Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of physical activity level with the eating attitude and body composition among female college teachers

The study attempts to investigate the relationship of physical activity level with eating attitude, anthropometric characteristics and body composition among female college teachers. Total 300 female college teachers from different colleges of Malwa Region of Punjab were selected thorough purposive sampling technique. The age of subjects ranged between 31 to 60 years. Height was measured by using the standard anthropometric rod. Body weight of the subjects was measured with a portable weighing machine. Body mass index was then calculated using the formula weight (kg)/height (m). The skinfolds thicknesses of body parts of the subjects were taken with Harpenden skinfold caliper. Percentage body fat as estimated from the sum of skinfolds was calculated using standardized equations. Physical activity level of the subjects was evaluated with the help of International Physical Activity Questionnaire (IPAQ). Eating attitude of the college teachers was assessed with the help of the questionnaire, 26 items eating attitude test (26-EAT). The results of the study revealed that 31 % of female college teachers were physically inactive, 56% were moderately active and 13% were highly physically active. It was shown from results that 38% of female college teachers had normal weight, 51.67% were overweight and 10% were reported to be obese. It was reported that 47.67 % of female college teachers had normal eating attitude and 52.33 % were reported to have disordered eating habits. The physical activity level of female college teachers was found to have significantly negative relationship with height (p<0.05), weight (p<0.05), BMI (p<0.05), fat percent (p<0.05) and lean body mass (p<0.05) and eating attitude (p<0.05). It is concluded that the physical activity level had significant effect on eating habits and body composition among the college teachers.
A Comparison of

- In this paper a comparison is made between two softwares MATLAB and PSIM. MATLAB and PSIM are used for teaching power electronics. Simulation of various power electronic circuits is presented like single phase controlled rectifier, dc-dc step down chopper and three phase dc-ac voltage source inverter. With computer modelling and simulation along with classroom courses and laboratory experiments, students can learn any power electronic circuit. The comparison between MATLAB and PSIM is made based on parameters like ease of design, time taken to build the model, functionality, research potential and the correctness of achieved results.
Equity duration: a dividend yield approach

Carmine De Franco, PhD Quantitative analyst carmine.de-franco@ossiam.com Bruno Monnier, CFA Quantitative analyst bruno.monnier@ossiam.com Ksenya Rulik, PhD, CFA Head of Quantitative Research ksenya.rulik@ossiam.com We look at the exposure of stock portfolios sorted by dividend yields to interest rate risk and determine if this can explain differences in risk and risk-adjusted performances between low and high dividend yield portfolios over a 25-year period for US equities. As for volatility sorted portfolios, we do find small but positive duration for high dividend yield portfolios. Nevertheless, the addition of the interest rate factor brings little explanatory power to the 4-Factor model and does not explain the alphas away. Because of the similarities in the results between volatility and dividend yield sorted portfolios with respect to interest rate risk, we try to highlight any potential overlap between the two. More precisely, we build double sorted volatility portfolios in a way that neutralizes the dividend yield difference between them. The model now fits the portfolios’ returns better than the pure volatility sorted case. The duration for lower (double sorted) volatility portfolios is almost 38% lower than the pure case, although the alphas are not explained away and remain significant. These findings shed light on the existing link between low volatility and high dividend yield portfolios, for which there is a substantial overlap. As such, a significant portion of low volatility duration can be attributed to their high dividend yield characteristic. JEL Classification: : C41, C52, G11, G12
Evaluation Of The Transfer Of The TOL Plasmid Pseudomonas Putida To Groundwater-Derived Biofilms In A Model Rock-Fracture Aquifer

