Miracle-dz/UniFAR-FTUs huggingface dataset:
RHOBIN Challenge: Reconstruction of Human Object Interaction

Modeling the interaction between humans and objects has been an emerging research direction in recent years. Capturing human-object interaction is however a very challenging task due to heavy occlusion and complex dynamics, which requires understanding not only 3D human pose, and object pose but also the interaction between them. Reconstruction of 3D humans and objects has been two separate research fields in computer vision for a long time. We hence proposed the first RHOBIN challenge: reconstruction of human-object interactions in conjunction with the RHOBIN workshop. It was aimed at bringing the research communities of human and object reconstruction as well as interaction modeling together to discuss techniques and exchange ideas. Our challenge consists of three tracks of 3D reconstruction from monocular RGB images with a focus on dealing with challenging interaction scenarios. Our challenge attracted more than 100 participants with more than 300 submissions, indicating the broad interest in the research communities. This paper describes the settings of our challenge and discusses the winning methods of each track in more detail. We observe that the human reconstruction task is becoming mature even under heavy occlusion settings while object pose estimation and joint reconstruction remain challenging tasks. With the growing interest in interaction modeling, we hope this report can provide useful insights and foster future research in this direction. Our workshop website can be found at \href{https://rhobin-challenge.github. io/}{https://rhobin-challenge.github. io/}.
Expanding Horizons in HCI Research Through LLM-Driven Qualitative Analysis

How would research be like if we still needed to"send"papers typed with a typewriter? Our life and research environment have continually evolved, often accompanied by controversial opinions about new methodologies. In this paper, we embrace this change by introducing a new approach to qualitative analysis in HCI using Large Language Models (LLMs). We detail a method that uses LLMs for qualitative data analysis and present a quantitative framework using SBART cosine similarity for performance evaluation. Our findings indicate that LLMs not only match the efficacy of traditional analysis methods but also offer unique insights. Through a novel dataset and benchmark, we explore LLMs' characteristics in HCI research, suggesting potential avenues for further exploration and application in the field.
Approximation Algorithms for Minimizing Congestion in Demand-Aware Networks

Emerging reconfigurable optical communication technologies allow to enhance datacenter topologies with demand-aware links optimized towards traffic patterns. This paper studies the algorithmic problem of jointly optimizing topology and routing in such demand-aware networks to minimize congestion, along two dimensions: (1) splittable or unsplittable flows, and (2) whether routing is segregated, i.e., whether routes can or cannot combine both demand-aware and demand-oblivious (static) links. For splittable and segregated routing, we show that the problem is generally $2$-approximable, but APX-hard even for uniform demands induced by a bipartite demand graph. For unsplittable and segregated routing, we establish upper and lower bounds of $O\left(\log m/ \log\log m \right)$ and $\Omega\left(\log m/ \log\log m \right)$, respectively, for polynomial-time approximation algorithms, where $m$ is the number of static links. We further reveal that under un-/splittable and non-segregated routing, even for demands of a single source (resp., destination), the problem cannot be approximated better than $\Omega\left(\frac{c_{\max}}{c_{\min}} \right)$ unless P=NP, where $c_{\max}$ (resp., $c_{\min}$) denotes the maximum (resp., minimum) capacity. It remains NP-hard for uniform capacities, but is tractable for a single commodity and uniform capacities. Our trace-driven simulations show a significant reduction in network congestion compared to existing solutions.
Fighting Fire with Fire: Adversarial Prompting to Generate a Misinformation Detection Dataset

The recent success in language generation capabilities of large language models (LLMs), such as GPT, Bard, Llama etc., can potentially lead to concerns about their possible misuse in inducing mass agitation and communal hatred via generating fake news and spreading misinformation. Traditional means of developing a misinformation ground-truth dataset does not scale well because of the extensive manual effort required to annotate the data. In this paper, we propose an LLM-based approach of creating silver-standard ground-truth datasets for identifying misinformation. Specifically speaking, given a trusted news article, our proposed approach involves prompting LLMs to automatically generate a summarised version of the original article. The prompts in our proposed approach act as a controlling mechanism to generate specific types of factual incorrectness in the generated summaries, e.g., incorrect quantities, false attributions etc. To investigate the usefulness of this dataset, we conduct a set of experiments where we train a range of supervised models for the task of misinformation detection.
Variability in exercise is linked to improved age-related dysfunctions: A potential role for the constrained-disorder principle-based second-generation artificial intelligence system

