Miracle-dz/UniFAR-FTUs huggingface dataset:
CLIMATIC VARIABILITY AND LIVELIHOOD VULNERABILITY IN KADUNA STATE, NIGERIA

Purpose: This paper focused on livelihood vulnerability induced by climatic variability amongst farming households in Kaduna state, Nigeria. Methodology: The research used a sample population of 400 using Taro Yamane formula which represents about 0.05% of the population of the three selected local government areas and it purposively targeted farming households heads (FHHH) in one of each of the three eco-climatic zones in the state. Kagarko, BirninGwari and Makarfi local government areas were based on their eco-climatic location and rurality to represent humid, sub-humid and dry sub humid zones of the state respectively. A multi stage sampling technique was further adopted in which farming districts and villages were selected for the administration of 400 structured questionnaires proportionately distributed proportionately to the three local government areas. The Department for International Development (DFID) sustainable livelihoods framework was adopted in the design of the structured questionnaires. Coefficient of Variation (CV %) was deployed to determine the variability of rainfall and temperature of the three eco-climatic zones of the past thirty six years (1981-2016) which was employed into the Micah Hahn’s Livelihood Vulnerability Index model.The results show that Kagarko (humid) had a CV% of 105.43 of rainfall, 9.06 CV% of maximum temperature and CV% of 17.63 in minimum temperature. BirninGwari (sub-humid) had a CV% of 119.64 in rainfall, CV% of 14.17 in maximum temperature and CV% of 15.92 in minimum temperature while Makarfi (dry sub-humid) had a CV% of 124.71 in rainfall, CV% of 9.72 in maximum temperature and 16.29 CV% in minimum temperature. The livelihood vulnerability index (LVI) of Kagarko was calculated to be 0.35, Makarfi and BirninGwari were calculated to be 0.36 respectively and vulnerability spider diagrams were used to capture and compare results. On a vulnerability scale of 0-1, the three eco-climatic zones were found to be very vulnerable to climatic variability. The paper has proved the applicability of Co-efficient of Variation (CV %) into the LVI model which is a departure from previous users who have consistently deployed Mean Standard Deviation into the model. Results: This study will serve as a spring board to meet the Sustainable Development Goals (SDGs) targets on vulnerable communities in Kaduna state. It is discovered that farmers in Makarfi and BirninGwari, even though in different eco-climatic zones of sub-humid and dry sub humid zones respectively, share equal level of livelihood vulnerability index of 0.36 while Kagarko area which is in humid zone, is having 0.35. These indicated that all the areas are within the very vulnerable values on a vulnerability scale of 0-1. The vulnerability levels of the study area can be attributed to weak Natural, Financial and Physical capitals. Recommendations: The paper recommended Integrated Farmers’ Livelihoods Support Strategy (IFLISS) so as to build the resilience of farming households’ livelihood capitals and reduce vulnerability levels.
Time-sensitive Personalized Query Auto-Completion

Query auto-completion (QAC) is a prominent feature of modern search engines. It is aimed at saving user's time and enhancing the search experience. Current QAC models mostly rank matching QAC candidates according to their past popularity, i.e., frequency. However, query popularity changes over time and may vary drastically across users. Hence, rankings of QAC candidates should be adjusted accordingly. In previous work time-sensitive QAC models and user-specific QAC models have been developed separately. Both types of QAC model lead to important improvements over models that are neither time-sensitive nor personalized. We propose a hybrid QAC model that considers both of these aspects: time-sensitivity and personalization. Using search logs, we return the top N QAC candidates by predicted popularity based on their recent trend and cyclic behavior. We use auto-correlation to detect query periodicity by long-term time-series analysis, and anticipate the query popularity trend based on observations within an optimal time window returned by a regression model. We rerank the returned top N candidates by integrating their similarities with a user's preceding queries (both in the current session and in previous sessions by the same user) on a character level to produce a final QAC list. Our experimental results on two real-world datasets show that our hybrid QAC model outperforms state-of-the-art time-sensitive QAC baseline, achieving total improvements of between 3% and 7% in terms of MRR.
Epistemic Foundation of Decision Making under Ambiguity

