Miracle-dz/UniFAR-FTUs huggingface dataset:
A General Analytical Framework for the Resolution of Near-Field Beamforming

The resolution is an important performance metric of near-field communication networks. In particular, the resolution of near field beamforming measures how effectively users can be distinguished in the distance-angle domain, which is one of the most significant features of near-field communications. In a comparison, conventional far-field beamforming can distinguish users in the angle domain only, which means that near-field communication yields the full utilization of user spatial resources to improve spectrum efficiency. In the literature of near-field communications, there have been a few studies on whether the resolution of near-field beamforming is perfect. However, each of the existing results suffers its own limitations, e.g., each is accurate for special cases only, and cannot precisely and comprehensively characterize the resolution. In this letter, a general analytical framework is developed to evaluate the resolution of near-field beamforming. Based on this derived expression, the impacts of parameters on the resolution are investigated, which can shed light on the design of the near-field communications, including the designs of beamforming and multiple access tequniques.
STDiff: Spatio-temporal Diffusion for Continuous Stochastic Video Prediction

Predicting future frames of a video is challenging because it is difficult to learn the uncertainty of the underlying factors influencing their contents. In this paper, we propose a novel video prediction model, which has infinite-dimensional latent variables over the spatio-temporal domain. Specifically, we first decompose the video motion and content information, then take a neural stochastic differential equation to predict the temporal motion information, and finally, an image diffusion model autoregressively generates the video frame by conditioning on the predicted motion feature and the previous frame. The better expressiveness and stronger stochasticity learning capability of our model lead to state-of-the-art video prediction performances. As well, our model is able to achieve temporal continuous prediction, i.e., predicting in an unsupervised way the future video frames with an arbitrarily high frame rate. Our code is available at https://github.com/XiYe20/STDiffProject.
Subject-Based Domain Adaptation for Facial Expression Recognition

Adapting a deep learning (DL) model to a specific target individual is a challenging task in facial expression recognition (FER) that may be achieved using unsupervised domain adaptation (UDA) methods. Although several UDA methods have been proposed to adapt deep FER models across source and target data sets, multiple subject-specific source domains are needed to accurately represent the intra- and inter-person variability in subject-based adaption. In this paper, we consider the setting where domains correspond to individuals, not entire datasets. Unlike UDA, multi-source domain adaptation (MSDA) methods can leverage multiple source datasets to improve the accuracy and robustness of the target model. However, previous methods for MSDA adapt image classification models across datasets and do not scale well to a larger number of source domains. In this paper, a new MSDA method is introduced for subject-based domain adaptation in FER. It efficiently leverages information from multiple source subjects (labeled source domain data) to adapt a deep FER model to a single target individual (unlabeled target domain data). During adaptation, our Subject-based MSDA first computes a between-source discrepancy loss to mitigate the domain shift among data from several source subjects. Then, a new strategy is employed to generate augmented confident pseudo-labels for the target subject, allowing a reduction in the domain shift between source and target subjects. Experiments on the challenging BioVid heat and pain dataset (PartA) with 87 subjects, and the UNBC-McMaster shoulder pain dataset with 25 subjects show that our Subject-based MSDA can outperform state-of-the-art methods yet scale well to multiple subject-based source domains.
A dynamic interactive learning framework for automated 3D medical image segmentation

Many deep learning based automated medical image segmentation systems, in reality, face difficulties in deployment due to the cost of massive data annotation and high latency in model iteration. We propose a dynamic interactive learning framework that addresses these challenges by integrating interactive segmentation into end-to-end weak supervised learning with streaming tasks. We develop novel replay and label smoothing schemes that overcome catastrophic forgetting and improve online learning robustness. For each image, our multi-round interactive segmentation module simultaneously optimizes both front-end predictions and deep learning segmenter. In each round, a 3D"proxy mask"is propagated from sparse user inputs based on image registration, serving as weak supervision that enable knowledge distillation from the unknown ground truth. In return, the trained segmenter explicitly guides next step's user interventions according to a spatial residual map from consecutive front or back-end predictions. Evaluation on 3D segmentation tasks (NCI-ISBI2013 and BraTS2015) shows that our framework generates online learning performances that match offline training benchmark. In addition, with a 62% reduction in total annotation efforts, our framework produces competitive dice scores comparing to online and offline learning which equipped with full ground truth. Furthermore, such a framework, with its flexibility and responsiveness, could be deployed behind hospital firewall that guarantees data security and easy maintenance.
Aspects of Machian Gravity (III): Testing Theory against Galaxy Cluster mass

