Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase I trial of chemoradiation with capecitabine and vorinostat in pancreatic cancer.

225 Background: Histone deacetylase inhibitors (HDACi) and fluoropyrimidines are synergistic and are radiation (R) sensitizers. The combination of the fluoropyrimidine capecitabine ( C) and the HDACi vorinostat (V) with R was evaluated in the treatment (tx) of non-metastatic pancreatic cancer (NMPC). Methods: Patients (Pts) with NMPC were treated with C + V + R. C dose was 1000 mg q12 Monday (M) - Friday (F) on the days of R. R was given via 3D conformal therapy to a total dose of 3000 cGy in 10 fractions over 2 weeks (wk). V dose was given daily M-F x 4 wk. V doses evaluated were: 100, 200, 300, and 400 mg. Resectability assessment occurred within 6 wk after completion of tx. Diffusion weighted- MRIs (DW-MRIs) were obtained pre tx and one wk after tx initiation to assess tumor cellularity. Peripheral blood mononuclear cells (PBMCs) were collected pre tx, during tx, and after completion to assess whole cell HDAC activity. Unresectable pts could opt for continuation of C and V (CCV) at systemic doses (C 10.
Factors Affecting Health Promoting Behaviors of Undergraduate Students in

The aim of this study was to determine the predictors of health-promoting behaviors among Ramkhamhaeng University students. This study was a cross-sectional survey. Data were collected from 329 undergraduate students who enrolled in the physical education courses in the Physical Education Department, Faculty of Education, Ramkhamhaeng University in academic year 2015. The instruments were questionnaires including a demographic section, prior health promoting behaviors, self esteem, perceived health benefits, perceived health barriers, perceived he alth self efficacy, affect related to health promoting behavior, social support, situation/environment to health promoting behavior, commitment to use of health promotion, and health promoting behaviors. All causal correlations among the variables in the m odel were examined using path analysis within Pender’s health promotion modeling framework. Results: The final health promotion model revealed a good model-data fit which followed the fitness index criteria. The results indicated that the important factors directly affected health promoting behaviors were perceived health self efficacy (HEF) perceived health benefit (HBF), prior health promoting behavior (PHB), social support (SS), commitment to use of health promotion (CHP), and perceived health barriers (HBA) which negatively affected health promoting behaviors. The factors indirectly influenced health promoting behaviors were prior health promoting behavior via perceived health self efficacy, via social support, and via perceived health benefits. In ov erall, the significant six factors could explain 64 percent of the variance in health promoting behaviors (R 2 = 0.64). The findings of this study showed that Pender’s health promotion model could predict health promoting behaviors among undergraduate Ramkhamhaeng University students well. The results provided information, especially, perceived health self efficacy and perceived health benefit, which are useful for curriculum planners, and health educators to develop health promotion programs that encourage students to learn the benefits and are confident in their own ability to practice good health habits.
Review on Health Informatics

Health informatics is informatics in health care. disciplines involved include information science, computer science, social science, behavioral science, management science, and others. It deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and biomedicine. Health informatics tools include amongst others computers, clinical guidelines, formal medical terminologies, and information and communication systems. The main goal of this work is review on Health Informatics.
Estimating microcredit impact with low take-up, contamination and inconsistent data. A replication study of Crépon, Devoto, Duflo, and Parienté (American Economic Journal: Applied Economics, 2015)

We replicate a flagship randomised control trial carried out in rural Morocco that showed substantial and significant impacts of microcredit on the assets, the outputs, the expenses and the profits of self-employment activities. The original results rely primarily on trimming, which is the exclusion of observation with the highest values on some variables. However, the applied trimming procedures are inconsistent between the baseline and the endline. Using identical specifications as the original paper reveals large and significant imbalances at the baseline and, at the endline, impacts on implausible outcomes, like household head gender, language or education. This calls into question the reliability of the data and the integrity of the experiment protocol. We find a series of coding, measurement and sampling errors. Correcting the identified errors lead to different results. After rectifying identified errors, we still find substantial imbalances at baseline and implausible impacts at the endline. Our re-analysis focused on the lack of internal validity of this experiment, but several of the identified issues also raise concerns about its external validity.
Invariant Diffs