Non-aqueous phase liquids (NAPLs) can become entrapped in subsurface rock fractures and become a long-term groundwater contaminant source. While remediation technologies exist, they can be expensive. Subsurface microorganisms can also degrade NAPLs trapped in the subsurface; however, this is a slow process. The possibility of enhancing microbial degradation of NAPLs via a plasmid transfer mechanism in a model rock fracture aquifer was explored. There was no indication that introduction of donor strain Pseudomonas putida SM1443::gfp2x-pWW0::dsRed into the model system led to transfer of the degradative TOL plasmid pWWO, or led to increased degradation of model NPL toluene. Plate matings with the donor strain and a groundwater-derived microbial consortium indicated that few potential recipients existed in the community. Nutrient concentration was ruled out as a limiting factor of plasmid transfer.
To interact or not? The convergence properties of interacting stochastic mirror descent

An open problem in optimization with imperfect information is the computation of an exact mini-mizer. Possible solutions include using a decreasing step size, an increasing batch size or variance reduction techniques. In this work we take a different approach and run replicas of mirror descent which we allow to interact. In particular, we study the convergence of interacting stochastic mirror descent and show that interaction can decrease the variance. Therefore, interaction is an alternative to decreasing the learning rate or to increasing the batch size. We show that in a convex, ill-conditioned problem interacting mirror descent results in faster and closer convergence to the optimum, and in a nonconvex setting interactions can help to escape from saddle points. In the distributed case we also discuss a variant of our algorithm that uses second-order information in order to encourage convergence to a solution that achieves both consensus and optimality.
Learning Latent Graph Representations for Relational VQA

Recent state-of-the-art Visual Question Answering (VQA) systems achieve impressive results by tokenizing image objects and language queries and learning a transformer-based model on top of these representations. The key mechanism of transformer-based models is cross-attentions, which implicitly form graphs over tokens and act as diffusion operators to facilitate information propagation through the graph for question-answering that requires some reasoning over the scene. We reinterpret and reformulate transformer-based model to explicitly construct latent graphs over tokens and thereby support improved performance for answering visual questions about relations between objects. We demonstrate the feasibility of our hypothesis by using ground-truth scene graphs to help answer questions in the relational GQA dataset, showing a human-like upper-bound performance. Then we pro-pose a graph-transformer-based model to learn latent graph representations in an end-to-end fashion without explicit supervision. Finally, we demonstrate that jointly learning latent graph representations along with VQA actually out-performs VQA using the current best supervised scene-graph parser.
A Mathematical Model Predicts Salt Tolerance in Upland Rice at the Germination Stage

Objective: Soil salinization is an important abiotic stressor limiting crop yield. Screening of upland rice germplasm resources during germination to choose salt-tolerant varieties for cultivation on saline land has great potential to improve crop yields. Results: Here, we measured the germination rate (GR), root fresh weight (RFW), root length (RL), shoot fresh weight (SFW), shoot length (SL), and total fresh weight (TFW) of 25 upland rice varieties under salt treatment of 0 or 150 mM NaCl at 10 days after sowing (DAS). Under the stress of 150 mM NaCl, the GR, RFW, RL, SFW, SL and TFW of the measured upland rice varieties decreased to different degrees. Through cluster analysis, the salt tolerance of the tested varieties was divided into four categories: five varieties showed high salt tolerance (HST), seven showed moderate salt tolerance (MST), six showed weak salt tolerance (WST), and seven showed salt sensitivity (SS). The established salt tolerance formula for upland rice (Y = (0.311 × STI of RFW) + (0.382 × STI of RL) + (0.239 × STI of SFW) + (0.388 × STI of SL) + (0.903 × STI of TFW) − 0.051) can used to assess the salt tolerance of every upland rice variety. With 150 mM NaCl treatment, TFW was a reliable indicator of the salt tolerance of upland rice. We then used this information to develop an equation for a salt tolerance index (STI) based on TFW that could predict the salt tolerance of any upland rice variety. Conclusion: In this study, 25 upland rice varieties were distributed into four classes according to cluster analysis: five HST, seven MST, six WST and seven SS. We put forward a mathematical equation that can predict any salt tolerance of upland rice, and conclude that TFW is a reliable physiological indicator to evaluate salt tolerance of upland rice. Our method has the potential to greatly improve upland rice yields in saline–alkali land by enabling salt tolerance evaluation and breeding of salt-tolerant crops.
A Cross-Sectional Study of Wisdom: A Matter of Age and Gender

