Miracle-dz/UniFAR-FTUs huggingface dataset:
Regression from dependent observations

The standard linear and logistic regression models assume that the response variables are independent, but share the same linear relationship to their corresponding vectors of covariates. The assumption that the response variables are independent is, however, too strong. In many applications, these responses are collected on nodes of a network, or some spatial or temporal domain, and are dependent. Examples abound in financial and meteorological applications, and dependencies naturally arise in social networks through peer effects. Regression with dependent responses has thus received a lot of attention in the Statistics and Economics literature, but there are no strong consistency results unless multiple independent samples of the vectors of dependent responses can be collected from these models. We present computationally and statistically efficient methods for linear and logistic regression models when the response variables are dependent on a network. Given one sample from a networked linear or logistic regression model and under mild assumptions, we prove strong consistency results for recovering the vector of coefficients and the strength of the dependencies, recovering the rates of standard regression under independent observations. We use projected gradient descent on the negative log-likelihood, or negative log-pseudolikelihood, and establish their strong convexity and consistency using concentration of measure for dependent random variables.
Een onderzoek naar de effectiviteit van welbevindentherapie bij cliënten met een Posttraumatische stressstoornis

The aim of this study is to examine on the one hand the effectiveness of the well-being therapy (WBT) compared to the ‘Treatment as usual’ (TAU) and on the other hand the elements of well-being, self-compassion and post traumatic growth during the rehabilitation phase in the treatment of clients with posttraumatic stress disorder (PTSD). This study also investigated whether the two-continua model is found by clients with PTSD. Data has been collected from 28 clients from Mediant, department of Psychotrauma. Pearson’s correlation coefficient is used to answer the question to which extent a connection between self-compassion, post traumatic growth and well-being was found by clients with PTSD, as well as to which extent the two-continua model was found by clients with PTSD. The results of this study showed a positive Pearson correlation between self-compassion, post traumatic growth and well-being. These experience wants to keep the two elements of self-compassion and posttraumatic during the WBT. Besides, there was found a strong negative Pearson correlation between psychopathology and well-being. This means that the two-continua model is not strongly demonstrable with clients with PTSD. Also a RM-ANOVA showed that the well-being and the self-compassion increase significantly during the rehabilitation phase. A rehabilitation phase in the treatment of PTSS seems to make sense. Both the WBT could be used as the TAU to improve well-being. The WBT seems to be more effective than the TAU to clients with a low well-being prior to the rehabilitation phase. This examine should be repeat in further research with a larger sample. Keywords: Posttraumatic stress disorder (PTSD); Positive psychology; Post traumatic growth; Self-compassion; Well-being; Well-being therapy.
Qualitative Study of Supervisor Feedback on Nurse Managers' Reflective Journals

Citation: Kuraoka Y (2018) Qualitative Study of Supervisor Feedback on Nurse Managers’ Reflective Journals. Int Arch Nurs Health Care 4:107. doi.org/10.23937/2469-5823/15100107 Accepted: November 15, 2018: Published: November 17, 2018 Copyright: © 2018 Kuraoka Y. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Importance of blood pressure variability in organ protection in spontaneously hypertensive rats treated with combination of nitrendipine and atenolol.

AIM To study the importance of reduction of blood pressure variability (BPV) in the organ protection of long-term treatment with combination of nitrendipine and atenolol, which was abbreviated as Nile, in spontaneously hypertensive rats (SHR). METHODS Combination of nitrendipine (10 mg/kg/d) and atenolol (20 mg/kg/d) was given in SHR chow for 12 weeks. Blood pressure (BP) was then recorded during 24 h in conscious state. After the determination of baroreflex sensitivity (BRS), rats were killed for organ-damage evaluation. RESULTS Long-term treatment with Nile significantly decreased BP and BPV, ameliorated impaired BRS, and obviously diminished end-organ damage in SHR. The indices of left ventricular and aortic hypertrophy, and glomerulosclerosis score were all positively related to BP and BPV, and negatively related to BRS in untreated and Nile-treated SHR. Multiple-regression analysis showed that decrease in left ventricular and aortic hypertrophy was mainly related to the decrease in systolic BPV, and amelioration in renal lesion was mainly determined by increase in BRS. CONCLUSION Long-term treatment with Nile possessed obvious organ protection in SHR. Besides the BP reduction, the decrease in BPV and the restoration of BRS may importantly contribute to this organ protection.
Dense 3D motion capture for human faces

