Miracle-dz/UniFAR-FTUs huggingface dataset:
Community norms for the Eating Disorder Examination Questionnaire among cisgender gay men.

OBJECTIVE Prior norms of the Eating Disorders Examination Questionnaire (EDE-Q) among men have not considered sexual orientation. This study's objective was to assess EDE-Q community norms among cisgender gay men. METHOD Participants were 978 self-identified cisgender gay men from The PRIDE Study recruited in 2018. RESULTS We present mean scores and standard deviations for the EDE-Q among cisgender gay men ages 18-82. Among cisgender gay men, 4.0% scored in the clinically significant range on the global score, 5.7% on the restraint, 2.1% on the eating concern, 10.5% on the weight concern, and 21.4% on the shape concern subscales of the EDE-Q. The global score as well as weight and shape concerns in a young adult subsample (18-26 years) from The PRIDE Study were higher than previously reported norms in young men (Lavender, 2010). Participants reported any occurrence (≥1/28 days) of dietary restraint (19.8%), objective binge episodes (10.9%), excessive exercise (10.1%), laxative misuse (1.1%), and self-induced vomiting (0.6%). Binge eating, excessive exercise, and self-induced vomiting in The PRIDE Study subsample were lower than previously reported in young men. DISCUSSION We provide EDE-Q norms among cisgender gay men, which should aid clinicians and researchers to interpret the EDE-Q scores of cisgender gay men.
Visually Grounded Video Reasoning in Selective Attention Memory

Visual reasoning in videos requires understanding temporal concepts in addition to the objects and their relations in a given frame. In analogy with human reasoning, we present Selective Attention Memory Network (SAMNet), an end-to-end differentiable recurrent model equipped with external memory. SAMNet can perform multi-step reasoning on a frame-by-frame basis, and dynamically control information ﬂow to the memory to store context-relevant representations to answer questions. We tested our model on the COG dataset (a multi-frame visual question answering test)
Weighting Estimation for Texture Based Face Recognition via Fisher Discriminant

available from http://ieeexplore.ieee. org/search/srchabstract.jsp? tp=&arnumber=5669242&openedRefi nements%3D*%26filter%3DAND%28NOT%284283010803%29%29%26matchBoolean% 3Dtrue%26searchField%3DSearch+All%26queryText%3D%28Authors%3Afeitosa%29 Weighting Estimation for Texture Based Face Recognition via Fisher Discriminant Raul Queiroz Feitosa, Dário Augusto Borges Oliveira, Alvaro de Lima Veiga Filho Department of Electrical Engineering Pontifical Catholic University of Rio de Janeiro Rio de Janeiro, Brazil raul@ele.puc-rio.br, dario@ele.puc-rio.br , alvf@ele.puc-rio.br Raphael Pithan Brito, José Luiz Buonomo de Pinho, Antonio Carlos Censi Montreal Informática Rio de Janeiro, Brazil raphaelpithan@yahoo.com. br , pinho@montreal.com. br , accensi@montreal.com. br Abstract – Texture based automatic face recognition (AFR) methods proposed in the last few years have been successful in large-scale applications where the database consists of a single frontal view per person. In those methods the global similarity between two faces is generally given by a linear combination of the local similarities computed upon each face region. Little attention has been given so far to the estimation of the weights that express the relative contribution of each face region to global similarity score. This paper addresses this issue and proposes a method to estimate the optimum weighting for texture based AFR. The solution is given by the most discriminative axis within a similarity space using Fisher discriminant analysis. The proposed method is evaluated in experiments conducted on the FERET and on the FEI face databases. For texture coding both Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) are considered. The experiments indicate that the proposed method brings a substantial improvement in terms of recognition performance in comparison to other weightings and weighting methods proposed in the literature. Texture based automatic face recognition (AFR) methods proposed in the last few years have been successful in large-scale applications where the database consists of a single frontal view per person. In those methods the global similarity between two faces is generally given by a linear combination of the local similarities computed upon each face region. Little attention has been given so far to the estimation of the weights that express the relative contribution of each face region to global similarity score. This paper addresses this issue and proposes a method to estimate the optimum weighting for texture based AFR. The solution is given by the most discriminative axis within a similarity space using Fisher discriminant analysis. The proposed method is evaluated in experiments conducted on the FERET and on the FEI face databases. For texture coding both Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) are considered. The experiments indicate that the proposed method brings a substantial improvement in terms of recognition performance in comparison to other weightings and weighting methods proposed in the literature.
SUB-CHANNEL AND POWER ALLOCATION IN FEMTOCELL NETWORKS USING OVERLAPPING COALITION FORMATION GAMES