Software development is inherently incremental. Nowadays, many software companies adopt an agile process and a shorter release cycle, where software needs to be delivered faster with quality assurances. On the other hand, the majority of existing program analysis tools still target single versions of programs and are slow and inflexible to handle changes. In the popular version control systems such as git, the program changes are still presented using source code diffs. It is hard to understand what program conditions are changed and which source code lines cause them. In this paper, we propose to compute "invariant diffs" to specify changes. Similar to source diffs that report common code and code churns, we define version invariants to represent program conditions that are common across versions, and invariant churns to show the changes of program conditions between versions. We designed a static demand-driven, path-sensitive analysis to compute and compare invariants for multiple versions of programs using multiversion control flow graphs. We report invariant diffs at the matched program points where comparing invariants are meaningful. Importantly, our analysis correlates source diffs with invariant diffs to explain what source code changes lead to the property changes. We implemented our algorithms in a tool called $H_2$ and performed experiments on 104 versions of programs. Our results show that we are able to compute invariant diffs correctly within reasonable amount of time. The version invariants can capture the common properties of program versions even constructed by different persons, and the invariant churns can specify the semantics of changes such as how a patch changed a buggy condition to a correct condition.
Quartz Crystal Microbalance Detection of Aflatoxin B1 by Self-Assembled Monolayer Technique

New biosensor techniques allowing detection of low concentrated substances show a great variety nowadays. The construction of a system with modified quartz as a part of a Quartz Crystal Microbalance (QCM) techniques helps the detection and confirmation of low toxin concentrations in a sample. The study aims to allow the application of methods for preparation and modification of the gold surface of piezoelectric crystal for detection of aflatoxin B1(AFB1) in the concentration range 0.2 - 2.0 µg/L by QCM technique (quartz - crystal microbalance). The procedure for the preparation of quartz crystal sensors for experimental purposes was performed. The quartz surface was activated and covered with self-assembled monolayer to immobilize antibody (rabbit anti-aflatoxin B1) for the detection of antigen - antibody reaction. The G” corresponds to viscous properties of the material, during applied deformation of the material in the presence of different concentrations, which revealed in the sensitivity of the used resonator. Detection of toxic pollutants may be achieved via QCM methods, ultrasound resonator and piezoelectric quartz techniques for measurement. These techniques allow detection of significantly low concentrations of toxic pollutants, in particular, AFB1, compared to analysis with direct and indirect ELISA immunoassays.
Getting Social in Selection: How Social Networking Website Content is Perceived and Used in Hiring

The use of social networking websites (SNWs) during employee selection is gaining popularity in organizations. Using a foundation that integrates identity and situational strength theories, we develop a conceptual framework that differentiates SNW information from information gathered through traditional selection procedures, and distinguishes between SNW types. Research questions and hypotheses are tested using a survey of 291 hiring professionals. Results indicate that SNW content is considered useful and is regularly utilized during hiring. Some SNW content is viewed positively (e.g., information supporting qualifications), while other information leads to negative perceptions (e.g., discriminatory comments). Finally, results support a differentiation between personal and professional SNW, as the kind of information sought and the effectiveness of assessing various work&#8208;related constructs differs between these SNW categories.
Bone: Osteosarcoma

Note Osteosarcoma is a malignant neoplasm of bone. It is among the most common non hematologic primary malignant tumors of bone in both children and adult s. The conventional type arises in the intramedullary cavity of the bone and represents 75% of all osteosarcomas.These tumors penetrate and destroy th e cortex of the bone and extend into surrounding soft tissues. In conventional intramedullary osteosarcom a, the predominant histologic pattern may be osteoblas tic, fibroblastic, chondroblastic, giant cell rich, mali gnant fibrous histiocytoma like or partially telangiectat ic.
Adaptive Quantization With a One-Word Memory

The adaptations are motivated by the assumption that the input signal variance is unknown, so that the quantizer is started off, in general, with a suboptimal step-size Astart. Multiplier functions that maximize the signal-to-quantization-error ratio (SNR) depend, in general, on Astart and the input sequence length N. For example, if the signal is stationary and N—*°°, best multipliers, irrespective of Astart, have values arbitrarily close to unity. On the other hand, small values of N and suboptimal values of Astart necessitate M values further away from unity. By including an adequate range of values for N and Astart in a generalized SNR definition, we show how one can determine stable multiplier functions Mopt that are optimal for a given signal. In computer simulations of 2and 3-bit quantizers with first-order Gauss-Markovian inputs, we note that, except when the magnitude of the correlation C between adjacent samples is very high, A/opt has the property of calling for fast increases and slow decreases of step-size. We derive optimum multipliers theoretically for two simple cases:
Revealing and Predicting Online Persuasion Strategy with Elementary Units

In online arguments, identifying how users construct their arguments to persuade others is important in order to understand a persuasive strategy directly. However, existing research lacks empirical investigations on highly semantic aspects of elementary units (EUs), such as propositions for a persuasive online argument. Therefore, this paper focuses on a pilot study, revealing a persuasion strategy using EUs. Our contributions are as follows: (1) annotating five types of EUs in a persuasive forum, the so-called ChangeMyView, (2) revealing both intuitive and non-intuitive strategic insights for the persuasion by analyzing 4612 annotated EUs, and (3) proposing baseline neural models that identify the EU boundary and type. Our observations imply that EUs definitively characterize online persuasion strategies.
Evolution: GenomicSignatures ofMimicry andMimicry of Genomic Signatures