The general theory of relativity (GR) has excelled in explaining gravitational phenomena at the scale of the solar system with remarkable precision. However, when extended to the galactic or cosmological scale, it requires dark matter and dark energy to explain observations. In our previous article arXiv:2308.04503, we've formulated a gravity theory based in Mach's principle, known as Machian gravity. We demonstrated that the theory successfully explains galactic velocity profiles without requiring additional dark matter components. In previous studies, for a selected set of galaxy clusters, we also showed its ability to explain the velocity dispersion in the clusters without extra unseen matter components. This paper primarily explores the mass profiles of galaxy clusters. We test the Machian Gravity acceleration law on two distinct sets comprising approximately 150 galaxy clusters sourced from various studies. We fitted the dynamic mass profiles using the Machian gravity model. The outcomes of our study show exceptional agreement between the theory and observational results.
Symmetry breaking operator for the reductive dual pair $(\mathrm{U}_l,\mathrm{U}_{l'})$

We consider the dual pair $(G,G')=(\mathrm{U}_l,\mathrm{U}_{l'})$ in the symplectic group $\mathrm{Sp}_{2ll'}(\mathbb{R})$. Fix a Weil representation of the metaplectic group $\tilde{\mathrm{Sp}}_{2ll'}(\mathbb{R})$. Let $\tilde{G\,}$ and $\tilde{G'}$ be the preimages of $G$ and $G'$ under the metaplectic cover $\tilde{\mathrm{Sp}}_{2ll'}(\mathbb{R})\to \mathrm{Sp}_{2ll'}(\mathbb{R})$, and let $\Pi\otimes\Pi'$ be a genuine irreducible representation of $\tilde{G\,}\times\tilde{G'}$. We study the Weyl symbol $f_{\Pi\otimes\Pi'}$ of the (unique up to a possibly zero constant) symmetry breaking operator (SBO) intertwining the Weil representation with $\Pi\otimes\Pi'$. This SBO coincides with the orthogonal projection of the space of the Weil representation onto its $\Pi$-isotypic component and also with the orthogonal projection onto its $\Pi'$-isotypic component. Hence $f_{\Pi\otimes\Pi'}$ can be computed in two different ways, one using $\Pi$ and the other using $\Pi'$. By matching the results, we recover Weyl's theorem stating that $\Pi\otimes\Pi'$ occurs in the Weil representation with multiplicity at most one and we also recover the complete list of the representations $\Pi\otimes\Pi'$ occurring in Howe's correspondence.
Loop group factorization method for the magnetic and thermostatic nonabelian ray transforms

We study the injectivity of the matrix attenuated and nonabelian ray transforms on compact surfaces with boundary for nontrapping $\lambda$-geodesic flows and the general linear group of invertible complex matrices. We generalize the loop group factorization argument of Paternain and Salo to reduce to the setting of the unitary group when $\lambda$ has the vertical Fourier degree at most $1$. This covers the magnetic and thermostatic flows as special cases. Our article settles the general injectivity question of the nonabelian ray transform for the simple magnetic geodesic flows in combination with an earlier result by Ainsworth. We stress that the injectivity question in the unitary case for simple Gaussian thermostats remains open. Furthermore, we observe that the loop group argument does not apply when $\lambda$ has higher Fourier modes.
Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy Labels

This paper delves into applying reinforcement learning (RL) in strategy games, particularly those characterized by parity challenges, as seen in specific positions of Go and Chess and a broader range of impartial games. We propose a simulated learning process, structured within a curriculum learning framework and augmented with noisy labels, to mirror the intricacies of self-play learning scenarios. This approach thoroughly analyses how neural networks (NNs) adapt and evolve from elementary to increasingly complex game positions. Our empirical research indicates that even minimal label noise can significantly impede NNs' ability to discern effective strategies, a difficulty that intensifies with the growing complexity of the game positions. These findings underscore the urgent need for advanced methodologies in RL training, specifically tailored to counter the obstacles imposed by noisy evaluations. The development of such methodologies is crucial not only for enhancing NN proficiency in strategy games with significant parity elements but also for broadening the resilience and efficiency of RL systems across diverse and complex environments.
Boosting the Cross-Architecture Generalization of Dataset Distillation through an Empirical Study

