Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Compliant Control

In many physical interactions such as opening doors and playing sports, humans act compliantly to move in various ways to avoid large impacts or to manipulate objects. This paper aims to build a framework for simulation and control of humanoids that creates physically compliant interactions with surroundings. We can generate a broad spectrum of movements ranging from passive reactions to external physical perturbations, to active manipulations with clear intentions. Technical challenges include defining compliance, reproducing physically reliable movements, and robustly controlling under-actuated dynamical systems. The key technical contribution is a two-level control architecture based on deep reinforcement learning that imitates human movements while adjusting their bodies to external perturbations. The controller minimizes the interaction forces and the control torques for imitation, and we demonstrate the effectiveness of the controller with various motor skills including opening doors, balancing a ball, and running hand in hand.
Lecturer Feedback in Writing Classes: An Investigation from Pre-University Students' Perspectives at a Private University inVietnam

This research was undertaken with the intention of eliciting information from students regarding their perceptions of lecturers' comments on written lectures, particularly in situations when all lessons were delivered online as a result of the Covid 19 pandemic. The data collection method consisted of conducting a survey with 52 pre-university students from Batch 16 who are currently enrolled in English Preparation Courses at the Ho Chi Minh Campus of FPT University. The findings of the study led to the discovery and subsequent drawing of the conclusion that students' perceptions are significantly influenced by the written comments of their instructors. Students placed a very high level of importance on receiving feedback from their instructors, as they regarded it as the factor that had the most impact on how much they improved. In addition, students put a larger value on information and lecturer feedback than they did on their results. This enabled them to enhance both their content and their language as they were in the process of learning. In addition, students were not aware that their teachers had provided them with any unfavorable feedback. However, the manner in which various lecturers deliver feedback and the characteristics of the students themselves will determine whether the input is perceived by the students as being detrimental or beneficial.
Rendering Iridescent Rock Dove Neck Feathers

Bird feathers exhibit fascinating reflectance, which is governed by fiber-like structures. Unlike hair and fur, the feather geometric structures follow intricate hierarchical patterns that span many orders of magnitude in scale. At the smallest scales, fiber elements have strongly non-cylindrical cross-sections and are often complemented by regular nanostructures, causing rich structural color. Therefore, past attempts to render feathers using fiber- or texture-based appearance models missed characteristic aspects of the visual appearance. We introduce a new feather modeling and rendering framework, which abstracts the microscopic geometry and reflectance into a microfacet-like BSDF. The R, TRT and T lobes, also known from hair and fur, here account for specular reflection off the cortex, diffuse reflection off the medulla, and transmission due to barbule spacing, respectively. Our BSDF, which does not require precomputation or storage, can be efficiently importance-sampled and readily integrated into rendering pipelines that represent feather geometry down to the barb level. We verify our approach using a BSDF-capturing setup for small biological structures, as well as against calibrated photographs of rock dove neck feathers.
ShaderTransformer: Predicting Shader Quality via One-shot Embedding for Fast Simplification

Given specific scene configurations and target functions, automatic shader simplification searches for the best simplified shader variant from an optimization space with many candidates. Although various speedup methods have been proposed, there is still a costly render-and-evaluate process to obtain variant’s performance and quality, especially when the scene changes. In this paper, we present a deep learning-based framework for predicting a shader’s simplification space, where the shader’s variants can be embedded into a metric space all at once for efficient quality evaluation. The novel framework allows the one-shot embedding of a space rather than a single instance. In addition, the simplification errors can be interpreted by mutual attention between shader fragments, presenting an informative focus-aware simplification framework that can assist experts in optimizing the codes. The results show that the new framework achieves significant speedup compared with existing search approaches. The focus-aware simplification framework reveals a new possibility of interpreting shaders for various applications.
Research Summary: Deterministic, Explainable and Efficient Stream Processing

The vast amounts of data collected and processed by technologies such as Cyber-Physical Systems require new processing paradigms that can keep up with the increasing data volumes. Edge computing and stream processing are two such paradigms that, combined, allow users to process unbounded datasets in an online manner, delivering high-throughput, low-latency insights. Moving stream processing to the edge introduces challenges related to the heterogeneity and resource constraints of the processing infrastructure. In this work, we present state-of-the-art research results that improve the facilities of Stream Processing Engines (SPEs) with data provenance, custom scheduling, and other techniques that can support the usability and performance of streaming applications, spanning through the edge-cloud contexts, as needed.
How children make sense of their permanent exclusion: a thematic analysis from semi-structured interviews

