Miracle-dz/UniFAR-FTUs huggingface dataset:
Fatigue presentation, severity, and related outcomes in a prospective cohort following post-COVID-19 hospitalization in British Columbia, Canada

Introduction Increasing evidence on long-term health outcomes following SARS CoV-2 infection shows post-viral symptoms can persist for months. These symptoms are often consistent with those of Myalgic Encephalomyelitis or Chronic Fatigue Syndrome (ME/CFS). The aim of the present study was to examine the prevalence and outcome predictors of post-viral fatigue and related symptoms 3- and 6-months following symptom onset. Methods A prospective cohort of patients hospitalized with Coronavirus disease (COVID-19) (n = 88) were recruited from a Post-COVID-19 Respiratory Clinic (PCRC) in Vancouver, Canada to examine predictors of long-term fatigue and substantial fatigue. Multivariable mixed effects analyses examined the relationship between patient predictors, including pre-existing comorbidities, patient reported outcome measures, and fatigue and substantial fatigue at follow-up. Results The number of patients experiencing fatigue or substantial fatigue at 3 months post-infection were 58 (67%) and 14 (16%) respectively. At 6 months these numbers declined to 47 (60%) patients experiencing fatigue and 6 (6%) experiencing substantial fatigue. Adjusted analysis, for sex, age, and time, revealed the number of pre-existing comorbidities to be associated with fatigue (OR 2.21; 95% CI 1.09–4.49; 0.028) and substantial fatigue (OR 1.73; 95% CI 1.06–2.95; 0.033) at 3 months follow-up. Except for shortness of breath, self-care, and follow-up time, all follow-up variables were found to be associated with fatigue and substantial fatigue at 3 months. Conclusion Fatigue and substantial fatigue are common after COVID-19 infection but often diminish over time. A significant number of patients continue to exhibit long-term fatigue at 6 months follow-up. Further research is needed to clarify the causality of viral infections in the development and severity of fatigue as a symptom and in meeting post-viral fatigue syndrome or ME/CFS diagnostic criteria.
SMG-BERT: integrating stereoscopic information and chemical representation for molecular property prediction

Molecular property prediction is a crucial task in various fields and has recently garnered significant attention. To achieve accurate and fast prediction of molecular properties, machine learning (ML) models have been widely employed due to their superior performance compared to traditional methods by trial and error. However, most of the existing ML models that do not incorporate 3D molecular information are still in need of improvement, as they are mostly poor at differentiating stereoisomers of certain types, particularly chiral ones. Also,routine featurization methods using only incomplete features are hard to obtain explicable molecular representations. In this paper, we propose the Stereo Molecular Graph BERT (SMG-BERT) by integrating the 3D space geometric parameters, 2D topological information, and 1D SMILES string into the self-attention-based BERT model. In addition, nuclear magnetic resonance (NMR) spectroscopy results and bond dissociation energy (BDE) are integrated as extra atomic and bond features to improve the model’s performance and interpretability analysis. The comprehensive integration of 1D, 2D, and 3D information could establish a unified and unambiguous molecular characterization system to distinguish conformations, such as chiral molecules. Intuitively integrated chemical information enables the model to possess interpretability that is consistent with chemical logic. Experimental results on 12 benchmark molecular datasets show that SMG-BERT consistently outperforms existing methods. At the same time, the experimental results demonstrate that SMG-BERT is generalizable and reliable.
SARS-CoV-2 infection dysregulates NAD metabolism

