Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding the optimal sequence of features selection based on reinforcement learning

This paper proposes a method for generating an optimal feature selecting sequence which is cost-effective for pattern classification. The sequence describes the order that feature selects for the process like classification. We model the procedure of feature selecting using Markov decision process (MDP), and use dynamic programming (DP) to learn a strategy to generate the orders only with the feedback of circumstance. To simplify the problem, we design a simple test scene that classifying three objects, whose values of synthetic features are generated randomly, into three classes. The results of experiments show that our method can reduce the computational time of extracting features.
Three Dimensional Discontinuous Galerkin Methods for Euler Equations on the Adaptive Consistent Meshes ∗

In the numerical simulation of three dimensional fluid dynamical equations, the huge computational quantity is a main challenge problem. Based on the three dimensional consistent unstructured tetrahedron meshes, we study the discontinuous Galerkin (DG) finite element method [1] combined with the adaptive mesh refinement (AMR) [2, 3] for solving Euler equations in this paper. That is according the equation solution variation to refine and coarsen grids so as to decrease total mesh number. The four space adaptive strategies are given and analyzed their advantages and disadvantages. Meanwhile, to overcome lower temporal computational efficiency in the explicit time discretization of AMR with a large number of refinement levels, we adopt the local time stepping (LTS) technique [4] and the arbitrary high order of accuracy (ADER) scheme [5] in time and space. The numerical examples show the validity of our methods.
Self-powered RNA nanomachine driven by metastable structure

Abstract Many non-coding and regulatory RNA elements have evolved to exploit transient or metastable structures that emerge during transcription to control complex folding pathways or to encode dynamic functions. However, efforts to engineer synthetic RNA devices have mostly focused on the thermodynamically stable structures. Consequently, significant challenges and opportunities exist in engineering functional RNAs that explicitly take advantage of cotranscriptionally generated transient or metastable structures. In this work, we designed a short RNA sequence that adopts a robust metastable structure when transcribed by an RNA polymerase. Although the metastable structure persists for hours at low temperature, it refolds almost completely into the thermodynamically stable structure upon heat denaturation followed by cooling. The synthetic RNA was also equipped with the Broccoli aptamer so that it can bind its ligand and become fluorescent only in the thermodynamically stable structure. We further demonstrated that the relaxation to the thermodynamically stable and fluorescent structure can be catalyzed by a short trigger RNA in a sequence-specific manner. Finally, the RNA architecture was redesigned to sense and respond to microRNA sequences. In summary, we designed RNA nanomachines that can detect an RNA sequence, amplify signal and produce an optical output, all encoded in a single RNA transcript, self-powered by a metastable structure.
Improved food sources in Artificial Bee Colony

Foraging behavior has inspired different algorithms to solve real-parameter optimization problems. One of the most popular algorithms within this class is the Artificial Bee Colony (ABC). In the present study the food source is initialized by comparing the food source with worst fitness and the evaluated mean of randomly generated food sources (population). Further the scout bee operator is modified to increase searching capabilities of the algorithm to sample solutions within the range of search defined by the current population. The proposed variant is called IFS-ABC and is tested on six unconstrained benchmark function. Further to test the efficiency of the proposed variant we implemented it on five constrained engineering optimization problems.
The Relationship Between Self-Esteem, Emotional Intelligence, and Empathy Among Students From Six Health Professional Programs