Objective: Regular physical activity (PA) promotes mental and physical health. Nevertheless, inactivity is a worldwide pandemic, and methods to augment exercise benefits are required. The constrained disorder principle (CDP) characterizes biological systems based on their inherent variability. We aimed to investigate the association between intra-individual variability in PA and disability among non-athlete adults. Methods: In this retrospective analysis of the longitudinal SHARE survey, we included non-disabled adults aged >50 with at least six visits over 14 years. Self-reported PA frequency was documented bi- to triennially. Low PA intensity was defined as vigorous PA frequency less than once a week. Stable PA was described as an unchanged PA intensity in all consecutive middle observations. The primary outcome was defined as a physical limitation in everyday activities at the end of the survey. Secondary outcomes were cognitive functions, including short-term memory, long-term memory, and verbal fluency. Results: The study included 2,049 non-disabled adults with a mean age of 53 and 49.1 % women. In the initially high PA intensity group, variability in PA was associated with increased physical disability prevalence (23.3% vs. 33.2%, stablevs. unstablePA; P<0.01; adjusted P<0.01). In the initially low PA intensity group, variability was associated with a reduced physical disability (45.6% vs. 33.3%, stablevs. unstable PA; P=0.02; adjusted P=0.03). There were no statistically significant differences in cognitive parameters between the groups. Among individuals with the same low PA intensity at the beginning and end of follow-up, variability was associated with reduced physical disability (56.9% vs. 36.5%, stablevs. unstablePA; P=0.02; adjusted P=0.04) and improved short-term memory (score change: −0.28 vs. +0.29, stablevs. unstable PA; P=0.05). Conclusion: Incorporating variability into PA regimens of inactive adults may enhance their physical and cognitive benefits.
Development of an anti-Pfs230 monoclonal antibody as a Plasmodium falciparum gametocyte blocker

Vector control is a crucial strategy for malaria elimination by preventing infection and reducing disease transmission. Most gains have been achieved through insecticide-treated nets (ITNs) and indoor residual spraying (IRS), but the emergence of insecticide resistance among Anopheles mosquitoes calls for new tools to be applied. Here, we present the development of a highly effective murine monoclonal antibody, targeting the N-terminal region of the Plasmodium falciparum gametocyte antigen Pfs230, that can decrease the infection prevalence by > 50% when fed to Anopheles mosquitoes with gametocytes in an artificial membrane feeding system. We used a standard mouse immunization protocol followed by protein interaction and parasite-blocking validation at three distinct stages of the monoclonal antibody development pipeline: post-immunization, post-hybridoma generation, and final validation of the monoclonal antibody. We evaluated twenty antibodies identifying one (mAb 13G9) with high Pfs230-affinity and parasite-blocking activity. This 13G9 monoclonal antibody could potentially be developed into a transmission-blocking single-chain antibody for expression in transgenic mosquitoes.
The lived experience of withdrawal from Selective Serotonin Reuptake Inhibitor (SSRI) antidepressants: A qualitative interview study

Abstract Background Our knowledge of the broader impacts of antidepressant withdrawal, beyond physical side effects, is limited. Further research is needed to investigate the lived experiences of withdrawal, to aid clinicians on how to guide patients through the process. Aim To explore antidepressant users’ experiences and views on the withdrawal process and how it affected their quality of life across multiple life domains. Design and Setting We conducted in‐depth qualitative interviews with 20 individuals from the community who had attempted to withdraw from Serotonin Reuptake Inhibitor antidepressants in the past year. Method Semi‐structured interviews were conducted online. A topic guide was used to ensure consistency across interviews. The interviews were audio‐recorded and transcribed verbatim and analysed using inductive reflexive thematic analysis. Results Five themes were generated. The first highlighted the challenges of managing the release from emotional blunting and cognitive suppression following antidepressant discontinuation. The second related to the negative impact of withdrawal on close relationships and social interactions. The third showed that concurrent with negative physical symptoms, there was a positive impact on health (exercise was reported by some as a coping mechanism). The fourth theme focused on support from GPs and families, emphasising the importance of mental health literacy in others. The final theme underscored the importance of gradual and flexible tapering in enabling a manageable withdrawal experience, and the consideration of timing. Conclusion The lived experience of withdrawal significantly impacts individuals’ well‐being. Participants emphasised that withdrawal is not just about physical side effects but also affects their emotional, cognitive, and social functioning. Patient and Public Involvement (PPI) Eight people attended individual online meetings to share their experiences of antidepressant withdrawal to help inform the study design and recruitment strategy. Insights from these meetings informed the development of the topic guide. Questions about GP involvement, family relationships, and mood and thinking changes were included based on this PPI work. This ensured the inclusion of topics important to antidepressant users and facilitated the researcher's questioning during the interviews.
Consistency of impact assessment protocols for non-native species