ABSTRACT A rising number of children are permanently excluded from school each year in England. Children’s experiences of exclusion are underrepresented in the literature, effectively giving prominence to the views and interpretations of researchers. This qualitative study uses semi-structured interviews to explore the ways in which excluded children story their experience of school exclusion (N = 18). Thematic analysis was used to identify trends in the children’s data, integrating key themes to develop an understanding of how children make sense of the exclusion situation. The main findings from this study are that excluded children tend to experience schools as misreading symptoms of social injustice, bullying, and special educational needs as misbehaviour and non-compliance. The children reported that exclusion behaviours were a communication of personal and social problems that were amplified by punitive school measures. Ways in which schools can implement these findings are discussed with key recommendations for employing these findings within schools and educational settings.
The effect of the COVID-19 pandemic on human rights practices: Findings from the Human Rights Measurement Initiative’s 2021 Practitioner Survey

Abstract Health is a human right; as such, a public health crisis is a human rights crisis. Yet the human rights impact of the 2020 COVID-19 pandemic seems to have varied widely, both across rights and across countries. How have human rights practices been affected by the pandemic so far? Which human rights were most negatively affected by the COVID-19 pandemic? Which states were most likely to experience these negative effects, and which states avoided a reduction in the enjoyment of human rights due to the pandemic? To provide some early answers to these questions, the Human Rights Measurement Initiative (HRMI) added questions to its annual practitioner survey that aimed at determining how a subset of civil, political, economic, and social rights was affected by COVID-19 in 2020 in 39 countries around the world. Using both quantitative and qualitative data from this survey, in combination with other indicators, this article provides a description of COVID-19’s human rights impact as seen by practitioners on the front lines around the world, as well as insight into the larger question of which factors enabled states to maintain a high level of enjoyment of human rights just when those rights were needed the most.
Findings from a survey on the current use of life-cycle assessment in building design

The built environment is facing environmental regulations more ambitious than ever before. In Europe, a law will lead all new buildings to the Nearly Zero-Energy performance level. However, even if a building does not have any energy consumption for its operation phase, it still has embodied impacts. To that end, Life-Cycle Assessment (LCA) methods have been developed and improved since the 1960s. However, LCAs are still not used as a standard practice among the architecture, engineering and construction industry. This study aims to discover the reasons for the low use of life-cycle performance approaches thanks to a web survey targeting practitioners, and to formulate key recommendations to improve their usability. This research reveals the low penetration rate of LCA software among building designers due to their limited efficiency within the design context. The main reasons for this situation are the cost of use, too heavy for the early design stage constraints, and the functionality, which is limited to the environmental assessment. Indeed, practitioners expect much more design support functionalities (multi-criteria approach, exploration mode, etc.). The survey findings aim to support the usability improvement of new LCA-based methods and the research and development of new tools at early design stages.
Genetic analysis of global faba bean germplasm maps agronomic traits and identifies strong selection signatures for geographical origin

Faba bean (Vicia faba L.) is a high-protein grain legume crop with great potential for further cultivation. However, little is known about the genetics underlying trait diversity. In this study, we use 21,345 high-quality SNP markers to genetically characterise 2,678 faba bean genotypes. We perform genome-wide association studies of key agronomic traits using a Seven-parent-MAGIC population and detect 238 significant marker-trait associations linked to 12 traits of agronomic importance, with 65 of these being stable across multiple environments. Using a non-redundant diversity panel of 685 accessions from 52 countries, we identify 3 subpopulations differentiated by geographical origin and 33 genomic regions subject to strong diversifying selection between subpopulations. We find that SNP markers associated with the differentiation of northern and southern accessions were able to explain a significant proportion of agronomic trait variance in the Seven-parent-MAGIC population, suggesting that some of these traits have played an important role in breeding. Altogether, our findings point to genomic regions associated with important agronomic traits and selection in faba bean, which can be used for breeding purposes. Key Message We identified marker-trait associations for key faba bean agronomic traits and genomic signatures of selection within a global germplasm collection.
Electronically tunable universal filter and quadrature oscillator using low- voltage differential difference transconductance amplifiers

