Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Cost Concurrent Error Detection for FSMs Implemented Using Embedded Memory Blocks of FPGAs

We present a number of low-cost concurrent error detection (CED) schemes for finite state machines (FSMs) implemented using embedded memory blocks available in FPGAs. The experimental results show that for many of the examined benchmark circuits, some of the proposed schemes provide for a reasonable level of error detection at a very low circuitry overhead, not exceeding 10%. The proposed set of CED schemes offers the designer an opportunity to trade-off error detection efficiency with implementation cost
Towards Reducing the Multidimensionality of Olap Cubes Using the Evolutionary Algorithms and Factor Analysis Methods

Data Warehouses are structures with large amount of data collected from heterogeneous sources to be used in a decision support system. Data Warehouses analysis identifies hidden patterns initially unexpected which analysis requires great memory and computation cost. Data reduction methods were proposed to make this analysis easier. In this paper, we present a hybrid approach based on Genetic Algorithms (GA) as Evolutionary Algorithms and the Multiple Correspondence Analysis (MCA) as Analysis Factor Methods to conduct this reduction. Our approach identifies reduced subset of dimensions from the initial subset p where p'<p where it is proposed to find the profile fact that is the closest to reference. GAs identify the possible subsets and the Khi formula of the ACM evaluates the quality of each subset. The study is based on a distance measurement between the reference and n facts profile extracted from the Warehouses.
The Abrams geriatric self‐neglect scale: introduction, validation and psychometric properties

Self‐neglect is an imprecisely defined entity with multiple clinical expressions and adverse health consequences, especially in the elderly. However, research has been limited by the absence of a measurement instrument that is both inclusive and specific. Our goal was to establish the psychometric properties of a quantitative instrument, the Abrams Geriatric Self‐Neglect Scale (AGSS).
Immunocytochemical localization of luteinizing hormone‐releasing hormone (LHRH) pathways in the sheep brain during anestrus and the mid‐luteal phase of the estrous cycle

The luteinizing hormone‐releasing hormone (LHRH) system of the sheep brain was examined by light microscopic immunocytochemistry with thick, unembedded sections. We compared the distribution and morphology of LHRH cells and their fibers in intact and ovariectomized anestrous ewes, and in breeding season ewes during the mid‐luteal phase of their estrous cycle. In all animals, a majority of LHRH neurons were found in the medial preoptic area adjacent to the organum vasculosum of the lamina terminalis. These cells formed a continuum rostrally with immunoreactive neurons in the diagonal band of Broca and medial septum and caudally with cells in the ventrolateral anterior hypothalamus and lateral hypothalamus. Relatively few cells (1–2%) were seen in the arcuate nucleus or its vicinity. Preoptic LHRH neurons project to the tubero‐infundibular sulcus of the median eminence by at least two routes: a major ventrolateral projection above the optic tract in the anterior and lateral hypothalamus, and a less prominent periventricular pathway along the third ventricle. LHRH fibers were also observed in a number of extrahypothalamic regions, including the medial amygdala and the accessory olfactory bulb.
The base-rate fallacy and its implications for the difficulty of intrusion detection

Many different demands can be made of intrusion detection systems. An important requirement is that it be effective i.e. that it should detect a substantial percentage of intrusions into the supervised system, while still keeping the false alarm rate at an acceptable level. This paper aims to demonstrate that, for a reasonable set of assumptions, the false alarm rate is the limiting factor for the performance of an intrusion detection system. This is due to the base-rate fallacy phenomenon, that in order to achieve substantial values of the Bayesian detection rate, P(Intrusion|Alarm), we have to achieve—a perhaps unattainably low—false alarm rate. A selection of reports of intrusion detection performance are reviewed, and the conclusion is reached that there are indications that at least some types of intrusion detection have far to go before they can attain such low false alarm rates.
Effects of intranasal oxytocin administration on memory for infant cues: Moderation by childhood emotional maltreatment

Oxytocin has been implicated in parent–infant attachment and social recognition. With respect to emotion recognition memory, both memory-enhancing and impairing effects have been observed, suggesting an influence of individual factors. We assessed the effects of oxytocin on memory for infant cues, and whether these effects are moderated by self-reported childhood emotional maltreatment. Nulliparous females (N = 102) participated in a randomized, double-blind, between-subjects study with intranasal oxytocin or placebo administration. Participants’ memory was tested using the Baby Social Reward Task, where participants were asked to select the happier infant from a pair of two infants based on the information that they received about the infants’ mood in the previous phase. Participants reporting more childhood emotional maltreatment were less accurate in this task after inhaling oxytocin. Our findings add to a growing body of literature showing that the effects of intranasal oxytocin on memory and social behavior are moderated by adverse early life experiences.
The Relationship Between Transactional Leadership, Conflict in the Work Team, Personality and Job Stress

