Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial organization in cyclic Lotka-Volterra systems.

We study the evolution of a system of {ital N} interacting species which mimics the dynamics of a cyclic food chain. On a one-dimensional lattice with {ital N}{lt}5 species, spatial inhomogeneities develop spontaneously in initially homogeneous systems. The arising spatial patterns form a mosaic of single-species domains with algebraically growing average size, {l_angle}l({ital t}){r_angle}{approximately}{ital t}{sup {alpha}}, where {alpha}=3/4 (1/2) and 1/3 for {ital N}=3 with sequential (parallel) dynamics and {ital N}=4, respectively. The domain distribution also exhibits a self-similar spatial structure which is characterized by an additional length scale, {l_angle}{ital L}({ital t}){r_angle}{approximately}{ital t}{sup {beta}}, with {beta}=1 and 2/3 for {ital N}=3 and 4, respectively. For {ital N}{ge}5, the system quickly reaches a frozen state with noninteracting neighboring species. We investigate the time distribution of the number of mutations of a site using scaling arguments as well as an exact solution for {ital N}=3. Some relevant extensions are also analyzed. {copyright} {ital 1996 The American Physical Society. }
Estimation level fusion in multisensor environment

The integration and fusion of information, from a combination of different types of instruments (sensors), is often used in the design of control systems. Typical applications that can benefit the use of multiple sensors are industrial tasks, military command, mobile robot navigation, multi-target tracking, and aircraft navigation. In recent years, there has been growing interest to fuse multisensor data to increase the accuracy of estimation parameters and system states. This interest is motivated by the availability of different types of sensors having different spectrum characteristics. The observations, used in the estimation process, are assigned to a common target through association process. If it is decided that all local sensors observe the same target, then the next problem is how to combine (fuse) the corresponding local estimates? A new algorithm, for estimation level fusion, is proposed that uses optimal mean square combination of arbitrary number of local estimates. Local estimates are produced by applying Kalman filter on individual sensor measurements.
The iterative algorithm for multiple incoherently distributed sources

Most algorithms for multiple incoherently distributed sources are based on multimensional search of the objective function to find the central angle and angular spread, and the satisfactory performance is obtained only under high SNR or for spaced separately sources. A new iterative algorithm — the EM algorithm is proposed to obtain the accurate estimation for multiple incoherently distributed sources with Gaussian angular power density function. The optimization of the likelihood function for multiple distributed sources is simplified into multiple optimizations for single source and the performance of the EM algorithm is very close to the CRLB even for low SNR. Another advantage of the EM algorithm is that it is insensitive to overestimate the number of sources compared with other methods. Computer simulations verify that the EM algorithm is more effective than most existing algorithms.
Reliability-and capacity-based selection in distributed hash tables

In a color television image pick-up device the light received through an objective lens is divided to follow two discrete paths, a part of the light being supplied to a brilliance image pick-up and the other part of the light being supplied through a Magenta filter to a chromaticity image pick-up tube including a stripe filter formed on the inner surface of a face plate and consisting of alternate monochromatic portions of red or blue color and transparent portions, thus obtaining color television signals from the output from two image pick-up tubes.
Dynamic trading strategies with stochastic and nonlinear price impact

In practice, the trader with a large block of shares usually faces endogenous liquidity risk of price impact. So the shares are usually broken up and the trader will choose the optimal strategy to trade. In this paper, the model of price impact is expanded. Supposing price impact with stochastic and nonlinear, we established the model of the stochastic and nonlinear price impact. The results show that the trader liquidation speed is obviously confined and the speed is constant in almost whole time under stochastic quadratic price impact function. The parameters sensitivity of optimal strategy is also analyzed in the dissertation: in early days, the greater sigma and alpha are, the greater liquidation speed is. The liquidation position reduction is more close to linear with gamma, beta and thetas increasing. We also highlight that the optimal liquidation time should be investigated on internal and external conditions.
Error Detection Tools for Parallel Languages

Debugging and finding errors in parallel programs is a non-trivial task. It becomes even harder when computation units (threads or processes) communicate with one another using one-sided operations: the memory held in a computation unit’s memory can be accessed by another thread without any notification. In this paper we present a framework for detecting errors in distributed applications that communicate using onesided operations. We present a set of algorithms and methods for detecting such errors in the case of UPC programs.
Robust Convex Clustering Analysis

