Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of sacubitril-valsartan in patients undergoing maintenance dialysis

Abstract Objectives Data on angiotensin receptor-neprilysin inhibitor (ARNI) sacubitril-valsartan (SV) in patients undergoing maintenance dialysis is scarce. Our study aimed to investigate the effect of SV on patients undergoing dialysis. Methods We retrospectively reviewed the data of end-stage kidney disease (ESRD) patients undergoing either peritoneal dialysis (PD) or hemodialysis (HD) in our center. A total of 51 patients receiving SV treatment were enrolled in the SV group. Another 51 age and sex-matched patients on dialysis without SV treatment were selected as the control group. All the patients were regularly followed up in the dialysis clinic. Their clinical, biochemical, and echocardiographic parameters were all recorded at baseline and during follow-up. The effect and safety of SV were further analyzed. Results A total of 102 ESRD patients on dialysis (51 patients in the SV group and 51 patients in the control group) were finally enrolled. The median follow-up time was 349 days (interquartile range [IQR]: 217–535 days). The level of B-type natriuretic peptide (BNP) (median [IQR] before and after SV treatment: 596.35 pg/ml [190.6–1714.85] vs. 188.7 pg/ml [83.34–600.35], p < 0.001) or N-terminal pro-B-type natriuretic peptide (NT-proBNP) (median [IQR]: 6316.00 pg/ml [4552.00–28598.00] vs. 5074.00 pg/ml [2229.00–9851.00], p = 0.022) were significantly decreased after treatment with SV. The variant rate of left ventricular ejection fraction (LVEF) was significantly higher in the SV group compared to the control group, especially in the PD subgroup. No significant difference was found in other echocardiographic parameters between SV and control group. Subgroup analysis of the PD group showed an increase in daily PD ultrafiltration (median [IQR]: 400 ml/d [200-500] vs. 500 ml/d [200–850], p = 0.114) after SV treatment. Variant rate of overhydration (OH) measured by the body composition monitor (BCM) of the SV group were significantly different from the control group (median [IQR]: −13.13% [−42.85%–27.84%] vs. 0% [−17.95%–53.85%], p = 0.049). The rate of hyperkalemia was slightly higher but without significant difference before and after the introduction of SV (19.6% vs. 27.5%, p = 0.350). No event of hypotension and angioedema were observed. Conclusions SV might have a cardio-protective role in ESRD patients undergoing dialysis, especially in PD patients. Serum potassium should be monitored during the treatment.
Fair Causal Feature Selection

Fair feature selection for classification decision tasks has recently garnered significant attention from researchers. However, existing fair feature selection algorithms fall short of providing a full explanation of the causal relationship between features and sensitive attributes, potentially impacting the accuracy of fair feature identification. To address this issue, we propose a Fair Causal Feature Selection algorithm, called FairCFS. Specifically, FairCFS constructs a localized causal graph that identifies the Markov blankets of class and sensitive variables, to block the transmission of sensitive information for selecting fair causal features. Extensive experiments on seven public real-world datasets validate that FairCFS has comparable accuracy compared to eight state-of-the-art feature selection algorithms, while presenting more superior fairness.
The Correlations of Stellar Tidal Disruption Rates with Properties of Massive Black Holes and Their Host Galaxies