Abstract Theory: Psychosocial skills such as communication, empathy, and emotional intelligence are now considered key attributes of health professionals. Self-esteem is another important construct that is less well studied. Self-esteem is important because low levels have been linked to depression, suicide, and eating disorders. Given that health professional students experience high levels of stress and are at increased risk for similar psychopathology, self-esteem may be an important variable in student well-being and performance after graduation. Hypotheses: This study sought to explore self-esteem during students’ 1st year of training hypothesizing that several would demonstrate low self-esteem. It is also hypothesized that emotional intelligence and empathy would be associated with self-esteem. Method: A cross-sectional survey was conducted, and data were gathered from dental, medical, nursing, optometry, pharmacy and veterinary students. Self-report questionnaires assessing self-esteem, emotional intelligence, and empathy were completed and demographic information was collected. Scores were calculated and differences between groups analyzed with analysis of variance and chi-square testing. Pearson’s correlation was used to assess associations between the constructs. Results: The mean self-esteem score was 26.2 ± 2.3 but 21% of the sample evidenced low self-esteem. There was no difference in the proportion of students demonstrating low self-esteem among programs. Gender did not have a significant effect on self-esteem scores, though ethnicity did. Emotional intelligence scores were higher among male individuals than among female. Emotional intelligence and empathy showed a small association with self-esteem. Conclusions: A significant proportion of health professional students suffer from low self-esteem during their 1st year of study. Such students may be more susceptible to the stresses associated with study and the development of psychopathology. More research needs to be conducted to explore the relationships between self-esteem, emotional intelligence, and empathy with a view to strengthening training in these areas and managing the challenges faced by health professional students.
Prediabetes and Risk for Cardiac Death Among Patients With Coronary Artery Disease: The ARTEMIS Study

OBJECTIVE To compare cardiac mortality in patients with CAD and prediabetes with that in CAD patients with normal glycemic status and type 2 diabetes. RESEARCH DESIGN AND METHODS The Innovation to Reduce Cardiovascular Complications of Diabetes at the Intersection (ARTEMIS) study included patients with CAD after revascularization (79%), optimal medical therapy, or both. Patients had type 2 diabetes (n = 834), impaired glucose tolerance (IGT; n = 314), impaired fasting glucose (IFG; n = 103), or normal glycemic status (n = 697) as defined on the basis of the results of an oral glucose tolerance test. The primary end point was cardiac death. Major adverse cardiac event (MACE: cardiac death, heart failure, or acute coronary syndrome) and all-cause mortality were secondary end points. RESULTS During a mean ± SD follow-up of 6.3 ± 1.6 years, 101 cardiac deaths, 385 MACEs, and 208 deaths occurred. Patients with IGT tended to have 49% lower adjusted risk for cardiac death (P = 0.069), 32% lower adjusted risk for all-cause mortality (P = 0.076), and 36% lower adjusted risk for MACE (P = 0.011) than patients with type 2 diabetes. The patients with IFG had 82% lower adjusted risk for all-cause mortality (P = 0.015) than the patients with type 2 diabetes, whereas risks for cardiac death and MACE did not differ significantly between the two groups. The adjusted risks for cardiac death, MACE, and all-cause mortality among patients with IGT and IFG did not significantly differ from those risks among patients with normal glycemic status. CONCLUSIONS Cardiac mortality or incidence of MACE in patients with CAD with prediabetes (i.e., IGT or IFG after revascularization, optimal medical therapy, or both) does not differ from those values in patients with normal glycemic status.
The evolution of learning algorithms for artificial neural networks

In this paper we investigate a neural network model in which weights between computational nodes are modiied according to a local learning rule. To determine whether local learning rules are suucient for learning, we encode the network architectures and learning dynamics genetically and then apply selection pressure to evolve networks capable of learning the four boolean functions of one variable. The successful networks are analysed and we show how learning behaviour emerges as a distributed property of the entire network. Finally the utility of genetic algorithms as a tool of discovery is discussed.
A Separation Algorithm for Improved LP-Decoding of Linear Block Codes

Maximum likelihood (ML) decoding is the optimal decoding algorithm for arbitrary linear block codes and can be written as an integer programming (IP) problem. Feldman relaxed this IP problem and presented linear programming (LP) based decoding. In this paper, we propose a new separation algorithm to improve the error-correcting performance of LP decoding for binary linear block codes. We use an IP formulation with indicator variables that help in detecting the violated parity checks. We derive Gomory cuts from the IP and use them in our separation algorithm. An efficient method of finding cuts induced by redundant parity checks (RPC) is also proposed. Under certain circumstances we can guarantee that these RPC cuts are valid and cut off the fractional optimal solutions of LP decoding. It is demonstrated on three LDPC codes and two BCH codes that our separation algorithm performs significantly better than LP decoding and belief propagation (BP) decoding.
Crystallization of Polytetrafluoroethylene in a Wide Range of Cooling Rates: Nucleation and Diffusion in the Presence of Nanosilica Clusters