Standardized tools are needed to identify and prioritize the most harmful non-native species (NNS). A plethora of assessment protocols have been developed to evaluate the current and potential impacts of non-native species, but consistency among them has received limited attention. To estimate the consistency across impact assessment protocols, 89 specialists in biological invasions used 11 protocols to screen 57 NNS (2614 assessments). We tested if the consistency in the impact scoring across assessors, quantified as the coefficient of variation (CV), was dependent on the characteristics of the protocol, the taxonomic group and the expertise of the assessor. Mean CV across assessors was 40%, with a maximum of 223%. CV was lower for protocols with a low number of score levels, which demanded high levels of expertise, and when the assessors had greater expertise on the assessed species. The similarity among protocols with respect to the final scores was higher when the protocols considered the same impact types. We conclude that all protocols led to considerable inconsistency among assessors. In order to improve consistency, we highlight the importance of selecting assessors with high expertise, providing clear guidelines and adequate training but also deriving final decisions collaboratively by consensus.
The Association of eHealth Literacy Skills and mHealth Application Use Among US Adults With Obesity: Analysis of Health Information National Trends Survey Data

Background Physical inactivity and a poor diet are modifiable behaviors that contribute to obesity. Obesity is a well-recognized risk factor for chronic diseases, including diabetes. Mobile health (mHealth) apps can play an important adjuvant role in preventing and treating chronic diseases and promoting positive health behavior change among people with obesity, and eHealth literacy skills have the potential to impact mHealth app use. Objective The purpose of this study was to explore the associations between the 2 dimensions, access and application, of eHealth literacy skills and mHealth app use among US adults (≥18 years of age) with obesity (BMI ≥30 kg/m2). Methods Data were obtained from February to June 2020 using the Health Information National Trends Survey 5. A total of 1079 respondents met the inclusion criteria of adults with obesity and owners of smartphones. Individual associations between mHealth app use and sociodemographic variables were explored using weighted chi-square and 2-tailed t tests. A multivariable weighted logistic regression model was fitted, and adjusted odds ratios (ORs) of using mHealth apps with corresponding 95% CIs were reported across multiple sociodemographic variables. An Ising model-weighted network visualization was produced. A receiver operating characteristic curve was calculated, and the area under the curve was reported with the corresponding Delong 95% CI. Results A majority of respondents were female (550/923, 59.6%) or non-Hispanic White (543/923, 58.8%). Individuals in households earning less than US $50,000 comprised 41.4% (382/923) of the sample. All sociodemographic variables were found to be univariately significant at the 5% level, except employment and region. Results from the multivariable weighted logistic regression model showed that the adjusted odds of using an mHealth app are 3.13 (95% CI 1.69-5.80) and 2.99 (95% CI 1.67-5.37) times higher among those with an access eHealth literacy skill of using an electronic device to look for health or medical information for themselves and an application eHealth literacy skill of using electronic communications with a doctor or doctor’s office, respectively. Several sociodemographic variables were found to be significant, such as education, where adjusted ORs comparing subgroups to the lowest educational attainment were substantial (ORs ≥7.77). The network visualization demonstrated that all eHealth literacy skills and the mHealth app use variable were positively associated to varying degrees. Conclusions This work provides an initial understanding of mHealth app use and eHealth literacy skills among people with obesity, identifying people with obesity subpopulations who are at risk of a digital health divide. Future studies should identify equitable solutions for people with obesity (as well as other groups) and their use of mHealth apps.
TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution

Transformer-based method has demonstrated promising performance in image super-resolution tasks, due to its long-range and global aggregation capability. However, the existing Transformer brings two critical challenges for applying it in large-area earth observation scenes: (1) redundant token representation due to most irrelevant tokens; (2) single-scale representation which ignores scale correlation modeling of similar ground observation targets. To this end, this paper proposes to adaptively eliminate the interference of irreverent tokens for a more compact self-attention calculation. Specifically, we devise a Residual Token Selective Group (RTSG) to grasp the most crucial token by dynamically selecting the top- $k$ keys in terms of score ranking for each query. For better feature aggregation, a Multi-scale Feed-forward Layer (MFL) is developed to generate an enriched representation of multi-scale feature mixtures during feed-forward process. Moreover, we also proposed a Global Context Attention (GCA) to fully explore the most informative components, thus introducing more inductive bias to the RTSG for an accurate reconstruction. In particular, multiple cascaded RTSGs form our final Top- $k$ Token Selective Transformer (TTST) to achieve progressive representation. Extensive experiments on simulated and real-world remote sensing datasets demonstrate our TTST could perform favorably against state-of-the-art CNN-based and Transformer-based methods, both qualitatively and quantitatively. In brief, TTST outperforms the state-of-the-art approach (HAT-L) in terms of PSNR by 0.14 dB on average, but only accounts for 47.26% and 46.97% of its computational cost and parameters. The code and pre-trained TTST will be available at https://github.com/XY-boy/TTST for validation.
Improvements on the ice cloud modeling capabilities of the Community Radiative Transfer Model

