Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and factors associated with sarcopenia among older adults in a post-acute hospital in Singapore

Background Sarcopenia is common in older adults worldwide, but its prevalence varies widely owing to differences in diagnostic criteria, population sampled, and care setting. We aimed to determine the prevalence and factors associated with sarcopenia in patients aged 65 and above admitted to a post-acute hospital in Singapore. Methods This was a cross-sectional study of 400 patients recruited from a community hospital in Singapore. Data including socio-demographics, physical activity, nutritional status, cognition, clinical and functional status, as well as anthropometric measurements were collected. Sarcopenia was defined using the Asian Working Group for Sarcopenia 2019 criteria [AWGS2019]. Results Of the 383 patients with complete datasets, overall prevalence of sarcopenia was 54% while prevalence of severe sarcopenia was 38.9%. Participants with increased age, male gender and a low physical activity level were more likely to be sarcopenic, while those with higher hip circumference and higher BMI of ≥27.5m/kg2 were less likely to be sarcopenic. Other than the above-mentioned variables, cognitive impairment was also associated with severe sarcopenia. Conclusions More than 1 in 2 older adults admitted to a post-acute hospital in Singapore are sarcopenic. There is an urgent need to address this important clinical syndrome burden and to identify patients at risk of sarcopenia in post-acute settings in Singapore for early intervention.
Convergence for Inactivation of TGFÎ² Signaling Is a Common Feature of Advanced Pancreatic Cancer

We performed WES of 250 unique tumor tissues from 30 multiregion sampled pancreatic cancer research autopsies from patients diagnosed with advanced stage disease. We find that most genetic alterations in PDAC occur in a subclonal manner, and some genes occurred in a subclonal manner exclusively. Convergent evolution within the TGF{beta} pathway was also identified as a common feature of advanced stage disease, with SMAD4 inactivation more common among metastatic PDACs compared to inactivation of TGF{beta} surface receptors that was more common in locally advanced tumors. The mode of clinical management (radiation versus chemotherapy) contributed distinct mutational signatures yet these mutations are not predicted to have functional relevance to tumor progression. Overall, these findings provide a first definition of the genetic features that distinguish among patients with locally advanced versus metastatic PDAC. These findings may have clinical relevance in upfront clinical decision making for the optimal candidates for neoadjuvant therapy.
Kinesthetic-based In-Hand Object Recognition with an Underactuated Robotic Hand

Tendon-based underactuated hands are intended to be simple, compliant and affordable. Often, they are 3D printed and do not include tactile sensors. Hence, performing in-hand object recognition with direct touch sensing is not feasible. Adding tactile sensors can complicate the hardware and introduce extra costs to the robotic hand. Also, the common approach of visual perception may not be available due to occlusions. In this paper, we explore whether kinesthetic haptics can provide in-direct information regarding the geometry of a grasped object during in-hand manipulation with an underactuated hand. By solely sensing actuator positions and torques over a period of time during motion, we show that a classifier can recognize an object from a set of trained ones with a high success rate of almost 95%. In addition, the implementation of a real-time majority vote during manipulation further improves recognition. Additionally, a trained classifier is also shown to be successful in distinguishing between shape categories rather than just specific objects.
Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chat

Large Language Models (LLMs) have the potential to fundamentally change the way people engage in computer programming. Agent-based modeling (ABM) has become ubiquitous in natural and social sciences and education, yet no prior studies have explored the potential of LLMs to assist it. We designed NetLogo Chat to support the learning and practice of NetLogo, a programming language for ABM. To understand how users perceive, use, and need LLM-based interfaces, we interviewed 30 participants from global academia, industry, and graduate schools. Experts reported more perceived benefits than novices and were more inclined to adopt LLMs in their workflow. We found significant differences between experts and novices in their perceptions, behaviors, and needs for human-AI collaboration. We surfaced a knowledge gap between experts and novices as a possible reason for the benefit gap. We identified guidance, personalization, and integration as major needs for LLM-based interfaces to support the programming of ABM.
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity

Instruction-tuned Large Language Models (LLMs) have recently showcased remarkable advancements in their ability to generate fitting responses to natural language instructions. However, many current works rely on manual evaluation to judge the quality of generated responses. Since such manual evaluation is time-consuming, it does not easily scale to the evaluation of multiple models and model variants. In this short paper, we propose a straightforward but remarkably effective evaluation metric called SemScore, in which we directly compare model outputs to gold target responses using semantic textual similarity (STS). We conduct a comparative evaluation of the model outputs of 12 prominent instruction-tuned LLMs using 8 widely-used evaluation metrics for text generation. We find that our proposed SemScore metric outperforms all other, in many cases more complex, evaluation metrics in terms of correlation to human evaluation. These findings indicate the utility of our proposed metric for the evaluation of instruction-tuned LLMs.
ReacLLaMA: Merging chemical and textual information in chemical reactivity AI models