Polytetrafluoroethylene (PTFE) is a polymer that displays exceptional properties. This synthetic fluoropolymer is also known to crystallize very fast upon cooling. The present work highlights for the first time the influence of nanosilica clusters on PTFE crystallization at fast cooling rates (up to 5000 K·s−1). The silica was synthesized from aqueous silicate solution and the surface modification was performed using TriEthoxyFluoroSilane (TEFS). In order to understand the crystallization behavior of PTFE/silica nanocomposite at a fast cooling rate, the measurements were carried out by Fast Scanning Calorimetry (FSC). The data were consequently combined with the measurements performed by conventional Differential Scanning Calorimetry (DSC). Interestingly, the results displayed variation of the crystallization behavior for the nanocomposite at fast cooling rates compared to slow cooling rates. The differences in crystal morphologies were then observed by Scanning Electron Microscopy (SEM) after slow and fast cooling rates. Finally, the effective activation energies (Eα) obtained from the crystallization under various cooling rates were combined in order to obtain one set of Hoffman-Lauritzen parameters. This procedure allowed us to show that the crystallization of PTFE in the presence of silica is promoted or hampered according to the cooling rates employed.
The Plant Cell: Beginnings[OPEN]

The 1980s were an exciting and revolutionary time for biology, and plant molecular biology in particular. The pioneering experiments of Paul Berg, Stanley Cohen, Herbert Boyer, and collaborators ([Cohen, 2013][1]) invented the field of recombinant DNA and led to an explosion of studies that
Decision-Making and Biases in Causal-Explanatory Reasoning

Decisions often rely on judgments about the probabilities of various explanations. Recent research has uncovered a host of biases that afflict explanatory inference: Would these biases also translate into decision-making? We find that although people show biased inferences when making explanatory judgments in decision-relevant contexts (Exp. 1A), these biases are attenuated or eliminated when the choice context is highlighted by introducing an economic framing (price information; Exp. 1B–1D). However, biased inferences can be “locked in” to subsequent decisions when the judgment and decision are separated in time (Exp. 2). Together, these results suggest that decisions can be more rational than the corresponding judgments—leading to choices that are rational in the output of the decision process, yet irrational in their incoherence with judgments.
Correlated subgraph search for multiple query graphs in graph streams

In real-world, there are many dynamic graph databases. Correlation mining is one of important analysis method in these dynamic graph databases. CGStream has been proposed to discover correlated graphs efficiently in graph streams. However, CGStream suffers from long running time for multiple queries. CGStream must perform frequent subgraph mining n times for n queries to generate candidate patterns. Many of the candidate patterns are redundant because multiple queries can have the same candidates. In this paper, we propose an efficient framework, MCGStream that supports correlated subgraph search for multiple queries in graph streams. The proposed framework generates no redundant candidate pattern for multiple queries by performing frequent subgraph mining with the global lower frequency bound. We propose the method that determines the global lower frequency bound for multiple queries. We build a correlation candidate tree to maintain the candidate patterns and their mapping to queries. Several optimization techniques are applied to the correlation candidate tree to reduce the search space. Experiments show that the proposed method efficiently processes multiple queries compared with CGStream by up to 35%.
Alanine Aminotransferase Is Associated with an Adverse Nocturnal Blood Glucose Profile in Individuals with Normal Glucose Regulation