In the seminal works, Mukerji (1997) and Ghirardato (2001) explain the notion of ambiguity in terms of incompleteness of what the decision maker (DM) knows and demonstrate that such limitation may lead to a non-additive specification of DM’s beliefs. However, these theories do not relate this issue to well established formal models of knowledge. We show that translating the decision theoretic set-up into epistemic logic may provide more deep insight into understanding of the sources of ambiguity on the one side and establish connection to the existing models of knowledge on the other side. In particular, we present that canonical knowledge systems are very well suited tools for this purposes. Furthermore, we show that DM subjective beliefs are represented by non-additive measures, whenever the knowledge operator and possibility operator disperse.
Cln3-Associated Kinase Activity inSaccharomyces cerevisiae Is Regulated by the Mating Factor Pathway

ABSTRACT The Saccharomyces cerevisiae cell cycle is arrested in G1 phase by the mating factor pathway. Genetic evidence has suggested that the G1 cyclins Cln1, Cln2, and Cln3 are targets of this pathway whose inhibition results in G1 arrest. Inhibition of Cln1- and Cln2-associated kinase activity by the mating factor pathway acting through Far1 has been described. Here we report that Cln3-associated kinase activity is inhibited by mating factor treatment, with dose response and timing consistent with involvement in cell cycle arrest. No regulation of Cln3-associated kinase was observed in a fus3 kss1 strain deficient in mating factor pathway mitogen-activated protein (MAP) kinases. Inhibition occurs mainly at the level of specific activity of Cln3-Cdc28 complexes. Inhibition of the C-terminally truncated Cln3-1-associated kinase is not observed; such truncations were previously identified genetically as causing resistance to mating factor-induced cell cycle arrest. Regulation of Cln3-associated kinase specific activity by mating factor treatment requires Far1. Overexpression of Far1 restores inhibition of C-terminally truncated Cln3-1-associated kinase activity. G2/M-arrested cells are unable to regulate Cln3-associated kinase, possibly because of cell cycle regulation of Far1 abundance. Inhibition of Cln3-associated kinase activity by the mating factor pathway may allow this pathway to block the earliest step in normal cell cycle initiation, since Cln3 functions as the most upstream G1-acting cyclin, activating transcription of the G1 cyclins CLN1 and CLN2 as well as of the S-phase cyclins CLB5 and CLB6.
The Role of Language in Ethnic Identity Measurement: A Multitrait-Multimethod Approach to Construct Validation

The authors focused on the role of language use in measuring ethnic identity. They demonstrated the construct validity of a 3-dimensional measure and examined potential biases introduced by alternative methods such as constant sum scale. Their findings support the distinction between the 3 subdimensions of language use (English use in family, in media consumption, and while shopping). The authors found evidence of discriminant validity. By using 3 approaches to construct validity, they found that the support for discriminant validity strengthens the discriminant and convergent properties of the instrument. Findings concerning method effects were less obvious. If the traditional multitrait-multimethod approach indicates no method effects, more stringent approaches (e.g., analysis of variance and correlated uniqueness approach) indicate the presence of limited method effects. The authors provide implications regarding measurement of ethnic identity.
Mobile target tracking by networked uninhabited autonomous vehicles via hospitability maps

An air vehicle detects a target at some position using its own sensor but delays attack. While the target is detected the air vehicle takes several looks at the target in order to classify it. Once the classification of the target is done the same air vehicle or another vehicle attacks it. During the process of classification the target has moved from its initial location. Since the target has moved away where should the UAVs look for it? This is a prediction and a search problem. Prediction uses the past information and search looks for the target in the predicted locations. As time increases the difference between prediction and search becomes blurrier. Therefore, the research objective now becomes more challenging. We need to explore innovative modeling and estimation techniques that result in more robust estimation and more robustness to model uncertainties. We can rely on terrain based state prediction to determine the likelihood of the new target position, but the probability density function (pdf) of the target position is propagate, as the target moves. The propagation of the pdf for the target position can be non-linear, non-Gaussian, and terrain dependent. The effects of such terrains are captured by something called hospitability maps. A hospitability map provides a likelihood or a "weight" for each point on the terrain's surface for the target to move and maneuver at that location. We introduce the idea of a time varying hospitability map by making the vehicles searching for the targets part of it.
Integrating multi-omic features exploiting Chromosome Conformation Capture data

