Miracle-dz/UniFAR-FTUs huggingface dataset:
A Texas Hold’em decision model based on Reinforcement Learning

Texas Hold ’em is a typical example of computer incomplete information game. The traditional machine learning method has been unable to deal with the huge search state space of Texas Hold ’em. In this paper, the value-based reinforcement learning algorithm is adopted, which can deal with the huge amount of data without manual extraction of data features, and can realize the unsupervised training of the model. However, the value based reinforcement learning algorithm has the problems of too complex acquisition process and over estimation of DQN algorithm. Therefore, this paper introduces DQN-S model, which combines reinforcement learning algorithm with Monte Carlo game search, and integrates Sarsa algorithm to solve the above problems to a certain extent. Finally, experimental data show that the over estimation effect of DQN algorithm is weakened to a certain extent after the Sarsa algorithm is incorporated. The average return of each game of DQN-S model and model is more than 5 chips, and the average return of each game of DQN-S model is more than 3 chips in the game with its strongest training version.
Advancing translational research with next‐generation protein microarrays

Protein microarrays are a high‐throughput technology used increasingly in translational research, seeking to apply basic science findings to enhance human health. In addition to assessing protein levels, posttranslational modifications, and signaling pathways in patient samples, protein microarrays have aided in the identification of potential protein biomarkers of disease and infection. In this perspective, the different types of full‐length protein microarrays that are used in translational research are reviewed. Specific studies employing these microarrays are presented to highlight their potential in finding solutions to real clinical problems. Finally, the criteria that should be considered when developing next‐generation protein microarrays are provided.
The cache complexity of multithreaded cache oblivious algorithms

We present a technique for analyzing the number of cache misses incurred by multithreaded cache oblivious algorithms on an idealized parallel machine in which each processor has a private cache. We specialize this technique to computations executed by the Cilk work-stealing scheduler on a machine with dag-consistent shared memory. We show that a multithreaded cache oblivious matrix multiplication incurs O(n3/√Z + (Pn)1/3n2) cache misses when executed by the Cilk scheduler on a machine with P processors, each with a cache of size Z, with high probability. This bound is tighter than previously published bounds. We also present a new multithreaded cache oblivious algorithm for 1D stencil computations, which incurs O(n2/Z+n+√Pn3+e) cache misses with high probability.
Maghreb Rural-Urban Migration: The Movement to Morocco’s Towns

Using data from national-level households survey, this study seeks to fill a gap in the migration literature by analyzing the determinant of rural migration in the case of Morocco. In contrast to the evidence in the international migration literature, our results support the negative selection into internal migration. Specifically, the probability of being a household without migrants decreases with educational level of the household and asset holdings compared to non-migrant households. This suggests that rural migration can benefit home communities and family members left behind by increasing household income and thus easing liquidity constraints, thereby promoting investment in physical and human capital. These results are in line with that related to the propensity to remit and the amount remitted that seemingly decrease with the individual level of education of household.
A General Class of Bayesian Survival Models with Zero and Nonzero Cure Fractions

Summary We propose a new class of survival models which naturally links a family of proper and improper population survival functions. The models resulting in improper survival functions are often referred to as cure rate models. This class of regression models is formulated through the Box–Cox transformation on the population hazard function and a proper density function. By adding an extra transformation parameter into the cure rate model, we are able to generate models with a zero cure rate, thus leading to a proper population survival function. A graphical illustration of the behavior and the influence of the transformation parameter on the regression model is provided. We consider a Bayesian approach which is motivated by the complexity of the model. Prior specification needs to accommodate parameter constraints due to the nonnegativity of the survival function. Moreover, the likelihood function involves a complicated integral on the survival function, which may not have an analytical closed form, and thus makes the implementation of Gibbs sampling more difficult. We propose an efficient Markov chain Monte Carlo computational scheme based on Gaussian quadrature. The proposed method is illustrated with an example involving a melanoma clinical trial.
CDS Momentum: Slow Moving Credit Ratings and Cross-Market Spillovers

