Miracle-dz/UniFAR-FTUs huggingface dataset:
Is lifetime traumatic brain injury a risk factor for mild cognitive impairment in veterans compared to non-veterans?

ABSTRACT Background: Traumatic brain injury (TBI) is prevalent in veterans and may occur at any stages of their life (before, during, or after military service). This is of particular concern, as previous evidence in the general population has identified TBI as a strong risk factor for mild cognitive impairment (MCI), a known precursor of dementia. Objectives: This study aimed to investigate whether exposure to at least one TBI across the lifetime was a risk factor for MCI in ageing UK veterans compared to non-veterans. Method: This cross-sectional study comprised of data from PROTECT, a cohort study comprising UK veterans and non-veterans aged ≥ 50 years at baseline. Veteran and TBI status were self-reported using the Military Service History Questionnaire (MSHQ) and the Brain Injury Screening Questionnaire (BISQ), respectively. MCI was the outcome of interest, and was defined as subjective cognitive impairment and objective cognitive impairment. Results: The sample population comprised of veterans (n = 701) and non-veterans (n = 12,389). TBI was a significant risk factor for MCI in the overall sample (OR = 1.21, 95% CI 1.11–1.31) compared to individuals without TBI. The prevalence of TBI was significantly higher in veterans compared to non-veterans (69.9% vs 59.5%, p < .001). There was no significant difference in the risk of MCI between veterans with TBI and non-veterans with TBI (OR = 1.19, 95% CI 0.98–1.45). Conclusion: TBI remains an important risk factor for MCI, irrespective of veteran status. The clinical implications indicate the need for early intervention for MCI prevention after TBI.
Response of the Kuroshio Extension path state to near‐term global warming in CMIP5 experiments with MIROC4h

In this study, responses of the Kuroshio Extension (KE) path state to near-term (2006–2035) global warming are investigated using a Kuroshio-resolving atmosphere-ocean coupled model. Under the representative concentration pathway 4.5 (RCP4.5) forcing, the KE system is intensified and its path state tends to move northward and becomes more stable. It is suggested that the local anticyclonic wind stress anomalies in the KE region favor the spin-up of the southern recirculation gyre, and the remote effect induced by the anticyclonic wind stress anomalies over the central and eastern midlatitude North Pacific also contributes to the stabilization of the KE system substantially. The dominant role of wind stress forcing on KE variability under near-term global warming is further confirmed by adopting a linear 1.5 layer reduced-gravity model forced by wind stress curl field from the present climate model. It is also found that the main contributing longitudinal band for KE index (KEI) moves westward in response to the warmed climate. This results from the northwestward expansion of the large-scale sea level pressure (SLP) field.
Nonsteroidal anti-inflammatory drug use and risk of acute kidney injury and hyperkalemia in older adults: a population-based study

Background. Clinical guidelines caution against nonsteroidal anti-inﬂammatory drug (NSAID) use in older adults. The study objective was to quantify the 30-day risk of acute kidney injury (AKI) and hyperkalemia in older adults after NSAID initiation and to develop a model to predict these outcomes. Methods. We conducted a population-based retrospective cohort study in Ontario, Canada from 2007 to 2015 of patients (cid:2) 66years. We matched 46 107 new NSAID users with 46 107 nonusers with similar baseline health. The primary outcome was 30-day risk of AKI and secondary outcomes were hyperkalemia and all-cause mortality. a AKI There was no association between NSAID use and all-cause mortality. A prediction model incorporated six predictors of AKI or hyperkalemia: older age, male gender, lower baseline estimated glomerular ﬁltration rate, higher baseline serum potassium, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker use or diuretic use. This model had moderate discrimination [C-statistic 0.72 (95% CI 0.70–0.74)] and good calibration. Conclusions. In older adults, new NSAID use compared with nonuse was associated with a higher 30-day risk of AKI and hyperkalemia but not all-cause mortality. Prescription NSAID use among many older adults may be safe, but providers should use caution and assess individual risk. ABSTRACT Background. Hypomagnesemia (Hypo-Mg) predicts mortality and chronic kidney disease (CKD) progression. However, in CKD, its prevalence, kidney-intrinsic risk factors, and the effectiveness of oral magnesium (Mg) therapy on serum Mg levels is uncertain. Methods. In a cross-sectional study enrolling pre-dialysis outpa-tients with CKD, the prevalence of electrolyte abnormalities (Mg,
Remote sensing of the correlation between breakpoint oscillations and infragravity waves in the surf and swash zone

