Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent advances in single-atom alloys: preparation methods and applications in heterogeneous catalysis

Single-atom alloys (SAAs) are a different type of alloy where a guest metal, usually a noble metal (e.g., Pt, Pd, and Ru), is atomically dispersed on a relatively more inert (e.g., Ag and Cu) host metal. As a type of atomic-scale catalyst, single-atom alloy catalysts have broad application prospects in the field of heterogeneous catalysis for hydrogenation, dehydrogenation, oxidation, and other reactions. Numerous experimental and characterization results and theoretical calculations have confirmed that the resultant electronic structure caused by charge transfer between the host metal and guest metal and the special geometric structure of the guest metal are responsible for the high selectivity and catalytic activity of SAA catalysts. In this review, the common methods for the preparation of single-atom alloys in recent years are introduced, including initial wet impregnation, physical vapor deposition, and laser ablation in liquid technique. Afterwards, the applications of single-atom alloy catalysts in selective hydrogenation, dehydrogenation, oxidation reactions, and hydrogenolysis reactions are emphatically reviewed. Finally, several challenges for the future development of SAA catalysts are proposed.
Comparative label-free proteomics of the neonatal meningitis-causing Escherichia coli K1 IHE3034 and RS218 morphotypes

ABSTRACT The proteome of two newborn meningitis Escherichia coli K1 (NMEC) morphotypes was examined via a label-free proteomics approach. Besides shared NMEC virulence factors, the two strains have different evolutionary strategies—strain IHE3034 tends to perform anaerobic respiration continuously, while strain RS218 maintains its filamentous morphotype due to active SOS response.
A gut aging clock using microbiome multi-view profiles is associated with health and frail risk

ABSTRACT Age-related changes in the microbiome have been reported in previous studies; however, direct evidence for their association with frailty is lacking. Here, we introduce biological age based on gut microbiota (gAge), an integrated prediction model that integrates gut microbiota data from different perspectives with potential background factors for aging assessment. Simulation results show that, compared with a single model, the ensemble model can not only significantly improve the prediction accuracy, but also make full use of the data in unpaired samples. From this, we identified markers associated with age development and grouped markers into accelerated aging and mitigated aging according to their effect on the prediction. Importantly, the application of gAge to an elderly cohort with different frailty levels confirmed that gAge and its predictive residuals are closely related to the individual’s health status and frailty stage, and age-related markers overlap significantly with disease and frailty characteristics. Furthermore, we applied the gAge prediction model to another independent cohort of the elderly population for aging assessment and found that gAge could effectively represent the aging population. Overall, our study explains the association between the gut microbiota and frailty, providing potential targets for the development of gut microbiota-based targeted intervention strategies for aging.
Young minds, deeper insights: a recap of the BMAS Summer School 2023, ranging from basic research to clinical implications of bone marrow adipose tissue

ABSTRACT Bone marrow adiposity (BMA) is a rapidly growing yet very young research field that is receiving worldwide attention based on its intimate relationship with skeletal and metabolic diseases, as well as hematology and cancer. Moreover, increasing numbers of young scientists and students are currently and actively working on BMA within their research projects. These developments led to the foundation of the International Bone Marrow Adiposity Society (BMAS), with the goal to promote BMA knowledge worldwide, and to train new generations of researchers interested in studying this field. Among the many initiatives supported by BMAS, there is the BMAS Summer School, inaugurated in 2021 and now at its second edition. The aim of the BMAS Summer School 2023 was to educate and train students by disseminating the latest advancement on BMA. Moreover, Summer School 2023 provided suggestions on how to write grants, deal with negative results in science, and start a laboratory, along with illustrations of alternative paths to academia. The event was animated by constructive and interactive discussions between early-career researchers and more senior scientists. In this report, we highlight key moments and lessons learned from the event.
A Comparison of Morphological, Jump, and Sprint Kinematic Asymmetries in Division I Track and Field Athletes

Inter-limb asymmetries are the difference in performance in one limb with respect to the other. Running events in track and field are considered symmetrical while jumping and throwing events are considered asymmetrical. It is unknown if competing in these different events result in differences in inter-limb asymmetries, thus, this study compared the magnitude of jump, sprint, and morphological asymmetries in track and field athletes who compete in symmetrical and asymmetrical events. Forty-six Division I track and field athletes performed a series of vertical jumps (VJ) and broad jumps (BJ) with force platforms measuring peak force of each limb, and 30-meter fly sprints with kinematics (step length (SL), flight time (FT), and contact time (CT)) recorded during the sprints. Additionally, thirty-eight of these subjects underwent body composition analysis via dual x-ray absorptiometry to determine morphological asymmetries. Asymmetries were calculated using the symmetry index and the asymmetry measures were compared between sprinters, distance runners, throwers, and jumpers utilizing a one-way analysis of variance or Kruskal-Wallis tests with post-hoc comparisons as necessary. There were no differences in VJ, BJ, and sprint kinematic asymmetries found between groups but there were differences in leg fat mass asymmetries (H(3)=8.259, p=0.041, eta2= 0.101) as well as arm lean mass (H(3)=9.404, p=0.024, eta2=0.152), fat mass (H(3)=17.822, p<0.001, eta2=0.353), and tissue mass (H(3)=11.632, p=0.009, eta2=0.206) asymmetry measures. While morphological asymmetry differences may be present in athletes competing in different events, there does not appear to be differences in asymmetries during the VJ, BJ, and 30-m fly sprint.
Development and evaluation of a computerized algorithm for the interpretation of pulmonary function tests