Using 5-year credit default swap (CDS) contracts on 1,247 U.S. firms from 2003-2011, we show a 3-month formation and 1-month holding period CDS momentum strategy yields 52 bps per month. By incorporating past CDS return signals, we further show traditional stock momentum strategies avoid abrupt losses during the crisis period and improve their performance by net 104 bps per month. Both within CDS market and across CDS-to-stock market momentum profits exist because CDS returns correctly anticipate future credit rating changes. Our results highlight the adverse effects of sluggish rating updates in creating information efficiency distortions and investment anomalies.
Poly[[tetrakis(μ2-pyrazine N,N′-dioxide-κ2 O:O′)neodymium(III)] tris(perchlorate)]

The title three-dimensional coordination network, {[Nd(C4H4N2O2)4](ClO4)3}n, is isostructural to that of other lanthanides. The Nd+3 cation lies on a fourfold roto-inversion axis. It is coordinated in a distorted square-antiprismatic fashion by eight O atoms from bridging pyrazine N,N′-dioxide ligands. There are two unique pyrazine N,N′-dioxide ligands. One ring is located around an inversion center, and there is a twofold rotation axis at the center of the other ring. There are also two unique perchlorate anions. One is centered on a twofold rotation axis and the other on a fourfold roto-inversion axis. The perchlorate anions are located in channels that run perpendicular to (001) and (110) and interact with the coordination network through C—H⋯O hydrogen bonds.
Practical Aspects of Mixed-Eccentricity Detection in PWM Voltage-Source-Inverter-Fed Induction Motors

In this paper, we present the results of an ongoing investigation relating to the detection of static and dynamic eccentricity in cage induction motors fed by a pulsewidth-modulation frequency converter using line-current spectral analysis. Two different motors fed by different supply sources (utility voltage and two different voltage converters at different assigned frequencies) were tested. A statistical analysis of the results obtained was carried out. These results allow us to present practical conclusions relating to the detection of mixed eccentricity.
Analog Silicon Photonic MEMS phase-shifter with double-step electrostatic actuation

We present the design of an analog Silicon Photonic MEMS phase shifter based on vertically moving adiabatic couplers. Using a two-step electrostatic actuation method, the phase shifter can be turned on selectively by applying an actuation voltage on a first pair of electrodes, and the phase of the optical signal can be tuned continuously by applying an actuation voltage at a second electrode pair. A mechanical movement of only 20nm at 19V actuation voltage allows for a full π phase shift. The design exhibits a compact footprint of 60um × 44um and low excess insertion loss below 0.1dB.
Extracting, representing and mining semantic metadata from text: facilitating knowledge discovery in biomedicine

The information access paradigm offered by most contemporary text information systems is a search-and-sift paradigm where users have to manually glean and aggregate relevant information from the large number of documents that are typically returned in response to keyword queries. Expecting the users to glean and aggregate information has lead to several inadequacies in these information systems. Owing to the size of many text databases, search-and-sift is a very tedious often requiring repeated keyword searches refining or generalizing queries terms. A more serious limitation arises from the lack of automated mechanisms to aggregate content across different documents to discover new knowledge. This dissertation focuses on processing text to assign semantic interpretations to its content (extracting Semantic metadata) and the design of algorithms and heuristics to utilize the extracted semantic metadata to support knowledge discovery operations over text content. Contributions in extracting semantic metadata in this dissertation cover the extraction of compound entities and complex relationships connecting entities. Extraction results are represented using a standard Semantic Web representation language (RDF) and are manually evaluated for accuracy. Knowledge discovery algorithms presented herein operate on RDF data. To further improve access mechanisms to text content, applications supporting semantic browsing and semantic search of text are presented.
Hypoglycemia in the intensive care unit