Clustering is an unsupervised learning approach that explores data and seeks groups of similar objects. Many classical clustering models such as k-means and DBSCAN are based on heuristics algorithms and suffer from local optimal solutions and numerical instability. Recently convex clustering has received increasing attentions, which leverages the sparsity inducing norms and enjoys many attractive theoretical properties. However, convex clustering is based on Euclidean distance and is thus not robust against outlier features. Since the outlier features are very common especially when dimensionality is high, the vulnerability has greatly limited the applicability of convex clustering to analyze many real-world datasets. In this paper, we address the challenge by proposing a novel robust convex clustering method that simultaneously performs convex clustering and identifies outlier features. Specifically, the proposed method learns to decompose the data matrix into a clustering structure component and a group sparse component that captures feature outliers. We develop a block coordinate descent algorithm which iteratively performs convex clustering after outliers features are identified and eliminated. We also propose an efficient algorithm for solving the convex clustering by exploiting the structures on its dual problem. Moreover, to further illustrate the statistical stability, we present the theoretical performance bound of the proposed clustering method. Empirical studies on synthetic data and real-world data demonstrate that the proposed robust convex clustering can detect feature outliers as well as improve cluster quality.
First-Person Experiments in Thinking

In psychology, thinking is typically studied in terms of a range of behavioral or physiological parameters, focusing, for instance, on the mental contents or the neuronal correlates of the thinking process proper. In the current article, by contrast, we seek to complement this approach with an exploration into the experiential or inner dimensions of thinking. These are subtle and elusive and hence easily escape a mode of inquiry that focuses on externally measurable outcomes. We illustrate how a sufficiently trained introspective approach can become a radar for facets of thinking that have found hardly any recognition in the literature so far. We consider this an important complement to third-person research because these introspective observations not only allow for new insights into the nature of thinking proper but also cast other psychological phenomena in a new light, for instance, attention and the self. We outline and discuss our findings and also present a roadmap for the reader interested in studying these phenomena in detail.
Gender Specific Differences in the Pros and Cons of Smoking among Current Smokers in Eastern Kentucky: Implications for Future Smoking Cessation Interventions

This study investigated gender differences in the perceived “pros” and “cons” of smoking using the constructs of decisional balance (DB) and stage of change from the Transtheoretical Model. The population distribution for stage of change among a population-based, cross-sectional survey of 155 current smokers over 40 years was: precontemplation (22.6%), contemplation (41.9%), preparation (35.5%). Results of stepwise regression models indicated significant gender differences in DB were in the preparation stage of change; scores on the DB measure increased 3.94 points (95% CI: 1.94, 5.93) for male smokers. Interventions targeting the “pros” and “cons” of smoking may need to be gender specific.
Clinical effects of lateral wedge arch support insoles in knee osteoarthritis

AbstractWe compared the short-term efficacy of rigid versus soft lateral wedge arch support (LWAS) insoles for patients with knee osteoarthritis (OA), as assessed using the International Classification of Functioning, Disability and Health (ICF) system, through a prospective, double-blind, randomized controlled trial.Participants who fulfilled the combined radiographic and clinical criteria for knee OA, as defined by the American College of Rheumatology, were randomly prescribed 1 pair of rigid or soft LWAS insoles. Body functions and structures were evaluated according to Kellgren–Lawrence scores, the Foot Posture Index, Hospital Anxiety and Depression Scale scores, the pain–pressure threshold, postural stability, dynamic balance, and fall risk; activities and participation were assessed according to 10-m fast speed walking, stair climbing and chair rising times, and Chronic Pain Grade questionnaire responses; and knee OA-related health status was evaluated using the Knee Injury and Osteoarthritis Outcome Score (KOOS). Hospital Anxiety and Depression Scale scores, the pain–pressure threshold, physical activity, balance, Chronic Pain Grade questionnaire responses, and the KOOS were recorded before treatment and at 1-, 2-, and 3-month follow-ups.We enrolled 90 participants, 70 women and 20 men, with mean ages of 60.6 ± 10.8 and 63.1 ± 10.8 years in the rigid and soft LWAS insole groups, respectively. Repeated-measures analysis of covariance revealed significant time × group effect improvements in pain (P = 0.008 for the KOOS), stair ascent time (P = 0.003), daily living function (P = 0.003 for the KOOS), sports and recreation function (P = 0.012 for the KOOS), and quality of life (P = 0.021 for the KOOS) in the soft LWAS insole group.Patients with knee OA who used soft LWAS insoles for a short term showed more significant improvement than did those who used rigid LWAS insoles in pain, physical activity, daily living function, sports and recreation function, and quality of life, which belong to the body functions and structures and the activities and participation components in the ICF scheme.
Analysis of a two-species stochastic competitive Lotka-Volterra system with dispersal