Introduction Severe COVID-19 results initially in pulmonary infection and inflammation. Symptoms can persist beyond the period of acute infection, and patients with Post-Acute Sequelae of COVID (PASC) often exhibit a variety of symptoms weeks or months following acute phase resolution including continued pulmonary dysfunction, fatigue, and neurocognitive abnormalities. We hypothesized that dysregulated NAD metabolism contributes to these abnormalities. Methods RNAsequencing of lungs from transgenic mice expressing human ACE2 (K18-hACE2) challenged with SARS-CoV-2 revealed upregulation of NAD biosynthetic enzymes, including NAPRT1, NMNAT1, NAMPT, and IDO1 6 days post-infection. Results Our data also demonstrate increased gene expression of NAD consuming enzymes: PARP 9,10,14 and CD38. At the same time, SIRT1, a protein deacetylase (requiring NAD as a cofactor and involved in control of inflammation) is downregulated. We confirmed our findings by mining sequencing data from lungs of patients that died from SARS-CoV-2 infection. Our validated findings demonstrating increased NAD turnover in SARS-CoV-2 infection suggested that modulating NAD pathways may alter disease progression and may offer therapeutic benefits. Specifically, we hypothesized that treating K18-hACE2 mice with nicotinamide riboside (NR), a potent NAD precursor, may mitigate lethality and improve recovery from SARS-CoV-2 infection. We also tested the therapeutic potential of an anti- monomeric NAMPT antibody using the same infection model. Treatment with high dose anti-NAMPT antibody resulted in significantly decreased body weight compared to control, which was mitigated by combining HD anti-NAMPT antibody with NR. We observed a significant increase in lipid metabolites, including eicosadienoic acid, oleic acid, and palmitoyl carnitine in the low dose antibody + NR group. We also observed significantly increased nicotinamide related metabolites in NR treated animals. Discussion Our data suggest that infection perturbs NAD pathways, identify novel mechanisms that may explain some pathophysiology of CoVID-19 and suggest novel strategies for both treatment and prevention.
E-prescription and invisible work in genomics in France

This article aims to analyze the transformations in medical prescription work and infrastructures brought by digitalization. Our fieldwork takes place in the context of precision medicine development based on genomics High Throughput Sequencing (HTS) in France, through the Plan France Médecine Génomique (PFMG 2025). The Plan aims at industrializing the production of genomic testing in clinical context at a national scale, particularly in oncology. To ensure the intensified flow of information between hospitals and HTS platforms required, a centralized process has been organized around two sequencing platforms and the introduction of a new e-prescription software (E-PRES). We start by analyzing how the e-prescription software changes the practices of health professionals by imposing new technological and professional standards. We show that, more than a mere prescription tool, this software is also a monitoring tool for the platforms and prescribers' work, and a support tool for the logistical and work organization. Secondly, we question the division of labor among the different professionals involved in the organizational or technical tasks required. We show that the feasibility of this new form of digitalized prescription relies on an important datawork performed by “small hands” to select, translate and process a vast amount of heterogeneous data.
A brief history of galectin evolution

Galectins are a family of carbohydrate-binding proteins found in vertebrates in great abundance and diversity in terms of both structure and ligand-binding properties as well as physiological function. Proteins with clear relationships to vertebrate galectins are already found in primitive Bilateria. The increasing amount of accessible well-annotated bilaterian genomes has allowed us to reveal, through synteny analyses, a new hypothesis about the phylogenetic history of the galectin family in this animal group. Thus, we can trace the genomic localization of the putative ancestral Bilateria galectin back to the scallops as a still very primitive slow-evolving bilaterian lineage. Intriguingly, our analyses show that the primordial galectin of the Deuterostomata most likely exhibited galectin-8-like characteristics. This basal standing galectin is characterized by a tandem-repeat type with two carbohydrate recognition domains as well as by a sialic acid binding property of the N-terminal domain, which is typical for galectin-8. With the help of synteny, the amplification of this potential primordial galectin to the broad galectin cosmos of modern jawed vertebrates can be reconstructed. Therefore, it is possible to distinguish between the paralogs resulting from small-scale duplication and the ohnologues generated by whole-genome duplication. Our findings support a substantially new hypothesis about the origin of the various members of the galectin family in vertebrates. This allows us to reveal new theories on the kinship relationships of the galectins of Gnatostomata. In addition, we focus for the first time on the galectines of the Cyclostomata, which as a sister group of jawed vertebrates providing important insights into the evolutionary history of the entire subphylum. Our studies also highlight a previously neglected member of the galectin family, galectin-related protein 2. This protein appears to be a widespread ohnologue of the original tandem-repeat ancestor within Gnathostomata that has not been the focus of galectin research due to its nonclassical galactose binding sequence motif and the fact that it was lost during mammalian evolution.
F EATURE -I NTERPRETABLE R EAL C ONCEPT D RIFT D ETECTION

