Miracle-dz/UniFAR-FTUs huggingface dataset:
Murine CD27(−) Vγ6(+) γδ T cells producing IL-17A promote ovarian cancer growth via mobilization of protumor small peritoneal macrophages

Significance Tumor development is impacted by a set of diverse infiltrating leukocyte populations that can either inhibit or, paradoxically, enhance tumor cell growth. This study characterizes a cellular cross-talk between γδ T lymphocytes and small peritoneal macrophages (SPMs) that is mediated by the proinflammatory cytokine, IL-17, and promotes ovarian cancer growth. IL-17 is preferentially produced by a population of γδ T cells, displaying a distinctive CD27(−) Vγ6(+) phenotype, that strongly proliferate in response to tumor challenge. This associates with the mobilization of SPMs that express protumor and proangiogenic molecular mediators upregulated by IL-17. Critically, these SPMs can directly enhance ovarian cancer cell growth. Our work identifies an IL-17–dependent γδ T cell/SPM axis that promotes tumor development and thus counteracts cancer immunosurveillance. Cancer-associated inflammation mobilizes a variety of leukocyte populations that can inhibit or enhance tumor cell growth in situ. These subsets include γδ T cells, which can infiltrate tumors and typically provide large amounts of antitumor cytokines, such as IFN-γ. By contrast, we report here that in a well-established transplantable (ID8 cell line) model of peritoneal/ovarian cancer, γδ T cells promote tumor cell growth. γδ T cells accumulated in the peritoneal cavity in response to tumor challenge and could be visualized within solid tumor foci. Functional characterization of tumor-associated γδ T cells revealed preferential production of interleukin-17A (IL-17), rather than IFN-γ. Consistent with this finding, both T cell receptor (TCR)δ-deficient and IL-17–deficient mice displayed reduced ID8 tumor growth compared with wild-type animals. IL-17 production by γδ T cells in the tumor environment was essentially restricted to a highly proliferative CD27(−) subset that expressed Vγ6 instead of the more common Vγ1 and Vγ4 TCR chains. The preferential expansion of IL-17–secreting CD27(−) Vγ6(+) γδ T cells associated with the selective mobilization of unconventional small peritoneal macrophages (SPMs) that, in comparison with large peritoneal macrophages, were enriched for IL-17 receptor A, and for protumor and proangiogenic molecular mediators, which were up-regulated by IL-17. Importantly, SPMs were uniquely and directly capable of promoting ovarian cancer cell proliferation. Collectively, this work identifies an IL-17–dependent lymphoid/myeloid cross-talk involving γδ T cells and SPMs that promotes tumor cell growth and thus counteracts cancer immunosurveillance.
Automated Data Curation for Robust Language Model Fine-Tuning

Large Language Models have become the de facto approach to sequence-to-sequence text generation tasks, but for specialized tasks/domains, a pretrained LLM lacks specific capabilities to produce accurate or well-formatted responses. Supervised fine-tuning specializes a LLM by training it on dataset of example prompts with target responses, but real-world data tends to be noisy. While many fine-tuning algorithms exist, here we consider a \emph{data-centric AI} perspective on LLM fine-tuning, studying how to \emph{systematically} curate the training dataset to improve the LLM produced via \emph{any} fine-tuning algorithm. We introduce an automated data curation pipeline CLEAR (Confidence-based LLM Evaluation And Rectification) for instruction tuning datasets, that can be used with any LLM and fine-tuning procedure. CLEAR estimates which training data is low-quality and either filters or corrects it. Automatically identifying which data to filter or correct is done via LLM-derived confidence estimates, to ensure only confident modifications to the dataset. Unlike existing data curation techniques, CLEAR is a comprehensive framework that can improve a dataset (and trained model outputs) without additional fine-tuning computations. We don't assume access to a stronger LLM than the model being fine-tuned (e.g.\ relying on GPT-4 when fine-tuning GPT-3.5), to see whether CLEAR can meaningfully improve the capabilities of any LLM. Experiments reveal that CLEAR consistently improves the performance of fine-tuned models across many datasets and models (like GPT-3.5 and Llama2).
Diffusion, viscosity and linear rheology of valence-limited disordered fluids