Resource allocation and interference mitigation are mainly challenge in the plug-and-play femtocell networks. Most of the previous work on resource allocation principally focuses on the non-cooperative way, while this paper further investigates the cooperative way in femtocell networks, in order to further optimize networks’ data rate. Additionally, in the cooperative ideas, the transmission power control is rarely considered. Based on the above, we propose a new overlapping coalition formation game framework with power control, where more efficient and flexible resource allocation is performed in a femtocell network. The key idea here is to cast femtocell base stations as an overlapping coalition formation game coupled with power allocation. On the basis of switching order and independent order, the femtocell base stations or resource units are spontaneously driven to less interfered coalitions. Within each coalition, we design a multi-objective optimal power control method and leverage the weighted sum method to solve it; thus the optimal unequal power is achieved. Simulations present that our algorithm can improve the system data rate by 14.93% for the number of 32 femtocell base stations, compared to the prior art.
Relations for Moments of Dual Generalized Order Statistics from Exponentiated Rayleigh Distribution and Associated Inference

In this paper we obtain exact expressions and some recurrence relations satisfied by single and product moments of dual generalized order statistics from exponentiated Rayleigh distribution. These relations are deduced for moments of order statistics and lower record values. Further, conditional expectation, recurrence relations for single moments and truncated moment are used to characterize this distribution.
PIE: A Data-Driven Payoff Inference Engine for Strategic Security Applications

Although most game theory models assume that payoff matrices are provided as input, getting payoff matrices in strategic games (e.g., corporate negotiations and counter-terrorism operations) has proven difficult. To tackle this challenge, we propose a payoff inference engine (PIE) that finds payoffs assuming that players in a game follow a myopic best response or a regret minimization heuristic. This assumption yields a set of constraints (possibly nonlinear) on the payoffs with a multiplicity of solutions. PIE finds payoffs by considering solutions of these constraints and their variants via three heuristics. First, we approximately compute a centroid of the resulting polytope of the constraints. Second, we use a soft constraint approach that allows violation of constraints by penalizing violations in the objective function. Third, we develop a novel approach to payoff inference based on support vector machines (SVMs). Unlike past work on payoff inference, PIE has the following advantages. PIE supports reasoning about multiplayer games, not just one or two players, it can use short histories, not long ones which may not be available in many real-world situations, it does not require all players to be fully rational, and it is one to two orders of magnitude more scalable than past work. We run experiments on a synthetic data set where we generate payoff functions for the players and see how well our algorithms can learn them, a real-world coarse-grained counter-terrorism data set about a set of different terrorist groups, and a real-world fine-grained data set about a specific terrorist group. As the ground truth about payoffs for the terrorist groups cannot be tested directly, we test PIE by using the payoffs to make predictions about the actions of the groups and corresponding governments (even though this is not the purpose of this article). We show that compared with recent work on payoff inference, PIE has both higher accuracy and much shorter runtime.
Status and distribution of anthracnose disease of dolichos bean in Southern Karnataka