The poor cross-architecture generalization of dataset distillation greatly weakens its practical significance. This paper attempts to mitigate this issue through an empirical study, which suggests that the synthetic datasets undergo an inductive bias towards the distillation model. Therefore, the evaluation model is strictly confined to having similar architectures of the distillation model. We propose a novel method of EvaLuation with distillation Feature (ELF), which utilizes features from intermediate layers of the distillation model for the cross-architecture evaluation. In this manner, the evaluation model learns from bias-free knowledge therefore its architecture becomes unfettered while retaining performance. By performing extensive experiments, we successfully prove that ELF can well enhance the cross-architecture generalization of current DD methods. Code of this project is at \url{https://github.com/Lirui-Zhao/ELF}.
KEN: Kernel Extensions using Natural Language

The ability to modify and extend an operating system is an important feature for improving a system's security, reliability, and performance. The extended Berkeley Packet Filters (eBPF) ecosystem has emerged as the standard mechanism for extending the Linux kernel and has recently been ported to Windows. eBPF programs inject new logic into the kernel that the system will execute before or after existing logic. While the eBPF ecosystem provides a flexible mechanism for kernel extension, it is difficult for developers to write eBPF programs today. An eBPF developer must have deep knowledge of the internals of the operating system to determine where to place logic and cope with programming limitations on the control flow and data accesses of their eBPF program enforced by the eBPF verifier. This paper presents KEN, an alternative framework that alleviates the difficulty of writing an eBPF program by allowing Kernel Extensions to be written in Natural language. KEN uses recent advances in large language models (LLMs) to synthesize an eBPF program given a user's English language prompt. To ensure that LLM's output is semantically equivalent to the user's prompt, KEN employs a combination of LLM-empowered program comprehension, symbolic execution, and a series of feedback loops. KEN's key novelty is the combination of these techniques. In particular, the system uses symbolic execution in a novel structure that allows it to combine the results of program synthesis and program comprehension and build on the recent success that LLMs have shown for each of these tasks individually. To evaluate KEN, we developed a new corpus of natural language prompts for eBPF programs. We show that KEN produces correct eBPF programs on 80% which is an improvement of a factor of 2.67 compared to an LLM-empowered program synthesis baseline.
Garden, greenhouse or climate chamber? Experimental conditions influence whether genetic differences are phenotypically expressed

Common-environment experiments are important to study genetically-based phenotypic variation within and among plant populations. Such experiments can be performed in an experimental garden, greenhouse or climate chamber. However, phenotypic expression may be strongly affected by the environmental conditions and influenced by parental and storage effects. Hence, it is unclear if results from common-environment experiments are reproducible across multiple experimental setups. In this study, we assessed the effects of three different growth facilities – outdoor garden, greenhouse, and climate chamber –, on phenotypic expression. We compared ancestral and descendant genotypes of the same population of Leontodon hispidus. We also evaluated differences in phenotypic expression between plants grown after one (F1) vs. two (F2) intermediate generations. We observed strong differences among plants growing in different growth facilities. Furthermore, we found that descendants had larger rosettes than ancestors only in the greenhouse and they flowered later than ancestors exclusively in the climate chamber. We did not find significant differences between intermediate generations within the growth facilities. Overall, our study demonstrates that environmental variation among growth facilities can dictate the presence and magnitude of phenotypic differences. This implies that absence of evidence for phenotypic differences is not evidence of absence. Experimental systems should be carefully designed to provide meaningful conditions related to the research question. Finally, growing a second intermediate generation did not impact the genetic differences of ancestors and descendants within the facilities, supporting that only one intermediate generation may be sufficient to reduce detectable parental and storage effects.
Sex-specific growth and lifespan effects of germline removal in the dioecious nematode Caenorhabditis remanei

Germline regulates the expression of life-history traits and mediates the trade-off between reproduction and somatic maintenance. However, germline maintenance in itself can be costly, and the costs can vary between the sexes depending on the number of gametes produced across the lifetime. We tested this directly by germline ablation using glp-1 RNAi in a dioecious nematode Caenorhabditis remanei. Germline removal strongly increased heat-shock resistance in both sexes, thus confirming the role of the germline in regulating somatic maintenance. However, germline removal resulted in increased lifespan only in males. High costs of mating strongly reduced lifespan in both sexes and obliterated the survival benefit of germline-less males even though neither sex produced any offspring. Furthermore, germline removal reduced male growth before maturation but not in adulthood, while female growth rate was reduced both before and especially after maturation. Thus, germline removal improves male lifespan without major growth costs, while germline-less females grow slower and do not live longer than reproductively functional counterparts in the absence of environmental stress. Overall, these results suggest that germline maintenance is costlier for males than for females.
Brain-wide topographic coordination of traveling spiral waves