A novel remote sensing methodology to determine the dominant infragravity mechanism in the inner surf and swash zone in the field is presented. Video observations of the breakpoint motion are correlated with the shoreline motion and inner surf zone water levels to determine the relationship between the time-varying breakpoint oscillations and the shoreline motion. The results of 13 field data sets collected from three different beaches indicate that, inside the surf zone, the dominance of bound wave or breakpoint forcing is strongly dependent on the surf zone width and the type of short wave breaking. Infragravity generation by bound wave release was stronger for conditions with relatively narrow surf zones and plunging waves; breakpoint forcing was dominant for wider surf zones and spilling breaker conditions.
Assessing the accuracy of MISR and MISR‐simulated cloud top heights using CloudSat‐ and CALIPSO‐retrieved hydrometeor profiles

Satellite retrievals of cloud properties are often used in the evaluation of global climate models, and in recent years satellite instrument simulators have been used to account for known retrieval biases in order to make more consistent comparisons between models and retrievals. Many of these simulators have seen little critical evaluation. Here we evaluate the Multiangle Imaging Spectroradiometer (MISR) simulator by using visible extinction profiles retrieved from a combination of CloudSat, CALIPSO, MODIS, and AMSR‐E observations as inputs to the MISR simulator and comparing cloud top height statistics from the MISR simulator with those retrieved by MISR. Overall, we find that the occurrence of middle‐ and high‐altitude topped clouds agrees well between MISR retrievals and the MISR‐simulated output, with distributions of middle‐ and high‐topped cloud cover typically agreeing to better than 5% in both zonal and regional averages. However, there are significant differences in the occurrence of low‐topped clouds between MISR retrievals and MISR‐simulated output that are due to differences in the detection of low‐level clouds between MISR and the combined retrievals used to drive the MISR simulator, rather than due to errors in the MISR simulator cloud top height adjustment. This difference highlights the importance of sensor resolution and boundary layer cloud spatial structure in determining low‐altitude cloud cover. The MISR‐simulated and MISR‐retrieved cloud optical depth also show systematic differences, which are also likely due in part to cloud spatial structure.
Effects of systemic oxytocin administration on ultraviolet B-induced nociceptive hypersensitivity and tactile hyposensitivity in mice

Ultraviolet B (UVB) radiation induces cutaneous inflammation, leading to thermal and mechanical hypersensitivity. Here, we examine the mechanical properties and profile of tactile and nociceptive peripheral afferents functionally disrupted by this injury and the role of oxytocin (OXT) as a modulator of this disruption. We recorded intracellularly from L4 afferents innervating the irradiated area (5.1 J/cm2) in 4-6 old week male mice (C57BL/6J) after administering OXT intraperitoneally, 6 mg/Kg. The distribution of recorded neurons was shifted by UVB radiation to a pattern observed after acute and chronic injuries and reduced mechanical thresholds of A and C- high threshold mechanoreceptors while reducing tactile sensitivity. UVB radiation did not change somatic membrane electrical properties or fiber conduction velocity. OXT systemic administration rapidly reversed these peripheral changes toward normal in both low and high-threshold mechanoreceptors and shifted recorded neuron distribution toward normal. OXT and V1aR receptors were present on the terminals of myelinated and unmyelinated afferents innervating the skin. We conclude that UVB radiation, similar to local tissue surgical injury, cancer metastasis, and peripheral nerve injury, alters the distribution of low and high threshold mechanoreceptors afferents and sensitizes nociceptors while desensitizing tactile units. Acute systemic OXT administration partially returns all of those effects to normal.
GALAXY CRUISE: Spiral and ring classifications for bright galaxies at z = 0.01–0.3

This paper presents a morphology classification catalog of spiral and ring features of 59854 magnitude-limited galaxies (r < 17.8 mag, and additional 628005 subsamples down to r = 20 mag) at z = 0.01–0.3 based on the Third Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program. We employ two deep-learning classifiers to determine the spiral and ring structures separately based on GALAXY CRUISE Data Release 1, which is dedicated to Hyper Suprime-Cam data. The number of spiral and ring galaxies contain 31864 and 8808 sources, respectively, which constitute 53% and 15% of the sample. A notable result of this study is the construction of a large sample of ring galaxies utilizing high-quality imaging data delivered by the Subaru Hyper Suprime-Cam. However, the accurate identification of ring galaxies remains difficult at a limited seeing resolution. Additionally, we confirm that most spiral galaxies are located on the star-forming main sequence, whereas ring galaxies preferentially reside in the green valley at stellar masses of 1010.5–1011 solar mass. Furthermore, decreasing fractions of spiral and ring galaxies are observed toward the centers of the galaxy clusters. The obtained morphology catalog is publicly available on the GALAXY CRUISE website.
Thermodynamics of active matter: Tracking dissipation across scales