Pulmonary function tests (PFTs) are usually interpreted by clinicians using rule-based strategies and pattern recognition. The interpretation, however, has variabilities due to patient and interpreter errors. Most PFTs have recognizable patterns that can be categorized into specific physiological defects. In this study, we developed a computerized algorithm using the python package (pdfplumber) and validated against clinicians’ interpretation. We downloaded PFT reports in the electronic medical record system that were in PDF format. We digitized the flow volume loop (FVL) and extracted numeric values from the reports. The algorithm used FEV1/FVC<0.7 for obstruction, TLC<80%pred for restriction and <80% or >120%pred for abnormal DLCO. The algorithm also used a small airway disease index (SADI) to quantify late expiratory flattening of the FVL to assess small airway dysfunction. We devised keywords for the python Natural Language Processing (NLP) package (spaCy) to identify obstruction, restriction, abnormal DLCO and small airway dysfunction in the reports. The algorithm was compared to clinicians’ interpretation in 6,889 PFTs done between March 1st, 2018, and September 30th, 2020. The agreement rates (Cohen’s kappa) for obstruction, restriction and abnormal DLCO were 94.4% (0.868), 99.0% (0.979) and 87.9% (0.750) respectively. In 4,711 PFTs with FEV1/FVC≥0.7, the algorithm identified 190 tests with SADI < lower limit of normal (LLN), suggesting small airway dysfunction. Of these, the clinicians (67.9%) also flagged 129 tests. When SADI was ≥ LLN, no clinician’s reports indicated small airway dysfunction. Our results showed the computerized algorithm agreed with clinicians’ interpretation in approximately 90% of the tests and provided a sensitive objective measure for assessing small airway dysfunction. The algorithm can improve efficiency and consistency and decrease human errors in PFT interpretation. The computerized algorithm works directly on PFT reports in PDF format and can be adapted to incorporate a different interpretation strategy and platform.
Exact SINR Analysis of Matched-filter Precoder

This paper answers a fundamental question about the exact distribution of the signal-to-interference-plus-noise ratio (SINR) under matched-filter (MF) precoding. Specifically, we derive the exact expressions for the cumulative distribution function (CDF) and the probability density function (PDF) of SINR under MF precoding over Rayleigh fading channels. Based on the exact analysis, we then rigorously prove that the SINR converges to some specific distributions separately in high SNR and in massive MIMO. To simplify the exact result in general cases, we develop a good approximation by modelling the interference as a Beta distribution. We then shift to the exact analysis of the transmit rate, and answer the fundamental question: How does the exact rate converge to the well-known asymptotic rate in massive MIMO? After that, we propose a novel approximation for the ergodic rate, which performs better than various existing approximations. Finally, we present some numerical results to demonstrate the accuracy of the derived analytical models.
Semantic Forwarding for Next Generation Relay Networks

We consider cooperative semantic text communications facilitated by a relay node. We propose two types of semantic forwarding: semantic lossy forwarding (SLF) and semantic predict-and-forward (SPF). Both are machine learning aided approaches, and, in particular, utilize attention mechanisms at the relay to establish a dynamic semantic state, updated upon receiving a new source signal. In the SLF model, the semantic state is used to decode the received source signal; whereas in the SPF model, it is used to predict the next source signal, enabling proactive forwarding. Our proposed forwarding schemes do not need any channel state information and exhibit consistent performance regardless of the relay's position. Our results demonstrate that the proposed semantic forwarding techniques outperform conventional semantic-agnostic baselines.
An Information Retrieval and Extraction Tool for Covid-19 Related Papers

