondrial protein synthesis and showed a corresponding decrease in mitochondrial oxygen consumption in cells. The nucleoside released by the prodrug balapiravir (R1626), 4′-azido cytidine, was a highly selective inhibitor of mitochondrial RNA transcription. The nucleotide prodrug of 2′-C-methyl guanosine, BMS-986094, showed a primary effect on mitochondrial function at submicromolar concentrations, followed by general cytotoxicity. In contrast, NI containing multiple ribose modifications, including the active forms of mericitabine and sofosbuvir, were poor substrates for PolRMT and did not show mitochondrial toxicity in cells. In general, these studies identified the prostate cell line PC-3 as more than an order of magnitude more sensitive to mitochondrial toxicity than the commonly used HepG2 cells. In conclusion, analogous to the role of mitochondrial DNA polymerase gamma in toxicity caused by some 2′-deoxynucleotide analogs, there is an association between HCV NI that interact with PolRMT and the observation of adverse events. More broadly applied, the sensitive methods for detecting mitochondrial toxicity described here may help in the identification of mitochondrial toxicity prior to clinical testing.
Swarm Robots: From Self-assembly to Locomotion

Inspired by the swarm behaviours of social insects, research into the self-assembly of swarm robots has become an attractive issue in the robotic community. Unfortunately, there are very few platforms for self-assembly and locomotion in the field of swarm robotics. The Sambot is a novel self-assembling modular robot that shares characteristics with swarm robots and self-reconfigurable robots. Each Sambot can move autonomously and connect with the other. This paper discusses the concept of combining self-assembly and locomotion for swarm robots. Distributed control algorithms for self-assembly and locomotion are proposed. Using five physical Sambots, experiments were carried out on autonomous docking, self-assembly and locomotion. Our control algorithm for self-assembly can also be used to realize the autonomous construction and self-repair of robotic structures consisting of a large number of Sambots.
Progressive transmission of appearance preserving octree-textures

The development of shape repositories and 3D databases rises the need of online visualization of 3D objects. The main issue with the remote visualization of large meshes is the transfer latency of the geometric information. The remote viewer requires the transfer of all polygons before allowing object's manipulation. To avoid this latency problem, an approach is to send several levels of details of the same object so that lighter versions can be displayed sooner and replaced with more detailed version later on. This strategy requires more bandwidth, implies abruptly changes in object aspect as the geometry refines as well as a non negligible precomputing time. Since the appearance of a 3D model is more influenced by its normal field than its geometry, we propose a framework in which the object's LOD is replaced with a single simplified mesh with a LOD of appearance. By using Appearance Preserving Octree-Textures (APO), this appearance LOD is encoded in a unique texture, and the details are progressively downloaded when they are needed. Our APO-based framework achieves a nearly immediate object rendering while details are transmitted and smoothly added to the texture. Scenes keep a low geometry complexity while being displayed at interactive framerate with a maximum of visual details, leading to a better visual quality over bandwith ratio than pure geometric LOD schemes. Our implementation is platform independent, as it uses JOGL and runs on a simple web browser. Furthermore the framework doesn't require processing on the server side during the client rendering.
MAMAC: A Multi-channel Asynchronous MAC Protocol for Wireless Sensor Networks

Optimal design of Medium Access Control (MAC) layer is one of the most important challenges in Wireless Sensor Networks (WSNs). Main problems in MAC layer of WSNs include sleep/wakeup scheduling and its overhead, collision and energy used for retransmission of collided frames. In this paper, an asynchronous and adaptive approach is proposed to design an energy-efficient multi-channel MAC protocol, named MAMAC, utilizing quorum system to extend network lifetime and increase network throughput. MAMAC is highly suitable for data collection applications. Simulations on the OPNET Modeler 14.0 manifest that the proposed protocol can prolong the network lifetime while increasing the network throughput.
DISSECTING THE GRAVITATIONAL LENS B1608+656. I. LENS POTENTIAL RECONSTRUCTION

Strong gravitational lensing is a powerful technique for probing galaxy mass distributions and for measuring cosmological parameters. Lens systems with extended source-intensity distributions are particularly useful for this purpose since they provide additional constraints on the lens potential (mass distribution). We present a pixelated approach to modeling the lens potential and source-intensity distribution simultaneously. The method makes iterative and perturbative corrections to an initial potential model. For systems with sources of sufficient extent such that the separate lensed images are connected by intensity measurements, the accuracy in the reconstructed potential is solely limited by the quality of the data. We apply this potential reconstruction technique to deep Hubble Space Telescope observations of B1608+656, a four-image gravitational lens system formed by a pair of interacting lens galaxies. We present a comprehensive Bayesian analysis of the system that takes into account the extended source-intensity distribution, dust extinction, and the interacting lens galaxies. Our approach allows us to compare various models of the components of the lens system, which include the point-spread function (PSF), dust, lens galaxy light, source-intensity distribution, and lens potential. Using optimal combinations of the PSF, dust, and lens galaxy light models, we successfully reconstruct both the lens potential and the extended source-intensity distribution of B1608+656. The resulting reconstruction can be used as the basis of a measurement of the Hubble constant. As an illustration of the astrophysical applications of our method, we use our reconstruction of the gravitational potential to study the relative distribution of mass and light in the lensing galaxies. We find that the mass-to-light ratio for the primary lens galaxy is (2.0 ± 0.2)h M☉ L−1B,☉ within the Einstein radius (3.9 h−1 kpc), in agreement with what is found for noninteracting lens galaxies at the same scales.
Discriminative Identification of Duplicates