We numerically investigate the dynamics and linear rheology of disordered systems made of patchy particles, focussing on the role of valence, temperature and bonding mechanism. We demonstrate that the dynamics is enslaved to bonding, giving rise to an activated behaviour at low temperature. By independently computing the diffusion constant and the viscosity from the simulations, we also confirm the validity of the Stokes-Einstein relation in valence-limited systems, with two caveats: (i) the diffusion constant requires a finite-size correction, at least at the intermediate density we investigate, and (ii) there is the onset of a breakdown that appears at the lowest temperatures considered. Finally, our results show that the storage and loss moduli of mixtures of divalent and $M$-valent particles exhibit an apparent power-law dependence on frequency, hinting at the possibility of using the composition to finely tune the rheological response of these materials. Our results compare well with literature experimental data on valence-limited DNA nanostars. In addition, the wealth of data we present and analyse here will help to develop and test theoretical frameworks aimed at describing the dynamics of flexible limited-valence particles that self-assemble into disordered networks.
Power Spectrum Estimation using Welch Method for various Window Techniques

A good resolution in PSE may be achieved by using optimum size of data sample. In this paper PSE has been performed for variable data length using Rectangular and Hamming window with Welch Method. Welch method is nonparametric method that include the periodogram that have the advantage of possible implementation using the fast Fourier Transform. The periodogram technique based on Welch method is capable of providing good resolution if data length samples are selected optimally. The PSE based on both Rectangular as well as Hamming window has been designed and simulated using MATLAB. It can be observed that the Rectangular and Hamming give better results than other windows like Bartlett, Hanning and Blackman window.
Recent Progress in the Development of Diagnostic Tests for Malaria

The impact of malaria on global health has continually prompted the need to develop effective diagnostic strategies. In malaria endemic regions, routine diagnosis is hampered by technical and infrastructural challenges to laboratories. These laboratories lack standard facilities, expertise or diagnostic supplies; thus, therapy is administered based on clinical or self-diagnosis. There is the need for accurate diagnosis of malaria due to the continuous increase in the cost of medication, and the emergence and spread of drug resistant strains. However, the widely utilized Giemsa-stained microscopy and immunochromatographic tests for malaria are liable to several drawbacks, including inadequate sensitivity and false-positive outcomes. Alternative methods that offer improvements in performance are either expensive, have longer turnaround time or require a level of expertise that makes them unsuitable for point-of-care (POC) applications. These gaps necessitate exploration of more efficient detection techniques with the potential of POC applications, especially in resource-limited settings. This minireview discusses some of the recent trends and new approaches that are seeking to improve the clinical diagnosis of malaria.
FaceXFormer: A Unified Transformer for Facial Analysis

In this work, we introduce FaceXformer, an end-to-end unified transformer model for a comprehensive range of facial analysis tasks such as face parsing, landmark detection, head pose estimation, attributes recognition, and estimation of age, gender, race, and landmarks visibility. Conventional methods in face analysis have often relied on task-specific designs and preprocessing techniques, which limit their approach to a unified architecture. Unlike these conventional methods, our FaceXformer leverages a transformer-based encoder-decoder architecture where each task is treated as a learnable token, enabling the integration of multiple tasks within a single framework. Moreover, we propose a parameter-efficient decoder, FaceX, which jointly processes face and task tokens, thereby learning generalized and robust face representations across different tasks. To the best of our knowledge, this is the first work to propose a single model capable of handling all these facial analysis tasks using transformers. We conducted a comprehensive analysis of effective backbones for unified face task processing and evaluated different task queries and the synergy between them. We conduct experiments against state-of-the-art specialized models and previous multi-task models in both intra-dataset and cross-dataset evaluations across multiple benchmarks. Additionally, our model effectively handles images"in-the-wild,"demonstrating its robustness and generalizability across eight different tasks, all while maintaining the real-time performance of 37 FPS.
A Magnetohydrodynamic Modeling of the Interchange Cycle for Oblique Northward Interplanetary Magnetic Field