Background: The COVID-19 pandemic has caused severe impacts on health systems worldwide. Its critical nature and the increased interest of individuals and organizations to develop countermeasures to the problem has led to a surge of new studies in scientific journals. Objetive: We sought to develop a tool that incorporates, in a novel way, aspects of Information Retrieval (IR) and Extraction (IE) applied to the COVID-19 Open Research Dataset (CORD-19). The main focus of this paper is to provide researchers with a better search tool for COVID-19 related papers, helping them find reference papers and hightlight relevant entities in text. Method: We applied Latent Dirichlet Allocation (LDA) to model, based on research aspects, the topics of all English abstracts in CORD-19. Relevant named entities of each abstract were extracted and linked to the corresponding UMLS concept. Regular expressions and the K-Nearest Neighbors algorithm were used to rank relevant papers. Results: Our tool has shown the potential to assist researchers by automating a topic-based search of CORD-19 papers. Nonetheless, we identified that more fine-tuned topic modeling parameters and increased accuracy of the research aspect classifier model could lead to a more accurate and reliable tool. Conclusion: We emphasize the need of new automated tools to help researchers find relevant COVID-19 documents, in addition to automatically extracting useful information contained in them. Our work suggests that combining different algorithms and models could lead to new ways of browsing COVID-19 paper data.
HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function

Support vector regression (SVR) has garnered significant popularity over the past two decades owing to its wide range of applications across various fields. Despite its versatility, SVR encounters challenges when confronted with outliers and noise, primarily due to the use of the $\varepsilon$-insensitive loss function. To address this limitation, SVR with bounded loss functions has emerged as an appealing alternative, offering enhanced generalization performance and robustness. Notably, recent developments focus on designing bounded loss functions with smooth characteristics, facilitating the adoption of gradient-based optimization algorithms. However, it's crucial to highlight that these bounded and smooth loss functions do not possess an insensitive zone. In this paper, we address the aforementioned constraints by introducing a novel symmetric loss function named the HawkEye loss function. It is worth noting that the HawkEye loss function stands out as the first loss function in SVR literature to be bounded, smooth, and simultaneously possess an insensitive zone. Leveraging this breakthrough, we integrate the HawkEye loss function into the least squares framework of SVR and yield a new fast and robust model termed HE-LSSVR. The optimization problem inherent to HE-LSSVR is addressed by harnessing the adaptive moment estimation (Adam) algorithm, known for its adaptive learning rate and efficacy in handling large-scale problems. To our knowledge, this is the first time Adam has been employed to solve an SVR problem. To empirically validate the proposed HE-LSSVR model, we evaluate it on UCI, synthetic, and time series datasets. The experimental outcomes unequivocally reveal the superiority of the HE-LSSVR model both in terms of its remarkable generalization performance and its efficiency in training time.
Proactive Detection of Voice Cloning with Localized Watermarking

In the rapidly evolving field of speech generative models, there is a pressing need to ensure audio authenticity against the risks of voice cloning. We present AudioSeal, the first audio watermarking technique designed specifically for localized detection of AI-generated speech. AudioSeal employs a generator/detector architecture trained jointly with a localization loss to enable localized watermark detection up to the sample level, and a novel perceptual loss inspired by auditory masking, that enables AudioSeal to achieve better imperceptibility. AudioSeal achieves state-of-the-art performance in terms of robustness to real life audio manipulations and imperceptibility based on automatic and human evaluation metrics. Additionally, AudioSeal is designed with a fast, single-pass detector, that significantly surpasses existing models in speed - achieving detection up to two orders of magnitude faster, making it ideal for large-scale and real-time applications.
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator

Imitation learning is often used in addition to reinforcement learning in environments where reward design is difficult or where the reward is sparse, but it is difficult to be able to imitate well in unknown states from a small amount of expert data and sampling data. Supervised learning methods such as Behavioral Cloning do not require sampling data, but usually suffer from distribution shift. The methods based on reinforcement learning, such as inverse reinforcement learning and Generative Adversarial imitation learning (GAIL), can learn from only a few expert data. However, they often need to interact with the environment. Soft Q imitation learning (SQIL) addressed the problems, and it was shown that it could learn efficiently by combining Behavioral Cloning and soft Q-learning with constant rewards. In order to make this algorithm more robust to distribution shift, we propose more efficient and robust algorithm by adding to this method a reward function based on adversarial inverse reinforcement learning that rewards the agent for performing actions in status similar to the demo. We call this algorithm Discriminator Soft Q Imitation Learning (DSQIL). We evaluated it on MuJoCo environments.
Kinematic Optimization of a Robotic Arm for Automation Tasks with Human Demonstration

