Miracle-dz/UniFAR-FTUs huggingface dataset:
One-Week L-Arginine Supplementation Had No Effect on 200m Freestyle Swimming Time Trial in Moderately-Trained Male Swimmers

Abstract Research on the effect of L-arginine supplementation on exercise performance is still inconsistent and its influence on single-bout swimming performance has not been investigated. Therefore, the aim of this study was to assess if one-week L-arginine supplementation would enhance 200-m freestyle swimming performance in trained/developmental (regularly training ∼3 times per week with a purpose to compete) male swimmers. In a randomized, cross-over, double-blind design, 8 trained/developmental male swimmers (age 25 ± 5 years; mean ± SD) completed 200-m freestyle swimming time-trial on 3 separate occasions: a control trial; and after 2 separate 7-d supplementation periods, with a daily dose of either 8 g/d of L-arginine or placebo trials. Blood lactate concentration was measured immediately post time-trial swimming. Completion time of the 200-m freestyle swimming time-trial did not differ significantly (F = 4.55; P = 0.060; ŋp2 = 0.394) between control (149.40 ± 9.88 s), L-arginine (146.02 ± 10.34 s) and placebo trials (147.58 ± 10.86 s). There was no statistically significant difference in post time-trial swimming blood lactate concentration between trials (control: 11.2 ± 2.7; L-arginine: 13.1 ± 1.8; Placebo: 12.2 ± 2.7, F = 3.52; P = 0.058; ŋp2 = 0.335). One-week of supplementation with 8 g/d of L-arginine, had no ergogenic effect on middle-distance (200-m), freestyle swimming performance in trained/developmental male swimmers.
Adjuvant nivolumab therapy may not improve disease‐free survival in resected acral lentiginous melanoma patients: A retrospective case series

Adjuvant nivolumab therapy has been reported to improve the survival of melanoma patients. Acral lentiginous melanoma (ALM) has been reported to be less likely to respond to immune checkpoint inhibitors (ICIs) than other subtypes. However, the efficacy of adjuvant nivolumab therapy for ALM patients remains uncertain due to the low number of cases. In this single‐center retrospective case series, we analyzed the clinical data of patients with resected stage III/IV ALM who were referred to our department between April 1, 2004 and March 31, 2022. The analyzed clinical data included age, sex, TNM stage, treatments, adverse events and disease‐free survival (DFS). Enrolled patients were divided into a nivolumab group and a non‐ICI group according to the adjuvant therapy they received. In total, 27 patients were included. The nivolumab and non‐ICI groups had 5 and 22 patients, respectively. There were no significant differences in patient characteristics between the two groups. There were no serious treatment‐related adverse events in the non‐ICI group, but one patient in the nivolumab group developed type 1 diabetes. In the survival analysis, the DFS for the nivolumab group did not exceed that of the non‐ICI group in postoperative adjuvant therapy for ALM patients. Given that adjuvant nivolumab therapy sometimes results in serious adverse effects, the administration of the therapy may need to be carefully considered, especially for ALM patients.
Laparoscopic versus Open Surgery in Colorectal Cancer: Is Laparoscopy Safe Enough?

OBJECTIVE To compare the early and long-term outcomes of laparoscopic and open surgery in colorectal cancer stages 1-3. STUDY DESIGN Comparative study. PLACE AND DURATION OF STUDY Department of Gastroenterological Surgery, Health Sciences University, Gulhane Training and Research Hospital, Ankara, Turkey, from January 1, 2017 to January 1, 2021. METHODOLOGY A total of 142 patients, who underwent surgery for colorectal cancer with a follow-up period of at least 3 years, were included in the study. The groups of the patients; (Group A) treated with laparoscopic surgery, and (Group B) treated with open surgery, were compared in respect of general characteristics, short and long-term morbidity, mortality, and oncological outcomes. RESULTS Body Mass Index (BMI) values were higher, and the duration of surgery was shorter in Group A patients compared to Group B (p<0.05). The re-operation rate (12.2%) was observed to be statistically and significantly high in Group B (p=0.040). In comparison of the oncological outcomes, a significantly greater number of metastatic lymph nodes were removed in Group B (p=0.004). Stage 2A (31.1%) was observed at a higher frequency in Group A, and Stage 3C was significantly higher in Group B (25.7%) (p=0.037). There was no statistically significant difference between the groups in terms of the number of lymph nodes removed, insufficient lymph nodes dissection (<12), surgical margin negativity, local recurrence, and distant metastasis. CONCLUSION For the selected patient group with early-stage colorectal tumours, laparoscopic surgery has short-term oncological outcomes similar to those of open surgery and relatively lower morbidity and mortality rates. KEY WORDS Laparoscopic surgery, Colorectal cancer, Open surgery, oncological Outcomes.
Features of Air Flow in the Trough-Crest Zone of Wind Waves