We perform numerical modeling of the interchange cycle in the magnetosphere‐ionosphere convection system for oblique northward interplanetary magnetic field (IMF). The interchange cycle results from the coupling of IMF‐to‐lobe reconnection and lobe‐to‐closed reconnection. Using a global magnetohydrodynamic simulation code, for an IMF clock angle of 20° (measured from due north), we successfully reproduced the following features of the interchange cycle. (1) In the ionosphere, for each hemisphere, there appears a reverse cell circulating exclusively in the closed field line region (the reciprocal cell). (2) The topology transition of the magnetic field along a streamline near the equatorial plane precisely represents the magnetic flux reciprocation during the interchange cycle. (3) Field‐aligned electric fields on the interplanetary‐open separatrix and on the open‐closed separatrix are those that are consistent with IMF‐to‐lobe reconnection and lobe‐to‐closed reconnection, respectively. These three features prove the existence of the interchange cycle in the simulated magnetosphere‐ionosphere system. We conclude that the interchange cycle does exist in the real solar wind‐magnetosphere‐ionosphere system. In addition, the simulation revealed that the reciprocal cell described above is not a direct projection of the diffusion region as predicted by the “vacuum” model in which diffusion is added a priori to the vacuum magnetic topology. Instead, the reciprocal cell is a consequence of the plasma convection system coupled to the so‐called NBZ (“northward Bz”) field‐aligned current system.
HSEmotion Team at the 6th ABAW Competition: Facial Expressions, Valence-Arousal and Emotion Intensity Prediction

This article presents our results for the sixth Affective Behavior Analysis in-the-wild (ABAW) competition. To improve the trustworthiness of facial analysis, we study the possibility of using pre-trained deep models that extract reliable emotional features without the need to fine-tune the neural networks for a downstream task. In particular, we introduce several lightweight models based on MobileViT, MobileFaceNet, EfficientNet, and DDAMFN architectures trained in multi-task scenarios to recognize facial expressions, valence, and arousal on static photos. These neural networks extract frame-level features fed into a simple classifier, e.g., linear feed-forward neural network, to predict emotion intensity, compound expressions, action units, facial expressions, and valence/arousal. Experimental results for five tasks from the sixth ABAW challenge demonstrate that our approach lets us significantly improve quality metrics on validation sets compared to existing non-ensemble techniques.
Decentralized Stochastic Subgradient Methods for Nonsmooth Nonconvex Optimization

In this paper, we concentrate on decentralized optimization problems with nonconvex and nonsmooth objective functions, especially on the decentralized training of nonsmooth neural networks. We introduce a unified framework, named DSM, to analyze the global convergence of decentralized stochastic subgradient methods. We prove the global convergence of our proposed framework under mild conditions, by establishing that the generated sequence asymptotically approximates the trajectories of its associated differential inclusion. Furthermore, we establish that our proposed framework encompasses a wide range of existing efficient decentralized subgradient methods, including decentralized stochastic subgradient descent (DSGD), DSGD with gradient-tracking technique (DSGD-T), and DSGD with momentum (DSGDm). In addition, we introduce SignSGD employing the sign map to regularize the update directions in DSGDm, and show it is enclosed in our proposed framework. Consequently, our convergence results establish, for the first time, global convergence of these methods when applied to nonsmooth nonconvex objectives. Preliminary numerical experiments demonstrate that our proposed framework yields highly efficient decentralized subgradient methods with convergence guarantees in the training of nonsmooth neural networks.
Association between adiposity indices and cardiometabolic risk factors among adults living in Puerto Rico