This paper proposes a novel approach to motion capture from multiple, synchronized video streams, specifically aimed at recording dense and accurate models of the structure and motion of highly deformable surfaces such as skin, that stretches, shrinks, and shears in the midst of normal facial expressions. Solving this problem is a key step toward effective performance capture for the entertainment industry, but progress so far has been hampered by the lack of appropriate local motion and smoothness models. The main technical contribution of this paper is a novel approach to regularization adapted to nonrigid tangential deformations. Concretely, we estimate the nonrigid deformation parameters at each vertex of a surface mesh, smooth them over a local neighborhood for robustness, and use them to regularize the tangential motion estimation. To demonstrate the power of the proposed approach, we have integrated it into our previous work for markerless motion capture [9], and compared the performances of the original and new algorithms on three extremely challenging face datasets that include highly nonrigid skin deformations, wrinkles, and quickly changing expressions. Additional experiments with a dataset featuring fast-moving cloth with complex and evolving fold structures demonstrate that the adaptability of the proposed regularization scheme to nonrigid tangential motion does not hamper its robustness, since it successfully recovers the shape and motion of the cloth without overfitting it despite the absence of stretch or shear in this case.
Integrability and ideal conductance at finite temperatures.

We analyse the finite temperature charge stiffness D(T>0), by a generalization of Kohn's method, for the problem of a particle interacting with a fermionic bath in one dimension. We present analytical evidence, using the Bethe ansatz method, that D(T>0) is finite in the integrable case where the mass of the particle equals the mass of the fermions and numerical evidence that it vanishes in the nonintegrable one of unequal masses. We conjecture that a finite D(T>0) is a generic property of integrable systems.
Characterization of inflammatory response induced by Potamotrygon motoro stingray venom in mice

Freshwater stingray accidents cause intense pain followed by edema, erythema, and necrosis formation. Treatment for stingray envenomation is based on administration of analgesic, antipyretic, and anti-inflammatory drugs. This report evaluated the local inflammatory reaction—including edema formation, leukocyte recruitment, release of inflammatory mediators, and histopathological changes—after the intraplantar injection of Potamotrygon motoro stingray venom in mice. Edema was observed as soon as 15 min after venom injection, peaking at 30 min, and lasted up to 48 h. In addition, P. motoro venom increased neutrophil counts in the site of injection, at all time periods and venom doses analyzed. Increased eosinophil and lymphocyte counts were detected mainly at 24 h. Moreover, monocytes/macrophages were observed in large amounts at 24 and 48 h. Microscopically, the venom induced leukocyte migration to the injured tissue, edema, mast cell degranulation, angiogenesis, and epidermal damage. Inflammatory mediator release (IL-6, MCP-1 and KC) was detected as soon as 1 h after venom injection, and it increased significantly at 4 h. At 24 h, the venom induced only the production of MCP-1. These results show that this stingray venom evokes a complex inflammatory reaction, with rapid and persistent edema formation, leukocyte recruitment, and release of cytokines and chemokines.
Impact of Physical Abuse on Adulthood Depressive Symptoms Among Women

This manuscript provides a systematic review of the literature to examine the relationship between childhood physical abuse and intimate partner physical abuse with adulthood depressive symptoms among women. Thirty-five studies that targeted women, measured depressive symptoms, childhood physical abuse, and intimate partner physical abuse were reviewed. Findings indicated an association between physical abuse experiences and depressive symptoms. However, the association of other risk factors, including other types of abuse, with depressive symptoms confounded this relationship. Recommendations, including control for other types of abuse, use of valid and reliable measures, and provision of a clear definition of physical abuse, are provided.
Analysis and detection of spam accounts in social networks

In recent years, social networks like Sina Weibo and Twitter have had rapid development. Meanwhile, social network platforms face threats imposed by spam accounts that propagate advertisements, phishing sites, fraud, etc. Such spam activities negatively affect normal users' experience and adverse to subsequent processing of users data. In this work, we present a new method using extreme learning machine (ELM), a supervised machine, for detecting spam accounts through their behavioral characteristics. Our analysis first collects messages crawling from Sina Weibo. Then, we select three categories of features extracted from message contents, social interactions and user profile properties applied to the ELM-based spam accounts detection algorithm. Finally, we verify the detectability of spam accounts through experiment and evaluation. Our proposed solution could achieve better results in system function and faster compared with other existing supervised machine leaning methods.
Loss of translational entropy in molecular associations