Vertical profiles for mean wind, standard deviations of velocity fluctuations, and wave-induced part of the momentum flux over a wavy fluid surface are calculated in the Cartesian coordinates on the basis of recent numerical results by Chalikov and Rainchik. Besides, calculations of spectra for surface elevation and wave-induced velocity components at this surface are carried out. Unlike the profiles typical to the near-wall turbulence, the calculated wind-velocity profiles deviate significantly from the logarithmic law throughout the entire trough-crest zone of wind waves, and the wave-induced part of the momentum flux changes its sign in close vicinity of the mean surface level. Vertical scales of the profiles features are estimated. Spectral analysis of the water-surface oscillations and wind-velocity components suggests nonlinear and strongly anisotropic dynamics of the system under consideration. Points of application of the results obtained are discussed.
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection

Anomaly detection on the attributed network has recently received increasing attention in many research fields, such as cybernetic anomaly detection and financial fraud detection. With the wide application of deep learning on graph representations, existing approaches choose to apply euclidean graph encoders as their backbone, which may lose important hierarchical information, especially in complex networks. To tackle this problem, we propose an efficient anomaly detection framework using hyperbolic self-supervised contrastive learning. Specifically, we first conduct the data augmentation by performing subgraph sampling. Then we utilize the hierarchical information in hyperbolic space through exponential mapping and logarithmic mapping and obtain the anomaly score by subtracting scores of the positive pairs from the negative pairs via a discriminating process. Finally, extensive experiments on four real-world datasets demonstrate that our approach performs superior over representative baseline approaches.
Chain of Explanation: New Prompting Method to Generate Quality Natural Language Explanation for Implicit Hate Speech

Recent studies have exploited advanced generative language models to generate Natural Language Explanations (NLE) for why a certain text could be hateful. We propose the Chain of Explanation (CoE) Prompting method, using the heuristic words and target group, to generate high-quality NLE for implicit hate speech. We improved the BLUE score from 44.0 to 62.3 for NLE generation by providing accurate target information. We then evaluate the quality of generated NLE using various automatic metrics and human annotations of informativeness and clarity scores.
Revisiting Active Sets for Gaussian Process Decoders

Decoders built on Gaussian processes (GPs) are enticing due to the marginalisation over the non-linear function space. Such models (also known as GP-LVMs) are often expensive and notoriously difficult to train in practice, but can be scaled using variational inference and inducing points. In this paper, we revisit active set approximations. We develop a new stochastic estimate of the log-marginal likelihood based on recently discovered links to cross-validation, and propose a computationally efficient approximation thereof. We demonstrate that the resulting stochastic active sets (SAS) approximation significantly improves the robustness of GP decoder training while reducing computational cost. The SAS-GP obtains more structure in the latent space, scales to many datapoints and learns better representations than variational autoencoders, which is rarely the case for GP decoders.
Vision Transformer with Convolutional Encoder–Decoder for Hand Gesture Recognition using 24-GHz Doppler Radar

Transformers combined with convolutional encoders have been recently used for hand gesture recognition (HGR) using micro-Doppler signatures. In this letter, we propose a vision-transformer-based architecture for HGR with multiantenna continuous-wave Doppler radar receivers. The proposed architecture consists of three modules: 1) a convolutional encoder–decoder, 2) an attention module with three transformer layers, and 3) a multilayer perceptron. The novel convolutional decoder helps to feed patches with larger sizes to the attention module for improved feature extraction. Experimental results obtained with a dataset corresponding to a two-antenna continuous-wave Doppler radar receiver operating at 24 GHz (published by Skaria et al.) confirm that the proposed architecture achieves an accuracy of 98.3% which substantially surpasses the state-of-the-art on the used dataset.
Complex analysis methods in noncommutative probability

In this thesis we study convolutions that arise from noncommutative probability theory. We prove several regularity results for free convolutions, and for measures in partially defined one-parameter free convolution semigroups. We discuss connections between Boolean and free convolutions and, in the last chapter, we prove that any infinitely divisible probability measure with respect to monotonic additive or multiplicative convolution belongs to a one-parameter semigroup with respect to the corresponding convolution. Earlier versions of some of the results in this thesis have already been published, while some others have been submitted for publication. We have preserved almost entirely the specific format for PhD theses required by Indiana University. This adds several unnecessary pages to the document, but we wanted to preserve the specificity of the document as a PhD thesis at Indiana University.
THE TEMPERATURE STATE SPACE OF A ＄C^{*}＄-DYNAMICAL SYSTEM, I