Chemical reactivity models are developed to predict chemical reaction outcomes in the form of classification (success/failure) or regression (product yield) tasks. The vast majority of the reported models are trained solely on chemical information such as reactants, products, reagents, and solvents, but not on the details of a synthetic protocol. Herein incorporation of procedural text with the aim to augment the Graphormer reactivity model and improve its accuracy is presented. Two major approaches are used: training an adapter Graphormer model that is provided with a GPT-2-derived latent representation of the text procedure (ReacLLaMA-Adapter) and labeling an unlabeled part of a dataset with the LLaMA 2 model followed by training the Graphormer on an extended dataset (Zero-Shot Labeling ReacLLaMA). Both methodologies enhance the discernment of unpromising reactions, thereby providing more accurate models with improved specificity.
Generating Bindings in MPICH

The MPI Forum has recently adopted a Python scripting engine for generating the API text in the standard document. As a by-product, it made available reliable and rich descriptions of all MPI functions that are suited for scripting tools. Using these extracted API information, we developed a Python code generation toolbox to generate the language binding layers in MPICH. The toolbox replaces nearly 70,000 lines of manually maintained C and Fortran 2008 binding code with around 5,000 lines of Python scripts plus some simple configuration. In addition to completely eliminating code duplication in the binding layer and avoiding bugs from manual code copying , the code generation also minimizes the effort for API extension and code instrumentation. This is demonstrated in our implementation of MPI-4 large count functions and the prototyping of a next generation MPI profiling interface, QMPI.
Analysis of Knowledge Tracing performance on synthesised student data

Knowledge Tracing (KT) aims to predict the future performance of students by tracking the development of their knowledge states. Despite all the recent progress made in this field, the application of KT models in education systems is still restricted from the data perspectives: 1) limited access to real life data due to data protection concerns, 2) lack of diversity in public datasets, 3) noises in benchmark datasets such as duplicate records. To resolve these problems, we simulated student data with three statistical strategies based on public datasets and tested their performance on two KT baselines. While we observe only minor performance improvement with additional synthetic data, our work shows that using only synthetic data for training can lead to similar performance as real data.
Rigorous Error Analysis for Logarithmic Number Systems

Logarithmic Number Systems (LNS) hold considerable promise in helping reduce the number of bits needed to represent a high dynamic range of real-numbers with finite precision, and also efficiently support multiplication and division. However, under LNS, addition and subtraction turn into non-linear functions that must be approximated - typically using precomputed table-based functions. Additionally, multiple layers of error correction are typically needed to improve result accuracy. Unfortunately, previous efforts have not characterized the resulting error bound. We provide the first rigorous analysis of LNS, covering detailed techniques such as co-transformation that are crucial to implementing subtraction with reasonable accuracy. We provide theorems capturing the error due to table interpolations, the finite precision of pre-computed values in the tables, and the error introduced by fix-point multiplications involved in LNS implementations. We empirically validate our analysis using a Python implementation, showing that our analytical bounds are tight, and that our testing campaign generates inputs diverse-enough to almost match (but not exceed) the analytical bounds. We close with discussions on how to adapt our analysis to LNS systems with different bases and also discuss many pragmatic ramifications of our work in the broader arena of scientific computing and machine learning.
Consistent algorithms for multi-label classification with macro-at-k metrics

We consider the optimization of complex performance metrics in multi-label classification under the population utility framework. We mainly focus on metrics linearly decomposable into a sum of binary classification utilities applied separately to each label with an additional requirement of exactly $k$ labels predicted for each instance. These"macro-at-$k$"metrics possess desired properties for extreme classification problems with long tail labels. Unfortunately, the at-$k$ constraint couples the otherwise independent binary classification tasks, leading to a much more challenging optimization problem than standard macro-averages. We provide a statistical framework to study this problem, prove the existence and the form of the optimal classifier, and propose a statistically consistent and practical learning algorithm based on the Frank-Wolfe method. Interestingly, our main results concern even more general metrics being non-linear functions of label-wise confusion matrices. Empirical results provide evidence for the competitive performance of the proposed approach.
Massive black holes in nuclear star clusters