Purpose of reviewTo review recent articles and evaluate hypoglycemia as a major complication of intensive insulin therapy in anticipation of emerging data from current clinical studies. Recent findingsFollowing the 2001 landmark Leuven study demonstrating that intensive insulin therapy in the surgical intensive care unit reduces mortality, many studies have evaluated aspects of intensive insulin therapy with respect to improved clinical outcome and the impact of hypoglycemia. Specific risk factors for hypoglycemia in the intensive care unit with intensive insulin therapy are diabetes, octreotide therapy, nutrition support, continuous venovenous hemofiltration with bicarbonate replacement fluid, sepsis and need for inotropic support. In prospective studies with a comparator group, the incidence of hypoglycemia in intensive care unit patients treated with intensive insulin therapy is up to 25%, corresponding to a relative risk of 5.0. In studies without a comparator group, however, the incidence is less than 7%. SummaryHypoglycemia is associated with adverse outcome in intensive care unit patients. It remains unclear whether intensive insulin therapy-induced hypoglycemia per se is responsible for this adverse outcome. The threat of hypoglycemia is a barrier to intensive insulin therapy in critical care, supporting the need for frequent glucose monitoring, readily available concentrated intravenous dextrose infusions, better training of nurses and technological advances in glucose-sensing and insulin-dosing algorithms.
Diversity selection combining to enhance the coding gain

Traditional diversity selection combining techniques are designed to minimize the probability of hard decision error (symbol-by-symbol) prior to channel decoding. When the diversity selection combining technique is employed in conjunction with the channel decoding, the traditional diversity selection combining technique does not necessarily minimize the post-decoding error probability because the decoder takes the soft decision of several different symbols in making a decision on a particular symbol. In this paper, we propose diversity combining techniques based on the log-likelihood ratio (LLR) that provide a higher coding gain than the traditional SNR- or LLR-based combining technique does. Simulation results are provided for the turbo and low-density parity-check (LDPC) codes in Rayleigh flat fading channels.
Trends in dietary carbohydrate consumption from 1991–2008 in the Framingham Heart Study offspring cohort

Longitudinal studies evaluating trends in dietary carbohydrate consumption among U.S. adults are limited. More recent studies reflective of the changes in food supply are needed to evaluate long‐term intakes and to compare them to dietary recommendations. We examined trends in dietary carbohydrate intake in the Framingham Heart Study (FHS) Offspring Cohort over a 17‐year period. The FHS offspring generation was recruited in 1971–75. Follow‐up exams were conducted every 4 years. Dietary data collection began at Exam 5 in 1991 using a semi‐quantitative food frequency questionnaire. This analysis included 3265 adults ages ≥25y with complete dietary data in at least three exams from 1991–2008. Mixed effects linear regression was used to examine trends in macronutrient and food intake using SASv9.3. Over 17 years of follow‐up, % energy from total carbohydrates (50.95% to 46.84%; p‐trend<0.0005) and from total sugars (18.19 % to 16.62%; p‐trend<0.0005) decreased. There was a decrease in % energy from fructose (5.41 to 4.76 %; p‐trend <0.0005) and % energy from sugar in beverages (1.43% to 0.9%, p‐trend<0.0005). Dietary fiber intake increased (18.01 to 19.21g, p‐trend<0.0005). Similar trends were observed when the analyses were stratified by sex. These results suggest some improvement in dietary carbohydrate quality over time in this cohort.
Feature reduction techniques for Arabic text categorization

This paper presents and compares three feature reduction techniques that were applied to Arabic text. The techniques include stemming, light stemming, and word clusters. The effects of the aforementioned techniques were studied and analyzed on the K-nearest-neighbor classifier. Stemming reduces words to their stems. Light stemming,by comparison, removes common affixes from words without reducing them to their stems. Word clusters group synonymous words into clusters and each cluster is represented by a single word. The purpose of employing the previous methods is to reduce the size of document vectors without affecting the accuracy of the classifiers.The comparison metric includes size of document vectors, classification time, and accuracy (in terms of precision and recall). Several experiments were carried out using four different representations of the same corpus: the first version uses stem-vectors, the second uses light stem-vectors, the third uses word clusters, and the fourth uses the original words (without any transformation) as representatives of documents. The corpus consists of 15,000 documents that fall into three categories: sports, economics, and politics. In terms of vector sizes and classification time, the stemmed vectors consumed the smallest size and the least time necessary to classify a testing dataset that consists of 6,000 documents. The light stemmed vectors superseded the other three representations in terms of classification accuracy.
Development of Circularly Polarized Synthetic Aperture Radar onboard microsatellite for Earth diagnosis