The representation, integration, and interpretation of omic data is a complex task, in particular considering the huge amount of information that is daily produced in molecular biology laboratories all around the world. The reason is that sequencing data regarding expression profiles, methylation patterns, and chromatin domains is difficult to harmonize in a systems biology view, since genome browsers only allow coordinate-based representations, discarding functional clusters created by the spatial conformation of the DNA in the nucleus. In this context, recent progresses in high throughput molecular biology techniques and bioinformatics have provided insights into chromatin interactions on a larger scale and offer a formidable support for the interpretation of multi-omic data. In particular, a novel sequencing technique called Chromosome Conformation Capture allows the analysis of the chromosome organization in the cell’s natural state. While performed genome wide, this technique is usually called Hi–C. Inspired by service applications such as Google Maps, we developed NuChart, an R package that integrates Hi–C data to describe the chromosomal neighborhood starting from the information about gene positions, with the possibility of mapping on the achieved graphs genomic features such as methylation patterns and histone modifications, along with expression profiles. In this paper we show the importance of the NuChart application for the integration of multi-omic data in a systems biology fashion, with particular interest in cytogenetic applications of these techniques. Moreover, we demonstrate how the integration of multi-omic data can provide useful information in understanding why genes are in certain specific positions inside the nucleus and how epigenetic patterns correlate with their expression.
Fear and the Defense Cascade: Clinical Implications and Management

AbstractEvolution has endowed all humans with a continuum of innate, hard-wired, automatically activated defense behaviors, termed the defense cascade. Arousal is the first step in activating the defense cascade; flight or fight is an active defense response for dealing with threat; freezing is a flight-or-fight response put on hold; tonic immobility and collapsed immobility are responses of last resort to inescapable threat, when active defense responses have failed; and quiescent immobility is a state of quiescence that promotes rest and healing. Each of these defense reactions has a distinctive neural pattern mediated by a common neural pathway: activation and inhibition of particular functional components in the amygdala, hypothalamus, periaqueductal gray, and sympathetic and vagal nuclei. Unlike animals, which generally are able to restore their standard mode of functioning once the danger is past, humans often are not, and they may find themselves locked into the same, recurring pattern of response tied in with the original danger or trauma. Understanding the signature patterns of these innate responses—the particular components that combine to yield the given pattern of defense—is important for developing treatment interventions. Effective interventions aim to activate or deactivate one or more components of the signature neural pattern, thereby producing a shift in the neural pattern and, with it, in mind-body state. The process of shifting the neural pattern is the necessary first step in unlocking the patient’s trauma response, in breaking the cycle of suffering, and in helping the patient to adapt to, and overcome, past trauma.
The sojourn time distribution in an infinite server resequencing queue with dependent interarrival and service times

We consider an infinite server resequencing queue, where arrivals are generated by jumps of a semi-Markov process and service times depend on the jumps of this process. The stationary distribution of the sojourn time, conditioned on the state of the semi-Markov process, is obtained both for the case of hyperexponential service times and for the case of a Markovian arrival process. For the general model, an accurate approximation is derived based on a discretisation of interarrival and service times.
Phase angle obtained by bioelectrical impedance analysis independently predicts mortality in patients with cirrhosis

AIM To evaluate the prognostic value of the phase angle (PA) obtained from bioelectrical impedance analysis (BIA) for mortality prediction in patients with cirrhosis. METHODS In total, 134 male cirrhotic patients prospectively completed clinical evaluations and nutritional assessment by BIA to obtain PAs during a 36-mo follow-up period. Mortality risk was analyzed by applying the PA cutoff point recently proposed as a malnutrition marker (PA ≤ 4.9°) in Kaplan-Meier curves and multivariate Cox regression models. RESULTS The patients were divided into two groups according to the PA cutoff value (PA > 4.9°, n = 73; PA ≤ 4.9°, n = 61). Weight, height, and body mass index were similar in both groups, but patients with PAs > 4.9° were younger and had higher mid-arm muscle circumference, albumin, and handgrip-strength values and lower severe ascites and encephalopathy incidences, interleukin (IL)-6/IL-10 ratios and C-reactive protein levels than did patients with PAs ≤ 4.9° (P ≤ 0.05). Forty-eight (35.80%) patients died due to cirrhosis, with a median of 18 mo (interquartile range, 3.3-25.6 mo) follow-up until death. Thirty-one (64.60%) of these patients were from the PA ≤ 4.9° group. PA ≤ 4.9° significantly and independently affected the mortality model adjusted for Model for End-Stage Liver Disease score and age (hazard ratio = 2.05, 95%CI: 1.11-3.77, P = 0.021). In addition, Kaplan-Meier curves showed that patients with PAs ≤ 4.9° were significantly more likely to die. CONCLUSION In male patients with cirrhosis, the PA ≤ 4.9° cutoff was associated independently with mortality and identified patients with worse metabolic, nutritional, and disease progression profiles. The PA may be a useful and reliable bedside tool to evaluate prognosis in cirrhosis.
Emotion Modeling and Machine Learning in Affective Computing