Michael Matschiner1,2 and Walter Salzburger3 1Department of Palaeontology and Museum, University of Zurich, Zurich, Switzerland 2Centre of Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Norway 3Zoological Institute, Department of Environmental Sciences, University of Basel, Basel, Switzerland Correspondence: michaelmatschiner@mac.com (M.M.), walter.salzburger@unibas.ch (W.S.) https://doi.org/10.1016/j.cub. 2019.04. 015
Board of Governors of the Federal Reserve System International Finance Discussion Papers Number 630 November 1998 A MODEL OF CRISES IN EMERGING MARKETS

This paper presents a “first generation” model of speculative attacks on emerging markets. Credit constrained governments accumulate liquid assets in order to self-insure against shocks to national consumption. Governments also insure poorly regulated domestic financial markets. Given this policy regime, a variety of internal and external shocks generate capital inflows followed by anticipated speculative attacks. The model suggests that a common shock generated capital inflows to emerging markets in Asia and Latin America after 1989. Country specific factors determined the timing of speculative attacks. Economic reform programs may also have generated capital inflow/crisis sequences.
SoK: Cyber Insurance – Technical Challenges and a System Security Roadmap

Cyber attacks have increased in number and complexity in recent years, and companies and organizations have accordingly raised their investments in more robust infrastructure to preserve their data, assets and reputation. However, the full protection against these countless and constantly evolving threats is unattainable by the sole use of preventive measures. Therefore, to handle residual risks and contain business losses in case of an incident, firms are increasingly adopting a cyber insurance as part of their corporate risk management strategy.As a result, the cyber insurance sector – which offers to transfer the financial risks related to network and computer incidents to a third party – is rapidly growing, with recent claims that already reached a $100M dollars. However, while other insurance sectors rely on consolidated methodologies to accurately predict risks, the many peculiarities of the cyber domain resulted in carriers to often resort to qualitative approaches based on experts opinions.This paper looks at past research conducted in the area of cyber insurance and classifies previous studies in four different areas, focused respectively on studying the economical aspects, the mathematical models, the risk management methodologies, and the predictions of cyber events. We then identify, for each insurance phase, a group of practical research problems where security experts can help develop new data-driven methodologies and automated tools to replace the existing qualitative approaches.
Normal diffusion Distributional Behaviors of Time-averaged Observables in Langevin Equation with Fluctuating Di ff usivity : Normal Di ff usion but Anomalous Fluctuations

where ψ1(t) is the normalized correlation function of diffusion coefficients, i.e., ψ1(t) ≡ (⟨D(t)D(0)⟩ − ⟨D⟩2) = ⟨D⟩2. Therefore, information on the underlying diffusion coefficient D(t) can be extracted by the RSD analysis [26, 27]. Here, we investigate ergodic properties of LEFD especially in non-equilibrium cases. In particular, we consider two-state models for the stochastic process D(t). When the mean sojourn time of a state in D(t) diverges, the stochastic process becomes non-stationary, which implies that the system is intrinsically in non-equilibrium. Using the Laplace analysis, we show distributional limit theorems for TMSD (time-averaged diffusivity). Langevin equation with dichotomously uctuating diffusivity. | Here, we consider dichotomous processes for diffusivity D(t) (see Fig. 1), i.e., there are two states, + state and − state, and the diffusion coefficient is given by D(t) = D+ if the state is + and D(t) = D− if the state is −. Sojourn times for + and − states are random variables following different probability density functions (PDFs), ρ+(x) and ρ−(x) for + and − states, respectively. We assume that one of the PDFs, ρ+(x), follow a narrow distribution which has nite second moment or a broad distribution of power law form, Eq. (2), and the other PDF follows a power-law distribution, whose Laplace transform is given by ρ̂−(s) = 1− a−s− + o(s) (α− < 1). Here, we consider three cases for ρ+(x): (1) 1 < α+: ρ̂+(s) = 1− +s+ a+s+ + o(sα+)
Distant Supervised Relation Extraction with Separate Head-Tail CNN

Distant supervised relation extraction is an efficient and effective strategy to find relations between entities in texts. However, it inevitably suffers from mislabeling problem and the noisy data will hinder the performance. In this paper, we propose the Separate Head-Tail Convolution Neural Network (SHTCNN), a novel neural relation extraction framework to alleviate this issue. In this method, we apply separate convolution and pooling to the head and tail entity respectively for extracting better semantic features of sentences, and coarse-to-fine strategy to filter out instances which do not have actual relations in order to alleviate noisy data issues. Experiments on a widely used dataset show that our model achieves significant and consistent improvements in relation extraction compared to statistical and vanilla CNN-based methods.
Pork Barrel Politics-Oxford Handbooks