Classifiers deployed in production degrade in performance due to changes in the posterior distribution, a phenomenon referred to as real concept drift. Knowledge of such distribution shifts is helpful for two main reasons: (i) it helps retain classifier performance across time by telling us when to retrain it; and (ii) understanding the nature of shift in the relationship between input features and output labels, which can be of value for business analytics (e.g., understanding change in demand helps manage inventory) or scientific study (e.g., understanding virus behavior across changing demographics helps distribute drugs better). An interpretable real concept drift detection method is ideal for achieving this knowledge. Existing interpretable methods in this space only track covariate shifts, thus, are insensitive to the optimal decision boundary (true posterior distribution) and vulnerable to benign drifts in streaming data. Our work addresses this issue by proposing an interpretable method that leverages gradients of a classifier in a feature-wise hypothesis-testing framework to detect real concept drift. We also extend our method to a more realistic unsupervised setting where labels are not available to detect drift. Our experiments on various datasets show that the proposed method outperforms existing interpretable methods and performs at par with state-of-theart supervised drift detection methods w.r. t the average model classification accuracy metric. Qualitatively, our method identifies features that are relevant to the drift in the USENET2 dataset, thus providing interpretability and accurate drift detection.
Investigating the Impact of Experience on a User's Ability to Perform Hierarchical Abstraction

—The field of Learning from Demonstration enables end-users, who are not robotics experts, to shape robot behavior. However, using human demonstrations to teach robots to solve long-horizon problems by leveraging the hierarchical structure of the task is still an unsolved problem. Prior work has yet to show that human users can provide sufficient demonstrations in novel domains without showing the demonstrators explicit teaching strategies for each domain. In this work, we investigate whether non-expert demonstrators can generalize robot teaching strategies to provide necessary and sufficient demonstrations to robots zero-shot in novel domains . We find that increasing participant experience with providing demonstrations improves their demonstration’s degree of sub-task abstraction ( p < . 001 ), teaching efficiency ( p < . 001 ), and sub-task redundancy ( p < . 05 ) in novel domains, allowing generalization in robot teaching. Our findings demonstrate for the first time that non-expert demonstrators can transfer knowledge from a series of training experiences to novel domains without the need for explicit instruction
SparseBTF: Sparse Representation Learning for Bidirectional Texture Functions

We propose a novel dictionary-based representation learning model for Bidirectional Texture Functions (BTFs) aiming at compact storage, real-time rendering performance, and high image quality. Our model is trained once, using a small training set, and then used to obtain a sparse tensor containing the model parameters. Our technique exploits redundancies in the data across all dimensions simultaneously, as opposed to existing methods that use only angular information and ignore correlations in the spatial domain. We show that our model admits efficient angular interpolation directly in the model space, rather than the BTF space, leading to a notably higher rendering speed than in previous work. Additionally, the high quality-storage cost tradeoff enabled by our method facilitates controlling the image quality, storage cost, and rendering speed using a single parameter, the number of coefficients. Previous methods rely on a fixed number of latent variables for training and testing, hence limiting the potential for achieving a favorable quality-storage cost tradeoff and scalability. Our experimental results demonstrate that our method outperforms existing methods both quantitatively and qualitatively, as well as achieving a higher compression ratio and rendering speed.
Plausibility Processing in Transformer Language Models: Focusing on the Role of Attention Heads in GPT

