Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear brain correlates of trait self-boundarylessness

Abstract Alterations of the sense of self induced by meditation include an increased sense of boundarylessness. In this study, we investigated behavioural and functional magnetic resonance imaging correlates of trait self-boundarylessness during resting state and the performance of two experimental tasks. We found that boundarylessness correlated with greater self-endorsement of words related to fluidity and with longer response times in a math task. Boundarylessness also correlated negatively with brain activity in the posterior cingulate cortex/precuneus during mind-wandering compared to a task targeting a minimal sense of self. Interestingly, boundarylessness showed quadratic relations to several measures. Participants reporting low or high boundarylessness, as compared to those in between, showed higher functional connectivity within the default mode network during rest, less brain activity in the medial prefrontal cortex during self-referential word processing, and less self-endorsement of words related to constancy. We relate these results to our previous findings of a quadratic relation between boundarylessness and the sense of perspectival ownership of experience. Additionally, an instruction to direct attention to the centre of experience elicited brain activation similar to that of meditation onset, including increases in anterior precentral gyrus and anterior insula and decreases in default mode network areas, for both non-meditators and experienced meditators.
Investigation of Correlation between Coronavirus Anxiety, Academic Motivation and Attitude towards the Field Education in Medical Students

Introduction: Coronavirus pandemic has created a wide range of psychological complications. Students of medical sciences, like health personnel, are at a high risk of infection with coronavirus. The present study is an attempt to examine the correlation between anxiety caused by coronavirus and attitude and motivation toward the field of study in medical sciences students at Ilam University of Medical Sciences. Methods: This correlational study was done on 373 students in different fields of medical sciences at Ilam University of Medical Sciences from April to September 2020. The participants were selected through stratified random sampling. Data gathering was done using Corona Disease Anxiety Scale (CDAS), Academic Motivation Scale (AMS), and Educational Attitude Standard Questionnaire. The questionnaires were completed online by the participants. The data were analyzed using SPSS software and Pearson's correlation test, independent t-test and analysis of variance at a significance level of (P<0.05). Results: Based on the Pearson correlation coefficient, the relationship between COVID-19 anxiety and educational motivation (P=0.001) and attitude (P=0.03) was inverse and significant. There was a significant statistical difference in the average score of anxiety caused by coronavirus in students of different fields. The highest mean anxiety score was in the operating room students and the lowest anxiety score was in the laboratory science field (P=0.001). Conclusions: Coronavirus pandemic has created anxiety and lowered educational motivation and attitude in students of different fields of medical sciences.
Preparedness toward participation in disaster management: An online survey among dental practitioners in a disaster-prone region of Eastern India

BACKGROUND: Dentists are often overlooked resources in the workforce for disaster management (DM). To determine the knowledge, attitudes, willingness, and self-perceived effectiveness to participate in DM among general dental practitioners (GDPs) of Eastern India. MATERIALS AND METHODS: A web-based online survey was conducted among 256 Dental Council of India registered GDPs of Cuttack district, Odisha. The 45-item survey comprised closed-ended questions on the demographic data of the participants, years of practice, previous experience in DM, and willingness to participate. Other domains assessed were the participants' objective knowledge of DM, attitude, and self-perceived effectiveness toward participation during disasters. Data were analyzed descriptively, and the Chi-square and Mann–Whitney U tests were used for statistical analysis, with a significance threshold of P < 0.05. RESULT: A total of 154 responses were analyzed, giving a response rate of 60.16%. The average age was ≤35 years, 59.1% were BDS dentists and 78.6% had less than 10 years of practice. Only 18% of them had previous experience with DM, and merely 3.2% were formerly trained; however, 95.5% of the dentists were willing to participate in DM. The mean DM knowledge and attitude scores were 16.12 (CI = 15.4–16.8) and 5.79 (5.45–6.13), respectively. Knowledge and attitude showed a significant correlation. About 56% indicated that they would be able to respond effectively to a disastrous event. Significant associations were observed between age group (P = 0.008), years of clinical practice (P = 0.001), qualification (P = 0.012), previous participation (P = 0.029), and self-perceived effectiveness. CONCLUSION: The knowledge regarding DM among respondents was at an average level. However, the majority of them were found to have a positive attitude toward participation in DM. Thus, including DM in dental curricula and drills for dental professionals might prove beneficial as almost all GDPs were showing greater self-perceived effectiveness and willingness to participate in disasters.
Medical students’ perceptions of the educational environment in a private medical school in Southwest Nigeria