The main objective of this study is to examine the influence of transactional leadership, conflict in the work team and personality on job stress. In order to measure that, transactional leadership, conflict in the work team and personality were used. Data were gathered through questionnaire survey of employee at PT Semen Baturaja head office (n=130). Correlation and regression result analysis were used to examine the relationship between independent variables which are transactional leadership, conflict in the work team and personality and dependent variable which is job stress. The result indicated that all the independent variables which are transactional leadership, conflict in the work team and personality were positively correlated to job stress. The regression result revealed that 43.6% of job stress has been significantly explained by transactional leadership, conflict in the work team and personality. Moreover, the result showed that the most influencing factor that causing job stress was conflict in the work team. The findings were discussed and recommendations for future research and practitioners were also addressed.
A General Euclidean Geometric Representation for the Classical Detection Theory

We propose an Euclidean geometric representation for the classical detection theory. The proposed representation is so generic that can be employed to almost all communication problems. The hypotheses and observations are mapped into R^N in such a way that a posteriori probability of an hypothesis given an observation decreases exponentially with the square of the Euclidean distance between the vectors corresponding to the hypothesis and the observation.
GAS FRACTION AND DEPLETION TIME OF MASSIVE STAR-FORMING GALAXIES AT z ∼ 3.2: NO CHANGE IN GLOBAL STAR FORMATION PROCESS OUT TO z > 3

The observed evolution of the gas fraction and its associated depletion time in main-sequence (MS) galaxies provides insights on how star formation proceeds over cosmic time. We report ALMA detections of the rest-frame ∼300 μm continuum observed at 240 GHz for 45 massive ( ), normal star-forming ( ), i.e., MS, galaxies at in the COSMOS field. From an empirical calibration between cold neutral, i.e., molecular and atomic, gas mass and monochromatic (rest-frame) infrared luminosity, the gas mass for this sample is derived. Combined with stellar mass and star formation rate (SFR) estimates (from MagPhys fits) we obtain a median gas fraction of and a median gas depletion time correction for the location on the MS will only slightly change the values. The reported uncertainties are the error on the median. Our results are fully consistent with the expected flattening of the redshift evolution from the 2-SFM (2 star formation mode) framework which empirically prescribes the evolution assuming a universal, log-linear relation between SFR and gas mass coupled to the redshift evolution of the specific star formation rate (sSFR) of MS galaxies. While shows only a mild dependence on location within the MS, a clear trend of increasing across the MS is observed (as known from previous studies). Further, we comment on trends within the MS and (in)consistencies with other studies.
Social inequalities in mortality: changes in the relative importance of income, education and household size over a 27-year period.

BACKGROUND The aim of this paper was to measure socio-economic inequalities in mortality over a 27-year period, and estimate the simultaneous effects of education and income adjusted for changing proportions and potential confounders. METHODS Census data in 1970, 1980 and 1990 with information about income, educational level and household size were linked to mortality records at the individual level and analysed with absolute mortality rates, Poisson regression (univariate and multivariate) and Relative Index of Inequality (RII). RESULTS Mortality differences increased between income quartiles and educational groups as well as between singles and non-singles. RII between income quartiles increased from 1.6 to 3.6 for men and 1.7 to 2.7 for women while RII between the educational groups increased from 1.6 to 2.8 for men and 1.5 to 2.1 for women. However, RII derived from the adjusted multivariate regression analysis was 1.8 (men) and 1.9 (women) between the income groups in 1990 and 2.2 (men) and 1.7 (women) between the educational groups. In the 1990s current income was more important than previous income, and the rate ratios (RR) increased for groups dropping into the lowest income quartile between two censuses. CONCLUSION Low education and single status have become relatively more important risk factors for mortality over time. Confounder effects of education and household size could partly explain the seemingly large increase in inequalities between income groups. Results may also indicate a stronger reverse causation (poor health causing low income) over time.
Empowering The Community: Information Strategies For Pollution Control

Disclosure strategies, which involve public and or private attempts to increase the availability of information on pollution, form the basis for what some have called the third wave in pollution control policy (after legal regulation - the first wave and market-based instruments - the second wave). While these strategies have become commonplace in natural resource settings (forest certification programs, for example), they are less familiar in a pollution control context. Yet both the research on, and experience with, this approach is now growing in both Organization of Economic Cooperation and Development (OECD) and developing countries. This paper will review what the authors know and don't know about the use of disclosure strategies to control pollution. Following a review of the conceptual foundations for disclosure strategies, the paper considers how the policy setting influences the type of information strategy employed. Examples of innovative disclosure strategies in the United States (U.S.), Latin America, and Asia, and the channels through which they operate, is followed by a review of the empirical research on their effectiveness. Finally the authors close with sense of where further research will be particularly helpful.
On the efficacy of Narrative Exposure Therapy: a reply to Mundt et al.