A two-species stochastic competitive Lotka-Volterra system with dispersal is studied in this paper. Firstly, we demonstrate that the diffused system has a globally sole and positive solution for any given positive initial conditions via applying ltd formula. Then, we discuss the stochastic final bounded ness of the solution. Finally, we obtain a sufficient condition for extinction.
Thermal effects on quantum communication through spin chains (7 pages)

We study the effect of thermal fluctuations in a recently proposed protocol for transmission of unknown quantum states through quantum spin chains. We develop a low-temperature expansion for general spin chains. We then apply this formalism to study exactly thermal effects on short spin chains of four spins. We show that optimal times for extraction of output states are almost independent of the temperature, which lowers only the fidelity of the channel. Moreover we show that thermal effects are smaller in the antiferromagnetic chains than the ferromagnetic ones.
Multivariate group-wise genetic analysis of white matter integrity using orientation distribution functions

Diffusion magnetic resonance imaging has become an important tool for comparing brain white matter fiber structure between groups of subjects. While voxel-wise statistical comparison are typically performed on scalar values derived from the diffusion tensors (DT ), several authors have advocated applying multivariate statistics to better exploit the information contained in the tensors, as they show significant improvements over their univariate counterparts. The DTs are good approximations to the fiber orientation in regions with no fiber crossings or partial volumed voxels; however, fiber crossings are ubiquitous in the brain, and the tensor approximation fails throughout a significant portion of the image. Consequently, here we treat this issue by analyzing the raw diffusion data directly and by building orientation distribution functions (ODF s), using the modified spherical harmonic decomposition from [7]. More precisely, we first perform linear and nonlinear registrations to transform these diffusion-weighted images to a common space, and a subsequent local rotation to reorient the diffusion signal and thus align it to the anatomy. We then build the ODF s. Our dataset consists of 25 identical and 25 fraternal twin pairs (100 healthy adult twins, age: 24.6± 1.8 SD years), for whom we aimed to study the genetic contribution to white matter integrity. A multivariate intraclass correlation value (ICC) is obtained from the coefficients of the spherical harmonics at each voxel, resulting in a map that represents similarity within twin pairs in each of the twin groups. From there, the heritability can be computed. These statistics obtained from the multivariate ICC are compared to those from a more commonly used scalar measure: the generalized fractional anisotropy (GFA). As shown here, accounting for directionality increases the signal detection.
Current Perspectives in Cardiac Laterality

The heart is the first organ to break symmetry in the developing embryo and onset of dextral looping is the first indication of this event. Looping is a complex process that progresses concomitantly to cardiac chamber differentiation and ultimately leads to the alignment of the cardiac regions in their final topology. Generation of cardiac asymmetry is crucial to ensuring proper form and consequent functionality of the heart, and therefore it is a highly regulated process. It has long been known that molecular left/right signals originate far before morphological asymmetry and therefore can direct it. The use of several animal models has led to the characterization of a complex regulatory network, which invariably converges on the Tgf-β signaling molecule Nodal and its downstream target, the homeobox transcription factor Pitx2. Here, we review current data on the cellular and molecular bases of cardiac looping and laterality, and discuss the contribution of Nodal and Pitx2 to these processes. A special emphasis will be given to the morphogenetic role of Pitx2 and to its modulation of transcriptional and functional properties, which have also linked laterality to atrial fibrillation.
Implementation of contextual clustering method for statistical parametric maps in functional magnetic resonance images

A contextual clustering procedure for Statistical Parametric Maps is calculated from time varying three- dimensional images. The algorithm can be used for the detection of neural activations from functional Magnetic Resonance Images. Ogawa et al. (1990) have discussed about the brain magnetic resonance imaging with contrast dependent on blood oxygenation concepts. Subsequently, the processing strategies for time-course data sets in functional magnetic resonance imaging of the human brain have been analyzed by Bandettini et al. (1993). By using the voxel by voxel testing technique, the neighborhood information is utilized and this is achieved by using a Markov random field prior concept and Iterated Conditional Modes algorithm. The simulation results and human functional magnetic resonance imaging experiments using visual stimulation demonstrate that a better sensitivity is achieved with a given specifications in comparison with the voxel-by- voxel thresholding technique.
Planning VANET infrastructures to improve safety awareness in curved roads

