Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping

Developing agents that can execute multiple skills by learning from pre-collected datasets is an important problem in robotics, where online interaction with the environment is extremely time-consuming. Moreover, manually designing reward functions for every single desired skill is prohibitive. Prior works targeted these challenges by learning goal-conditioned policies from offline datasets without manually specified rewards, through hindsight relabelling. These methods suffer from the issue of sparsity of rewards, and fail at long-horizon tasks. In this work, we propose a novel self-supervised learning phase on the pre-collected dataset to understand the structure and the dynamics of the model, and shape a dense reward function for learning policies offline. We evaluate our method on three continuous control tasks, and show that our model significantly outperforms existing approaches, especially on tasks that involve long-term planning.
Volumetric Modeling and Experimental Validation of Normal Contact Dynamic Forces

A volumetric contact dynamics model has been proposed for the purpose of generating reliable and rapid simulations of contact dynamics. Forces and moments between bodies in contact can be expressed in terms of the volume of interference between the undeformed geometries. This allows for the modeling of contact between complex geometries and relatively large contact surfaces, while being computationally less expensive than finite element methods. However, the volumetric model requires experimental validation. Models for simple geometries in contact have been developed for stationary and dynamic contact, and an apparatus has been developed to experimentally validate these models. This paper focuses on validation of the normal contact models. Measurements of forces and displacements will be used to identify the parameters related to the normal force, i.e., the volumetric stiffness and hysteretic damping factor for metallic surfaces. The experimental measurements are compared with simulated results to assess the validity of the volumetric model.
Indonesian Students’ Prejudice Against Homosexuals: Religious Fundamentalism and Intergroup Threat as Predictors

The research aimed to examine and explain the relationship between religious fundamentalism and intergroup threats toward undergraduate students’ prejudice against gays and lesbians. Bhinneka Tunggal Ika, a slogan about tolerance, is supposed to protect and enhance diversity in Indonesia. On the contrary, sexual minority groups, gays and lesbians, often accept stereotypes, prejudices, and discrimination from the majority of Indonesian because they are considered against the religious values and morality of Indonesian. The research used a cross-sectional survey design with accidental sampling methods. The research subjects were students aged 18 to 24 years who studied at universities in Surabaya, identified as heterosexuals, and with diverse religious backgrounds (N = 414). The instruments used were the Attitudes Towards Lesbians and Gay Men (ATLG), the Revised Religious Fundamentalism Scale (RFS-12), and the Perceived Threat of Homosexuals (PTHS) Scale. Multiple regression analysis shows that religious fundamentalism and intergroup threat plays a significant role in explaining prejudice against gays and lesbians (R = 0,872; R² = 0,761; F = 654,817; p < 0,001). The role of intergroup threat as a predictor of prejudice against gays and lesbians is higher than religious fundamentalism.Additional analysis shows that academic discipline, contact, empathy, religion, and university characteristics affect students’ prejudice.
Stability of the Flow Past a Row of Square Bars.

The stability of the flow past a row of square bars, which is placed across a uniform flow, is investigated numerically. Two-dimensional and incompressible flow field is assumed. It is found that each jet which flows between the square bars is independent of each other when the pitch-to-diameter ratio of the row is large. However the confluence of several jets occurs when the pitch-to-diameter ratio is small. It is found that the confluence of couples or triplets of jets is a consequence of a pitchfork bifurcation and the critical Reynolds numbers for the pitchfork bifurcations are evaluated for each value of the pitch-to-diameter ratio. An experiment to visualize the flow field is also made and the confluence of jets is confirmed.
Patterns of Social Vulnerability - An Interactive Dashboard to Explore Risks to Public Health on the US County Level