Affective computing is a computing related to, arise from, or influences emotions. Various emotion modeling and machine learning methods are used in affective computing. To explain human emotional states, psychologists developed various emotion models. Their models and methods have been adapted to affective computing. Machine learning, a field of study that gives computers the ability to learn without being explicitly programmed, is also essential to make computers deal with uncertain object like emotion. In this survey, we first review several emotion modeling techniques. Then we review machine learning techniques which is used in affective computing.
Investigation on the performance of a new multiple choice strategy for PSO Algorithm in the task of large scale optimization problems

In this paper, a novel strategy for particle swarm optimization is presented and investigated over its ability to improve the performance of PSO algorithm in the task of large scale optimization problems. This proposed strategy alters the way the velocity of each particle is determined. Promising results of this innovative strategy are presented in the results section and briefly analyzed.
Robust Automatic Facial Expression Detection Method

Recently, the recognition of occluded facial expressions attract more and more people’s attention . Sparse representation based classification (SRC) method gives good performance on face recognition (FR) and facial expression recognition (FER), well-known for its robustness to occlusion. Histograms of Oriented Gradient (HOG) descriptors are very efficient to represent the shape information of different facial expressions and robust to various illumination. Since, this paper propos es a novel method by using HOG descriptors conjunction with SRC framework for FER. Experiment results show that the proposed method gives better performance than the existing state-of-the-art methods. Furthermore, the proposed method is not only robust to assigned occlusion s , but also to random occlusion s .
In vivo fiber tractography using DT‐MRI data

Fiber tract trajectories in coherently organized brain white matter pathways were computed from in vivo diffusion tensor magnetic resonance imaging (DT‐MRI) data. First, a continuous diffusion tensor field is constructed from this discrete, noisy, measured DT‐MRI data. Then a Frenet equation, describing the evolution of a fiber tract, was solved. This approach was validated using synthesized, noisy DT‐MRI data. Corpus callosum and pyramidal tract trajectories were constructed and found to be consistent with known anatomy. The method's reliability, however, degrades where the distribution of fiber tract directions is nonuniform. Moreover, background noise in diffusion‐weighted MRIs can cause a computed trajectory to hop from tract to tract. Still, this method can provide quantitative information with which to visualize and study connectivity and continuity of neural pathways in the central and peripheral nervous systems in vivo, and holds promise for elucidating architectural features in other fibrous tissues and ordered media. Magn Reson Med 44:625–632, 2000. Published 2000 Wiley‐Liss, Inc.
Clustering of ellipses based on their distinctiveness: An aid to ellipse detection algorithms

Many ellipse detection algorithms produce multiple elliptic hypotheses corresponding to a single elliptic object. Thus, it is needed to identify similar ellipses that possibly belong to the same object and cluster them as a single object. This will reduce the computational and memory requirement for further higher level processing of ellipse detection algorithms. Here, we present a method better than Basca's method [4] to detect similar ellipses and cluster them. This method is independent of the ellipse detection and hypotheses generation scheme. The presented method is simple, intuitive, requires few computations, and is applicable to wide range of scenarios.
Distributed under Creative Commons Cc-by 4.0 a Comparison of Least Squares Regression and Geographically Weighted Regression Modeling of West Nile Virus Risk Based on Environmental Parameters

Background. The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity. Methods. We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model. Results. LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R 2 = 0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R 2 = 0.71). Conclusions. The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.
Real Map Virtualization for Indoor Positioning using Smartphone

Indoor maps are highly essential for indoor positioning and location-based services. People existing in big building such as mall, airport and hospitals leads to rapid growth in location based applications. Todays, typical indoor positioning systems employ a training/positioning model using Wi-Fi fingerprints. While these approaches have practical results in terms of coverage, they require an indoor map, which is rarely available to the user and involves significant training costs. In this paper, we present a virtual map, generated automatically inside user smartphone, which lets the user watching her/himself moving forward, backward, left or right from well-known landmarks on the floor map. This map is generated in the form of rectangle of grid points with no prior information of the actual indoor map; therefore, it is suitable for using it at any building. An indoor positioning system based on Smartphone sensor (Accelerometer and Magnetometer) incorporated with existing APs in the building is used to test the suggested virtual map. General Terms Algorithms, Measurement, Experimentation.