In their review article, Mundt et al. (2014), ‘Evaluating interventions for posttraumatic stress disorder in low and middle income countries: Narrative Exposure Therapy’, (Intervention, this issue) evaluated Narrative Exposure Therapy (NET) as an example of a short-term treatment for posttraumatic stress disorder that is applied in low and middle income, post conflict settings. They concluded that it is premature to recommend NET as a treatment approach. In this response, the clinicians and researchers who developed NET argue that the paper by Mundt et al. does not correctly represent the rationale of NET and that the methodological critique of the NET trials in the article is biased. Compared to other types of psychotherapy and psychosocial interventions in post conflict settings, the evidence base of NET is strong, and the application of NET within a sustainable mental healthcare system can be recommended.
The science of emotion: Research and tradition in the psychology of emotion.

Originally published in Contemporary Psychology: APA Review of Books, 1997, Vol 42(3), 269. This introductory text (see record 1996-97076-000) introduces readers to four major theoretical perspectives on emotion: Darwinian, Jamesian, cognitive, and social constructivist. The author presents the cent
Philosophical foundations for a constructivist and institutionalist relationship between the European Union and Australia.

vii Acknowledgement . viii List of tables & abbreviations.ix Chapter One: Introduction 1.1. Thesis about the international relationship between the European Union and Australia: A sociological constructivist and sociological institutionalist approach.1 1.2. Methodology of the thesis.4 1.3. Objectives for the research of the thesis.5 Chapter Two: Sociological constructivism and sociological institutionalism in international relations, and in the EU-Australia cooperation 2.
Pathway Analysis of Expression Data: Deciphering Functional Building Blocks of Complex Diseases

Identification of differentially expressed pathways from expression data is an important problem because it allows us to gain insights into the functional working mechanism of cells beyond the detection of differentially expressed genes. In this paper we present a brief guide to methods for the pathway analysis of expression data. Despite the vast amount of different statistical methods that have been developed so far, there is a considerable similarity among them, allowing a systematic classification and a reduction to a few null hypotheses that are effectively tested. Systems biology aims to find emergent phenomena by the integration of heterogeneous data. In general, data integration itself is a part of any scientific inference: its elementary steps are the integration of observations (measurements) into the context of biological knowledge. However, in the case of systems biology, the scale of integration is many folds higher, resulting in a prodigious number of new computational approaches for the simultaneous analyses of heterogeneous data. In this paper we discuss one popular way of integrating biological knowledge into large-scale genome-wide measurements, namely the identification of functionally related genes (pathways) enriched or differentially expressed in gene expression data [1]. It should be noted that the approaches discussed are also applicable to the analyses of, e.g., RNA-seq, metabolomics or proteomics data and, generally, different types of biological measurements when preexisting biological knowledge is available. In the early stages of methodological developments for gene expression data analyses, most approaches were focused on producing so-called gene lists. This is a set of individual genes called differentially expressed as identified by univariate test statistics (e.g., a t-test) [2]–[4]. Instead, more recent approaches clearly reflect systems biology's trend of data integration and interpretation [5]–[7], focusing on sets of functionally related genes (e.g., from the same signaling or metabolic pathway) rather than individual genes. The purpose of this paper is to provide a brief guide to methods for the analysis of differentially expressed pathways or gene sets, which we simply call pathway-based methods. For this reason, we emphasize an illustration of the methods rather than their technical description. The reader is encouraged to follow the cited literature for technical details.
A novel framework to elucidate core classes in a dataset

In this paper we present an original framework to extract representative groups from a dataset, and we validate it over a novel case study. The framework specifies the application of different clustering algorithms, then several statistical and visualisation techniques are used to characterise the results, and core classes are defined by consensus clustering. Classes may be verified using supervised classification algorithms to obtain a set of rules which may be useful for new data points in the future. This framework is validated over a novel set of histone markers for breast cancer patients. From a technical perspective, the resultant classes are well separated and characterised by low, medium and high levels of biological markers. Clinically, the groups appear to distinguish patients with poor overall survival from those with low grading score and better survival. Overall, this framework offers a promising methodology for elucidating core consensus groups from data.
Miniaturized portable image-recording device for paper-based nucleotide assays with nanoparticles