Anthracnose disease of dolichos bean caused by the fungus Colletotrichum lindemuthianum is a serious disease in southern Karnataka where the crop is grown during kharif season. In the present investigation, an intensive roving survey was conducted for 2 years during kharif 2010 and 2011 to record the incidence and severity of foliar and pod infection of anthracnose of dolichos bean. Survey of major dolichos bean growing areas of southern Karnataka indicated that the occurrence of disease ranged from 23.36 per cent to 47.54 per cent. The highest average foliar infection was recorded in Mysore district (55.42%) followed by Chamarajanagar (46.55%) and Chikkaballapur (40.81%) districts during kharif 2010 and the least was recorded in Ramanagar district (25.69%). During 2011 highest average foliar infection was recorded in Mysore district (39.67%) followed by Chamarajanagar (34.87%) and Hassan (27.88%) districts and the least was recorded in Ramanagar district (21.46%). Average pod infection was highest in Mysore district (26.68%) followed by Chamarajanagar district (21.42%) and the least in Ramanagar district (9.92%) during kharif 2010. During 2011 also, average pod infection was highest in Mysore district (22.06%) followed by Chamarajanagar (15.44%) and Chikkaballapur (12.78%) districts and the least was observed in Ramanagar district (6.97%).
Chinese Enterprise Empirical Data Analysis: Relationship of Lean Production and Energy Consumption- A New Fuzzy Math Application

Lean production and energy consumption has become a new trend of every industrial management. For example, the traditional Chinese medicine department, lean cover overall treatments operations. If every step did not waste resource, cost down, carbon elimination, and saving energy consumption would be possible. This study attempted to explore the exactly correlation between lean production and energy consumption. After checked the data set from 288 MBA (which are managers in companies) personnel, this study offered valuable empirical data from real world. A new MIP model had been proposed and the fuzzy decision-making model was applied to validate the correlation. With the problem formulation and calculation, the results showed that lean production implementation in Chinese enterprises would raise energy consumption. Moreover, energy consumption would increase with lean production level. The results indicated that the overall level of lean production in Chinese enterprises was relative low as it needed to consume plenty of resources to implement lean production. It further suggested that enterprises in less developed countries needed more fundamental understanding of lean production and implemented lean production without increasing resource consumption. KeywordsLean production; Energy consumption; Fuzzy math model; Health development aspect; Empirical study
MARKETI G STRATEGY AS A EXPLA ATORY VARIABLE OF MARKET SELECTIO : A SIMULTA EOUS MODELI G APPROACH

This study develops a model that explains export sales volume by destination based on a company’s export marketing strategy. A Seemingly Unrelated Regression Model (SURE) simultaneously estimates the explanatory value of the different elements of the marketing strategy as well as company characteristics, such as experience, size and motivation to export, on entry decisions to six different regional markets made by exporting companies in a southern European country. The data was collected from a sample size of 2.264 exporting companies. Our findings confirm the importance of exporting experience and proactiveness in determining high export sales volumes in every regional market except for those psychologically close. Nevertheless, different marketing strategies depending on the region lead to high export sales volumes. For example, low price strategies in the case of Latin America or differentiation strategies based on the augmented product in the case of the U.S. generate high export sales. Promotional expenditures are of higher importance for distant markets, but for closer markets channel development is the key.
The Impact of Hurricanes Strikes on Tourist Arrivals in the Caribbean Charley Granvorka Université des Antilles et de la Guyane (CEREGMIA)

We quantify the impact of hurricane strike on the tourism industry in the Caribbean. To this end we first derive a hurricane damages index by using historical 3-hourly hurricane track data within a physically based wind field model which allows one to calculate the actual wind speed experienced at any locality relative to the hurricane eye of a passing or land falling hurricane. We then employ this hurricane destruction index within a cross-country panel data context to estimate its impact on country-level tourist numbers. Our results suggest that an average hurricane strike causes tourism arrivals to be about 2% lower of what it would be if no strike had occurred.
Tools for Predictive Analytics: An Overview

Predictive analytics deals with analysis of historical data to uncover hidden pattern and provide new insights using techniques from statistics, modeling, machine learning and artificial intelligence. Predictive analytic tools help in the process of extraction and prediction which help the users in better decision making. Predictive Analytic tools provide business users with simple self explanatory charts, graphs and scores which help them to understand the likelihood of possible outcomes. In this work we have presented few popular state-of-the-art predictive analytic tools with their most important feature and approach and also the programming language in which they are implemented.
Situated Grounding Facilitates Multimodal Concept Learning for AI