Context. Massive black holes (MBHs) are typically hosted in the centres of massive galaxies but they appear to become rarer in lower mass galaxies, where nuclear star clusters (NSCs) frequently appear instead. The transition region, where both an MBH and NSC can co-exist, has been poorly studied to date and only a few dozen galaxies are known to host them. One avenue for detecting new galaxies with both an MBH and NSC is to look for accretion signatures of MBHs. Aims. Here, we use new SRG/eROSITA all-sky survey eRASS:4 data to search for X-ray signatures of accreting MBHs in NSCs, while also investigating their combined occupation fraction. Methods. We collected more than 200 galaxies containing an NSC, spanning multiple orders in terms of galaxy stellar mass and morphological type, within the footprint of the German eROSITA Consortium survey. We determined the expected X-ray contamination from binary stellar systems using the galaxy stellar mass and star formation rate as estimated from far-ultraviolet and mid-infrared emission. Results. We find significant detections for 18 galaxies (~8.3%), including one ultra-luminous X-ray source; however, only three galaxies (NGC 2903, 4212, and 4639) have X-ray luminosities that are higher than the expected value from X-ray binaries, indicative of the presence of an MBH. In addition, the X-ray luminosity of six galaxies (NGC 2903, 3384, 4321, 4365, 4639, and 4701) differs from previous studies and could indicate the presence of a variable active galactic nucleus. For NGC 4701 specifically, we find a variation of X-ray flux within the eRASS:4 data set. Stacking X-ray non-detected galaxies in the dwarf regime M*gal ≤ 109 M⊙) results in luminosity upper limits of a few times 1038 erg s−1. The combined occupation fraction of accreting MBHs and NSCs becomes non-zero for galaxy masses above ~ 107.5 M⊙ and this result is slightly elevated as compared to the literature data. Conclusions. Our data extend, for the first time, towards the dwarf elliptical galaxy regime and identify promising MBH candidates for higher resolution follow-up observations. At most galaxy masses (and with the exception of three cases), the X-ray constraints are consistent with the expected emission from binary systems or an Eddington fraction of at most 0.01%, assuming a black holes mass of 106.5 M⊙. This work confirms the known complexities in similar-type of studies, while providing the appealing alternative of using X-ray survey data of in-depth observations of individual targets with higher resolution instruments.
OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering

State estimation for legged robots is challenging due to their highly dynamic motion and limitations imposed by sensor accuracy. By integrating Kalman filtering, optimization, and learning-based modalities, we propose a hybrid solution that combines proprioception and exteroceptive information for estimating the state of the robot's trunk. Leveraging joint encoder and IMU measurements, our Kalman filter is enhanced through a single-rigid body model that incorporates ground reaction force control outputs from convex Model Predictive Control optimization. The estimation is further refined through Gated Recurrent Units, which also considers semantic insights and robot height from a Vision Transformer autoencoder applied on depth images. This framework not only furnishes accurate robot state estimates, including uncertainty evaluations, but can minimize the nonlinear errors that arise from sensor measurements and model simplifications through learning. The proposed methodology is evaluated in hardware using a quadruped robot on various terrains, yielding a 65% improvement on the Root Mean Squared Error compared to our VIO SLAM baseline. Code example: https://github.com/AlexS28/OptiState
Towards Generating Executable Metamorphic Relations Using Large Language Models

Metamorphic testing (MT) has proven to be a successful solution to automating testing and addressing the oracle problem. However, it entails manually deriving metamorphic relations (MRs) and converting them into an executable form; these steps are time-consuming and may prevent the adoption of MT. In this paper, we propose an approach for automatically deriving executable MRs (EMRs) from requirements using large language models (LLMs). Instead of merely asking the LLM to produce EMRs, our approach relies on a few-shot prompting strategy to instruct the LLM to perform activities in the MT process, by providing requirements and API specifications, as one would do with software engineers. To assess the feasibility of our approach, we conducted a questionnaire-based survey in collaboration with Siemens Industry Software, focusing on four of their software applications. Additionally, we evaluated the accuracy of the generated EMRs for a web application. The outcomes of our study are highly promising, as they demonstrate the capability of our approach to generate MRs and EMRs that are both comprehensible and pertinent for testing purposes.
ViTree: Single-path Neural Tree for Step-wise Interpretable Fine-grained Visual Categorization

As computer vision continues to advance and finds widespread applications across various domains, the need for interpretability in deep learning models becomes paramount. Existing methods often resort to post-hoc techniques or prototypes to explain the decision-making process, which can be indirect and lack intrinsic illustration. In this research, we introduce ViTree, a novel approach for fine-grained visual categorization that combines the popular vision transformer as a feature extraction backbone with neural decision trees. By traversing the tree paths, ViTree effectively selects patches from transformer-processed features to highlight informative local regions, thereby refining representations in a step-wise manner. Unlike previous tree-based models that rely on soft distributions or ensembles of paths, ViTree selects a single tree path, offering a clearer and simpler decision-making process. This patch and path selectivity enhances model interpretability of ViTree, enabling better insights into the model's inner workings. Remarkably, extensive experimentation validates that this streamlined approach surpasses various strong competitors and achieves state-of-the-art performance while maintaining exceptional interpretability which is proved by multi-perspective methods. Code can be found at https://github.com/SJTU-DeepVisionLab/ViTree.
Quantum motions of scalar particles under rainbow gravity effects in spherical symmetrical G\"odel-type metric with topological defects