Molecular associations in solution are opposed by the loss of entropy (ΔS) that results from the restriction of motion of each component in the complex. Theoretical estimates of ΔS are essential for rationalizing binding affinities, as well as for calculating entropic contribution to enzyme catalysis. Recently a statistical–mechanical framework has been proposed for estimating efficiently the translational entropy loss (ΔStrsl), while taking explicitly into account the complex intermolecular interactions between the solute and the solvent. This framework relates the translational entropy of a solute in solution to its “free volume,” defined as the volume accessible to the center of mass of the solute in the presence of the solvent and calculated by using an extension of the cell model (CM) for condensed phases. The translational entropy of pure water, estimated with the CM algorithm, shows good agreement with the experimental information. The free volume of various solutes in water, calculated within the CM by using molecular dynamics simulations with explicit solvent, displays a strong correlation with the solutes' polar and total surface areas. This correlation is used to propose a parameterization that can be used to calculate routinely the translational entropy of a solute in water. We also applied the CM formalism to calculate the free volume and translational entropy loss (ΔStrsl) on binding of benzene to a cavity in a mutant T4‐lysozyme. Our results agree with previously published estimates of the binding of benzene to this mutant T4‐lysozyme. These and other considerations suggest that the cell model is a simple yet efficient theoretical framework to evaluate the translational entropy loss on molecular association in solution. Proteins 2004. © 2003 Wiley‐Liss, Inc.
The Structure of Corporate Political Action: Interfirm Relations and Their Consequences.

The power of big business is one of the perennial issues in American political life. In this book Mark Mizruchi asks to what extent are large corporations unified politically - or, more precisely, when do companies agree on issues and when do they disagree? This is an important question in a democracy whose vitality depends on a wide sharing of power. Adopting a structural model of social action. Mizruchi examines the effects of factors such as geographic proximity, common industry membership stock ownership, and interlocking directorates on the campaign contributions of corporate PACs and corporate testimony before Congress. He finds that organizational and social networks among firms have the greatest effect in determining similar political behaviour. These results establish for the first time that the nature of relationships among firms is what generates business unity, more so than other forms of common interest. This kind of structural united front is what leads to the important political successes of big business.
Data Gathering System in Sensor Network

Network lifetime, scalability and load balancing are important requirements for data gathering sensor network applications. A major issue is the technique used for data collection and data gathering. Many protocols were introduced for better performance. LEACH, PEGASIS and TREEPSI were used as established energy-efficient routing protocols. This paper proposes as efficient approach for data collection in wireless sensor networks by introducing an energy efficient priority algorithm (EEPA) model, which is a hybrid model, derived from a combination of LEACH and Member Forward List technique to enhance the cluster-head selection process. This priority algorithm model list includes the nodes with highest priority for forwarding the data. When a node fails or dies, this list is used to select the next node with higher priority. The EEPA prevents the traditional LEACH algorithm clusterhead selection algorithm from repeating when a node fails or dies. The result show that EEPA decreases power consumption and energy loss in wireless sensor networks.
Evaluation of driver steering performance with haptic guidance under passive fatigued situation

Haptic steering technology has been developed to support drivers for more accurate and reliable vehicular control. This paper investigates the evaluation of steering performance when a haptic guidance steering system is implemented for passive fatigued drivers. The haptic system continuously produces torque on the steering wheel to inform drivers about the lateral lane deviations; consequently, drivers are aware of the active torque and then contribute to the steering task by interacting with the system. An experiment with 12 participants was conducted in a high-fidelity driving simulator. In a within-subject counterbalanced design, one session included implement of haptic system and the other excluded haptic system. A monotonous long-period driving course was designed to induce drivers' passive fatigue. Electromyography signals of the brachioradialis muscles were measured to examine grip strength of a driver holding a steering wheel. Results show that the risk of lane departure was significantly reduced when the haptic system was activated. In addition, grip strength of the drivers decreased when cooperating with the haptic system.
SolarDB : Towards a Shared-Everything Database on Distributed Log-Structured Storage 39

Efficient transaction processing over large databases is a key requirement for manymission-critical applications. Thoughmodern databases have achieved good performance through horizontal partitioning, their performance deteriorates when cross-partition distributed transactions have to be executed. This paper presents SolarDB, a distributed relational database system that has been successfully tested at a large commercial bank. The key features of SolarDB include: 1) a shared-everything architecture based on a two-layer log-structured merge-tree; 2) a new concurrency control algorithm that works with the log-structured storage, which ensures efficient and non-blocking transaction processing even when the storage layer is compacting data among nodes in the background; 3) fine-grained data access to effectively minimize and balance network communication within the cluster. According to our empirical evaluations on TPC-C, Smallbank and a real-world workload, SolarDB outperforms the existing shared-nothing systems by up to 50x when there are close to or more than 5% distributed transactions.
Risk Factors for Frailty in a Large Prevalent Cohort of Hemodialysis Patients