The concept of entropy has been pivotal in the formulation of thermodynamics. For systems driven away from thermal equilibrium, a comparable role is played by entropy production and dissipation. Here we provide a comprehensive picture how local dissipation due to effective chemical events manifests on large scales in active matter. We start from a microscopic model for a single catalytic particle involving explicit solute molecules and show that it undergoes directed motion. Leveraging stochastic thermodynamics, we calculate the average entropy production rate for interacting particles. We then show how the model of active Brownian particles emerges in a certain limit and we determine the entropy production rate on the level of the hydrodynamic equations. Our results augment the model of active Brownian particles with rigorous expressions for the dissipation that cannot be inferred from their equations of motion, and we illustrate consequences for wall aggregation and motility-induced phase separation. Notably, our bottom-up approach reveals that a naive application of the Onsager currents yields an incorrect expression for the local dissipation.
Generalist embedding models are better at short-context clinical semantic search than specialized embedding models

The increasing use of tools and solutions based on Large Language Models (LLMs) for various tasks in the medical domain has become a prominent trend. Their use in this highly critical and sensitive domain has thus raised important questions about their robustness, especially in response to variations in input, and the reliability of the generated outputs. This study addresses these questions by constructing a textual dataset based on the ICD-10-CM code descriptions, widely used in US hospitals and containing many clinical terms, and their easily reproducible rephrasing. We then benchmarked existing embedding models, either generalist or specialized in the clinical domain, in a semantic search task where the goal was to correctly match the rephrased text to the original description. Our results showed that generalist models performed better than clinical models, suggesting that existing clinical specialized models are more sensitive to small changes in input that confuse them. The highlighted problem of specialized models may be due to the fact that they have not been trained on sufficient data, and in particular on datasets that are not diverse enough to have a reliable global language understanding, which is still necessary for accurate handling of medical documents.
IoT in the Era of Generative AI: Vision and Challenges

Equipped with sensing, networking, and computing capabilities, Internet of Things (IoT) such as smartphones, wearables, smart speakers, and household robots have been seamlessly weaved into our daily lives. Recent advancements in Generative AI exemplified by GPT, LLaMA, DALL-E, and Stable Difussion hold immense promise to push IoT to the next level. In this article, we share our vision and views on the benefits that Generative AI brings to IoT, and discuss some of the most important applications of Generative AI in IoT-related domains. Fully harnessing Generative AI in IoT is a complex challenge. We identify some of the most critical challenges including high resource demands of the Generative AI models, prompt engineering, on-device inference, offloading, on-device fine-tuning, federated learning, security, as well as development tools and benchmarks, and discuss current gaps as well as promising opportunities on enabling Generative AI for IoT. We hope this article can inspire new research on IoT in the era of Generative AI.
Federated Class-Incremental Learning with Prototype Guided Transformer

Existing federated learning methods have effectively addressed decentralized learning in scenarios involving data privacy and non-IID data. However, in real-world situations, each client dynamically learns new classes, requiring the global model to maintain discriminative capabilities for both new and old classes. To effectively mitigate the effects of catastrophic forgetting and data heterogeneity under low communication costs, we designed a simple and effective method named PLoRA. On the one hand, we adopt prototype learning to learn better feature representations and leverage the heuristic information between prototypes and class features to design a prototype re-weight module to solve the classifier bias caused by data heterogeneity without retraining the classification layer. On the other hand, our approach utilizes a pre-trained model as the backbone and utilizes LoRA to fine-tune with a tiny amount of parameters when learning new classes. Moreover, PLoRA does not rely on similarity-based module selection strategies, thereby further reducing communication overhead. Experimental results on standard datasets indicate that our method outperforms the state-of-the-art approaches significantly. More importantly, our method exhibits strong robustness and superiority in various scenarios and degrees of data heterogeneity. Our code will be publicly available.
SwitchTab: Switched Autoencoders Are Effective Tabular Learners

Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing (NLP), where data samples exhibit explicit spatial or semantic dependencies. However, applying these methods to tabular data is challenging due to the less pronounced dependencies among data samples. In this paper, we address this limitation by introducing SwitchTab, a novel self-supervised method specifically designed to capture latent dependencies in tabular data. SwitchTab leverages an asymmetric encoder-decoder framework to decouple mutual and salient features among data pairs, resulting in more representative embeddings. These embeddings, in turn, contribute to better decision boundaries and lead to improved results in downstream tasks. To validate the effectiveness of SwitchTab, we conduct extensive experiments across various domains involving tabular data. The results showcase superior performance in end-to-end prediction tasks with fine-tuning. Moreover, we demonstrate that pre-trained salient embeddings can be utilized as plug-and-play features to enhance the performance of various traditional classification methods (e.g., Logistic Regression, XGBoost, etc.). Lastly, we highlight the capability of SwitchTab to create explainable representations through visualization of decoupled mutual and salient features in the latent space.
DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace

