Miracle-dz/UniFAR-FTUs huggingface dataset:
Face matching in a long task: enforced rest and desk-switching cannot maintain identification accuracy

In face matching, observers have to decide whether two photographs depict the same person or different people. This task is not only remarkably difficult but accuracy declines further during prolonged testing. The current study investigated whether this decline in long tasks can be eliminated with regular rest-breaks (Experiment 1) or room-switching (Experiment 2). Both experiments replicated the accuracy decline for long face-matching tasks and showed that this could not be eliminated with rest or room-switching. These findings suggest that person identification in applied settings, such as passport control, might be particularly error-prone due to the long and repetitive nature of the task. The experiments also show that it is difficult to counteract these problems.
Investigation of failure causes in workload-driven reliability testing

Virtual execution environments and middleware are required to be extremely reliable because applications running on top of them are developed assuming their correctness, and platform-level failures can result in serious and unexpected application-level problems. Since software platforms and middleware are often executed for long time without any interruption, large part of the testing process is devoted to investigate their behavior when long and stressful executions occur (these test cases are called workloads). When a problem is identified, software engineers examine log files to find its root cause. Unfortunately, since of the workloads length, log files can contain a huge amount of information and manual analysis is often prohibitive. Thus, de-facto, the identification of the root cause is mostly left to the intuition of the software engineer. In this paper, we propose a technique to automatically analyze logs obtained from workloads to retrieve important information that can relate the failure to its cause. The technique works in three steps: (1) during workload executions, the system under test is monitored; (2) logs extracted from workloads that have been successfully completed are used to derive compact and general models of the expected behavior of the target system; (3) logs corresponding to workloads terminated unsuccessfully are compared with the inferred models to identify anomalous event sequences. Anomalies help software engineers to identify failure causes. The technique can also be used during operational phase, to discover possible causes of unexpected failures by comparing logs corresponding to failing executions with models derived at testing time. Preliminary experimental results conducted on the Java Virtual Machine indicate that several bugs can be rapidly identified thanks to the feedbacks provided by our technique.
Prokaryotic Diversity--Magnitude, Dynamics, and Controlling Factors

There are probably millions of species in the microorganismal domains Bacteria and Archaea (the prokaryotes), and we are only just beginning to work out the basic principles governing their distribution and abundance in natural environments. One characteristic that has become clear is that prokaryote diversity in aquatic environments is orders of magnitude less than in sediments and soils. Hypotheses and models explaining such differences are under development and are beginning to offer promising insights into the mechanisms governing prokaryote diversity and ecosystem function.
Decreased IP-10 and Elevated TGFβ1 Levels are Associated with Viral Clearance Following Therapy in Patients with Hepatitis C Virus

The role of pro-fibrogenic cytokines in the outcome of infections with hepatitis C virus (HCV) and the response to treatment with pegylated interferon-alpha (pegIFNα) and ribavirin remains unclear. To address this issue, we assessed hepatic fibrosis and plasma markers pertinent to T-cell mediated fibrogenesis and inflammation at the start of treatment. Levels of soluble (s)CD30, interleukin-13 receptor alpha 2 (IL-13Rα2), total and active transforming growth factor-beta 1 (TGFβ1), interleukin-18 (IL-18) and interferon-gamma inducible protein-10 (IP-10, CXCL10) were correlated with the severity of fibrosis and with treatment outcome using multiple logistic regression modelling. The Hepascore algorithm was confirmed as a marker of fibrosis, but was a poor predictor of treatment outcome. Inclusion of all immunological markers improved prediction based on Hepascore alone (p = 0.045), but optimal prediction was achieved with an algorithm (“TIPscore”) based on TGFβ1 (total), IP-10, age, sex and HCV genotype (p = 0.003 relative to Hepascore). Whilst this was only marginally more effective than predictions based on HCV genotype age and sex (p = 0.07), it associates high TGFβ1 and low IP-10 levels with a failure of therapy.
Innitely many insolvable Diophantine equations II

Let f(X1;:::;Xm) be a quadratic form in m variables X1;:::;Xm with integer coecients. Then it is well-known that the Diophantine equation f(X1;:::;Xm) = 0 has a nontrivial solution in integers if and only if the equation has a nontrivial solution in real numbers and the congruence f(X1;:::;Xm) 0 (mod N) has a nontrivial solution for every integer N > 1. Such a principle is called the Hasse principle. In this paper, we explicitly give several types of families of the Diophantine equations of degree two, not homogeneous, for which the Hasse principle fails. 2000 Mathematics Subject Classication : 11D09, 11A07
Managing Careers for Ambidexterity and Organizational Alignment: Why It Matters Today to HR Practice