Robotic arms are highly common in various automation processes such as manufacturing lines. However, these highly capable robots are usually degraded to simple repetitive tasks such as pick-and-place. On the other hand, designing an optimal robot for one specific task consumes large resources of engineering time and costs. In this paper, we propose a novel concept for optimizing the fitness of a robotic arm to perform a specific task based on human demonstration. Fitness of a robot arm is a measure of its ability to follow recorded human arm and hand paths. The optimization is conducted using a modified variant of the Particle Swarm Optimization for the robot design problem. In the proposed approach, we generate an optimal robot design along with the required path to complete the task. The approach could reduce the time-to-market of robotic arms and enable the standardization of modular robotic parts. Novice users could easily apply a minimal robot arm to various tasks. Two test cases of common manufacturing tasks are presented yielding optimal designs and reduced computational effort by up to 92%.
A compact and cost-effective laser-powered speckle visibility spectroscopy (SVS) device for measuring cerebral blood flow

In the realm of cerebrovascular monitoring, primary metrics typically include blood pressure, which influences cerebral blood flow (CBF) and is contingent upon vessel radius. Measuring CBF non-invasively poses a persistent challenge, primarily attributed to the difficulty of accessing and obtaining signal from the brain. This study aims to introduce a compact speckle visibility spectroscopy (SVS) device designed for non-invasive CBF measurements, offering cost-effectiveness and scalability while tracking CBF with remarkable sensitivity and temporal resolution. The wearable hardware has a modular design approach consisting solely of a laser diode as the source and a meticulously selected board camera as the detector. They both can be easily placed on a subject’s head to measure CBF with no additional optical elements. The SVS device can achieve a sampling rate of 80 Hz with minimal susceptibility to external disturbances. The device also achieves better SNR compared with traditional fiber-based SVS devices, capturing about 70 times more signal and showing superior stability and reproducibility. It is designed to be paired and distributed in multiple configurations around the head, and measure signals that exceed the quality of prior optical CBF measurement techniques. Given its cost-effectiveness, scalability, and simplicity, this laser-centric tool offers significant potential in advancing non-invasive cerebral monitoring technologies.
Using Motion Forecasting for Behavior-Based Virtual Reality (VR) Authentication

Task-based behavioral biometric authentication of users interacting in virtual reality (VR) environments enables seamless continuous authentication by using only the motion trajectories of the person’s body as a unique signature. Deep learning-based approaches for behavioral biometrics show high accuracy when using complete or near complete portions of the user trajectory, but show lower performance when using smaller segments from the start of the task. Thus, any system designed with existing techniques are vulnerable while waiting for future segments of motion trajectories to become available. In this work, we present the first approach that forecasts future user behavior using Transformer-based forecasting and using the forecasted trajectory to perform user authentication. Our work leverages the notion that given the current trajectory of a user in a task-based environment we can forecast the future trajectory of the user as they are unlikely to dramatically shift their behavior since it would preclude the user from successfully completing their task goal. Using the publicly available 41-subject ball throwing dataset of Miller et al. we show improvement in user authentication when using forecasted data. When compared to no forecasting, our approach reduces the authentication equal error rate (EER) by an average of 23.85% and a maximum reduction of 36.14%.
The Wetland Dilemma: Nitrogen Removal at the Expense of Methane Generation

ii Acknowledgments. iv Vita . vi List of Tables . xii Chapter 1: Introduction . 1 1.1 Wetlands in America . 1 1.2 Major Biochemical Processes in Wetlands . 4 1.2. 1 Aerobic Processes . 5 1.2. 2 Suboxic Processes. 8 1.2. 3 Anaerobic Processes . 10 1.3 Microbial Communities in Wetland Environments . 14 1.3. 2 Microorganisms Involved in Methane Production . 19
Effect of recommending users and opinions on the network connectivity and idea generation process

The growing reliance on online services underscores the crucial role of recommendation systems, especially on social media platforms seeking increased user engagement. This study investigates how recommendation systems influence the impact of personal behavioral traits on social network dynamics. It explores the interplay between homophily, users' openness to novel ideas, and recommendation-driven exposure to new opinions. Additionally, the research examines the impact of recommendation systems on the diversity of newly generated ideas, shedding light on the challenges and opportunities in designing effective systems that balance the exploration of new ideas with the risk of reinforcing biases or filtering valuable, unconventional concepts.
Wrist movement classification for adaptive mobile phone based rehabilitation of children with motor skill impairments

Rehabilitation exercises performed by children with cerebral palsy are tedious and repetitive. To make them more engaging, we propose to use an exergame approach, where an adaptive application can help the child remain stimulated and interested during exercises. In this paper, we describe how the mobile phone sensors can be used to classify wrist movements of the user during the rehabilitation exercises to detect if the user is performing the correct exercise and illustrate the use of our approach in an actual mobile phone application. We also show how an adaptive difficulty system was added to the application to allow the system to adjust to the user. We present experimental results from a pilot with healthy subjects that were constrained to simulate restricted wrist movements, as well as from tests with a target group of children with cerebral palsy. Our results show that wrist movement classification is successfully achieved and results in improved interactions.