Social vulnerability is the susceptibility of a community to be adversely impacted by natural hazards and public health emergencies, such as drought, earthquakes, flooding, virus outbreaks, and the like. Climate change is at the root of many recent natural hazards while the COVID-19 pandemic is still an active threat. Social vulnerability also refers to resilience, or the ability to recover from such adverse events. To gauge the many aspects of social vulnerability the US Center of Disease Control (CDC) has subdivided social vulnerabilities into distinct themes, such as socioeconomic status, household composition, and others. Knowing a community's social vulnerabilities can help policymakers and responders to recognize risks to community health, prepare for possible hazards, or recover from disasters. In this paper we study social vulnerabilities on the US county level and present research that suggests that there are certain combinations, or patterns, of social vulnerability indicators into which US counties can be grouped. We then present an interactive dashboard that allows analysts to explore these patterns in various ways. We demonstrate our methodology using COVID-19 death rate as the hazard and show that the patterns we identified have high predictive capabilities of the pandemic's local impact.
An Efficient Framework for the Clustering of Human Activity Datausing Kernelized Robust Covariance Descriptors

In this paper, a new method for the efficient clustering of human activity data is proposed. Unlike the traditional human activity clustering approaches, our method relies on the skeletal data recorded with the help of motion capture (mocap) systems to achieve the goal. The proposed method is structured around the kernel-based robust covariance descriptor. By introducing a data re-framing technique that efficiently utilizes the temporal properties of the human activity data, we have alleviated the data redundancy and insufficiency issues associated with action sequences. The optimization model developed encompasses the combined benefits of low-rank representation and least square regression. The formulation is strengthened by incorporating the temporal dependency of the human activity sequences with the help of a temporal Laplacian regularizer. With the proposed algorithm, a representation matrix is learned from the raw data, which is then used to perform subspace clustering. Experiments conducted on multiple human activity datasets reveal the ability of the proposed method to achieve better clustering results compared to state-of-the-art counterparts.
Behaviour Change Techniques in mobile apps targeting self-harm in young people: a systematic review

Background: Despite the high prevalence of self-harm among young people, as well as their extensive use of mobile apps for seeking support with their mental healthcare, it is unclear whether the design of mobile apps aimed at targeting self-harm is underpinned by behaviour change techniques (BCTs). Purpose: To systematically analyse and identify 1) what BCTs and 2) what theories are present in self-harm apps for young people in an attempt to understand their active components. Methods: Systematic searches in Apple store, followed by thorough screening, were conducted to identify free mobile apps targeting self-harm in young people. Five apps met the inclusion criteria and were used by trained researchers, who coded identified features against the BCT Taxonomy V1. Results: Despite the majority of apps being underpinned by principles of Dialectical Behaviour Therapy (DBT), no other information is available about specific theories underpinning these apps. Nineteen of the 93 BCTs were identified across the five apps. The most frequently used BCT was “Distraction” (54.2%), offering ideas for activities to distract users from self-harming. Other techniques that were used often were “Social Support (unspecified)” (27.0%) and “Behaviour Substitution” (10.6%). Conclusions: This study provides the first analysis of BCTs present in mental health apps which are designed to target the reduction of self-harm in young people. Future research is needed to evaluate the effectiveness of the apps, as well as assess the effectiveness of the BCTs present.
A Mixed Methods Multicenter Study on the Capabilities, Barriers, and Opportunities for Diabetes Screening and Management in the Public Health System of Southern Ethiopia