In this research contribution, we explore the relativistic quantum dynamics of spin-0 scalar particles within a curved space-time background containing topological defects, while incorporating the effects of rainbow gravity. The Klein Gordon equation is solved in a spherically symmetrical Godel type space time, considering the presence of topological defects and the influence of rainbow gravity, described by the line element:
Deformed Laurent series rings and completions of the Weyl division ring

Let $ L((T^{-1}))$ be the space of (inverse) Laurent serieswith coefficients in some field $L$. It has a standard degree map and the induced topology. With its usual addition and a new product on this space which is continuous and preserves the standard degree map, it will be a complete topological division ring, and called a deformed Laurent series ring. Under mild restrictions, we give the necessary and sufficient conditions for a product on $ L((T^{-1}))$ to make it a deformed Laurent series ring. Then we apply the above theory to construct the completions of the Weyl division ring $D_1$, over some field of characteristic 0, with respect to a class of discrete valuations on it. Such completions are topological division rings with nice properties. For instance, their valuation rings are non-commutative Henselian rings; the centralizer of each element not in the center is commutative.
Emerging two-dimensional conductivity at Mott-band insulator interface

Intriguingly conducting perovskite interfaces between ordinary band insulators are widely explored, whereas similar interfaces with Mott insulators are still not quite understood. Here we address the (001), (110), and (111) interfaces between the LaTiO$_{3}$ Mott- and large band gap KTaO$_{3}$ insulators. Based on first-principles calculations, we reveal a mechanism of interfacial conductivity, which is distinct from a formerly studied one applicable to interfaces between polar wide band insulators. Here the key factor causing conductivity is the matching of oxygen octahedra tilting in KTaO$_{3}$ and LaTiO$_{3}$ which, due to a small gap in the LaTiO$_{3}$ results in its sensitivity to the crystal structure, yields metalization of its overlayer and following charge transfer from Ti to Ta. Our findings, also applicable to other Mott insulators interfaces, shed light on the emergence of conductivity observed in LaTiO$_{3}$/KTaO$_{3}$~(110) where the ''polar`` arguments are not applicable and on the emergence of superconductivity in these structures.
Refined Inverse Rigging: A Balanced Approach to High-fidelity Blendshape Animation

In this paper, we present an advanced approach to solving the inverse rig problem in blendshape animation, using high-quality corrective blendshapes. Our algorithm introduces novel enhancements in three key areas: ensuring high data fidelity in reconstructed meshes, achieving greater sparsity in weight distributions, and facilitating smoother frame-to-frame transitions. While the incorporation of corrective terms is a known practice, our method differentiates itself by employing a unique combination of $l_1$ norm regularization for sparsity and a temporal smoothness constraint through roughness penalty, focusing on the sum of second differences in consecutive frame weights. A significant innovation in our approach is the temporal decoupling of blendshapes, which permits simultaneous optimization across entire animation sequences. This feature sets our work apart from existing methods and contributes to a more efficient and effective solution. Our algorithm exhibits a marked improvement in maintaining data fidelity and ensuring smooth frame transitions when compared to prior approaches that either lack smoothness regularization or rely solely on linear blendshape models. In addition to superior mesh resemblance and smoothness, our method offers practical benefits, including reduced computational complexity and execution time, achieved through a novel parallelization strategy using clustering methods. Our results not only advance the state of the art in terms of fidelity, sparsity, and smoothness in inverse rigging but also introduce significant efficiency improvements. The source code will be made available upon acceptance of the paper.
Isolated and group environment dependence of stellar mass and different star formation rates

In this study, we explored the impact of isolated and group environments on stellar mass, star formation rate (SFR), and specific star formation rate (SSFR, i.e., the rate of star formation per unit stellar mass) using the galaxy dataset from the Sloan Digital Sky Survey Data Release 12 (SDSS DR12) for $z\lesssim0.2$. To mitigate the Malmquist bias, we partitioned the entire dataset into fifteen subsamples with a redshift bin size of $\Delta z = 0.01$ and examined the environmental dependencies of these properties within each redshift bin. A strong correlation between environment, stellar mass, SFR, and SSFR was observed across nearly all redshift bins. In the lower redshift bins $(z<0.1)$, the proportion of galaxies within the isolated environment exceeded that within the group environment. On the other hand, in the higher redshift bins $(z\geq 0.12)$, the isolated environment's galaxy fraction is found to be lower than that of the group environment. For the intermediate redshift bins $(0.1 \leq z<0.12)$, an approximately equal proportion of galaxies is observed in both isolated and group environments.