On etudie l'espace des etats KMS aux temperatures inverses finies d'un systeme dynamique (A,γ), ou A est une C*-algebre simple initiale et γ une representation d'un groupe de Lie Γ comme automorphismes de A. On etudie le cas ou l'algebre de Lie de Γ est R n
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning

Effective exploration is critical for reinforcement learning agents in environments with sparse rewards or high-dimensional state-action spaces. Recent works based on state-visitation counts, curiosity and entropy-maximization generate intrinsic reward signals to motivate the agent to visit novel states for exploration. However, the agent can get distracted by perturbations to sensor inputs that contain novel but task-irrelevant information, e.g. due to sensor noise or changing background. In this work, we introduce the sequential information bottleneck objective for learning compressed and temporally coherent representations by modelling and compressing sequential predictive information in time-series observations. For efficient exploration in noisy environments, we further construct intrinsic rewards that capture task-relevant state novelty based on the learned representations. We derive a variational upper bound of our sequential information bottleneck objective for practical optimization and provide an information-theoretic interpretation of the derived upper bound. Our experiments on a set of challenging image-based simulated control tasks show that our method achieves better sample efficiency, and robustness to both white noise and natural video backgrounds compared to state-of-art methods based on curiosity, entropy maximization and information-gain.
Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems

Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user. In recent years, methods relying on stochastic ordering have been developed to create “fairer" rankings that reduce inequality in who or what is shown to users. Complete randomization–ordering candidate items randomly, independent of estimated relevance–is largely considered a baseline procedure that results in the most equal distribution of exposure. In industry settings, recommender systems often operate via a two-step process in which candidate items are first produced using computationally inexpensive methods and then a full ranking model is applied only to those candidates. In this paper, we consider the effects of inequality at the first step and show that, paradoxically, complete randomization at the second step can result in a higher degree of inequality relative to deterministic ordering of items by estimated relevance scores. In light of this observation, we then propose a simple post-processing algorithm in pursuit of reducing exposure inequality that works both when candidate sets have a high level of imbalance and when they do not. The efficacy of our method is illustrated on both simulated data and a common benchmark data set used in studying fairness in recommender systems.
Support Recovery in Mixture Models with Sparse Parameters

Mixture models are widely used to fit complex and multimodal datasets. In this paper we study mixtures with high dimensional sparse parameter vectors and consider the problem of support recovery of those vectors. While parameter learning in mixture models is well-studied, the sparsity constraint remains relatively unexplored. Sparsity of parameter vectors is a natural constraint in variety of settings, and support recovery is a major step towards parameter estimation. We provide efficient algorithms for support recovery that have a logarithmic sample complexity dependence on the dimensionality of the latent space. Our algorithms are quite general, namely they are applicable to 1) mixtures of many different canonical distributions including Uniform, Poisson, Laplace, Gaussians, etc. 2) Mixtures of linear regressions and linear classifiers with Gaussian covariates under different assumptions on the unknown parameters. In most of these settings, our results are the first guarantees on the problem while in the rest, our results provide improvements on existing works.
Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation

Transformers have revolutionized vision and natural language processing with their ability to scale with large datasets. But in robotic manipulation, data is both limited and expensive. Can manipulation still benefit from Transformers with the right problem formulation? We investigate this question with PerAct, a language-conditioned behavior-cloning agent for multi-task 6-DoF manipulation. PerAct encodes language goals and RGB-D voxel observations with a Perceiver Transformer, and outputs discretized actions by ``detecting the next best voxel action''. Unlike frameworks that operate on 2D images, the voxelized 3D observation and action space provides a strong structural prior for efficiently learning 6-DoF actions. With this formulation, we train a single multi-task Transformer for 18 RLBench tasks (with 249 variations) and 7 real-world tasks (with 18 variations) from just a few demonstrations per task. Our results show that PerAct significantly outperforms unstructured image-to-action agents and 3D ConvNet baselines for a wide range of tabletop tasks.
Sparsity-guided Network Design for Frame Interpolation

—DNN-based frame interpolation, which generates intermediate frames from two consecutive frames, is often dependent on model architectures with a large number of features, preventing their deployment on systems with limited resources, such as mobile devices. We present a compression-driven network design for frame interpolation that leverages model pruning through sparsity-inducing optimization to greatly reduce the model size while attaining higher performance. Concretely, we begin by compressing the recently proposed AdaCoF model and demonstrating that a 10 × compressed AdaCoF performs similarly to its original counterpart, where different strategies for using layerwise sparsity information as a guide are comprehensively investigated under a variety of hyperparameter settings. We then enhance this compressed model by introducing a multi-resolution warping module, which improves visual consistency with multi-level details. As a result, we achieve a considerable performance gain with a quarter of the size of the original AdaCoF. In addition, our model performs favorably against other state-of-the-art approaches on a wide variety of datasets. We note that the suggested compression-driven framework is generic and can be easily transferred to other DNN-based frame interpolation algorithms. The source code is available at https://github.com/tding1/CDFI.
Secure Shapley Value for Cross-Silo Federated Learning