This article discusses pork barrel politics which is an integral part of congressional political life and the legislative process. The first two sections of the article discuss pork barrel benefits and their connection to the reelection goal of the legislators. The second section looks at research and the literature on the patterns by which pork barrel benefits are awarded by Congress. Central to this discussion are the individual-benefit and collective-benefit motivations for supplying pork barrel benefits. The remainder of the section discusses scholarship on the strategies for pork barrel allocation and evaluates the contribution of research on the understanding of pork barrel politics.
Safe Reinforcement Learning for Control Systems: A Hybrid Systems Perspective and Case Study

Reinforcement learning (RL) is a general method for learning optimal policies through exploration and experience. Although impressive results have been achieved with RL in recent years, particularly in the domain of video games, RL has been predominantly used in systems with both discrete environments and agents. A key barrier to using RL in safety-criftical cyber-physical system applications is the difficulty of ensuring their safety while enabling learning. This paper presents a framework called FoRShield for learning safe policies for control systems with nonlinear continuous dynamics. A generic hybrid systems model of a control system using online RL is developed. This model is then used to formalize the notion of a shield that can filter unsafe action choices and provide feedback to the learning system. A concrete approach for computing the shield is presented using existing hybrid reachability analysis tools. Finally, to illustrate the feasibility of the approach, a substantial case study is presented involving a quadcopter that uses RL to plan for a dynamic fire-fighting task. The implementation of the approach shows that interesting fire-fighting strategies can be safely learned for discrete environment with 232 states and a 9-dimensional plant model using a standard laptop computer. 1
CONTEXT COLLAPSE IN NEWS

Social media is becoming a much more dynamic power, affecting how news is consumed, shared, and created (Morejon, 2012), including fake news and disinformation (Silverman et al., 2016). Recent reports show that an increasing number of people consume news via social media on a regular basis. According to a Pew Research Centre survey (2016), 62% of US adults obtain news via social media. Sixtysix percent of Facebook users reported reading news stories on the site, while nearly 59% of Twitter users reported the same. Similarly, the Reuters Institute (2016) suggests that 51% of people in UK with online access often use social media as a news source. Thus, it is important to understand social media as an environment for news consumption.
Performance Evaluation of Empirical Equations for Compression Index Using Experimental Data and Statistical Analysis

A quick and economical solution to find the consolidation settlement is to use empirical equations to estimate the soil compression index. These equations may not be appropriate for soils in different regions. In this study, the performance of some empirical equations was determined by estimating the compression index of Kerman clayey soil, Iran. In order to evaluate the compression index, 122 standard odometer tests were performed on undisturbed specimens and the compression indices calculated. Four quantity criteria including error and ranking were selected. The computational measures included determination of the root mean square error (RMSE), ratio of the estimated compression index to the measured value in the laboratory (K), the ranking index (RI) and the ranking distance (RD). Ranking index and ranking distance are related to the accuracy and precision of the estimated results compared to real data. Finally, evaluation of performance of the empirical equations was carried out according to the RD, since it is a self-inclusive statistical parameter that gives accurate and precise estimation compared to other parameters. Results of the analysis indicated that the best estimate of compression index of Kerman soil with empirical equations is based on the initial void ratio compared to other soil properties.
A new method based on the bundle idea and gradient sampling technique for minimizing nonsmooth convex functions

In this paper, we combine the positive aspects of the Gradient Sampling (GS) and bundle methods, as the most efficient methods in nonsmooth optimization, to develop a robust method for solving unconstrained nonsmooth convex optimization problems. The main aim of the proposed method is to take advantage of both GS and bundle methods, meanwhile avoiding their drawbacks. At each iteration of this method, to find an efficient descent direction, the GS technique is utilized for constructing a local polyhedral model for the objective function. If necessary, via an iterative improvement process, this initial polyhedral model is improved by some techniques inspired by the bundle and GS methods. The convergence of the method is studied, which reveals the following positive features (i) The convergence of our method is independent of the number of gradient evaluations required to establish and improve the initial polyhedral models. Thus, the presented method needs much fewer gradient evaluations in comparison to the original GS method. (ii) As opposed to GS type methods, the objective function need not be continuously differentiable on a full measure open set in $\mathbb R^n$ to ensure the convergence for the class of convex problems. Apart from the mentioned advantages, by means of numerical simulations, we show that the presented method provides promising results in comparison with GS methods, especially for large scale problems. Moreover, in contrast with bundle methods, our method is not very sensitive to the accuracy of supplied gradients.