ABSTRACT Today’s competitive environment increasingly calls for organizations and their employees to align competencies and individual capabilities for ambidexterity. Ambidexterity is defined as the need to exploit competencies while allowing for innovative potential. The role of human capital development, and specifically understanding how existing human resources (HR) practices may limit ambidexterity, is central to career management. While career management spans both individual and organizational interests, we approach this issue from the question of how firms can manage careers to build organizational ambidexterity. We also explore what HR professionals can do to address this issue. As part of our approach, we focus on three central and interrelated issues: (a) the role of legacy effects in HR practices, which may over- or underestimate the respective competencies and capabilities needed for exploitation and exploration; we relate this issue to “goal displacement” and the “Peter Principle”; (b) the management of psychological contracts, and how implied expectations may compromise or facilitate ambidexterity; and finally, (c) the role of social networks. Our conceptual article reviews these challenges with recommendations for HR professionals and academics.
Archaea and Fungi of the Human Gut Microbiome: Correlations with Diet and Bacterial Residents

Diet influences health as a source of nutrients and toxins, and by shaping the composition of resident microbial populations. Previous studies have begun to map out associations between diet and the bacteria and viruses of the human gut microbiome. Here we investigate associations of diet with fungal and archaeal populations, taking advantage of samples from 98 well-characterized individuals. Diet was quantified using inventories scoring both long-term and recent diet, and archaea and fungi were characterized by deep sequencing of marker genes in DNA purified from stool. For fungi, we found 66 genera, with generally mutually exclusive presence of either the phyla Ascomycota or Basiodiomycota. For archaea, Methanobrevibacter was the most prevalent genus, present in 30% of samples. Several other archaeal genera were detected in lower abundance and frequency. Myriad associations were detected for fungi and archaea with diet, with each other, and with bacterial lineages. Methanobrevibacter and Candida were positively associated with diets high in carbohydrates, but negatively with diets high in amino acids, protein, and fatty acids. A previous study emphasized that bacterial population structure was associated primarily with long-term diet, but high Candida abundance was most strongly associated with the recent consumption of carbohydrates. Methobrevibacter abundance was associated with both long term and recent consumption of carbohydrates. These results confirm earlier targeted studies and provide a host of new associations to consider in modeling the effects of diet on the gut microbiome and human health.
A Crow or a Blackbird?: Using True Social Network and Tweeting Behavior to Detect Malicious Entities in Twitter

The growing popularity of Twitter and its ability to enable near instantaneous sharing of information has made it a target of attacks by malicious entities who use it to spam and provide links to malware. There is evidence that these entities are using increasingly sophisticated techniques that mimic the behavior of reputed sources to avoid detection. We use novel mechanisms that utilize the true social network of users, the quality of information produced by them and their tweeting behavior to identify such entities. A scheme based on these mechanisms is even able to detect malicious entities that collude to establish dense social networks. Using actual data from a representative sample of 278, 758 Twitter users, we demonstrate the effectiveness of this approach by showing that (1) we identified 5334 accounts that had links to unsafe websites, and (2) over a period of 31 days, 181 accounts that our algorithm identified as potentially malicious were subsequently suspended by Twitter. We believe our algorithm is one of the first to automatically deal with a broad range of malicious entities present in Twitter.
Day-to-Day Variability in Home Blood Pressure Is Associated With Cognitive Decline: The Ohasama Study

Although an association between high blood pressure and cognitive decline has been reported, no studies have investigated the association between home blood pressure and cognitive decline. Home blood pressure measurements can also provide day-to-day blood pressure variability calculated as the within-participant SD. The objectives of this prospective study were to clarify whether home blood pressure has a stronger predictive power for cognitive decline than conventional blood pressure and to compare the predictive power of the averaged home blood pressure with day-to-day home blood pressure variability for cognitive decline. Of 485 participants (mean age, 63 years) who did not have cognitive decline (defined as Mini-Mental State Examination score, <24) initially, 46 developed cognitive decline after a median follow-up of 7.8 years. Each 1-SD increase in the home systolic blood pressure value showed a significant association with cognitive decline (odds ratio, 1.48; P=0.03). However, conventional systolic blood pressure was not significantly associated with cognitive decline (odds ratio, 1.24; P=0.2). The day-to-day variability in systolic blood pressure was significantly associated with cognitive decline after including home systolic blood pressure in the same model (odds ratio, 1.51; P=0.02), whereas the odds ratio of home systolic blood pressure remained positive, but it was not significant. Home blood pressure measurements can be useful for predicting future cognitive decline because they can provide information not only on blood pressure values but also on day-to-day blood pressure variability.
Literacy scholars coming to know the people in the parks, their literacy practices and support systems