This study describes the development of a portable image-recording device that is lightweight, inexpensive, and compatible with commercial smartphones and paper-based analytical devices. A light-unaffected environment is essential for optical quantification in colorimetric-based or fluorescence-based assays, and we have previously developed a portable fluorescent image-recording device. Here, we attempted to miniaturize the portable image-recording device by using a thin-film LED strip and minimize the cost of the device by using colored glassine as a light filter. Finally, we demonstrated the capacity to measure DNA-AuNPs concentration with a paper-based device.
Sequence analysis ARCS : an aggregated related column scoring scheme for aligned sequences

Motivation: Biologists frequently align multiple biological sequences to determine consensus sequences and/or search for predominant residues and conserved regions. Particularly, determining conserved regions in an alignment is one of the most important activities. Since protein sequences are often several-hundred residues or longer, it is difficult todistinguishbiologically importantconserved regions (motifsor domains) from others. The widely used tools, Logos, Al2co, Confind, and theentropy-basedmethod, often fail to highlight such regions.Thus a computational tool that can highlight biologically important regions accurately will be highly desired. Results: This paper presents a new scoring scheme ARCS (Aggregated Related Column Score) for aligned biological sequences. ARCS method considers not only the traditional character similarity measure but also column correlation. In an extensive experimental evaluation using 533 PROSITE patterns, ARCS is able to highlight the motif regions with up to 77.7% accuracy corresponding to the top three peaks. Availability: The source code is available on http://bio.informatics. indiana.edu/projects/arcs and http://goldengate.case.edu/projects/ arcs Contacts: jiong.yang@case.edu, sunkim2@indiana.edu Supplementary Material: http://bio.informatics. indiana.edu/projects/ arcs and http://goldengate.case.edu/projects/arcs
Load balancing in heterogeneous networks based on distributed learning in potential games

We present a novel approach for distributive load balancing in heterogeneous networks that use cell range expansion (CRE) for user association. First, we formulate the problem as a minimisation of an α-fairness objective function. Depending on α, different objectives in terms of network performance or fairness can be achieved. Next, we model the interactions among the base stations for load balancing as a potential game, in which the potential function is the α-fairness function. The optimal Nash equilibrium of the game is found by using distributed learning algorithms. We use log-linear and binary log-linear learning algorithms for complete and partial information settings, respectively. By running extensive simulations, we show that the proposed algorithms converge within a few tens of iterations. The convergence speed in the case of partial information setting is comparable to that of the complete information setting. We also show that the best response algorithm does not necessarily converge to the optimal Nash equilibrium.
Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer's Disease

The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer's disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support Vector Machines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.
Positive and Negative Autoregulation ofREB1 Transcription in Saccharomyces cerevisiae

ABSTRACT Reb1p is a DNA binding protein of Saccharomyces cerevisiae that has been implicated in the activation of transcription by polymerase (Pol) II, in the termination of transcription by Pol I, and in the organization of nucleosomes. Studies of the transcriptional control of the REB1 gene have led us to identify three Reb1p binding sites in the 5′ region of the its gene, termed A, B, and C, at positions −110, −80, and +30 with respect to transcription initiation. In vitro, Reb1p binds to the three sites with the relative affinity of A ≥ C > B. Kinetic parameters suggest that when both A and C sites are present on the same DNA molecule, the C site may recruit Reb1p for the A site. In vivo the A and B sites each contribute to the transcription activity ofREB1 in roughly additive fashion. Mutation of both A and B sites abolishes transcription. On the other hand, the C site is a negative element, reducing transcription by 40%. In cells overexpressing Reb1p, the C site reduces transcription by more than 80%. This effect can be transposed to another transcription unit, demonstrating that the effect of Reb1p binding at the C site does not depend on interaction with upstream Reb1p molecules. Relocation of the C site to a position 105 bp downstream of the transcription initiation site abolishes its effect, suggesting that it does not act as a conventional attenuator of transcription. We conclude that binding of Reb1p at the C site hinders formation of the initiation complex. This arrangement of Reb1p binding sites provides a positive and negative mechanism to autoregulate the expression of REB1. Such an arrangement could serve to dampen the inevitable fluctuation in Rep1p levels caused by the intermittent presence of its mRNA within an individual cell.