Peer-to-peer human-computer interactions require a minimum level of capability that remains beyond current unimodal approaches. Computers must recognize and generate communicative acts within multiple modalities, understand the grounding of communicative acts within the shared context and situation of both interlocutors, and appreciate the consequences of behavior and actions within the interaction. In this short paper, we discuss an approach to interactive concept learning using multimodal simulations that situate and contextualize the interaction, thereby visually demonstrating what the computer believes and understands. We examine an example of situated grounding in a collaborative task, and its uses in probing learned models and interactive learning
From doc2query to docTTTTTquery

The setup in this work follows doc2query, but with T5 [8] as the expansion model. T5 is a sequence-tosequence model that uses a similar pretraining objective as BERT [3] to pretrain its encoder-decoder architecture. In this model, all target tasks are cast as sequence-to-sequence tasks. In our case, we feed as input the passage and train the model to generate the question. We train the model with a constant learning rate of 10−4 for 4k iterations with batches of 256, which corresponds to 2 epochs with the MS MARCO training set. We use a maximum of 512 input tokens and 64 output tokens. In the MS MARCO dataset, none of the inputs or outputs have to be truncated when using these lengths. Similar to Nogueira et al. [7], we find that the top-k sampling decoder [4] produces more effective queries than beam search. We use k = 10. In all experiments, we use T5-base as we did not notice any improvement in retrieval effectiveness with the large model. We did not experiment with T5-3B and T5-11B due to their computational cost.
Anomaly Detection for In-Vehicle Networks using a Sensor-based Approach

Modern vehicles made a meaningful development to more complexity as well as connectivity in the last decade. Therefore, they cannot be seen as a closed system anymore. As the openness of the vehicle is increasing, so does the security risk for the in-vehicle networks and its components. Apart from threats for comfort and confidentiality, these attacks can also affect safety critical systems of the vehicle and hence endanger the driver and other road users. This paper discusses anomaly detection capabilities for in-vehicle networks based on sensors monitoring the internal network traffic. With respect to characteristics of typical vehicular networks, like the Controller Area Network (CAN), a recognition model for potential attacks during the operation of the vehicle without causing false positives is presented. Moreover, important design and application criteria for such an extension of the vehicle’s security architecture are explained and discussed.
Inefficiency of Equilibria in Task Allocation by Crowdsourcing

Crowdsourcing is a promising way for solving problems in a distributed manner, which outsources tasks to an undefined, generally large group of agents in the form of an open call. The important feature in crowdsourcing is that contractors choose tasks to solve, which is different from a procurement auction that a contractee chooses contractors. How efficient crowdsourcing is has not been sufficiently studied, although a lot of studies about auction mechanisms exist. To clarify the efficiency of crowdsourcing-style problem solving, we provide two analysis models: one is for the case that all the contractors have the same type and another is for the case that two types of contractors exist. Then, we examine the efficiency by comparing social surplus in an equilibrium allocation to social surplus in an optimal allocation. The theoretical analysis and the experimental results show how efficiency deteriorates in terms of the values of parameters such as the probability of success, an amount of reward, and the cost for completing the task.
Men's Involvement in Safe Motherhood