This paper presents a new electronically tunable universal filter and quadrature oscillator for low frequency biomedical and biosensor applications employing low-voltage differential difference transconductance amplifier (DDTA). The DDTA CMOS structure uses 0.5 V of supply voltage and consumes 277 nW of power. Unlike the previous universal filters, the proposed filter provides many transfer functions of the standard five transfer functions such as low-pass, high-pass, band-pass, band-stop and all-pass with both unity and controlled voltage gains as well as both inverting and non-inverting transfer functions. The natural frequency and the voltage gain of the five standard transfer functions can be controlled electronically. For the band-pass filter, the third intermodulation distortion (IMD3) was 0.37% for 20 mVpp input signal while the output integrated noise was 61.37 μV. The dynamic range (DR) was 53.27 dB for 1% IMD3. The quadrature oscillator has electronically and orthogonal control of the condition and frequency of oscillation. The proposed circuit and its applications were designed and verified via Cadence simulator tool using 0.13 μm UMC CMOS technology. Further, the circuit was evaluated by PSPICE simulation and experiment test using commercial OTA LM13700.
BOUNDED INFLUENCE APPROACHES TO CONSTRAINED MIXED VECTOR AUTOREGRESSIVE MODELS

The proliferation of many clinical studies obtaining multiple biophysical signals from several individuals repeatedly in time is increasingly recognized, a recognition generating growth in statistical models that analyze cross-sectional time series data. In general, these statistical models try to answer two questions: (i) intra-individual dynamics of the response and its relation to some covariates; and, (ii) how this dynamics can be aggregated consistently in a group. In response to the first question, we propose a covariate-adjusted constrained Vector Autoregressive model, a technique similar to the STARMAX model (Stoffer, JASA 81, 762-772), to describe serial dependence of observations. In this way, the number of parameters to be estimated is kept minimal while offering flexibility for the model to explore higher order dependence. In response to (ii), we use mixed effects analysis that accommodates modelling of heterogeneity among cross-sections arising from covariate effects that vary from one cross-section to another. Although estimation of the model can proceed using standard maximum likelihood techniques, we believed it is advantageous to use bounded influence procedures in the modelling (such as choosing constraints) and parameter estimation so that the effects of outliers can be controlled. In particular, we use M-estimation with a redescending bounding function because its influence function is always bounded. Furthermore, assuming consistency, this influence function is useful to obtain the limiting distribution of the estimates. However, this distribution may not necessarily yield accurate inference in the presence of contamination as the actual asymptotic distribution might have wider tails. This led us to investigate bootstrap approximation techniques. A sampling scheme based on IID innovations is modified to accommodate the cross-sectional structure of the data. Then the M-estimation is applied to each bootstrap sample naively to obtain the asymptotic distribution of the estimates.We apply these strategies to the extracted BOLD activation from several regions of the brain from a group of individuals to describe joint dynamic behavior between these locations. We used simulated data with both innovation and additive outliers to test whether the estimation procedure is accurate despite contamination.
Evaluating Public Service Delivery Smartness and Impact on Citizens’ Well-being

Public sectors are increasingly adopting emerging technologies to innovate and deliver smart services to enhance citizens’ overall well-being. Although the idea of smartness in public sector service innovations has been explored from the perspective of service providers, limited study has been done from the perspective of end users. Furthermore, the impact of smart service delivery on citizens’ quality of life has been widely studied quantitatively, but qualitative evidence has been sparse. This paper aims to address those research gaps using a mobile-based innovation in the motor vehicle annual registration services (SAMBARA) recently introduced by the West Java Province in Indonesia as a case study. We use a qualitative smartness measurement framework based on efficiency, effectiveness, transparency, and collaboration metrics to assess the smartness of the service. We also evaluate the service’s influence on enhancing citizens’ overall quality of life using the well-being framework, which is based on aspects of usefulness, safety, and convenience experiences. We verified the significance of our findings across various participants’ background using statistical analysis ANOVA. The outcome of the study shows that mobile-based innovation services not only create smartness in public service delivery but also improve citizens’ well-being regardless of their various backgrounds. This research contributes to the public service innovation knowledge base and offers a baseline study for researchers and practitioners to carry out similar study in other emergent nations like Indonesia.
Low-Density Lipoprotein Cholesterol and Mortality in Peritoneal Dialysis