Traveling waves of activity are a prevalent phenomenon within neural networks of diverse brain regions and species1–4, and have been implicated in myriad brain functions including sensory perception5–8, memory9, spatial navigation10–12 and motor control13,14. However, the anatomical basis for these waves, and whether they are locally confined versus distributed across the brain, remains unclear. Here we use cortex-wide imaging and large-scale electrophysiology in awake mice to reveal the organization of traveling waves across spatial scales. Traveling waves formed spiral patterns predominantly centered on somatosensory cortex. Strikingly, the local axonal architecture of neurons in sensory cortex exhibited a matching circular arrangement. At the cortex-wide scale, these spiral waves were mirrored between hemispheres and between sensory and motor cortex, reflecting topographic long-range axons. Finally, at the brain-wide scale, cortical spiral waves were coordinated with subcortical spiking patterns in the thalamus, striatum and midbrain. These results establish that traveling waves are structured by axonal pathways and globally impact neural activity across diverse brain systems.
Atlas of nascent RNA transcripts reveals enhancer to gene linkages

Gene transcription is controlled and modulated by regulatory regions, including enhancers and promoters. These regions are abundant in unstable, non-coding bidirectional transcription. Using nascent RNA transcription data across hundreds of human samples, we identified over 800,000 regions containing bidirectional transcription. We then identify highly correlated transcription between bidirectional and gene regions. The identified correlated pairs, a bidirectional region and a gene, are enriched for disease associated SNPs and often supported by independent 3D data. We present these resources as an SQL database which serves as a resource for future studies into gene regulation, enhancer associated RNAs, and transcription factors.
Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a “meta-matching” approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants (“meta-matching with dataset stacking” and “multilayer meta-matching”) to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original “meta-matching with stacking” approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.
Democratizing biomedical simulation through automated model discovery and a universal material subroutine

Personalized computational simulations have emerged as a vital tool to understand the biomechanical factors of a disease, predict disease progression, and design personalized intervention. Material modeling is critical for realistic biomedical simulations, and poor model selection can have life-threatening consequences for the patient. However, selecting the best model requires a profound domain knowledge and is limited to a few highly specialized experts in the field. Here we explore the feasibility of eliminating user involvement and automate the process of material modeling in finite element analyses. We leverage recent developments in constitutive neural networks, machine learning, and artificial intelligence to discover the best constitutive model from thousands of possible combinations of a few functional building blocks. We integrate all discoverable models into the finite element workflow by creating a universal material subroutine that contains more than 60,000 models, made up of 16 individual terms. We prototype this workflow using biaxial extension tests from healthy human arteries as input and stress and stretch profiles across the human aortic arch as output. Our results suggest that constitutive neural networks can robustly discover various flavors of arterial models from data, feed these models directly into a finite element simulation, and predict stress and strain profiles that compare favorably to the classical Holzapfel model. Replacing dozens of individual material subroutines by a single universal material subroutine–populated directly via automated model discovery–will make finite element simulations more user-friendly, more robust, and less vulnerable to human error. Democratizing finite element simulation by automating model selection could induce a paradigm shift in physics-based modeling, broaden access to simulation technologies, and empower individuals with varying levels of expertise and diverse backgrounds to actively participate in scientific discovery and push the boundaries of biomedical simulation.
SNPs in genes related to the repair of damage to DNA in clinical isolates of M. tuberculosis: a transversal and longitudinal approach

The presence of SNPs in genes related to DNA damage repair in M. tuberculosis can trigger hypermutagenic phenotypes with a higher probability of generating drug resistance. The aim of this research was to compare the presence of SNPs in genes related to DNA damage repair between sensitive and DR isolates, as well as to describe the dynamics in the presence of SNPs in M. tuberculosis isolated from recently diagnosed TB patients of the state of Veracruz, Mexico. The presence of SNPs in the coding regions of 65 genes related to DNA damage repair was analyzed. Eighty-six isolates from 67 patients from central Veracruz state, Mexico, were sequenced. The results showed several SNPs in 14 genes that were only present in drug-resistant genomes. In addition, by following of 15 patients, it was possible to describe three different dynamics of appearance and evolution of non-synonymous SNPs in genes related to DNA damage repair: 1) constant fixed SNPs, 2) population substitution, and 3) gain of fixed SNPs. Further research is required to discern the biological significance of each of these pathways and their utility as markers of DR or for treatment prognosis.