Background More than half of diabetics’ in Ethiopia live undiagnosed, and the majority of those who already knew their status also struggle to manage their diseases. However, the underlying challenges are less understood in the study area. Therefore, this study aimed to assess diabetes screening and management capabilities, barriers, and opportunities in southern Ethiopia. Methods We applied a mixed methods study. To assess the healthcare systems’ capabilities, we collected quantitative data from randomly selected ten hierarchically organized healthcare facilities, and purposive maximum variation sampling was applied to recruit twenty-nine individuals for face-to-face in-depth interviewing. The interviews were audio recorded, transcribed verbatim, thematically analyzed, and presented accordingly. Results Our study findings indicated that there were good opportunities and encouraging capabilities like government commitment and expansion of services to improve diabetes screening and management in southern Ethiopia. Nevertheless, poor governance, the system’s structural problems, skilled professionals’ inaccessibility and lack of teamwork, poor service integration, poor planning, and lack of monitoring and evaluation mechanisms have been hampering the service delivery at the system level. While service unaffordability, low awareness level, and lifestyle modification problems were the main challenges at the patient level. Furthermore, outdated paper-based medical record documentation, frequent essential drug stock-outing, essential laboratory service interruptions, and none-use of some available services like HbA1c have been contributing to the barriers. Conclusion Despite favorable capabilities available, diabetes management in southern Ethiopia has been struggling with solvable structural defects, poor service delivery and inaccessibility, and patients’ poor lifestyle modification. Therefore, public health system restructuring, optimum financing, computerization of medical records documentation, and health system and patient capacity building are strongly recommended interventions to tackle the problem at the grass-root level.
Security Analysis of SplitFed Learning

Split Learning (SL) and Federated Learning (FL) are two prominent distributed collaborative learning techniques that maintain data privacy by allowing clients to never share their private data with other clients and servers, and find extensive IoT applications in smart healthcare, smart cities and smart industry. Prior work has extensively explored the security vulnerabilities of FL in the form of poisoning attacks. To mitigate the effect of these attacks, several defenses have also been proposed. Recently, a hybrid of both learning techniques has emerged (commonly known as SplitFed) that capitalizes on their advantages (fast training) and eliminates their intrinsic disadvantages (centralized model updates). In this paper, we perform the first empirical analysis of SplitFed's robustness to strong model poisoning attacks. We observe that the model updates in SplitFed have significantly smaller dimensionality as compared to FL that is known to have curse of dimensionality. We show that large models that have higher dimensionality are more susceptible to privacy and security attacks, whereas the clients in SplitFed do not have the complete model and have lower dimensionality, making them more robust to existing model poisoning attacks. Our results show that the accuracy reduction due to the model poisoning attack is 5x lower for SplitFed compared to FL.
Application of Data Enhancement Method Based on Generative Adversarial Networks for Soybean Leaf Disease Identification

: Soybean leaf disease data collection is an expensive and time-consuming task. Convolutional neural network training requires a large amount of data, but traditional data enhancement methods (such as rotation, flipping, translation) are restricted by fixed rules and cannot generate images with diversity and variability. Aiming at the problem of the lack of soybean leaf disease data set, this study proposes a data enhancement method based on Generative Adversarial Networks (GANs) for soybean leaf disease identification. The method is based on a cyclic adversarial network and its discriminator uses dense connections. Strategies to reduce the size and computational complexity of the final model. Using a cyclic adversarial network to convert between healthy and diseased leaves, unsupervised learning can be performed, using limited images to learn disease characteristics, thereby generating highly recognizable soybean leaf images. Synthesis images generated from GANs and original images are fed together as the model training set input and the recognition model for recognizing 9 types of soybean leaf images is obtained. An accuracy rate of 95.89% can be achieved on the verification set. Experimental results show that the generative adversarial network provided in this article can: Generate soybean leaf disease image data with high discriminative features, increase the size of the data set and provide a feasible solution for soybean leaf disease image data enhancement; as A regularization strategy to reduce over-fitting problems and improve the performance of the recognition model.
Fully-connected bond percolation on Z d