Stars can be either disrupted as tidal disruption events (TDEs) or swallowed whole by massive black holes (MBHs) at galactic centers when they approach sufficiently close to these MBHs. In this work, we investigate the correlations of such stellar consumption rates with both the MBH mass M BH and the inner slope of the host-galaxy mass density distribution α. We introduce a simplified analytical power-law model with a power-law stellar-mass density distribution surrounding MBHs and separate the contributions of two-body relaxation and stellar orbital precession for the stellar orbital angular momentum evolution in nonspherical galaxy potentials. The stellar consumption rates derived from this simplified model can be well consistent with the numerical results obtained with a more realistic treatment of stellar distributions and dynamics around MBHs, providing an efficient way to estimate TDE rates. The origin of the correlations of stellar consumption rates with M BH and α is explained by the dependence of this analytical model on those MBH/host-galaxy properties and by the separation of the stellar angular momentum evolution mechanisms. We propose that the strong positive correlation between the rates of stellar consumption due to two-body relaxation and α provides one interpretation for the overrepresentation of TDEs found in some rare E+A/poststarburst galaxies. We find high TDE rates for giant stars, up to those for solar-type stars. Understanding the origin of the correlations of the stellar consumption rates will be necessary for obtaining the demographics of MBHs and their host galaxies via TDEs.
Length-scale Dependence of Stokes-Einstien Breakdown in Active Glass-forming Liquids

Stokes-Einstein (SE) relation, which relates diffusion constant with the viscosity of a liquid at high temperatures in equilibrium, is violated in the supercooled temperature regime. Whether this relation is obeyed in nonequilibrium active liquids is a question of significant current interest to the statistical physics community trying to develop the theoretical framework of nonequilibrium statistical mechanics. Via extensive computer simulations of model active glass-forming liquids in three dimensions, we show that SE is obeyed at a high temperature similar to the equilibrium behaviour, and it gets violated in the supercooled temperature regimes. The degree of violation increases systematically with the increasing activity which quantifies the amount the system is driven out of equilibrium. First passage-time (FPT) distributions helped us to gain insights into this enhanced breakdown from the increased short-time peak, depicting hoppers. Subsequently, we study the wave vector dependence of SE relation and show that it gets restored at a wave vector that decreases with increasing activity, and the cross-over wave vector is found to be proportional to the inverse of the dynamical heterogeneity length scale in the system. Our work showed how SE violation in active supercooled liquids could be rationalized using the growth of dynamic length scale, which is found to grow enormously with increasing activity in these systems.
Security of One-Way Entanglement Purification with Quantum Sampling Against a Restricted Adversary

Entanglement purification protocols promise to play a critical role in the future of quantum networks by distributing entanglement across noisy channels. However, only the security of two-way purification protocols have been closely studied. To address this, we propose a one-way entanglement purification protocol which utilizes quantum sampling and prove its security against an adversary restricted to single qubit Pauli gates. This is done through leveraging the equivalence of one-way entanglement purification protocols with error-correcting codes. To prove the security of this protocol, we first use the quantum sampling framework introduced by Bouman and Fehr to estimate the Hamming weight of the qubits which passed through the channel and then use the estimated relative Hamming weight $\omega$ to determine the amount of interference that Eve has subjected to the quantum channel. Since Eve is restricted to single qubit Pauli gates, the number of applied gates can be directly estimated using the Hamming weight. Estimating the number of adversarial single qubit gates, allows us to perform error correction and disentangle the logical qubit from Eve with probability $1-\epsilon_{qu}^\delta$. Since this protocol allows communication only in one direction, the distance of the code must be decided before transmission, and therefore Bob will be forced to abort the protocol if he finds that Eve has applied more gates than the code can correct. One-way protocols may find use when communication is limited, or when we desire to decrease latency compared to the multiple rounds of communication needed in two-way protocols. Further research may investigate the security of this protocol against arbitrary single or multi-qubit gates to obtain security guarantees against a more general adversary.
A Generalized FDTD Scheme for Moving Electromagnetic Structures With Arbitrary Space–Time Configurations