ABSTRACT In this research, literacy scholars present the voices of the people who live in the parks near their state capital and university in a south-eastern city in the United States. Through the recorded, transcribed and analyzed conversations, we report the literacy practices of the people in the parks and their insights into the nested state and university structures that restrict and empower their quality of life opportunities. The general findings show our participants to be avid readers of a variety of genres and users of technology but with limited access to state and university resources and infrastructures due to laws and regulations and community members’ negative perceptions that restrict their use. Recommendations are made for more urban community state and university collaborative initiatives to increase understandings and respect for people who live in the parks to honor their motivations for greater social capital and attainment of their life goals.
Discrete step sizes of molecular motors lead to bimodal non-Gaussian velocity distributions under force

Fluctuations in the physical properties of biological machines are inextricably linked to their functions. Distributions of run-lengths and velocities of processive molecular motors, like kinesin-1, are accessible through single molecule techniques, yet there is lack a rigorous theoretical model for these probabilities up to now. We derive exact analytic results for a kinetic model to predict the resistive force (F) dependent velocity (P(v)) and run-length (P(n)) distribution functions of generic finitely processive molecular motors that take forward and backward steps on a track. Our theory quantitatively explains the zero force kinesin-1 data for both P(n) and P(v) using the detachment rate as the only parameter, thus allowing us to obtain the variations of these quantities under load. At non-zero F, P(v) is non-Gaussian, and is bimodal with peaks at positive and negative values of v. The prediction that P(v) is bimodal is a consequence of the discrete step-size of kinesin-1, and remains even when the step-size distribution is taken into account. Although the predictions are based on analyses of kinesin-1 data, our results are general and should hold for any processive motor, which walks on a track by taking discrete steps.
Continuous nematic-isotropic transition in submicron-size liquid-crystal droplets.

La RHN du deuterium de gouttelettes nematiques dans une matrice de polymere revele l'effet de confinement de l'ordre d'orientation moleculaire pres de la transition de phase nematique-isotropique (N-I). Pour de larges gouttelettes de 0. 35μm de diametre, la transition N-I est du premier ordre. Pour des gouttelettes de moins de 0. 035μm de diametre, la phase isotropique a un ordre paranematique et la transition N-I est remplacee par un developpement continu de l'ordre paranematique a l'ordre nematique. Ce resultat fournit la premiere demonstration de la prediction de Sheng sur la courbe de coexistence de N-I
QoS Feasibility in MIMO Broadcast Channels With Widely Linear Transceivers

The use of proper, i.e., circularly symmetric, complex Gaussian signals for all users is known to be optimal in broadcast channels with proper complex Gaussian noise from an information theoretic point of view, i.e., they are employed in the capacity-achieving strategy. However, such proper per-user transmit signals are not necessarily optimal for problems with quality-of-service (QoS) constraints if the transmit strategy is restricted to widely linear transceivers without time-sharing. This is shown by deriving the QoS feasibility region of the multiple-input multiple-output broadcast channel with improper Gaussian per-user transmit signals and widely linear transceivers.
An Embarrassment of Riches: Confronting Omitted Variable Bias and Multi-Scale Capitalization in Hedonic Price Models

Abstract Many researchers have addressed concerns of omitted variable bias in hedonic price models through the use of spatial fixed effects. We argue that this approach does not consider the biases introduced by effects that overlap the zone of capitalization for nonmarket goods. We show this bias can dominate the usual omitted variable bias using data on developer-provided open space. We control for multiscale capitalization and omitted variables simultaneously by extending the Hausman-Taylor estimator where the panel component of the estimator is defined over repeated transactions within neighborhoods rather than the traditional definition following a unique cross-sectional unit through time.
Does Bilateral Trust Affect International Movement of Goods and Labor?

Trust in the citizens of a potential partner country may affect the decision to trade with or to migrate to a foreign country. This paper employs panel data to examine the causal impact of such bilateral trust on international trade and migration patterns. We apply instrumental variables (IV) approaches that capture the exogenous variance of bilateral trust separately with eight indicators of genetic ("somatic") distance between country-pairs. These indicators work equally well at the first stage. However, second-stage results very much depend on the exact measure employed as instrument. Overall, we find little evidence that bilateral trust affects international movements of goods and labor. More generally, we highlight the potential fragility of IV estimations even when the instruments seem plausible on theoretical grounds and when standard statistical tests confirm their validity.
Condition monitoring methods, failure identification and analysis for Induction machines