The current study was designed to explore the potential relationship of wisdom with age and gender. A sample of 400 individuals, representative of both genders (men=185 & women=215 with age range 17-50 years) was drawn from various educational institutions and work places of Khyber Pukhtunkhwa. Ardelt’s (2003) three-dimensional wisdom scale (3D-WS) was used as a tool to collect the relevant information. According to the results, gender accounted for significant variation in wisdom. Men outperformed women on affective and reflective dimensions of wisdom. However, there was no significant gender difference on the cognitive dimension that indicates that, on average, men and women are equally high in cognitive aspect of wisdom. A strong relationship was also observed between age and wisdom attesting wisdom as dependent on age.
Microbial safety of meat sold in Orathanadu region, Thanjavur

Microbial contamination of foods is a significant vital food safety issue throughout the world. Reducing the microbial contamination of meat will reduce the transmission of food-borne pathogens to consumers. Therefore, the study determined the prevalence and antimicrobial sensitivity pattern of food-borne pathogens in chevon and chicken samples sold at commercial markets in Orathanadu region, Tamil Nadu. A total of 50 meat samples were collected randomly from different regions of Orathanadu and analyzed for food-borne pathogens using selective medium. The characteristic Salmonella, E. coli and Staphylococcus aureus colonies isolated were subjected to biochemical tests and antibiotic resistance study. The level of Salmonella, E. coli and S. aureus in the chicken samples was observed as 53%, 37% and 27% respectively. Chevon samples had 65% Salmonella, 40% E. coli and 55% S. aureus. Higher prevalence of Salmonella followed by S. aureus and E. coli was observed in meat samples indicating the possible cross-contamination that occurred during slaughtering process may pose potential public health risk to consumers and meat handlers. S. aureus had showed higher resistance to antibiotics compared to E. coli and Salmonella. It is concluded that strict hygienic practices especially during slaughtering, appropriate use of use of antibiotics in animal husbandry practices; proper washing of meat before cooking and thorough cooking of meat could reduce and control the contamination of food-borne pathogens and the emergence of antimicrobial resistance in foods of animal origin.
A comparative study of mental toughness among the cricket players of different age group from Delhi

Mental toughness is a phrase often used to describe how we observe an athlete’s behavior especially when we perceive that they overcome challenging events beyond what is normally expected in a given context. To achieve this purpose, twenty female Cricket players each at the age group of 9-12, 13-16 and 17-20 years were selected from Delhi, who regularly practice the game and participate in various tournaments. “Mental Toughness Questionnaire” designed by Dr. Goldberg, was responded by all the subjects. Sports Mental Toughness Questionnaire was used as a tool for collection of data. All the filled in questionnaires were collected from the subjects and scoring was done according to the scoring key. Usually an individual took 5 to 10 minutes in completing the test. The collected data underwent descriptive analysis followed by one way analysis of Variance. The level of significance was set at 0.05. Comparing the pair wise difference of means with critical difference it was evident that there was no difference between means of 9-12 years and 13-16 years group, whereas mean of the 17-20 years group showed there was higher difference between than that of first and second. Hence it proved that the age groups from 17-20 years are mentally tough when compared to 9-12 years and 13-16 years of female Cricket players.
Attention Option-Critic

Temporal abstraction in reinforcement learning is the ability of an agent to learn and use high-level behaviors, called options. The option-critic architecture provides a gradient-based end-to-end learning method to construct options. We propose an attention-based extension to this framework, which enables the agent to learn to focus different options on different aspects of the observation space. We show that this leads to behaviorally diverse options which are also capable of state abstraction, and prevents the degeneracy problems of option domination and frequent option switching that occur in option-critic, while achieving a similar sample complexity. We also demonstrate the more efficient, interpretable, and reusable nature of the learned options in comparison with option-critic, through different transfer learning tasks. Experimental results in a relatively simple four-rooms environment and the more complex ALE (Arcade Learning Environment) showcase the efficacy of our approach.
Speech Representation Learning for Emotion Recognition Using End-to-End ASR with Factorized Adaptation