The problem of finding duplicates in data is ubiquitous in data mining. We cast the problem of finding duplicates in sequential data into a poly-cut problem on a fully connected graph. The edge weights can be identified with parameterized pairwise similarities between objects that are optimized by structural support vector machines on labeled training sets. Our approach adapts the similarity measure to the data and is independent of the number of clusters. We present three large margin approximations of learning the pairwise similarities: an integrated QPformulation, a sequential multi-class approach and a pairwise classifier. We report on experimental results.
CTCP : A Cross-Layer Information Based TCP for MANET

Traditional TCP cannot detect link contention losses and route failure losses which occur in MANET and considers every packet loss as congestion. This results in severe degradation of TCP performance. In this research work, we modified the operations of TCP to adapt to network states. The cross-layer notifications are used for adapting the congestion window and achieving better performance. We propose Cross-layer information based Transmission Control Protocol (CTCP) which consists of four network states. Decelerate state to recover from contention losses, Cautionary state to deal with route failures, Congested state to handle network congestion and Normal state to be compatible with traditional TCP. Decelerate state makes TCP slow down if the packet loss is believed to be due to contention rather than congestion. Cautionary state suspends the TCP variables and after route reestablishment resumes with conservative values. Congestion state calls congestion control when network is actually congested and normal state works as standard TCP. Simulation results show that network state based CTCP is more appropriate for MANET than packet loss based traditional TCP.
Effects of Neurotoxic and Neuroprotective Agents on Peripheral Nerve Regeneration Assayed by Time-Lapse Imaging In Vivo

A direct histological assay of axonal regeneration would have many advantages over currently available behavioral, electrophysiological, and radiometric assays. We show that peripheral sensory axons marked with the yellow fluorescent protein in transgenic mice can be viewed transcutaneously in superficial nerves. Degenerating and regenerating axons can be followed in live animals with a dissecting microscope and then, after fixation, studied at high resolution by confocal microscopy. Using this approach, we document differences in regenerative ability after nerve transection, crush injury, and crush injury after a previous “conditioning” lesion. We also show that the chemotherapeutic drug vincristine rapidly but transiently blocks regeneration and that the immunosuppressive drug FK506 modestly enhances regeneration. Moreover, FK506 nearly restores normal regenerative ability in animals treated with submaximal doses of vincristine. Because neuropathy is the major dose-limiting side effect of vincristine, we propose that its efficacy could be enhanced by coadministration of FK506 analogs that are neuroactive but not immunosuppressive.
Exploration on continuous Gaussian process frontier maps

An information-driven autonomous robotic exploration method on a continuous representation of unknown environments is proposed in this paper. The approach conveniently handles sparse sensor measurements to build a continuous model of the environment that exploits structural dependencies without the need to resort to a fixed resolution grid map. A gradient field of occupancy probability distribution is regressed from sensor data as a Gaussian process providing frontier boundaries for further exploration. The resulting continuous global frontier surface completely describes unexplored regions and, inherently, provides an automatic stop criterion for a desired sensitivity. The performance of the proposed approach is evaluated through simulation results in the well-known Freiburg and Cave maps.
Barriers and responses to the disclosure of non-suicidal self-injury: a thematic analysis

ABSTRACT Non-suicidal self-injury (NSSI) is a prevalent behaviour among youth and young adults, yet little is known about their NSSI disclosure experiences. Disclosure of NSSI may have important implications for accessing treatment and eliciting support from family and friends; it may also highlight where efforts are needed to combat potential barriers (e.g. shame). This study sought to better understand the factors that facilitate and discourage NSSI disclosure in a sample of undergraduate students as well as gather a richer understanding of young adults’ experiences disclosing NSSI. To do this, a thematic analysis of interview transcripts with 17 students (16 women and 1 man) was conducted. Themes related to barriers to disclosure (shame, concern about others) and disclosure recipients’ responses (silence/avoidance, understanding) were explored. Results underscored the central role of shame in NSSI disclosures, both as an experience impacting the difficulty and likelihood of disclosure and as a potential consequence of receiving avoidant responses to disclosure. Among the clinical implications discussed is the import of initiatives to reduce NSSI stigma and foster supportive and understanding responses to NSSI disclosures.
Viscoelasticity of F-actin measured with magnetic microparticles