BACKGROUND: The educational environment plays a very important role in determining the overall performance of students. The aim of this study is to determine the perceptions of undergraduate medical students about their educational environment in a Nigerian university. MATERIALS AND METHODS: This was an observational, cross-sectional study conducted among final-year (600 level) undergraduate medical students. The Dundee Ready Education Environment Measure (DREEM) questionnaire was used for the study to assess the educational environment of the medical students. RESULTS: A total number of 100 final-year (600 level) undergraduate medical students participated in the study, out of which 27 (27.0%) were males and 73 (73.0%) were females with a male-to-female ratio of 0.37 to 1. The age range of the respondents was 21–30 years with a mean (±SD) of 23.54 (±1.403). The total mean DREEM score was 116.2/200. The total score of SPL domain was 30.2/48 (62.9%). The total score of SPT domain was 27.3/44 (62.0%), while that of SASP was 19.2/32 (60.0%). The total score of SPA domain was 26.6/48 (55.4%) and that of SSP was 13.1/28 (46.8%). For the SPL, SPT, SASP, and SPA domains, the score was above 50%. However, for the SSP domain the score was less than 50%. CONCLUSIONS: The total mean DREEM score in this study was 116.2/200, more positives than negatives, with the students' social perspective having the least domain score. There is a need to provide adequate social support system for medical students in general and particularly for those who get stressed.
The effect of educational intervention on the quality of life of family caregivers of hemodialysis patients: A randomized controlled trial

BACKGROUND: Family caregivers of hemodialysis patients experience various physical, psychological, social, economic, and spiritual problems that reduce their quality of life. The present study aimed to determine the effect of a family-centered education program on the quality of life of family caregivers of patients undergoing hemodialysis. MATERIALS AND METHODS: This was a randomized controlled trial that was performed on 70 caregivers of patients undergoing hemodialysis in the medical centers of Hazrate Ali Asghar and Hazrate Zahraye Marzieh in Isfahan. Caregivers were randomly divided into experimental and control groups, and the experimental group received an eight-session family-centered education program. Data were collected using the Quality of Life Scale (QOLS)-short form immediately after and 1 month after the intervention. Data analysis was performed using the Statistical Package for the Social Sciences (SPSS) software version 18 and analysis of variance and covariance. RESULTS: The results showed that both experimental and control groups were homogeneous in terms of demographic information and there was no significant difference between them in this regard. Analysis of data on quality of life and its four domains showed that the mean scores of quality of life (P = 0.089) and its four domains including physical health (P = 0.367), mental health (P = 0.429), community relations (P = 0.132), and environmental health (P = 0.232) increased significantly immediately after and 1 month after the intervention (P < 0.001 in all cases). CONCLUSION: Educational programs can improve the quality of life of family caregivers of hemodialysis patients. Therefore, it is recommended that programs be developed and evaluated in various studies in the future.
High-throughput shoot phenotyping reveals temporal growth responses to nitrogen and inorganic and organic phosphorus sources in tomato

Abstract The application of nitrogen (N) and phosphorus (P) fertilizers to soils is required to maintain crop yields, so the sufficient and timely delivery of nutrients to match crop demand is important in fertilizer management. We quantified temporal growth responses of tomato plants with different rates of N and P application using high-throughput shoot phenotyping. The tomato plants were grown in soil that had organic, inorganic or a combination of sources of P incorporated. Additional N was added to each pot at low and high rates, 13 days after planting. At the same rate of total P application, the inorganic P source resulted in greater shoot growth at the early time points. Later on, the plants supplied with organic or mixed P sources grew faster than those that received the inorganic P source, resulting in comparable shoot biomass in all treatments at the time of destructive harvest. The shoot phenotyping data demonstrated that readily available soil P was important for early tomato growth while available N was more important in later stages of vegetative growth. These results suggest that a fertilizer formulation of combined inorganic and organic P sources may be able to sustain rapid and great shoot growth in tomato plants, while also reducing additional N input.
Post-emergence herbicides effect on weeds, yield and economics of Bt cotton