Developing robust speech emotion recognition (SER) systems is challenging due to small-scale of existing emotional speech datasets. However, previous works have mostly relied on hand-crafted acoustic features to build SER models that are difﬁcult to handle a wide range of acoustic variations. One way to alleviate this problem is by using speech representations learned from deep end-to-end models trained on large-scale speech database. Speciﬁcally, in this paper, we leverage an end-to-end ASR to extract ASR-based representations for speech emotion recognition. We further devise a factorized domain adaptation approach on the pre-trained ASR model to improve both the speech recognition rate and the emotion recognition accuracy on the target emotion corpus, and we also provide an analysis in the effectiveness of representations extracted from different ASR layers. Our experiments demonstrate the importance of ASR adaptation and layer depth for emotion recognition.
Defining and characterizing Urban Logistics Spaces: insights from a port city and generalization issues

This paper presents a preliminary analysis of Urban Logistics Spaces (ULS) in Barranquilla, Colombia, with the aim of defining a typology in port cities in developing countries and examining the relevance of using a specific typology for those cities or having a unique and general typology, for any city in the world. Firstly, the background to the research is presented. Then, the methodology for the characterization of urban facilities and the definition of the typology is presented. A qualitative characterization was carried out through semi-structured interviews, in order to understand the specificities of the ULS located in Barranquilla, and to analyses how the logistic spaces that are not yet in that context could improve the city's logistic system. The data collection was carried out with the participation of nine (9) actors belonging to the logistic system of the city of Barranquilla. The results show that the "standard" typology chosen, typical of the European context, is partially recognized by the professionals of Barranquilla, and therefore the identification of other types of ULS currently operating in the Colombian context was obtained. Consequently, it is necessary to standardize the typology of urban logistic spaces and to deepen the analyses to define a general typology, which would be reduced in relation to the type of city, but which would have a unified general base capable of being later adapted to any city.
Development of a Swarming Algorithm Based on Reynolds Rules to control a group of multi-rotor UAVs using ROS

The objective of this article is to present a swarming algorithm based on Reynolds ﬂocking rules to drive a group of Unmanned Aerial Vehicles (UAV) together as a coherent swarm. We used quadrotors controlled at the low level by a Pixhawk autopilot board and carrying an embedded Linux board (Raspberry Pi) to execute the control algorithms. Each robot is also equipped with distance sensors used to detect other robots next to itself. The swarming algorithm runs on the ROS platform and was implemented in C++. A simulating environment based on the Gazebo Simulator was prepared to test and evaluate the algorithm in a way as close to the reality as possible.
Radio-isotope Identification Using Dictionary Learning Approach for Plastic Spectra