We consider the bond percolation model on the lattice Z d ( d ≥ 2) with the constraint to be fully connected. Each edge is open with probability p ∈ ( 0 , 1 ) , closed with probability 1 − p and then the process is conditioned to have a unique open connected component (bounded or unbounded). The model is deﬁned on Z d by passing to the limit for a sequence of ﬁnite volume models with general boundary conditions. Several questions and problems are investigated: existence, uniqueness, phase transition, DLR equations. Our main result involves the existence of a threshold 0 < p ∗ ( d ) < 1 such that any inﬁnite volume model is necessary the vacuum state in subcritical regime (no open edges) and is non trivial in the supercritical regime (existence of a stationary unbounded connected cluster). Bounds for p ∗ ( d ) are given and show that it is drastically smaller than the standard bond percolation threshold in Z d . For instance 0 . 128 < p ∗ ( 2 ) < 0 . 202 (rigorous bounds) whereas the 2D bond percolation threshold is equal to 1 / 2.
Dual-Aircraft Investigation of the Inner Core of Hurricane Norbert. Part I: Kinematic Structure

Abstract On 24–25 September 1984 air motions and physical processes in the eyewall region of Hurricane Norbert were investigated with two National Oceanic and Atmospheric Administration (NOAA) Aircraft Operations Center (AOC) WP-3D research aircraft. One aircraft, equipped with the airborne Doppler radar, flew repeated radial penetrations of the eyewall, mapping the three-dimensional wind field throughout a 40-km radius of the storm center. These data provide the first complete documentation of the three-dimensional wind field of the inner core of a hurricane on spatial scales of 10–25 km in the horizontal and 1 km in the vertical and temporal scales of 1–2 h. The wind field was asymmetric, and the asymmetry varied with altitude. As altitude increased the location of the maximum tangential wind sloped upwind, from left of the storm track at 1-km altitude to right of the storm track above 3 km. The radial wind at 1-km altitude was inward in front of the storm and outward behind. This pattern in the radial .
An Investigation about the Interrelationship of Student-Teacher in Higher Hospitality Management and Tourism Education in Cyprus

This study is intended to investigate the interrelationship of student- in the private Hospitality and Tourism Higher Education in Cyprus. The researchers of this study formulated three main themes, especially sought to identify the qualities of a good teacher, positive relationship with a teacher, characteristics of caring academic demonstrate, and attributes of a good professor/lecturer. The purpose of this was to investigate the effects of student perceptions of their college teacher's interpersonal teaching behaviors on student achievement and affective learning outcomes. Analyses were conducted in order to investigate the effects of differing student perceptions of their teacher's interpersonal teaching behaviors. A qualitative approach was employed, whereby face-to-face and semi-structured interviews were conducted with 100 students in private tourism higher education in Cyprus. Using thematic analysis, the study concludes that it is imperative for teachers to develop positive academic relationships and establish close rapport with their students to facilitate the learning process.
Stroke-related length of hospitalization trends and in-hospital mortality in Peru

Background Peru faces challenges to provide adequate care to stroke patients. Length of hospitalization and in-hospital mortality are two well-known indicators of stroke care. We aimed to describe the length of stay (LOS) of stroke in Peru, and to assess in-hospital mortality risk due to stroke, and subtypes. Methods This retrospective cohort study used hospitalization registries coding with ICD-10 from 2002 to 2017 (N = 98,605) provided by the Ministry of Health; in-hospital mortality was available for 2016–2017 (N = 6,566). Stroke cases aged ≥35 years were divided into subarachnoid hemorrhage (I60), intracerebral hemorrhage (I61), cerebral infarction (I63), and stroke not specified as hemorrhage or infarction (I64). Data included stroke LOS and in-hospital mortality; socio-demographic and clinical variables. We fitted a region- and hospital level-stratified Weibull proportional hazard model to assess the in-hospital mortality. Results The median LOS was 7 days (IQR: 4–13). Hemorrhagic strokes had median LOS longer than ischemic strokes and stroke not specified as hemorrhage or infarction (P = <0.001). The case fatality rate (CFR) of patients with stroke was 11.5% (95% CI [10–12%]). Subarachnoid hemorrhage (HR = 2.45; 95% CI [1.91–3.14]), intracerebral hemorrhage (HR = 1.95; 95% CI [1.55–2.46]), and stroke not specified as hemorrhage or infarction (HR = 1.45; 95% CI [1.16–1.81]) were associated with higher in-hospital mortality risk in comparison to ischemic strokes. Discussion Between 2002 and 2017, LOS due to stroke has not changed in Peru in stroke patients discharged alive. Hemorrhagic cases had the longest LOS and highest in-hospital mortality risk during 2016 and 2017. The findings of our study seem to be consistent with a previous study carried out in Peru and similar to that of HIC and LMIC, also there is an increased median LOS in stroke cases managed in specialized centers. Likewise, LOS seems to depend on the type of stroke, where ischemic stroke cases have the lowest LOS. Peru needs to improve access to stroke care.
A Microsimulation Modelling Approach to Quantify Environmental Footprint of Autonomous Buses