Software development in the aerospace domain requires adhering to strict, high-quality standards. While there exist regulatory guidelines for commercial software in this domain (e.g., ARP-4754 and DO-178), these do not apply to software with deep neural network (DNN) components. Consequently, it is unclear how to allow aerospace systems to benefit from the deep learning revolution. Our work here seeks to address this challenge with a novel, output-centric approach for DNN certification. Our method employs statistical verification techniques, and has the key advantage of being able to flag specific inputs for which the DNN's output may be unreliable - so that they may be later inspected by a human expert. To achieve this, our method conducts a statistical analysis of the DNN's predictions for other, nearby inputs, in order to detect inconsistencies. This is in contrast to existing techniques, which typically attempt to certify the entire DNN, as opposed to individual outputs. Our method uses the DNN as a black-box, and makes no assumptions about its topology. We hope that this work constitutes another step towards integrating DNNs in safety-critical applications - especially in the aerospace domain, where high standards of quality and reliability are crucial.
A complete characterization of spectra of the Randic matrix of level-wise regular trees

Let $G$ be a simple finite connected graph with vertex set $V(G) = \{v_1,v_2,\ldots,v_n\}$. Denote the degree of vertex $v_i$ by $d_i$ for all $1 \leq i \leq n$. The Randi\'c matrix of $G$, denoted by $R(G) = [r_{i,j}]$, is the $n \times n$ matrix whose $(i,j)$-entry $r_{i,j}$ is $r_{i,j} = 1/\sqrt{d_id_j}$ if $v_i$ and $v_j$ are adjacent in $G$ and 0 otherwise. A tree is a connected acyclic graph. A level-wise regular tree is a tree rooted at one vertex $r$ or two (adjacent) vertices $r$ and $r'$ in which all vertices with the minimum distance $i$ from $r$ or $r'$ have the same degree $m_i$ for $0 \leq i \leq h$, where $h$ is the height of $T$. In this paper, we give a complete characterization of the eigenvalues with their multiplicity of the Randi\'c matrix of level-wise regular trees. We prove that the eigenvalues of the Randi\'c matrix of a level-wise regular tree are the eigenvalues of the particular tridiagonal matrices, which are formed using the degree sequence $(m_0,m_1,\ldots,m_{h-1})$ of level-wise regular trees.
Mining Fine-Grained Image-Text Alignment for Zero-Shot Captioning via Text-Only Training

Image captioning aims at generating descriptive and meaningful textual descriptions of images, enabling a broad range of vision-language applications. Prior works have demonstrated that harnessing the power of Contrastive Image Language Pre-training (CLIP) offers a promising approach to achieving zero-shot captioning, eliminating the need for expensive caption annotations. However, the widely observed modality gap in the latent space of CLIP harms the performance of zero-shot captioning by breaking the alignment between paired image-text features. To address this issue, we conduct an analysis on the CLIP latent space which leads to two findings. Firstly, we observe that the CLIP's visual feature of image subregions can achieve closer proximity to the paired caption due to the inherent information loss in text descriptions. In addition, we show that the modality gap between a paired image-text can be empirically modeled as a zero-mean Gaussian distribution. Motivated by the findings, we propose a novel zero-shot image captioning framework with text-only training to reduce the modality gap. In particular, we introduce a subregion feature aggregation to leverage local region information, which produces a compact visual representation for matching text representation. Moreover, we incorporate a noise injection and CLIP reranking strategy to boost captioning performance. We also extend our framework to build a zero-shot VQA pipeline, demonstrating its generality. Through extensive experiments on common captioning and VQA datasets such as MSCOCO, Flickr30k and VQAV2, we show that our method achieves remarkable performance improvements. Code is available at https://github.com/Artanic30/MacCap.
Multi-segmented non-isothermal compositional liquid gas well model for geothermal processes

We consider a non-isothermal compositional gas liquid model for the simulation of well operations in geothermal processes. The model accounts for phase transitions assumed to be at thermodynamical equilibrium and is based on an hydrodynamical Drift Flux Model (DFM) combined with a No Pressure Wave approximation of the momentum equation. The focus of this work is on the design of a robust discretization accounting for slanted and multibranch wells with the ability to simulate both transient behavior such as well opening as well as coupled simulations at the time scale of the reservoir. It is based on a staggered finite volume scheme in space combined with a fully implicit Euler time integration. The construction of consistent and stable numerical fluxes is a key feature for a robust numerical method. It is achieved by combining a monotone flux approximation for the phase superficial velocities with an upwind approximation of the phase molar fractions, density and enthalpy. In order to facilitate the coupling of the well and reservoir models, the Newton linearization accounts for the elimination of the hydrodynamical unknowns leading to Jacobian systems using the same primary unknowns than those of the reservoir model. The efficiency of our approach is investigated on both stand alone well test cases without and with cross flow, and on a fully coupled well-reservoir simulation.