Objective Although the association between alanine aminotransferase (ALT) levels and risk of type 2 diabetes is well-studied, the effects of slightly increased ALT levels within the normal range on the temporal normal glucose profile remains poorly understood. Methods A total of 322 Chinese subjects without impaired glucose tolerance or previous diagnoses of diabetes were recruited for study from 10 hospitals in urban areas across China. All subjects wore a continuous glucose monitoring (CGM) system for three consecutive days. The diurnal (06∶00–20∶00) and nocturnal (20∶00–06∶00) mean blood glucose (MBG) levels were calculated. Subjects were stratified by ALT quartile level and correlation analyses were performed. Results The median ALT level was 17 IU/L, and subjects with ALT ≥17 IU/L had higher nocturnal MBG level than those with ALT <17 IU/L (P<0.05). Nocturnal MBG was positively correlated with ALT levels (Pearson correlation analysis: r = 0.187, P = 0.001), and the correlation remained significant after correction for the homeostatic model assessment of insulin resistance index (HOMA-IR) (r = 0.105, P = 0.041). No correlations were found between diurnal MBG and ALT, and nocturnal or diurnal MBG and aspartate aminotransferase or gamma-glutamyltransferase (all, P>0.05). Multivariate stepwise regression analysis of elevated nocturnal MBG identified increased HOMA-IR, elevated ALT levels, and decreased homeostatic model assessment of ß-cell function as independent factors (all, P<0.05). Conclusions Mildly elevated ALT levels, within the normal range, are associated with unfavorable nocturnal glucose profiles in Chinese subjects with normal glucose regulation.
Brownian Simulation of Many-Particle Binding to a Reversible Receptor Array

The principles and practice of a many-body Brownian dynamics algorithm of reversible binding to a static three-dimensional receptor array are presented. The mixed boundary conditions at the array are compared with single-particle direct propagation using a novel operator discretization. The many-body aspects are checked against previous one-dimensional Brownian simulations. The long-time behavior agrees with expected analytic solutions.
A Survey on LDA Approach in Predicting Link Behavior in Social Networks

Social network sites (SNS) in recent times are focusing mainly on user interactions. These SNS are attracting the attention of academic and industry researchers who are intrigued by their accordance and reach rapidly. Mainly data mining techniques have been very effective in using the content and graph structure that was available to solve various problems such as friendship link prediction, estimating the percentage of their friendship…etc. Topic models are one among the most effective approaches to discover latent topic analysis and text data mining. One desirable feature of a social network is to be capable to suggest potential friends to its existing users and the approach must be proved to be effective in improving the predictions. Topic modeling approach provides an easy way to analyze large volume of data and the topic modeling techniques like Latent Dirichlet Allocation (LDA) to uncover latent structure in user interests which have to be explored is going to be implemented. By using LDA, the users are predicting their friends and with the how much amount of percentage ratio they are becoming friends. In this review paper, it has been identified that LDA has a limitation of topic correlation modeling which can be overcome by using CTM (correlated topic model) and it can work better than Arm (Association Rule Mining) for list of 4 or more communities while the tagging can be effectively done when both LDA and association rules are used together.
COMPUTATIONAL PERFORMANCE OF CONTACT SEARCH DURING DEM SIMULATION OF HOPPER FILLING

The Discrete Element Method (DEM) recently presents numerical tool widely used in simulation of heterogeneous solids. The method captures dynamic behaviour of solid by considering motion and interaction of material particles. The most computationally expensive procedure of the DEM is search of contacts for particles, therewith each pair of colliding particles has to be identified. To investigate and accelerate computational performance of the DEM, computational performance study of two popular cell and sorting contact search algorithms was done in the present work. Both algorithms are implemented into DEMMAT code. Series of numerical tests was conducted for the above purposes by considering distributed filling of hopper. Mono-sized and poly-dispersed assemblies of elastic spherical particles were examined and computational performance during particle flow is illustrated.
Neutralizing Linguistically Problematic Annotations in Unsupervised Dependency Parsing Evaluation