Background:Although individuals with kidney disease, including those dependent on dialysis, often present clinically with signs and symptoms consistent with frailty, there is limited information about sociodemographic and clinical risk factors that may be associated. Methods:Seven hundred forty-five patients undergoing hemodialysisbetween 2009 and 2011 in 7 Atlanta dialysis clinics and 7 San Francisco bay area dialysis clinics were assessed using the validated Fried frailty index (recent unintentional weight loss, reported exhaustion, low grip strength, slow walk speed, low physical activity) that defines frailty as the presence of 3 or more criteria. Study coordinators interviewed participants; measured grip strength, walk speed, and body composition; and reviewed records for clinical and laboratory parameters. Logistic regression models were used to estimate the association of patient characteristics with frailty. Results:In adjusted analyses, peripheral vascular disease and cardiac diseases, including dysrhythmia, atrial fibrillation, tachycardia, pericarditis, and cardiac arrest, were associated with higher odds for frailty, whereas black race and higher serum albumin concentration were associated with lower odds for frailty. Conclusions:In multivariable analyses, the risk for frailty in patients undergoing hemodialysis, as assessed by the presence of 3 or more criteria that comprise the Fried frailty index, was increased in association with peripheral vascular disease and cardiac conditions, such as dysrhythmia and atrial fibrillation, and was decreased for those with higher serum albumin concentration and for blacks compared with whites. Among patients who met the Fried definition of frailty, 78% scored as frail on walk speed and 56% scored as frail on grip strength, the 2 physical performance measures.
Burst Firing is a Neural Code in an Insect Auditory System

Various classes of neurons alternate between high-frequency discharges and silent intervals. This phenomenon is called burst firing. To analyze burst activity in an insect system, grasshopper auditory receptor neurons were recorded in vivo for several distinct stimulus types. The experimental data show that both burst probability and burst characteristics are strongly influenced by temporal modulations of the acoustic stimulus. The tendency to burst, hence, is not only determined by cell-intrinsic processes, but also by their interaction with the stimulus time course. We study this interaction quantitatively and observe that bursts containing a certain number of spikes occur shortly after stimulus deflections of specific intensity and duration. Our findings suggest a sparse neural code where information about the stimulus is represented by the number of spikes per burst, irrespective of the detailed interspike-interval structure within a burst. This compact representation cannot be interpreted as a firing-rate code. An information-theoretical analysis reveals that the number of spikes per burst reliably conveys information about the amplitude and duration of sound transients, whereas their time of occurrence is reflected by the burst onset time. The investigated neurons encode almost half of the total transmitted information in burst activity.
An Entailment-Based Approach to the QA4MRE Challenge

This paper describes our entry to the 2012 QA4MRE Main Task (English dataset). The QA4MRE task poses a significant challenge as the expression of knowledge in the question and answer (in the document) typically substantially differs. Ultimately, one would need a system that can perform full machine reading – creating an internal model of the document’s meaning – to achieve high performance. Our approach is a preliminary step toward this, based on estimating the likelihood of textual entailment between sentences in the text, and the question Q and each candidate answer Ai. We first treat the question Q and each answer Ai independently, and find sets of sentences SQ, SA that each plausibly entail (the target of) Q or one of the Ai respectively. We then search for the closest (in the document) pair of sentences in these sets, and conclude that the answer Ai entailed by SAi in the closest pair is the answer. This approach assumes coherent discourse, i.e., that sentences close together are usually “talking about the same thing”, and thus conveying a single idea (namely an expression of the Q+Ai pair). In QA4MRE it is hard to "prove" entailment, as a candidate answer A may be expressed using a substantially different wording in the document, over multiple sentences, and only partially (as some aspects of A may be left implicit in the document, to be filled in by the reader). As a result, we instead estimate the likelihood of entailment (that a sentence S entails A) by look for evidence, namely entailment relationships between components of S and A such as words, bigrams, trigrams, and parse fragments. To identify these possible entailment relationships we use three knowledge resources, namely WordNet, ParaPara (a large paraphrase database from Johns Hopkins University), and the DIRT paraphrase database. Our best run scored 40% in the evaluation, and around 42% in additional (unsubmitted) runs afterwards. In ablation studies, we found that the majority of our score (approximately 38%) could be attributed to the basic algorithm, with the knowledge resources adding approximately 4% to this baseline score. Finally we critique our approach with respect to the broader goal of machine reading, and discuss what is needed to move closer to that goal.