We analyze the effect of using a vehicular ad-hoc network (VANET) on accident avoidance. As shown in our analysis, a higher frequency of safety packets can prevent accidents, even for high speed vehicles and dense roads. To overcome connectivity problems in blind crossing situations, a genetic algorithm (GA) based method is presented for VANET infrastructure planning. The proposed approach tries to remove coverage sight holes in low sight distance cases in a traveling path in the road. In such places, drivers might not have enough sight for proper action and also environmental obstacles prevent direct communication between vehicles. Furthermore, curved roads affect mobility. Simulation results show that the density of vehicles is increased right before a curve and is decreased after that. Therefore, in this kind of road, a high frequency of packet generation may not act well in accident avoidance. The method proposed in this paper tries to cover such places considering the lowest safety distances according to traffic theory. For this, the road must be covered directly by infrastructure. Therefore, the problem is to find the best number and also positions of road side units. Using GA, the algorithm minimizes the summation of total uncovered and overlapped points in the roads which are covered by more than one antenna. Simulation on a real road map confirmed the capabilities of the proposed approach.
Evaluation of Iron Oxide Nanoparticles Toxicity on Liver Cells of BALB/c Rats

Background Because of their unique magnetic properties, Fe3O4 nanoparticles (Fe3O4-NPs) have extensive applications in various biomedical aspects. Investigation of the possible adverse aspects of these particles has lagged far behind their fast growing application. Objectives The current study aimed to evaluate the toxicity of Fe3O4-NPs in the liver of mice. Materials and Methods In the present clinical trial, 90 BALB/c mice were randomly divided in 15 groups. Five control groups were fed by usual water and food. Five placebo groups were gavaged with physiological serum in doses of 25, 50, 75, 150, and 300 micrograms per gram of body weight (μg/gr). Five experimental groups were gavaged with Fe3O4-NPs, in doses of 25, 50, 75, 150, and 300 μg/gr. This pattern was repeated every other day, for 3 days. Then, the levels of liver enzymes [alanine transaminase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (ALP)] were compared between these groups. The histological alterations of livers were examined, as well. For statistical analysis, Kruskal-Wallis and Mann-Whitney, with type I Bonferroni correction, as post-hoc, have been used. Results The administration of 150 and 300 μg/gr doses of Fe3O4-NPs were associated with significant elevation in liver enzymes, compared to controls (P < 0.0001). Furthermore, the histopathological effects were observed in the liver tissue of these groups. However, in groups treated with lower doses of Fe3O4-NPs, no significant adverse effect was observed. Conclusions Based on our results, the administration of Fe3O4-NPs causes dose dependent adverse effects on liver.
pads / t : A Language for Describing and Transforming Ad Hoc Data

pads/t is a declarative data description language paired with an error-aware transformation language. This pairing provides rich support for programming with ad hoc data sources. Such data is common in a wide range of domains including networking, financial analysis, biology, and physics. pads/t’s data description language is based on polymorphic, recursive, and dependent data types. From such descriptions, the compiler generates robust parsing and pretty printing code. The transformation language supports error-aware computation by automatically maintaining an association between data and a description of the errors in that data.
Adenosine-Mediated Presynaptic Modulation of Glutamatergic Transmission in the Laterodorsal Tegmentum

The laterodorsal tegmentum (LDT) neurons supply most of the cholinergic tone to the brainstem and diencephalon necessary for physiological arousal. It is known that application of adenosine in the LDT nucleus increases sleep in vivo (Portas et al., 1997) and directly inhibits LDT neurons in vitro by activating postsynaptic adenosine A1 receptors (Rainnie et al., 1994). However, adenosine effects on synaptic inputs to LDT neurons has not been previously reported. We found that both evoked glutamatergic EPSCs and GABAergic IPSCs were reduced by adenosine (50 μm). A presynaptic site of action for adenosine A1 receptors on glutamatergic afferents was suggested by the following: (1) adenosine did not affect exogenous glutamate-mediated current, (2) adenosine reduced glutamatergic miniature EPSC (mEPSC) frequency, without affecting the amplitude, and (3) inhibition of the evoked EPSC was mimicked by the A1 agonist N6-cyclohexyladenosine (100 nm) but not by the A2 agonist N6-[2-(3,5-dimethoxyphenyl)-2-(methylphenyl)-ethyl]-adenosine (10 nm). The A1 receptor antagonist 8-cyclopentyltheophylline (CPT; 200 nm) potentiated the evoked EPSCs, suggesting the presence of a tonic activation of presynaptic A1 receptors by endogenous adenosine. The adenosine kinase inhibitor, 5-iodotubercidin (10 μm), mimicked adenosine presynaptic and postsynaptic effects. These effects were antagonized by CPT or adenosine deaminase (0.8 IU/ml), suggesting mediation by increased extracellular endogenous adenosine. Together, these data suggest that the activity of LDT neurons is under inhibitory tone by endogenous adenosine through the activation of both presynaptic A1receptors on excitatory terminals and postsynaptic A1receptors. Furthermore, an alteration of adenosine kinase activity modifies the degree of this inhibitory tone.