Dependency parsing is a central NLP task. In this paper we show that the common evaluation for unsupervised dependency parsing is highly sensitive to problematic annotations. We show that for three leading unsupervised parsers (Klein and Manning, 2004; Cohen and Smith, 2009; Spitkovsky et al., 2010a), a small set of parameters can be found whose modification yields a significant improvement in standard evaluation measures. These parameters correspond to local cases where no linguistic consensus exists as to the proper gold annotation. Therefore, the standard evaluation does not provide a true indication of algorithm quality. We present a new measure, Neutral Edge Direction (NED), and show that it greatly reduces this undesired phenomenon.
Leaky gut: mechanisms, measurement and clinical implications in humans

The objectives of this review on ‘leaky gut’ for clinicians are to discuss the components of the intestinal barrier, the diverse measurements of intestinal permeability, their perturbation in non-inflammatory ‘stressed states’ and the impact of treatment with dietary factors. Information on ‘healthy’ or ‘leaky’ gut in the public domain requires confirmation before endorsing dietary exclusions, replacement with non-irritating foods (such as fermented foods) or use of supplements to repair the damage. The intestinal barrier includes surface mucus, epithelial layer and immune defences. Epithelial permeability results from increased paracellular transport, apoptosis or transcellular permeability. Barrier function can be tested in vivo using orally administered probe molecules or in vitro using mucosal biopsies from humans, exposing the colonic mucosa from rats or mice or cell layers to extracts of colonic mucosa or stool from human patients. Assessment of intestinal barrier requires measurements beyond the epithelial layer. ‘Stress’ disorders such as endurance exercise, non-steroidal anti-inflammatory drugs administration, pregnancy and surfactants (such as bile acids and dietary factors such as emulsifiers) increase permeability. Dietary factors can reverse intestinal leakiness and mucosal damage in the ‘stress’ disorders. Whereas inflammatory or ulcerating intestinal diseases result in leaky gut, no such disease can be cured by simply normalising intestinal barrier function. It is still unproven that restoring barrier function can ameliorate clinical manifestations in GI or systemic diseases. Clinicians should be aware of the potential of barrier dysfunction in GI diseases and of the barrier as a target for future therapy.
Continued Fraction Digit Averages and Maclaurin’s Inequalities

A classical result of Khinchin says that for almost all real numbers α, the geometric mean of the first n digits ai(α) in the continued fraction expansion of α converges to a number K ≈ 2.6854520… (Khinchin’s constant) as n → ∞. On the other hand, for almost all α, the arithmetic mean of the first n continued fraction digits ai(α) approaches infinity as n → ∞. There is a sequence of refinements of the AM-GM inequality, Maclaurin’s inequalities, relating the 1/kth powers of the kth elementary symmetric means of n numbers for 1 ≤ k ≤ n. On the left end (when k = n), we have the geometric mean, and on the right end (k = 1), we have the arithmetic mean. We analyze what happens to the means of continued fraction digits of a typical real number in the limit as one moves f(n) steps away from either extreme. We prove sufficient conditions on f(n) to ensure divergence when one moves f(n) steps away from the arithmetic mean and convergence when one moves f(n) steps away from the geometric mean. We show for almost all α and appropriate k as a function of n that S(α, n, k)1/k is of order log (n/k). For typical α, we find the limit for f(n) = cn, 0 < c < 1. We also study the limiting behavior of such means for quadratic irrational α, providing rigorous results, as well as numerically supported conjectures.
InGaAs HEMT with InAs-rich InAlAs barrier spacer for reduced source resistance

An InAlAs/InGaAs HEMT with an InAs-rich barrier spacer (In0.52Al0.48As) to reduce the parasitic resistance is reported. Devices were obtained with a source resistance of 170 Ω-μm. A 40 nm gate length In0.7Ga0. 3As HEMT with Lside=100 nm and tins=10 nm shows excellent transconductance and subthreshold characteristics including gm=1.6 mS/μm, DIBL=122 mV/V and S=80 mV/dec at VDS=0.5 V. In addition, this device exhibits an fT=530 GHz and fmax=445 GHz at VDS=0.7 V. These excellent characteristics mainly arise from a reduction in the source resistance through the use of the InAs-rich InAlAs spacer.