We present a generalized finite-difference time-domain (FDTD) scheme to simulate moving electromagnetic structures with arbitrary space–time configurations. This scheme is a local adaptation and 2+1-D extension of the uniform and 1+1-D scheme recently reported in Deck-Léger et al., 2023. The local adaptation, which is allowed by the inherently matched nature of the generalized Yee cell to the conventional Yee cell, extends the range of applicability of the scheme in Deck-Léger et al., 2023 to moving structures that involve multiple and arbitrary velocity profiles while being fully compatible with the conventional absorbing boundary conditions and standard treatments of medium dispersion. We show that a direct application of the conventional FDTD scheme predicts qualitatively correct spectral transitions but quantitatively erroneous scattering amplitudes, we infer from this observation generalized, hybrid—physical and auxiliary (nonphysical)—fields that automatically satisfy moving boundary conditions in the laboratory frame, and accordingly establish local update equations based on the related Maxwell’s equations and constitutive relationships. We subsequently provide a detailed stability analysis with a generalization of the Courant criterion for the generalized scheme. We finally validate and illustrate the proposed method by several representative examples. The proposed scheme fills an important gap in the open literature on computational electromagnetics and offers an unprecedented, direct solution for moving structures in commercial software platforms.
End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization

Robot localization using a previously built map is essential for a variety of tasks including highly accurate navigation and mobile manipulation. A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based localization. However, the recent works for image-to-point cloud registration either divide the registration into separate modules or project the point cloud to the depth image to register the RGB and depth images. In this paper, we present I2PNet, a novel end-to-end 2D-3D registration network. I2PNet directly registers the raw 3D point cloud with the 2D RGB image using differential modules with a unique target. The 2D-3D cost volume module for differential 2D-3D association is proposed to bridge feature extraction and pose regression. 2D-3D cost volume module implicitly constructs the soft point-to-pixel correspondence on the intrinsic-independent normalized plane of the pinhole camera model. Moreover, we introduce an outlier mask prediction module to filter the outliers in the 2D-3D association before pose regression. Furthermore, we propose the coarse-to-fine 2D-3D registration architecture to increase localization accuracy. We conduct extensive localization experiments on the KITTI Odometry and nuScenes datasets. The results demonstrate that I2PNet outperforms the state-of-the-art by a large margin. In addition, I2PNet has a higher efficiency than the previous works and can perform the localization in real-time. Moreover, we extend the application of I2PNet to the camera-LiDAR online calibration and demonstrate that I2PNet outperforms recent approaches on the online calibration task.
Noncontact Monitoring of Dehydration Using RF Data Collected Off the Chest and the Hand

We report a novel noncontact method for dehydration monitoring. We utilize a transmit software-defined radio (SDR) that impinges a wideband radio-frequency (RF) signal (of frequency 5.23 GHz) onto either the chest or the hand of a subject who sits nearby. Furthermore, another SDR in the closed vicinity collects the reflected RF signals. The two SDRs exchange orthogonal frequency division multiplexing (OFDM) signal, whose individual subcarriers get modulated once it reflects off (passes through) the chest (the hand) of the subject. This way, the signal collected by the receive SDR consists of channel frequency response (CFR) that captures the variation in the blood osmolality due to dehydration. The received raw CFR data is then passed through a handful of machine-learning (ML) classifiers that classify each subject as either hydrated or dehydrated. To train our ML classifiers, we have constructed our custom dataset by collecting data from five Muslim subjects who were fasting during the month of Ramadan. Specifically, we have implemented and tested the following ML classifiers: ${k}$ -nearest neighbor (KNN), support vector machines (SVMs), decision tree (DT), ensemble classifier, and a neural network (NN) classifier. Among all the classifiers, the neural network classifier achieved the best classification accuracy, that is, an accuracy of 93.8% (96.15%) for the proposed chest-based (hand-based) method. Compared to the prior contact-based method where the reported accuracy is 97.83%, our proposed noncontact method provides slightly less accuracy than that reported in the literature for the contact-based method; nevertheless, the advantages of our noncontact dehydration method speak for themselves.
Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition

Audio-visual speech recognition (AVSR) provides a promising solution to ameliorate the noise-robustness of audio-only speech recognition with visual information. However, most existing efforts still focus on audio modality to improve robustness considering its dominance in AVSR task, with noise adaptation techniques such as front-end denoise processing. Though effective, these methods are usually faced with two practical challenges: 1) lack of sufficient labeled noisy audio-visual training data in some real-world scenarios and 2) less optimal model generality to unseen testing noises. In this work, we investigate the noise-invariant visual modality to strengthen robustness of AVSR, which can adapt to any testing noises while without dependence on noisy training data, a.k. a. , unsupervised noise adaptation. Inspired by human perception mechanism, we propose a universal viseme-phoneme mapping (UniVPM) approach to implement modality transfer, which can restore clean audio from visual signals to enable speech recognition under any noisy conditions. Extensive experiments on public benchmarks LRS3 and LRS2 show that our approach achieves the state-of-the-art under various noisy as well as clean conditions. In addition, we also outperform previous state-of-the-arts on visual speech recognition task.
The relationship between the incidence of X-ray selected AGN in nearby galaxies & star-formation rate

We present the identification and analysis of an X-ray selected AGN sample that lie within the local (z < 0.35) galaxy population. From a parent sample of 22 079 MPA-JHU (based on SDSS DR8) galaxies, we identified 917 galaxies with central, excess X-ray emission (from 3XMM-DR7) likely originating from an AGN. We measured the host galaxies’ star formation rates and classified them as either star-forming or quiescent based on their position relative to main sequence of star formation. Only 72 per cent of the X-ray selected sample were identified as AGN using BPT selection; this technique is much less effective in quiescent hosts, only identifying 50 per cent of the X-ray AGN. We also calculated the growth rates of the black holes powering these AGN in terms of their specific accretion rate (∝ LX/M*) and found quiescent galaxies, on average, accrete at a lower rate than star-forming galaxies. Finally, we measured the sensitivity function of 3XMM so we could correct for observational bias and construct probability distributions as a function of accretion rate. AGN were found in galaxies across the full range of star formation rates ($\log _{10} \, \mathrm{SFR/M_\odot \ yr^{-1}} = -3\ \mathrm{to}\ 2$) in both star-forming and quiescent galaxies. The incidence of AGN was enhanced by a factor 2 (at a 3.5σ significance) in star-forming galaxies compared to quiescent galaxies of equivalent stellar mass and redshift, but we also found a significant population of AGN hosted by quiescent galaxies.
DreamCatcher: Revealing the Language of the Brain with fMRI using GPT Embedding

The human brain possesses remarkable abilities in visual processing, including image recognition and scene summarization. Efforts have been made to understand the cognitive capacities of the visual brain, but a comprehensive understanding of the underlying mechanisms still needs to be discovered. Advancements in brain decoding techniques have led to sophisticated approaches like fMRI-to-Image reconstruction, which has implications for cognitive neuroscience and medical imaging. However, challenges persist in fMRI-to-image reconstruction, such as incorporating global context and contextual information. In this article, we propose fMRI captioning, where captions are generated based on fMRI data to gain insight into the neural correlates of visual perception. This research presents DreamCatcher, a novel framework for fMRI captioning. DreamCatcher consists of the Representation Space Encoder (RSE) and the RevEmbedding Decoder, which transform fMRI vectors into a latent space and generate captions, respectively. We evaluated the framework through visualization, dataset training, and testing on subjects, demonstrating strong performance. fMRI-based captioning has diverse applications, including understanding neural mechanisms, Human-Computer Interaction, and enhancing learning and training processes.
Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions

A major challenge in stroke research and stroke recovery predictions is the determination of a stroke lesion's extent and its impact on relevant brain systems. Manual segmentation of stroke lesions from 3D magnetic resonance (MR) imaging volumes, the current gold standard, is not only very time-consuming, but its accuracy highly depends on the operator's experience. As a result, there is a need for a fully automated segmentation method that can efficiently and objectively measure lesion extent and the impact of each lesion to predict impairment and recovery potential which might be beneficial for clinical, translational, and research settings. We have implemented and tested a fully automatic method for stroke lesion segmentation which was developed using eight different 2D-model architectures trained via transfer learning (TL) and mixed data approaches. Additionally, the final prediction was made using a novel ensemble method involving stacking and agreement window. Our novel method was evaluated in a novel in-house dataset containing 22 T1w brain MR images, which were challenging in various perspectives, but mostly because they included T1w MR images from the subacute (which typically less well defined T1 lesions) and chronic stroke phase (which typically means well defined T1-lesions). Cross-validation results indicate that our new method can efficiently and automatically segment lesions fast and with high accuracy compared to ground truth. In addition to segmentation, we provide lesion volume and weighted lesion load of relevant brain systems based on the lesions' overlap with a canonical structural motor system that stretches from the cortical motor region to the lowest end of the brain stem.
Bootstrapped Representations in Reinforcement Learning

In reinforcement learning (RL), state representations are key to dealing with large or continuous state spaces. While one of the promises of deep learning algorithms is to automatically construct features well-tuned for the task they try to solve, such a representation might not emerge from end-to-end training of deep RL agents. To mitigate this issue, auxiliary objectives are often incorporated into the learning process and help shape the learnt state representation. Bootstrapping methods are today's method of choice to make these additional predictions. Yet, it is unclear which features these algorithms capture and how they relate to those from other auxiliary-task-based approaches. In this paper, we address this gap and provide a theoretical characterization of the state representation learnt by temporal difference learning (Sutton, 1988). Surprisingly, we find that this representation differs from the features learned by Monte Carlo and residual gradient algorithms for most transition structures of the environment in the policy evaluation setting. We describe the efficacy of these representations for policy evaluation, and use our theoretical analysis to design new auxiliary learning rules. We complement our theoretical results with an empirical comparison of these learning rules for different cumulant functions on classic domains such as the four-room domain (Sutton et al, 1999) and Mountain Car (Moore, 1990).
Handwritten Text Recognition from Crowdsourced Annotations

In this paper, we explore different ways of training a model for handwritten text recognition when multiple imperfect or noisy transcriptions are available. We consider various training configurations, such as selecting a single transcription, retaining all transcriptions, or computing an aggregated transcription from all available annotations. In addition, we evaluate the impact of quality-based data selection, where samples with low agreement are removed from the training set. Our experiments are carried out on municipal registers of the city of Belfort (France) written between 1790 and 1946. The results show that computing a consensus transcription or training on multiple transcriptions are good alternatives. However, selecting training samples based on the degree of agreement between annotators introduces a bias in the training data and does not improve the results. Our dataset is publicly available on Zenodo.
CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents

In this letter, we focus on inferring whether the given user command is clear, ambiguous, or infeasible in the context of interactive robotic agents utilizing large language models (LLMs). To tackle this problem, we first present an uncertainty estimation method for LLMs to classify whether the command is certain (i.e., clear) or not (i.e., ambiguous or infeasible). Once the command is classified as uncertain, we further distinguish it between ambiguous or infeasible commands leveraging LLMs with situational aware context prompts. For ambiguous commands, we disambiguate the command by interacting with users via question generation with LLMs. We believe that proper recognition of the given commands could lead to a decrease in malfunction and undesired actions of the robot, enhancing the reliability of interactive robot agents. We present a dataset for robotic situational awareness consisting of pairs of high-level commands, scene descriptions, and labels of command type (i.e., clear, ambiguous, or infeasible). We validate the proposed method on the collected dataset and pick-and-place tabletop simulation environment. Finally, we demonstrate the proposed approach in real-world human-robot interaction experiments.
Handle decompositions and Kirby diagrams for the complement of plane algebraic curves

The complement of plane algebraic curves are well studied from topological and algebro-geometric viewpoints. In this paper, we will describe the explicit handle decompositions and the Kirby diagrams for the complement of plane algebraic curves. The method is based on the notion of braid monodromy. We refined this technique to obtain handle decompositions and Kirby diagrams.