The goal of this paper is to explore how Transformer language models process semantic knowledge, especially regarding the plausibility of noun-verb relations. First, I demonstrate GPT2 exhibits a higher degree of similarity with humans in plausibility processing compared to other Transformer language models. Next, I delve into how knowledge of plausibility is contained within attention heads of GPT2 and how these heads causally contribute to GPT2's plausibility processing ability. Through several experiments, it was found that: i) GPT2 has a number of attention heads that detect plausible noun-verb relationships; ii) these heads collectively contribute to the Transformer's ability to process plausibility, albeit to varying degrees; and iii) attention heads' individual performance in detecting plausibility does not necessarily correlate with how much they contribute to GPT2's plausibility processing ability.
Scaling Covariance Matrix Adaptation MAP-Annealing to High-Dimensional Controllers

Pre-training a diverse set of robot controllers in simulation has enabled robots to adapt online to damage in robot locomotion tasks. However, ﬁnding diverse, high-performing controllers requires specialized hardware and extensive tuning of a large number of hyperparameters. On the other hand, the Covariance Matrix Adaptation MAP-Annealing algorithm, an evolution strategies (ES)-based quality diversity algorithm, does not have these limitations and has been shown to achieve state-of-the-art performance in standard benchmark domains. However, CMA-MAE cannot scale to modern neural network controllers due to its quadratic complexity. We leverage efﬁcient approximation methods in ES to propose three new CMA-MAE variants that scale to very high dimensions. Our experiments show that the variants outperform ES-based baselines in benchmark robotic locomotion tasks, while being comparable with state-of-the-art deep reinforcement learning-based quality diversity algorithms. Source code and videos are available at https: //scalingcmamae.github. io
OpenLock Grid: Learning Causal Structure with Deep RL Models

Causal reasoning is considered by many to be the cornerstone of human intelligence. Humans have the ability to develop causal schemas to solve novel problems and establish explanations of latent constraints while interacting with the environment. In this paper, we explore and examine agents’ ability to discover causal schemas and generalize to more complex environments based on the OpenLock task, in which participants are required to escape a room by moving levers, and the sequential movements of the correct levers form a causal sequence beginning with either a common-cause (CC) or a common-effect (CE) structure. We develop a deep Q-learning model to solve a simplified OpenLock problem by learning the transition probability between solutions. We argue that the simplification is reasonable since the original task is a complex combination of several diverse sub-tasks and it’s difficult to solve all sub-tasks in one single model. The simplified task is equivalent to the original one on the ability to examine agents’ causal transferring capability, as argued in Sec. 1. We focus on the agents’ exploring the latent causal structure of levers, which is challenging and meaningful enough to show the potential of RL in causal reasoning. Experiments show that our method learns the underlying causal mechanisms perfectly and pertains to a great generalization ability that can transfer the knowledge trained in previous rooms to new rooms, and in simple conditions to more complex tasks.
TGRL: T EACHER G UIDED R EINFORCEMENT L EARN - ING A LGORITHM FOR POMDP S

In many real-world problems, an agent must operate in an uncertain and partially observable environment. Due to partial information, a policy directly trained to operate from these restricted observations tends to perform poorly. In some scenarios, during training more information about the environment is available, which can be utilized to find a superior policy. Because this privileged information is unavailable at deployment, such a policy cannot be deployed. The teacher-student paradigm overcomes this challenge by using actions of privileged (or teacher) policy as the target for training the deployable (or student) policy operating from the restricted observation space using supervised learning. However, due to information asymmetry, it is not always feasible for the student to perfectly mimic the teacher. We provide a principled solution to this problem, wherein the student policy dynamically balances between following the teacher’s guidance and utilizing reinforcement learning to solve the partially observed task directly. The proposed algorithm is evaluated on diverse domains and fares favorably against strong baselines.
Responses of intraspecific metabolic scaling to temperature and activity differ between water- and air-breathing ectothermic vertebrates.