In this paper, we proposed the circularly polarized Synthetic aperture radar onboard microsatellite (μSAT CP-SAR). The CP-SAR is designed as the small, light in weight and low power consumption system. The CP-SAR sensor is developed to radiate and receives circularly polarized wave. This CP-SAR sensor will be applicable for land cover, disaster monitoring, snow cover, and oceanography mapping, etc. We also proposed the CP-SAR UAV for ground experiment at Shikabe airport (Japan) and Sulaiman airport (Indonesia) in 2011–1012.
3D boiler tube leak detection technique using acoustic emission signals for power plant structure health monitoring

This paper propose a 3D source location technique for locating leak in boiler tube of power plant using Acoustic Emission technique. In order to verify the effectiveness of the suggested technique, this paper presents a case study to locate the source of leak in the boiler tube of a 500MW class thermal power plant. Test were carry out using soot blower signal from 62 points with a pressure of 10 bar when plant is under a normal working condition. The conventional approaches for detection leak in power plant involved the measurements of RMS signal and peak value. In this study boiler tube leak from the inside of boiler is investigated using 3D source location technique as well as determination of the actual leak based on various analysis method such as amplitude, duration, RMS, and so on. Our results show that we can detect and prevent unexpected plant accident early, and contributes to maximize the effectiveness of power generation.
Current advances in drug development in spinal muscular atrophy

Purpose of reviewSpinal muscular atrophy (SMA) is a pediatric neuromuscular condition characterized by progressive proximal muscle weakness. It is one of the most common genetic causes of infant mortality across different races and is caused by mutation of the survival of motor neuron 1 (SMN1) gene on chromosome 5q13. Recent findingsTo date, there have been many therapeutics developments for SMA targeting various potential pathways such as increasing SMN gene expression, enhancing SMN2 exon 7 inclusion, neuroprotection, cell replacement, and gene therapy. SummaryAlthough SMA remains an incurable disease to date, recent advances in the field of basic and translational research have enhanced our understanding of the pathogenesis of the disease and opened new possibilities for therapeutic intervention. This article reviews and highlights past and current translational research on SMA therapeutics.
Hysteresis-band control based PWM strategy of three-level neutral-point balance

A new method of neutral-point balance based on hysteresis-band control for NPC three-level inverters is proposed in this paper. The neutral-point is balanced by PWM control according to the direction of neutral-point current and the state of neutral-point voltage. The neutral-point current direction is got by output current of each phase and PWM group. The state of neutral point voltage is decided by hysteresis-band control. PWM group is regrouped when it goes against the neutral point potential balance. Being independent of PWM mode, this scheme can be used in any PWM mode for neutral-point balance control. The amplitude of neutral point voltage ripple and the switch times of IGBT can be adjusted by regulating the width of hysteresis-band. The method is verified by simulation and experiment of three-level converter with SHEPWM control scheme.
Positive Autoregulation of mrkHI by the Cyclic Di-GMP-Dependent MrkH Protein in the Biofilm Regulatory Circuit of Klebsiella pneumoniae