Objective: Safe motherhood incentives have not been successful in reducing maternal mortality in Nigeria probably because interventions are centered around the women who are rather passive in decision making on reproductive health in an average African family setting. The aim of this study is to look critically into men involvement in pregnancy care to assess their role in decision making in pregnancy care, the level of men’s awareness of danger signs of obstetric emergency and to investigate the attitude, action and perception of men in obstetric emergencies. Materials and Methods: This study was conducted in Ife Central Local Government of Osun State, Nigeria. Obalufon and Sabo communities were purposely selected to incorporate the three major ethnic backgrounds in Nigeria. Multi-stage sampling technique and serial recruitment were used. Gender-specific focus group discussions were also done. Data analysis was done using computer statistical package for social sciences. Results: Three-hundred married (131 men and 169 women) respondents in reproductive age group were selected and interviewed. Men were the major decision makers in pregnancy with women playing supportive role. Less than 15% of female respondents took part in decision making. Men’s role was found to be mainly in the area of financial support. The level of men attendance at the antenatal clinic was very low (7.9%). Men’s awareness of danger signs in pregnancy was high and in obstetric emergency many men believed the women should make the decision. Health seeking behaviour when in need of emergency obstetric care was independent of age, educational status and ethnicity. Discussion: The study showed male gender supremacy in decision making. It also showed that a high level of male knowledge on reproductive health and obstetric care was needful to improve pregnancy outcome. To a varying extent, men supported their wives during pregnancy in terms of financial contribution and accompanying them to health facility for delivery but major decisions were made by men. Spousal attendance especially at antenatal clinic and delivery should be encouraged as this served as avenue to educate them.
EFFECT OF LOWDOSE INTRAVENOUS KETAMINE IN POSTOPERATIVE ANALGESIA FOR HYSTERECTOMY AND ADNEXECTOMY

OBJECTIVE: To evaluate the efficacy of low dose ketamine (0.15 mg/Kg i.v.) as a pre-emptive pain relief after general anesthesia. METHOD: Sixty-nine gynecological surgery patients were enrolled in this prospective, double-blind study. All received total intravenous anesthesia and were randomized to group A (n = 23) to receive preoperative ketamine, to group B (n = 23) to receive postoperative ketamine or to group C (n = 23) to receive placebo. A patient-controlled anesthesia device with morphine and ketorolac was used to provide postoperative pain relief. We evaluated the time taken to resume spontaneous breathing and to administer the first analgesic dose; the pain score on a visual analogue scale at rest at 1, 6, 24, 48 h and on the fifth day, and upon movement on the first and fifth days; and morphine consumption at 6, 24 and 48 h. RESULTS: Recovery of spontaneous breathing occurred later in group B (10 +/- 5 min) than in group A (6.68 +/- 3.64 min; p < 0.05) or in group C (8 +/- 4 min). The first analgesic dose was infused later in group B (45.24 +/- 16.16 min; p < 0.001) than in group A (26.45 +/- 11.65 min) or in group C (25.47 +/- 9.19 min). The pain scores at rest were similar. On the fifth day, pain upon movement was less intense for patients treated with ketamine (group A 12 [10-21] and group B 13.5 [2-22]) than in patients treated with placebo (group C 23 [15.5-36.75]) (p < 0.05 group C vs. group A; and p < 0.01 group C vs. group B). Morphine consumption at 6 h was less in group B than group C (p < 0.01). CONCLUSIONS: A low dose of ketamine has no preemptive analgesic effect.
POLYCYSTIC OVARY SYNDROME TREATMENT OF SUBFERTILITYWITH INSULIN LOWERING MEDICATIONS

Background: Polycystic ovarian syndrome (PCOS) is one of the most common hormonal disorder affecting an estimated 10 percent of women of reproductive age and is a leading cause of infertility. (Franks S 1995. Nazir et all in 1999 have reported 20.7% prevalence of polycystic ovarian syndrome in Pakistan. One of the major biochemical features of polycystic ovary syndrome is insulin resistance accompanied by compensatory hyperinsulinemia (elevated fasting blood insulin levels). Women with polycystic ovarian syndrome are currently treated according to their presenting features but it is suggested by most of the researchers that endocrinopathy which is the main cause of all these symptoms can only be treated by reducing hyperinsulinemia. Method: We studied 60 patients (age range 20-3oyers) and they were divided into group A group B. Group A was given Clomiphene Citrate and group B was given Metformin. Result: A non-significant effect was observed between the two groups when %age of conception and abortion was observed. While considering the ovarian hyper stimulation syndrome (OHSS) a significant difference was seen that is 10% of patients in group A showed OHSS while non in group B.