Metabolism underpins all life-sustaining processes and varies profoundly with body size, temperature and locomotor activity. A current theory explains some of the size-dependence of metabolic rate (its mass exponent, b) through changes in metabolic level (L). We propose two predictive advances that: (a) combine the above theory with the evolved avoidance of oxygen limitation in water-breathers experiencing warming, and (b) quantify the overall magnitude of combined temperatures and degrees of locomotion on metabolic scaling across air- and water-breathers. We use intraspecific metabolic scaling responses to temperature (523 regressions) and activity (281 regressions) in diverse ectothermic vertebrates (fish, reptiles and amphibians) to show that b decreases with temperature-increased L in water-breathers, supporting surface area-related avoidance of oxygen limitation, whereas b increases with activity-increased L in air-breathers, following volume-related influences. This new theoretical integration quantitatively incorporates different influences (warming, locomotion) and respiration modes (aquatic, terrestrial) on animal energetics.
Lightweight Curvature Estimation on Point Clouds with Randomized Corrected Curvature Measures

The estimation of differential quantities on oriented point cloud is a classical step for many geometry processing tasks in computer graphics and vision. Even if many solutions exist to estimate such quantities, they usually fail at satisfying both a stable estimation with theoretical guarantee, and the efficiency of the associated algorithm. Relying on the notion of corrected curvature measures [LRT22, LRTC20] designed for surfaces, the method introduced in this paper meets both requirements. Given a point of interest and a few nearest neighbours, our method estimates the whole curvature tensor information by generating random triangles within these neighbours and normalising the corrected curvature measures by the corrected area measure. We provide a stability theorem showing that our pointwise curvatures are accurate and convergent, provided the noise in position and normal information has a variance smaller than the radius of neighbourhood. Experiments and comparisons with the state‐of‐the‐art confirm that our approach is more accurate and much faster than alternatives. The method is fully parallelizable, requires only one nearest neighbour request per point of computation, and is trivial to implement.
Bongard-Tool: Tool Concept Induction from Few-Shot Visual Exemplars

There is no one-to-one mapping from objects to tool concepts. In fact, objects can support diversified tool uses, enabling compositional and flexible functionalities. These tool-like functionalities are mostly context-dependent and vary across scenes. To address this unique property of tool concepts, we propose the Bongard-Tool challenge and formulate the context-dependent tool understanding as a few-shot concept induction problem. Specifically, to build Bongard-Tool, we employ large language models for knowledge building, web crawling, and vision-language models for content retrieval and filtering. We also perform extensive experiments on recent few-shot and meta-learning methods to show the hardship of understanding compositional tool concepts from pure visual perception. We hope to shed light on future studies by introducing Bongard-Tool benchmark as a testbed for building machines that can flexibly understand and use tools. 1
On the Side Distortion Lower Bound in "Generalized Multiple Description Coding with Correlating Transforms"

The rate-distortion region for two-description cod- ing of a memoryless Gaussian source is a celebrated result of Ozarow. In the above paper (IEEE Trans. Inform. Theory, 47(6):2199-2224), Ozarow's rate-distortion region is translated to a lower bound on the side distortion in terms of the central distortion D0 and redundancy ρ = R1 + R2 � R(D0) for the bal- anced distortion case (D1 = D2). Since the impact of the (single- description) Shannon bound in neglected, the expressions and accompanying figure are misleading. A better form of the side distortion bound is presented and discussed here. Extension of the bound to jointly Gaussian vectors is also discussed briefly, again to clarify the above paper.
A simple generalization of the O'Brien and Fleming group sequential test procedure to more than two treatment groups

As a modification of a fixed single-sample trial, O'Brien and Fleming 1979 proposed a group sequential test procedure for clinical trials for comparing only two treatments. The purpose of this paper is to provide a direct generalization of this procedure to accomodate more than two treatment groups. On the basis of Monte Carlo simulations, a table that summarizes the critical values for α-levels equal to . 05 and . 10 in the situation where the number of treatment groups ranges from 2 to 10 and the number of sequential tests ranges from 1 to 10 has been derived as well.