Abstract Objective To compare the general adiposity index (BMI) with abdominal obesity indices (waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR)) in order to examine the best predictor of cardiometabolic risk factors among Hispanics living in Puerto Rico. Design Secondary analysis of measurements taken from a representative sample of adults. Logistic regression models (prevalence odds ratios (POR)), partial Pearson's correlations (controlling for age and sex) and receiver-operating characteristic (ROC) curves were calculated between indices of obesity (BMI, WC, WHR and WHtR) and blood pressure, HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), total cholesterol (TC):HDL-C, TAG, fasting blood glucose, glycosylated Hb, high-sensitivity C-reactive protein (hs-CRP), fibrinogen, plasminogen activator inhibitor-1 (PAI-1) and an aggregated measure of cardiometabolic risk. Setting Household study conducted between 2005 and 2007 in the San Juan Metropolitan Area in Puerto Rico. Subjects A representative sample of 858 non-institutionalized adults. Results All four obesity indices significantly correlated with the cardiometabolic risk factors. WHtR had the highest POR for high TC:HDL-C, blood pressure, hs-CRP, fibrinogen and PAI-1; WC had the highest POR for low HDL-C and high LDL-C and fasting blood glucose; WHR had the highest POR for overall cardiometabolic risk, TAG and glycosylated Hb. BMI had the lowest POR for most risk factors and smallest ROC curve for overall cardiometabolic risk. Conclusions The findings of the study suggest that general adiposity and abdominal adiposity are both associated with cardiometabolic risk in this population, although WC, WHR and WHtR appear to be slightly better predictors than BMI.
Multimodal Human-Autonomous Agents Interaction Using Pre-Trained Language and Visual Foundation Models

In this paper, we extended the method proposed in [17] to enable humans to interact naturally with autonomous agents through vocal and textual conversations. Our extended method exploits the inherent capabilities of pre-trained large language models (LLMs), multimodal visual language models (VLMs), and speech recognition (SR) models to decode the high-level natural language conversations and semantic understanding of the robot's task environment, and abstract them to the robot's actionable commands or queries. We performed a quantitative evaluation of our framework's natural vocal conversation understanding with participants from different racial backgrounds and English language accents. The participants interacted with the robot using both spoken and textual instructional commands. Based on the logged interaction data, our framework achieved 87.55% vocal commands decoding accuracy, 86.27% commands execution success, and an average latency of 0.89 seconds from receiving the participants' vocal chat commands to initiating the robot's actual physical action. The video demonstrations of this paper can be found at https://linusnep.github. io/MTCC-IRoNL/.
Reinforcement Learning with Latent State Inference for Autonomous On-ramp Merging under Observation Delay

This paper presents a novel approach to address the challenging problem of autonomous on-ramp merging, where a self-driving vehicle needs to seamlessly integrate into a flow of vehicles on a multi-lane highway. We introduce the Lane-keeping, Lane-changing with Latent-state Inference and Safety Controller (L3IS) agent, designed to perform the on-ramp merging task safely without comprehensive knowledge about surrounding vehicles' intents or driving styles. We also present an augmentation of this agent called AL3IS that accounts for observation delays, allowing the agent to make more robust decisions in real-world environments with vehicle-to-vehicle (V2V) communication delays. By modeling the unobservable aspects of the environment through latent states, such as other drivers' intents, our approach enhances the agent's ability to adapt to dynamic traffic conditions, optimize merging maneuvers, and ensure safe interactions with other vehicles. We demonstrate the effectiveness of our method through extensive simulations generated from real traffic data and compare its performance with existing approaches. L3IS shows a 99.90% success rate in a challenging on-ramp merging case generated from the real US Highway 101 data. We further perform a sensitivity analysis on AL3IS to evaluate its robustness against varying observation delays, which demonstrates an acceptable performance of 93.84% success rate in 1-second V2V communication delay.
Using Generative Text Models to Create Qualitative Codebooks for Student Evaluations of Teaching