The Shapley value (SV) is a fair and principled metric for contribution evaluation in cross-silo federated learning (cross-silo FL), wherein organizations, i.e., clients, collaboratively train prediction models with the coordination of a parameter server. However, existing SV calculation methods for FL assume that the server can access the raw FL models and public test data. This may not be a valid assumption in practice considering the emerging privacy attacks on FL models and the fact that test data might be clients' private assets. Hence, we investigate the problem of secure SV calculation for cross-silo FL. We first propose HESV , a one-server solution based solely on homomorphic encryption (HE) for privacy protection, which has limitations in efficiency. To overcome these limitations, we propose SecSV , an efficient two-server protocol with the following novel features. First, SecSV utilizes a hybrid privacy protection scheme to avoid ciphertext-ciphertext multiplications between test data and models, which are extremely expensive under HE. Second, an efficient secure matrix multiplication method is proposed for SecSV. Third, SecSV strategically identifies and skips some test samples without significantly affecting the evaluation accuracy. Our experiments demonstrate that SecSV is 7.2--36.6× as fast as HESV, with a limited loss in the accuracy of calculated SVs.
Reproducibility in machine learning for medical imaging

Reproducibility is a cornerstone of science, as the replication of findings is the process through which they become knowledge. It is widely considered that many fields of science are undergoing a reproducibility crisis. This has led to the publications of various guidelines in order to improve research reproducibility. This didactic chapter intends at being an introduction to reproducibility for researchers in the field of machine learning for medical imaging. We first distinguish between different types of reproducibility. For each of them, we aim at defining it, at describing the requirements to achieve it and at discussing its utility. The chapter ends with a discussion on the benefits of reproducibility and with a plea for a non-dogmatic approach to this concept and its implementation in research practice.
Ask Before You Act: Generalising to Novel Environments by Asking Questions

Solving temporally-extended tasks is a challenge for most reinforcement learning (RL) algorithms [arXiv:1906.07343]. We investigate the ability of an RL agent to learn to ask natural language questions as a tool to understand its environment and achieve greater generalisation performance in novel, temporally-extended environments. We do this by endowing this agent with the ability of asking"yes-no"questions to an all-knowing Oracle. This allows the agent to obtain guidance regarding the task at hand, while limiting the access to new information. To study the emergence of such natural language questions in the context of temporally-extended tasks we first train our agent in a Mini-Grid environment. We then transfer the trained agent to a different, harder environment. We observe a significant increase in generalisation performance compared to a baseline agent unable to ask questions. Through grounding its understanding of natural language in its environment, the agent can reason about the dynamics of its environment to the point that it can ask new, relevant questions when deployed in a novel environment.
53Mn‐53Cr chronology and ε54Cr‐Δ17O genealogy of Erg Chech 002: The oldest andesite in the solar system

The meteorite Erg Chech (EC) 002 is the oldest felsic igneous rock from the solar system analyzed to date and provides a unique opportunity to study the formation of felsic crusts on differentiated protoplanets immediately after metal–silicate equilibration or core formation. The extinct 53Mn‐53Cr chronometer provides chronological constraints on the formation of EC 002 by applying the isochron approach using chromite, metal–silicate–sulfide, and whole‐rock fractions as well as “leachates” obtained by sequential digestion of a bulk sample. Assuming a chondritic evolution of its parent body, a 53Cr/52Cr model age is also obtained from the chromite fraction. The 53Mn‐53Cr isochron age of 1.73 ± 0.96 Ma (anchored to D'Orbigny angrite) and the chromite model age constrained between 1.46−0.68+0.78 and 2.18−1.06+1.32 Ma after the formation of calcium‐aluminium‐rich inclusions (CAIs) agree with the 26Al‐26Mg ages (anchored to CAIs) reported in previous studies. This indicates rapid cooling of EC 002 that allowed near‐contemporaneous closure of multiple isotope systems. Additionally, excess in the neutron‐rich 54Cr (nucleosynthetic anomalies) combined with mass‐independent isotope variations of 17O provides genealogical constraints on the accretion region of the EC 002 parent body. The 54Cr and 17O isotope compositions of EC 002 confirm its origin in the “noncarbonaceous” reservoir and overlap with the vestoid material Northwest Africa 12217 and anomalous eucrite Elephant Moraine 92023. This indicates a common feeding zone during accretion in the protoplanetary disk between the source of EC 002 and vestoids. The enigmatic origin of iron meteorites remains still unresolved as EC 002, which is more like a differentiated crust, has an isotope composition that does not match known iron meteorite groups that were once planetesimal cores.