ABSTRACT Klebsiella pneumoniae is an important cause of nosocomial infections, primarily through the formation of surface-associated biofilms to promote microbial colonization on host tissues. Expression of type 3 fimbriae by K. pneumoniae facilitates surface adherence, a process strongly activated by the cyclic di-GMP (c-di-GMP)-dependent transcriptional activator MrkH. In this study, we demonstrated the critical importance of MrkH in facilitating K. pneumoniae attachment on a variety of medically relevant materials and demonstrated the mechanism by which bacteria activate expression of type 3 fimbriae to colonize these materials. Sequence analysis revealed a putative MrkH recognition DNA sequence (“MrkH box”; TATCAA) located in the regulatory region of the mrkHI operon. Mutational analysis, electrophoretic mobility shift assay, and quantitative PCR experiments demonstrated that MrkH binds to the cognate DNA sequence to autoregulate mrkHI expression in a c-di-GMP-dependent manner. A half-turn deletion, but not a full-turn deletion, between the MrkH box and the −35 promoter element rendered MrkH ineffective in activating mrkHI expression, implying that a direct interaction between MrkH and RNA polymerase exists. In vivo analyses showed that residues L260, R265, N268, C269, E273, and I275 in the C-terminal domain of the RNA polymerase α subunit are involved in the positive control of mrkHI expression by MrkH and revealed the regions of MrkH required for DNA binding and transcriptional activation. Taken together, the data suggest a model whereby c-di-GMP-dependent MrkH recruits RNA polymerase to the mrkHI promoter to autoactivate mrkH expression. Increased MrkH production subsequently drives mrkABCDF expression when activated by c-di-GMP, leading to biosynthesis of type 3 fimbriae and biofilm formation. IMPORTANCE Bacterial biofilms can cause persistent infections that are refractory to antimicrobial treatments. This study investigated how a commonly encountered hospital-acquired pathogen, Klebsiella pneumoniae, controls the expression of MrkH, the principal regulator of type 3 fimbriae and biofilm formation. We discovered a regulatory circuit whereby MrkH acts as a c-di-GMP-dependent transcriptional activator of both the gene cluster of type 3 fimbriae and the mrkHI operon. In this positive-feedback loop, whereby MrkH activates its own production, K. pneumoniae has evolved a mechanism to ensure rapid MrkH production, expression of type 3 fimbriae, and subsequent biofilm formation under favorable conditions. Deciphering the molecular mechanisms of biofilm formation by bacterial pathogens is important for the development of innovative treatment strategies for biofilm infections.
Total bregman divergence, a robust divergence measure, and its applications

Divergence measures provide a means to measure the pairwise dissimilarity between “objects”, e.g., vectors and probability density functions (pdfs). Kullback-Leibler (KL) divergence and the square loss (SL) function are two examples of commonly used dissimilarity measures which along with others belong to the family of Bregman divergences (BD). In this thesis, we present a novel divergence dubbed the Total Bregman divergence (TBD), which is inherently very robust to outliers, a very desirable property in many applications. Further, we derive the TBD center, called the t-center (using the e1-norm), for a population of positive definite matrices is in closed form and show that it is invariant to transformations from the special linear group. This t-center, which is also robust to outliers, is then used in shape retrieval, diffusion tensor imaging (DTI) estimation, interpolation and segmentation. Furthermore, TBD is used to regularize the conventional boosting algorithms, which have been applied to applications in pattern classification. (Full text of this dissertation may be available via the University of Florida Libraries web site. Please check http://www.uflib.ufl.edu/etd.html)
Research of fuzzy two degree of freedom PID control for permanent magnet synchronous linear motor

Considering the disadvantages of the traditional PID controller in system tracking ability, anti-disturbance performance and a strong dependence on the accurate model of the Permanent magnet synchronous linear motor(PMSLM), this paper proposed a fuzzy two degree of freedom(2DOF) Proportional-Integral-Differential (PID) control. Having studied the equation of the PMSLM on the direct-quadrature(d-q) axis, the paper not only gives the mathematical model of PMSLM vector control system, but designs the fuzzy 2DOF PID position controller. Through the on-line adjustment of the 2DOF PID parameters by fuzzy rules, the PMSLM control system can not only make the tracking ability and anti-disturbance to achieve the best, but also get high positioning accuracy and keep the robustness of the outputs in special conditions. Simulation and several experimental results are proved to validate the theoretical analysis.