Feedback is a critical aspect of improvement. Unfortunately, when there is a lot of feedback from multiple sources, it can be difficult to distill the information into actionable insights. Consider student evaluations of teaching (SETs), which are important sources of feedback for educators. They can give instructors insights into what worked during a semester. A collection of SETs can also be useful to administrators as signals for courses or entire programs. However, on a large scale as in high-enrollment courses or administrative records over several years, the volume of SETs can render them difficult to analyze. In this paper, we discuss a novel method for analyzing SETs using natural language processing (NLP) and large language models (LLMs). We demonstrate the method by applying it to a corpus of 5,000 SETs from a large public university. We show that the method can be used to extract, embed, cluster, and summarize the SETs to identify the themes they express. More generally, this work illustrates how to use the combination of NLP techniques and LLMs to generate a codebook for SETs. We conclude by discussing the implications of this method for analyzing SETs and other types of student writing in teaching and research settings.
DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding

Recent research on Simultaneous Localization and Mapping (SLAM) based on implicit representation has shown promising results in indoor environments. However, there are still some challenges: the limited scene representation capability of implicit encodings, the uncertainty in the rendering process from implicit representations, and the disruption of consistency by dynamic objects. To address these challenges, we propose a real-time dynamic visual SLAM system based on local-global fusion neural implicit representation, named DVN-SLAM. To improve the scene representation capability, we introduce a local-global fusion neural implicit representation that enables the construction of an implicit map while considering both global structure and local details. To tackle uncertainties arising from the rendering process, we design an information concentration loss for optimization, aiming to concentrate scene information on object surfaces. The proposed DVN-SLAM achieves competitive performance in localization and mapping across multiple datasets. More importantly, DVN-SLAM demonstrates robustness in dynamic scenes, a trait that sets it apart from other NeRF-based methods.
Alcohol Consumption and Subclinical Findings on Magnetic Resonance Imaging of the Brain in Older Adults: The Cardiovascular Health Study

Background and Purpose— Subclinical findings on MRI of the brain are associated with poorer cognitive and neurological function among older adults. We sought to determine how alcohol consumption is related to these findings. Methods— As part of the Cardiovascular Health Study, 3660 adults aged 65 years and older underwent MRI of the brain from 1992 to 1994. We excluded 284 participants with a confirmed history of cerebrovascular disease. We assessed self-reported intake of beer, wine, and liquor at the annual clinic visit closest to the date of the MRI and grouped participants into 6 categories: abstainers, former drinkers, <1 drink weekly, 1 to <7 drinks weekly, 7 to <15 drinks weekly, and ≥15 drinks weekly. Neuroradiologists assessed white matter grade, infarcts, ventricular size, and sulcal size in a standardized and blinded manner. We used multivariate regression to control for sociodemographic and clinical characteristics. Results— We found a U-shaped relationship between alcohol consumption and white matter abnormalities. Compared with abstainers, individuals consuming 1 to <7 drinks had an OR of 0.68, and those consuming ≥15 drinks weekly had an OR of 0.95 (p for quadratic term=0.01). Heavier alcohol consumption was associated with a lower prevalence of infarcts (OR for ≥15 drinks weekly relative to abstainers 0.59;P for trend=0.004), but larger ventricular size (OR for ≥15 drinks weekly relative to abstainers 1.32;P for trend=0.006) and sulcal size (OR for ≥15 drinks weekly relative to abstainers 1.53;P for trend=0.007). Conclusions— Moderate alcohol consumption is associated with a lower prevalence of white matter abnormalities and infarcts, thought to be of vascular origin, but with a dose-dependent higher prevalence of brain atrophy on MRI among older adults. The extent to which these competing associations influence overall brain function will require further study.
Hubble tension in a nonminimally coupled curvature-matter gravity model

The presently open problem of the Hubble tension is shown to be removed in the context of a modified theory of gravity with a non-minimal coupling between curvature and matter. By evolving the cosmological parameters that match the cosmic microwave background data until their values from direct late-time measurements, we obtain an agreement between different experimental methods without disrupting their individual validity. These modified gravity models are shown to provide adequate fits for other observational data from recent astrophysical surveys and to reproduce the late-time accelerated expansion of the Universe without the inclusion of a cosmological constant. This compatibility with observations presents further evidence of the versatility of these models in mimicking diverse cosmological phenomena in a unified manner.