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment and industrial processes. The studies of induction motor behavior during abnormal conditions, and the possibility to diagnose different types of faults have been a challenging topic for many electrical machine researchers. The Motor Current Signature Analysis (MCSA) is considered the most popular fault detection method now a day because it can easily detect the common machine fault such as turn to turn short ckt, cracked /broken rotor bars, bearing deterioration etc. This paper presents theory and some experimental results of Motor current signature analysis. The MCSA uses the current spectrum of the machine for locating characteristic fault frequencies. The spectrum is obtained using a Fast Fourier Transformation (FFT) that is performed on the signal under analysis. The fault frequencies that occur in the motor current spectra are unique for different motor faults. However this method does not always achieve good results with non-constant load torque. Therefore, different signal processing methods, such as Short- time Fourier Transform (STFT) and Wavelet transforms techniques are also proposed and compared in this paper. Keywords—Fault diagnosis, Fast Fourier transforms, MCSA, Short time Fourier transform (STFT), Wavelet transform I. INTRODUCTION DVANCES in digital signal processing technology have enabled researchers to process more data in less time. As a result, information that is not previously available can be extracted from the collected data. In the light of these developments, condition monitoring via MCSA has recently drawn more attention from researchers. MCSA focuses its efforts on the spectral analysis of the stator current and has been successfully used in the detection of broken rotor bars, bearing damage and the dynamic eccentricity (1). MCSA analyzes the stator current in search of current harmonics directly related to new rotating flux components, which are caused by faults in the motor-flux distribution (2). The advantage of this technique is that it is well recognized nowadays as a standard due to its simplicity: It needs only one current sensor per machine and is based on straightforward signal-processing techniques such as fast Fourier transforms (FFT). The MCSA uses the current spectrum of the machine for locating characteristic fault frequencies. The spectrum is obtained using a Fast Fourier Transformation (FFT) that is performed on the signal under analysis. But, the FFT in the stator current is quite difficult to apply with accuracy due to problems such as frequency resolution, magnitude accuracy at steady state, and more generally, due to data processing (3). Therefore, this paper also discusses some new methods (Short Time Fourier Transform, Wavelet transform) based on stator- current analysis for online fault detection in induction machines, which would overcome the averaging problems of classical FFT.
Analysis of reinforcement learning strategies for predation in a mimic-model prey environment

In this paper we propose a mathematical learning model for a stochastic automaton simulating the behaviour of a predator operating in a random environment occupied by two types of prey: palatable mimics and unpalatable models. Specifically, a well known linear reinforcement learning algorithm is used to update the probabilities of the two actions, eat prey or ignore prey, at every random encounter. Each action elicits a probabilistic response from the environment that can be either favorable or unfavourable. We analyse both fixed and varying stochastic responses for the system. The basic approach of mimicry is defined and a short review of relevant previous approaches in the literature is given. Finally, the conditions for continuous predator performance improvement are explicitly formulated and precise definitions of predatory efficiency and mimicry efficiency are also provided.
POLYCHRONY for System Design

Rising complexities and performances of integrated circuits and systems, shortening time-to-market demands for electronic equipments, growing installed bases of intellectual property (IP), requirements for adapting existing IP blocks with new services, all stress high-level design as a prominent research topic and call for the development of appropriate methodological solutions. In this aim, system design based on the so-called "synchronous hypothesis" consists of abstracting the nonfunctional implementation details of a system and lets one benefit from a focused reasoning on the logics behind the instants at which the system functionalities should be secured. With this point of view, synchronous design models and languages provide intuitive (ontological) models for integrated circuits. This affinity explains the ease of generating synchronous circuits and verify their functionalities using compilers and related tools that implement this approach. In the relational mathematical model behind the design language SIGNAL, this affinity goes beyond the domain of purely synchronous circuits, and embraces the context of complex architectures consisting of synchronous circuits and desynchronization protocols: globally asynchronous and locally synchronous architectures (GALS). The unique features of the relational model behind SIGNAL are to provide the notion of polychrony: the capability to describe circuits and systems with several clocks; and to support refinement: the ability to assist and support system design from the early stages of requirement specification, to the later stages of synthesis and deployment. The SIGNAL model provides a design methodology that forms a continuum from synchrony to asynchrony, from specification to implementation, from abstraction to concretization, from interfaces to implementations. SIGNAL gives the opportunity to seamlessly model circuits and devices at multiple levels of abstractions, by implementing mechanisms found in many hardware simulators, while reasoning within a simple and formally defined mathematical model. In the same manner, the flexibility inherent to the abstract notion of signal, handled in the synchronous-desynchronized design model of SIGNAL, invites and favors the design of correct by construction systems by means of well-defined transformations of system specifications (morphisms) that preserve the intended semantics and stated properties of the architecture under design. The aim of the present article is to review and summarize these formal, correct-by-construction, design transformations. Most of them are implemented in the POLYCHRONY tool-set, allowing for a mixed bottom–up and top–down design of an embedded hardware–software system using the SIGNAL design language.