In this study a novel microsimulation-based methodology for environmental assessment of urban systems is developed to address the performance of autonomous mass-mobility against conventional approaches. Traffic growth and microsimulation models, calibrated using real data, are utilised to assess four traffic management scenarios: business-as-usual; public bus transport case; public-bus rapid transit (BRT) case; and, a traffic-demand-responsive-autonomous-BRT case, focusing on fuel energy efficiency, headways, fleet control and platooning for lifecycle analysis (2015–2045) of a case study 3.5 km long 5-lane dual-carriageway section. Results showed that both energy consumption and exhaust emission rates depend upon traffic volume and flow rate factors of vehicle speed-time curves; acceleration-deceleration; and braking rate. The results measured over-reliance of private cars utilising fossil fuel that cause congestions and high environmental footprint on urban roads worsen causing excessive travel times. Public transport promotion was found to be an effective and easy-to-implement environmental burden reduction strategy. Results showed significant potential of autonomous mass-mobility systems to reduce environmental footprint of urban traffic, provided adequate mode-shift can be achieved. The study showed utility of microsimulations for energy and emissions assessment, it linked bus network performance assessment with environmental policies and provided empirical models for headway and service frequency comparisons at vehicle levels. The developed traffic fleet operation prediction methodology for long-term policy implications and tracking models for accurate yearly simulation of real-world vehicle operation profiles are applicable for other sustainability-oriented urban traffic management studies.
The structure of normal shock waves in a binary gas mixture

A previous study of normal shock waves through numerical experiments with a simulated gas on a digital computer is extended to binary gas mixtures. The mixture is simulated by two sets of rigid elastic sphere molecules with the appropriate mass and diameter ratios. Velocity profile results for medium strength waves in a mixture of equal parts argon and helium are in qualitative agreement with the continuum calculations of Sherman (1960), but there is no initial acceleration of the argon in mixtures containing a very small initial mole fraction of this gas. The temperature profiles are similar to those for the velocity in that the argon profile lags behind the helium profile. However, when there is a small proportion of heavy gas, the profiles cross-over and the temperature of the heavy gas overshoots the Rankine-Hugoniot downstream value. For very strong shock waves, the overall shock thickness expressed in upstream mean free paths becomes larger, but the profiles are generally similar to those for the medium strength waves.
%GI: A SAS Macro for Measuring and Testing Global Imbalance of Covariates within Subgroups

The global imbalance (GI) measure is a way for checking balance of baseline covariates that confound efforts to draw valid conclusions about treatment effects on outcomes of interest. In addition, GI is tested by means of a multivariate test. The GI measure and its test overcome some limitations of the common way for assessing the presence of imbalance in observed covariates that were discussed in D'Attoma and Camillo (2011). A user written SAS macro called %GI, to simultaneously measure and test global imbalance of baseline covariates is described. Furthermore, %GI also assesses global imbalance by subgroups obtained through several matching or classification methods (e.g., cluster analysis, propensity score subclassification, Rosenbaum and Rubin'84), no matter how many groups are examined. %GI works with mixed categorical, ordinal and continuous covariates. Continuous baseline covariates need to be split into categories. It also works in the multi-treatment case. The use of the %GI macro will be illustrated using two artificial examples.