Background In dialysis patients, lowering low-density lipoprotein cholesterol (LDL-C) did not provide benefits, which seemed implausible in clinical practice. We hypothesized a U-shaped association between LDL-C and mortality in dialysis patients. Methods In this multi-center retrospective real-world cohort study, 3,565 incident Chinese peritoneal dialysis (PD) patients between January 1, 2005, and May 31, 2020, were included. The associations between baseline LDL-C and mortality were examined using cause-specific hazard models. Results Of 3,565 patients, 820 died, including 415 cardiovascular deaths. As compared with the reference range (2.26-2.60 mmol/L), both higher levels of LDL-C (> 2.60 mmol/L) and lower levels of LDL-C (< 2.26 mmol/L) were associated with increased risks of all-cause mortality (hazard ratio [HR],1.35, 95% confidence index [CI], 1.09-1.66; HR 1.36, 95%CI, 1.13-1.64) and cardiovascular mortality (HR, 1.31, 95% CI, 1.10-1.72; HR, 1.64; 95% CI, 1.22-2.19). Malnutrition (albumin < 36.0 g/L) modified the association between LDL-C and cardiovascular mortality (P for interaction = 0.01). A significantly increased risk of cardiovascular mortality was observed among patients with malnutrition and lower levels of LDL-C (HR 2.96, 95%CI 1.43-6.12) or higher levels of LDL-C (HR 2.81, 95%CI 1.38-5.72). Conclusion Low and high levels of LDL-C at the start of PD procedure were associated with increased all-cause and cardiovascular mortality risks. Malnutrition may modify the association of LDL-C with cardiovascular mortality.
Tuning brain networks: The emerging role of transcranial direct current stimulation on structural plasticity

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique (NIBS) that has been proven to promote beneficial effects in a range of neurological and psychiatric disorders. Unfortunately, although has been widely investigated, the mechanism comprehension around tDCS effects presents still some gaps. Therefore, scientists are still trying to uncover the cellular and molecular mechanisms behind its positive effects to permit a more suitable application. Experimental models have provided converging evidence that tDCS elicits improvements in learning and memory by modulating both excitability and synaptic plasticity in neurons. Recently, among tDCS neurobiological effects, neural synchronization and dendritic structural changes have been reported in physiological and pathological conditions, suggesting possible effects at the neuronal circuit level. In this review, we bring in to focus the emerging effects of tDCS on the structural plasticity changes and neuronal rewiring, with the intent to match these two aspects with the underpinning molecular mechanisms identified so far, providing a new perspective to work on to unveil novel tDCS therapeutic use to treat brain dysfunctions.
Relationship Between Hardiness and Social Anxiety in Chinese Impoverished College Students During the COVID-19 Pandemic: Moderation by Perceived Social Support and Gender

During the COVID-19 epidemic, quarantine and financial disadvantages might exacerbate social anxiety among impoverished college students. Based on the hardiness model and the social support buffering model, the present study proposed and verified a dual moderation model to investigate the effects of hardiness on social anxiety and the moderating roles of gender and perceived social support. The hardiness scale, the perceived social support scale, and the social anxiety subscale of the self-consciousness scale were administered to 673 Chinese college students aged 18 to 23 years who were recognized as impoverished by the Chinese authorities and provided with funding. The results revealed that (1) hardiness had a significant negative effect on social anxiety, (2) perceived social support moderated the effect of hardiness on social anxiety, and (3) gender moderated the effect of hardiness on social anxiety. The dual moderated model proposed in the study provides practical implications for helping impoverished college students cope with social anxiety during the COVID-19 pandemic.
Preoperative Strength Training for Clinical Outcomes Before and After Total Knee Arthroplasty: A Systematic Review and Meta-Analysis

Background There is an increasing interest in preoperative strength training for promoting post-operative rehabilitation, but the effectiveness of preoperative strength training for clinical outcomes after total knee arthroplasty (TKA) remains controversial. Objective This study aims to systematically evaluate the effect of preoperative strength training on clinical outcomes before and after TKA. Methods We systematically searched PubMed, Cochrane Library, Web of Science, and EMBASE databases from the inception to November 17, 2021. The meta-analysis was performed to evaluate the effects of preoperative strength training on clinical outcomes before and after TKA. Results Seven randomized controlled trials (RCTs) were included (n = 306). Immediately before TKA, the pooled results showed significant improvements in pain, knee function, functional ability, stiffness, and physical function in the strength training group compared with the control group, but not in strength (quadriceps), ROM, and WOMAC (total). Compared with the control group, the results indicated strength training had a statistically significant improvement in post-operative knee function, ROM, and functional ability at less than 1 month and 3 months, and had a statistically significant improvement in post-operative strength (quadriceps), stiffness, and WOMAC (total) at 3 months, and had a statistically significant improvement in post-operative pain at 6 months. However, the results indicated strength training had no statistically significant improvement in post-operative strength (quadriceps) at less than 1 month, 6, and 12 months, had no statistically significant improvement in post-operative pain at less than 1 month, 3, and 12 months, had no statistically significant improvement in post-operative knee function at 6 and 12 months, and had no statistically significant improvement in post-operative physical function at 3 months. Conclusions Preoperative strength training may be beneficial to early rehabilitation after TKA, but the long-term efficacy needs to be further determined. At the same time, more caution should be exercised when interpreting the clinical efficacy of preoperative strength training for TKA.