Noticeable improvements on the ice cloud modeling capabilities of the Community Radiative Transfer Model (CRTM) are reported, which are based on the most recent advances in understanding ice cloud microphysical (particularly, ice particle habit/shape characteristics) and optical properties. The new CRTM ice cloud model is derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 ice cloud habit model, which represents ice particles as severely roughened hexagonal ice column aggregates with a gamma size distribution. The single‐scattering properties of the new ice particle model are derived from a state‐of‐the‐art ice optical property library and are constructed as look‐up tables for rapid CRTM computations. Various sensitivity studies concerning instrument‐specific applications and simulations are performed to validate CRTM against satellite observations. In particular, radiances in a spectral region covering the infrared wavelengths are simulated. Comparisons of brightness temperatures between CRTM simulations and observations (from MODIS, the Atmospheric Infrared Sounder, and the Advanced Microwave Sounding Unit) show that the new ice cloud optical property look‐up table substantially enhances the performance of the CRTM under ice cloud conditions.
GRAF1 Regulates Brown and Beige Adipose Differentiation and Function

Adipose tissue, which is crucial for the regulation of energy within the body, contains both white and brown adipocytes. White adipose tissue (WAT) primarily stores energy, while brown adipose tissue (BAT) plays a critical role in energy dissipation as heat, offering potential for therapies aimed at enhancing metabolic health. Regulation of the RhoA/ROCK pathway is crucial for appropriate specification, differentiation and maturation of both white and brown adipocytes. However, our knowledge of how this pathway is controlled within specific adipose depots remains unclear, and to date a RhoA regulator that selectively controls adipocyte browning has not been identified. Our study shows that expression of GRAF1, a RhoGAP highly expressed in metabolically active tissues, closely correlates with brown adipocyte differentiation in culture and in vivo. Mice with either global or adipocyte-specific GRAF1 deficiency exhibit impaired BAT maturation, reduced capacity for WAT browning, and compromised cold-induced thermogenesis. Moreover, defects in differentiation of mouse or human GRAF1-deficient brown preadipocytes can be rescued by treatment with a Rho kinase inhibitor. Collectively, these studies indicate that GRAF1 can selectively induce brown and beige adipocyte differentiation and suggest that manipulating GRAF1 activity may hold promise for the future treatment of diseases related to metabolic dysfunction.
Spatio-temporal variability in transmission risk of human schistosomes and animal trematodes in a seasonally desiccating East African landscape

Different populations of hosts and parasites experience distinct seasonality in environmental factors, depending on local-scale biotic and abiotic factors. This can lead to highly heterogeneous disease outcomes across host ranges. Variable seasonality characterizes urogenital schistosomiasis, a neglected tropical disease caused by parasitic trematodes (Schistosoma haematobium). Their intermediate hosts are aquatic Bulinus snails that are highly adapted to extreme rainfall seasonality, undergoing prolonged dormancy yearly. While Bulinus snails have a remarkable capacity for rebounding following dormancy, we investigated the extent to which parasite survival within snails is diminished. We conducted an investigation of seasonal snail schistosome dynamics in 109 ponds of variable ephemerality in Tanzania from August 2021 to July 2022. First, we found that ponds have two synchronized peaks of schistosome infection prevalence and observed cercariae, though of lower magnitude in the fully desiccating than non-desiccating ponds. Second, we evaluated total yearly schistosome prevalence across an ephemerality gradient, finding ponds with intermediate ephemerality to have the highest infection rates. We also investigated dynamics of non-schistosome trematodes, which lacked synonymity with schistosome patterns. We found peak schistosome transmission risk at intermediate pond ephemerality, thus the impacts of anticipated increases in landscape desiccation could result in increases or decreases in transmission risk with global change.
The applications and limitations of using remote sensing platforms to detect harvest activity

Multispectral remote sensing applications that detect changes in vegetative reflectance in the near infrared are used to identify forest cover change in forests across the globe. Remote sensing platforms are used to identify changes in vegetative density often using the NDVI (Normalized Difference Vegetative Index) or other similar methods based a change of vegetative reflectance over time. Changes in forest cover may represent any number of disturbance events, fire, hurricane damage, human harvest behavior, and land use change. This paper aims to develop an accuracy assessment of using remote sensing products that identify indiscriminate changes in vegetative density to identify human harvest activity across multiple geophysical regions and forest cover types in the Southeastern United States.