A plastic scintillator has been widely used as a radiation portal monitor (RPM) for homeland security deployed at airports, seaports, and border crossing to detect illegal radioactive materials. To adequately identify variable radio-isotopes, it should contain an advanced spectroscopic processing to overcome its inherent low resolution and absence of photoelectric peaks. Our focus is the radio-isotope identification (RIID) for  spectra obtained from the plastic scintillation detector. Many algorithms including region of interest, energy windowing, and inverse calibration matrix algorithm has been intensively studied for RIID of  spectra. Recently, algorithm using machine learning such as artificial neutral network, principle components analysis, and so forth has been implemented into RIID and shown the outstanding performance [1]. Compressive sensing (CS) is an advanced sampling theory with efficiently acquiring signals than NyquistShannon sampling theorem by solving sparse coefficient. The basic structure of CS theory could be expressed as Eq. (1). Y = DX (1) where Y∈R is a matrix consisting of measured data, D∈R is a overcomplete matrix (K≫M) called dictionary, and X∈R is a sparse matrix. The very dictionary indicates a proper representation basis of data sets by means of reduced dimensionality subspaces, which can be adaptive to both the input signal and the processing tasks. Fourier, discrete cosine, and wavelet basis are commonly used as a predescribed dictionary for signal reconstruction. In fact, these prespecified dictionary could not be suitable for sparse representation of  spectra because the spectra show a variant of different Gaussian distribution in each measurement. In this work, dictionary learning as a supervised learning approach was proposed and applied to RIID for plastic (EJ-200)  spectra. Label consistent K-SVD (LCKSVD) algorithm was exploited to adapting the dictionary to a given training spectra, Y [2-3]. Labels shown in Table I were made by combining radioisotopes (Ba, Na, Cs, Co). To find such an optimized dictionary and test its performance, Monte Carlo simulation and experimental measurement was carried out. 2. Methods and Results
Learning Without Forgetting Approaches for Lifelong Robotic Vision

—Recent advances in deep learning have achieved exciting results in the ares such as object detection, image recognition and object localization. However, robotic vision poses new challenges for applying visual algorithms due to varying distribution of images from real world and it requires that the model is able to learn knowledge continuously. This competition is about developing lifelong learning algorithms which can be applied to the robotic vision system. This work describes the approach that we submit to this open competition.
WHAT FORM OF FINANCIAL DEVELOPMENT WORKS BEST FOR POOR IN RURAL INDIA?

This paper examines the direct (microcredit), medium-direct (bank credit), and indirect (through economic growth) effect of financial sector development (FSD) on rural poor in India using state-wise annual data from 1999-00 to 2011-12 (for the years 1999-00, 2004-05, 2009-10, and 2011-12). A dynamic panel data analysis for a sample of 15 major Indian states using the generalized method of moments estimators provides an empirical evidence for the direct (microcredit), medium-direct (bank credit), and indirect (economic growth) effect of FSD on rural poor. The paper proposes that formal financial services by banks are primarily availed by non-poor and urban population and hence acts as a medium-direct channel whereas the semi-formal financial services by microfinance institutions specifically target the rural poor and act as a direct channel to affect the poor. It is the first ever study to use statewise data on microcredit disbursed under Self-help Group Bank Linkage Programme to assess the direct impact of FSD on rural poor. Our findings suggest that the reduction in poverty seems to be not fully converting into reduction in income inequality between the richest and the poorest 20 percent. While bank credit disproportionately increases the income of the bottom 20 percent and reduces income inequality, microcredit with high chances of doing so, fails. The poorest seem to be the hardest hit victim of access to finance channel with both the channels increasing the income inequality among the poor. The study questions the social banking initiatives of the government in rural areas where more than 80 percent of the poor reside. There is a need for restructuring financial inclusion programmes with a shift in their focus on rural areas and an improved mechanism to target the poor.
Two-Stage Technique for LTLf Synthesis Under LTL Assumptions

In synthesis, assumption are constraints on the environments that rule out certain environment behaviors. A key observation is that even if we consider system with LTLf goals on finite traces, assumptions need to be expressed considering infinite traces, using LTL on infinite traces, since the decision to stop the trace is controlled by the agent. To solve synthesis of LTLf goals under LTL assumptions, we could reduce the problem to LTL synthesis. Unfortunately, while synthesis in LTLf and in LTL have the same worst-case complexity (both are 2EXPTIME-complete), the algorithms available for LTL synthesis are much harder in practice than those for LTLf synthesis. Recently, it has been shown that in basic forms of fairness and stability assumptions we can avoid such a detour to LTL and keep the simplicity of LTLf synthesis. In this paper, we generalize these results and show how to effectively handle any kind of LTL assumptions. Specifically, we devise a two-stage technique for solving LTLf under general LTL assumptions and show empirically that this technique performs much better than standard LTL synthesis.