Dispersed submicroscopic magnetic particles were used to probe viscoelasticity for cytoplasm and purified components of cytoplasm. An externally applied magnetic field exerted force on particles in cells, in filamentous actin (F-actin) solutions, or in F-actin gels formed by the addition of the actin gelation factor, actin-binding protein (ABP). The particle response to magnetic torque can be related to the viscoelastic properties of the fluids. We compared data obtained on F- actin by the magnetic particle method with data obtained on F-actin by means of a sliding plane viscoelastometer. F-actin solutions had a significant elasticity, which increased by 20-fold when gels were formed by ABP addition. Both methods gave consistent results, but the dispersed magnetic particles indicated quantitatively greater rigidity than the viscoelastometer (two and six times greater for F-actin solutions and for F-actin plus ABP gels, respectively). These differences may be due to the fact that, compared with traditional microrheometers, dispersed particle measurements are less affected by long-range heterogeneity or domain-like structure. The magnetometric method was used to examine the mechanical properties of cytoplasm within intact macrophages; the application of the same magnetometric technique to both cells and well-defined, purified protein systems is a first step toward interpreting the results obtained for living cells in molecular terms. The magnetic particle probe system is an effective nonoptical technique for determining the motile and mechanical properties of cells in vitro and in vivo.
A Sentence Judgment System for Grammatical Error Detection

This study develops a sentence judgment system using both rule-based and n-gram statistical methods to detect grammatical errors in Chinese sentences. The rule-based method provides 142 rules developed by linguistic experts to identify potential rule violations in input sentences. The n-gram statistical method relies on the n-gram scores of both correct and incorrect training sentences to determine the correctness of the input sentences, providing learners with improved understanding of linguistic rules and n-gram frequencies.
Numerical Analysis of Granular Flows in a Silo Bed on Flow Regime Characterization

The flow characteristics of a gravity-driven dense granular flow in a granular bed with a contracted drainage orifice are studied by using discrete element method and quantitative analysis. Three values of discharging rates, ranging from fast to slow dense flows, are investigated. Time variations and derivatives of mean forces and velocities, as well as their respective correlations, are analyzed to quantitatively depict the characteristics of granular flow as well as flow regime categorization. The auto-correlation functions, as well as their Fourier spectrums, are utilized to characterize the differences between the mechanisms of slow and fast granular flows. Finally, it is suggested that the flow regimes of slow and fast flows can be characterized by the kinetic and kinematic flow properties of particles.
A genetic algorithm to attribute reduction with test cost constraint

In many machine learning applications, we need to pay test cost for each data item. Due to limited money and/or time, we also have a constraint on the total test cost. This issue have been recently formalized as the optimal sub-reduct with test cost constraint problem. An information gain based heuristic algorithm has been proposed to deal with it. In this paper, we propose a genetic algorithm which takes advantages of both the test cost information and the search potential of GA. Experimental results on four UCI datasets indicate that the new algorithm generally produces better results than the existing one.
How Persuasive Are Health Advertisements in First-Person Shooter Games? Exploring Knowledge-Activation and Thought-Disruption Mechanisms

This study examined whether persuasive health messages embedded in shooter games have broad or targeted effects on players’ willingness to engage in risk behaviors (N = 145). Participants presented with in-game health messages discouraging alcohol-impaired driving of motor vehicles showed reduced willingness to drink alcohol and to operate both motor and nonmotor vehicles, compared to those in a no-message gaming control condition. There were no spillover effects on willingness to smoke cigarettes or marijuana, thus implying targeted persuasive effects. In addition, players experiencing high instead of low cognitive load showed decreased postexperiment willingness to drink and operate nonmotor vehicles, thus suggesting that playing a game under high cognitive load can influence players’ attitudes. The findings replicate previous research and further expand on knowledge-activation and thought-disruption mechanisms underlying the persuasiveness of health messages.
Iterative Methods for Finding a Trust-region Step

We consider methods for large-scale unconstrained minimization based on finding an approximate minimizer of a quadratic function subject to a two-norm trust-region inequality constraint. The Steihaug-Toint method uses the conjugate-gradient algorithm to minimize the quadratic over a sequence of expanding subspaces until the iterates either converge to an interior point or cross the constraint boundary. The benefit of this approach is that an approximate solution may be obtained with minimal work and storage. However, the method does not allow the accuracy of a constrained solution to be specified. We propose an extension of the Steihaug-Toint method that allows a solution to be calculated to any prescribed accuracy. If the Steihaug-Toint point lies on the boundary, the constrained problem is solved on a sequence of evolving low-dimensional subspaces. Each subspace includes an accelerator direction obtained from a regularized Newton method applied to the constrained problem. A crucial property of this direction is that it can be computed by applying the conjugate-gradient method to a positive-definite system in both the primal and dual variables of the constrained problem. The method includes a parameter that allows the user to take advantage of the tradeoff between the overall number of function evaluations and matrix-vector products associated with the underlying trust-region method. At one extreme, a low-accuracy solution is obtained that is comparable to the Steihaug-Toint point. At the other extreme, a high-accuracy solution can be specified that minimizes the overall number of function evaluations at the expense of more matrix-vector products.