A total of eight treatments were evaluated in a randomized block design (RBD) with three replications. The treatments consisted of pre-emergence application of pendimethalin (30.0% EC) at 1.0 kg/ha + 1 hoeing on 45 DAS, post-emergence application of quizalofop-ethyl 50 g/ha (30 DAS) + 1 hoeing, pendimethalin at 1.0 kg/ha fb quizalofop-ethyl 50 g/ha + 1 hoeing on 45 DAS, post-emergence application of pyrithiobac-sodium 62.5 g/ha (30 DAS) + 1 hoeing on 45 DAS, combination of pyrithiobac-sodium 62.5 g/ha + quizalofop-ethyl 50 g/ha + 1 hoeing on 45 DAS, glyphosate 1.0 kg/ha (45 DAS) as directed spray and control plots of weed free check and unweeded check. The results revealed that application of preemergence herbicide pendimethalin at 1.0 kg/ha at 3 DAS followed by post-emergence herbicide (quizalofop-ethyl 50 g/ha at 30 DAS) + one hoeing or combined post-emergence application of pyrithiobac-sodium + quizalofop-ethyl + one hoeing on 45 DAS recorded lesser weed population and weed dry weight and higher weed control efficiency with lower weed index. These treatments were also comparable with pre-emergence application of pendimethalin + 1 hoeing. Higher yield attributes, viz. number of sympodia per plant, number of bolls per plant and boll weight and seed cotton yield were also registered by these treatments. The yield reduction due to weeds accounted 44.8 and 80.3% during 201213 and 2013–14, respectively The economic analysis indicated that higher total income, net income and benefit-cost ratio were associated with pre-emergence application of pendimethalin followed by postemergence application of quizalofop-ethyl + one hoeing, combined post-emergence application of pyrithiobac-sodium + quizalofop-ethyl + one hoeing and pre-emergence application of pendimethalin + 1 hoeing in both the years of study.
Dual skip connections in U-Net, ResUnet and U-Net3+ for remote extraction of buildings

Urban buildings are extracted from high-resolution Earth observation (EO) images using semantic segmentation networks like U-Net and its successors. Each re-iteration aims to improve performance by employing a denser skip connection mechanism that harnesses multi-scale features for accurate object mapping. However, denser connections increase network parameters and do not necessarily contribute to precise segmentation. In this paper, we develop three dual skip connection mechanisms for three networks (U-Net, ResUnet, and U-Net3+) to selectively deepen the essential feature maps for improved performance. The three mechanisms are evaluated on feature maps of different scales, producing nine new network configurations. They are evaluated against their original vanilla configurations on four building footprint datasets of different spatial resolutions, including a multi-resolution (0.3+0.6+1.2m) dataset that we develop for complex urban environments. The evaluation revealed that densifying the large- and small-scale features in U-Net and U-Net3+ produce up to 0.905 F1, more than TransUnet (0.903) and Swin-Unet (0.882) in our new dataset with up to 19x fewer parameters. The results conclude that selectively densifying feature maps and skip connections enhances network performance without a substantial increase in parameters. The findings and the new dataset will contribute to the computer vision domain and urban planning decision processes.
T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification

Cancer is a complex disease characterized by uncontrolled cell growth and proliferation. T cell receptors (TCRs) are essential proteins for the adaptive immune system, and their specific recognition of antigens plays a crucial role in the immune response against diseases, including cancer. The diversity and specificity of TCRs make them ideal for targeting cancer cells, and recent advancements in sequencing technologies have enabled the comprehensive profiling of TCR repertoires. This has led to the discovery of TCRs with potent anti-cancer activity and the development of TCR-based immunotherapies. In this study, we investigate the use of sparse coding for the multi-class classification of TCR protein sequences with cancer categories as target labels. Sparse coding is a popular technique in machine learning that enables the representation of data with a set of informative features and can capture complex relationships between amino acids and identify subtle patterns in the sequence that might be missed by low-dimensional methods. We first compute the k-mers from the TCR sequences and then apply sparse coding to capture the essential features of the data. To improve the predictive performance of the final embeddings, we integrate domain knowledge regarding different types of cancer properties. We then train different machine learning (linear and non-linear) classifiers on the embeddings of TCR sequences for the purpose of supervised analysis. Our proposed embedding method on a benchmark dataset of TCR sequences significantly outperforms the baselines in terms of predictive performance, achieving an accuracy of 99.8\%. Our study highlights the potential of sparse coding for the analysis of TCR protein sequences in cancer research and other related fields.
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning

We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments. We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. We first trained individual skills in isolation and then composed those skills end-to-end in a self-play setting. The resulting policy exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more; and transitions between them in a smooth, stable, and efficient manner - well beyond what is intuitively expected from the robot. The agents also developed a basic strategic understanding of the game, and learned, for instance, to anticipate ball movements and to block opponent shots. The full range of behaviors emerged from a small set of simple rewards. Our agents were trained in simulation and transferred to real robots zero-shot. We found that a combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training in simulation enabled good-quality transfer, despite significant unmodeled effects and variations across robot instances. Although the robots are inherently fragile, minor hardware modifications together with basic regularization of the behavior during training led the robots to learn safe and effective movements while still performing in a dynamic and agile way. Indeed, even though the agents were optimized for scoring, in experiments they walked 156% faster, took 63% less time to get up, and kicked 24% faster than a scripted baseline, while efficiently combining the skills to achieve the longer term objectives. Examples of the emergent behaviors and full 1v1 matches are available on the supplementary website.
Regularized lattice theory for spatially dispersive nonlinear optical conductivities

Nonlinear optical responses are becoming increasingly relevant for characterizing the symmetries and quantum geometry of electronic phases in materials. Here, we develop an expanded diagrammatic scheme for calculating spatially dispersive corrections to nonlinear optical conductivities, which we expect to enhance or even dominate even-order responses in materials of recent interest. Building upon previous work that enforces gauge invariance of spatially uniform nonlinear optical responses, we review the cancellation of diagrams required to ensure the equivalence between velocity gauge and length gauge formulations, and provide a simple vertex rule for extending optical responses to first order in the light wave vector q. We then demonstrate the method with calculations on a prototypical centrosymmetric model where spatial dispersion admits anomalous secondharmonic generation, a response that is symmetry-forbidden under the dipole approximation.
Exponentially Convergent Numerical Method for Abstract Cauchy Problem with Fractional Derivative of Caputo Type

We present an exponentially convergent numerical method to approximate the solution of the Cauchy problem for the inhomogeneous fractional differential equation with an unbounded operator coefficient and Caputo fractional derivative in time. The numerical method is based on the newly obtained solution formula that consolidates the mild solution representations of sub-parabolic, parabolic and sub-hyperbolic equations with sectorial operator coefficient A and non-zero initial data. The involved integral operators are approximated using the sinc-quadrature formulas that are tailored to the spectral parameters of A, fractional order α and the smoothness of the first initial condition, as well as to the properties of the equation’s right-hand side f(t). The resulting method possesses exponential convergence for positive sectorial A, any finite t, including t=0 and the whole range α∈(0,2). It is suitable for a practically important case, when no knowledge of f(t) is available outside the considered interval t∈[0,T]. The algorithm of the method is capable of multi-level parallelism. We provide numerical examples that confirm the theoretical error estimates.
PROSPECTIVE TEACHER'S SPECIALIZED CONTENT KNOWLEDGE ON DERIVATIVE

Mathematics teacher knowledge has been widely studied, and recently a remarkable advancement has been reach with the proposal of the “Mathematical Knowledge for Teaching” (MKT) model for describing the complex of knowledge that a teacher should have to teach a specific mathematics topic. Nonetheless there are still questions to be addressed, such as, how or under what criteria can the MKT be assessed? How the teacher educators can help the prospective teacher to develop the different components of the MKT? How are related the different components of MKT? In this report, we have tackled, though partially, such questions, by advancing specific criteria to explore the prospective teachers’ knowledge about the notion of derivative: common, specialized and extended knowledge. In this report we inform specifically on the specialized content knowledge.
Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models

With growing size of Neural Networks (NNs), model sparsification to reduce the computational cost and memory demand for model inference has become of vital interest for both research and production. While many sparsification methods have been proposed and successfully applied on individual models, to the best of our knowledge their behavior and robustness has not yet been studied on large populations of models. With this paper, we address that gap by applying two popular sparsification methods on populations of models (so called model zoos) to create sparsified versions of the original zoos. We investigate the performance of these two methods for each zoo, compare sparsification layer-wise, and analyse agreement between original and sparsified populations. We find both methods to be very robust with magnitude pruning able outperform variational dropout with the exception of high sparsification ratios above 80%. Further, we find sparsified models agree to a high degree with their original non-sparsified counterpart, and that the performance of original and sparsified model is highly correlated. Finally, all models of the model zoos and their sparsified model twins are publicly available: modelzoos.cc.
Fundamental Tradeoffs in Learning with Prior Information

We seek to understand fundamental tradeoffs between the accuracy of prior information that a learner has on a given problem and its learning performance. We introduce the notion of prioritized risk, which differs from traditional notions of minimax and Bayes risk by allowing us to study such fundamental tradeoffs in settings where reality does not necessarily conform to the learner's prior. We present a general reduction-based approach for extending classical minimax lower-bound techniques in order to lower bound the prioritized risk for statistical estimation problems. We also introduce a novel generalization of Fano's inequality (which may be of independent interest) for lower bounding the prioritized risk in more general settings involving unbounded losses. We illustrate the ability of our framework to provide insights into tradeoffs between prior information and learning performance for problems in estimation, regression, and reinforcement learning.