Miracle-dz/UniFAR-FTUs huggingface dataset:
Short Severe Energy Restriction with Refueling Reduces Body Mass without Altering Training-Associated Performance Improvement

ABSTRACT Purpose We investigated short-term (9 d) exposure to low energy availability (LEA) in elite endurance athletes during a block of intensified training on self-reported well-being, body composition, and performance. Methods Twenty-three highly trained race walkers undertook an ~3-wk research-embedded training camp during which they undertook baseline testing and 6 d of high energy/carbohydrate (HCHO) availability (40 kcal·kg FFM−1·d−1) before being allocated to 9 d continuation of this diet (n = 10 M, 2 F) or a significant decrease in energy availability to 15 kcal·kg FFM−1·d−1 (LEA: n = 10 M, 1 F). A real-world 10,000-m race walking event was undertaken before (baseline) and after (adaptation) these phases, with races being preceded by standardized carbohydrate fueling (8 g·kg body mass [BM]−1 for 24 h and 2 g·kg BM−1 prerace meal). Results Dual-energy x-ray absorptiometry–assessed body composition showed BM loss (2.0 kg, P < 0.001), primarily due to a 1.6-kg fat mass reduction (P < 0.001) in LEA, with smaller losses (BM = 0.9 kg, P = 0.008; fat mass = 0.9 kg, P < 0.001) in HCHO. The 76-item Recovery–Stress Questionnaire for Athletes, undertaken at the end of each dietary phase, showed significant diet–trial effects for overall stress (P = 0.021), overall recovery (P = 0.024), sport-specific stress (P = 0.003), and sport-specific recovery (P = 0.012). However, improvements in race performance were similar: 4.5% ± 4.1% and 3.5% ± 1.8% for HCHO and LEA, respectively (P < 0.001). The relationship between changes in performance and prerace BM was not significant (r = −0.08 [−0.49 to 0.35], P = 0.717). Conclusions A series of strategically timed but brief phases of substantially restricted energy availability might achieve ideal race weight as part of a long-term periodization of physique by high-performance athletes, but the relationship between BM, training quality, and performance in weight-dependent endurance sports is complicated.
School-based cognitive-behavioural therapy for children and adolescents with social anxiety disorder and social anxiety symptoms: A systematic review

Background Social anxiety disorder (SAD) is prevalent among children and adolescents. Cognitive-behavioural therapy (CBT) has been used as the first-line treatment. However, evaluation of CBT conducted in a school setting has been scarce. Objectives This study aims to review the CBT and its effectiveness in the school setting for children and adolescents with SAD or social anxiety symptoms. Quality assessment on individual studies was conducted. Methods Studies were identified through the search in PsycINFO, ERIC, PubMed and Medline targeting CBT conducted in a school setting with an aim to treat children and adolescents with SAD or social anxiety symptoms. Randomised controlled trials and quasi-experimental studies were selected. Results A total of 7 studies met the inclusion criteria. Five studies were randomised controlled trials, and two were quasi-experimental studies with 2558 participants aged 6–16 years from 138 primary schools and 20 secondary schools. There were minor effects to reduce social anxiety symptoms for children and adolescents at post-intervention in 86% of the selected studies. Friend for Life (FRIENDS), Super Skills for Life (SSL) and Skills for Academic and Social Success (SASS) conducted in school were more effective than the control conditions. Conclusions There is a lack of quality of the evidence for FRIENDS, SSL and SASS, due to inconsistencies on the outcome assessments, statistical analyses, and the fidelity measures adopted in individual studies. Insufficient school funding and workforce with relevant health background, and the low level of parental involvement in the intervention would be the major challenges in school-based CBT for children and adolescents with SAD or social anxiety symptoms.
Parathyroid hormone receptor-1 signaling aggravates hepatic fibrosis through upregulating cAMP response element-binding protein-like 2

Background and Aims: Parathyroid hormone receptor-1 (PTH1R) is a class B G protein–coupled receptor central to skeletal development, bone turnover, and calcium homeostasis. However, the role of PTH1R signaling in liver fibrosis is largely unknown. Here, the role of PTH1R signaling in the activation of HSCs and hepatic fibrosis was examined. Approach and Results: PTH1R was highly expressed in activated HSCs and fibrotic liver by using human liver specimens or carbon tetrachloride (CCl4)-treated or methionine and choline-deficient diet (MCD)-fed C57/BL6 mice. The mRNA level of hepatic PTH1R was positively correlated to α-smooth muscle actin in patients with liver cirrhosis. Mice with HSCs-specific PTH1R deletion were protected from CCl4, MCD, or western diet, plus low-dose CCl4-induced liver fibrosis. Conversely, parathyroid hormone (PTH) aggravated liver fibrosis in CCl4-treated mice. Mouse primary HSCs and LX2 cell lines were used for in vitro experiments. Molecular analyses by luciferase reporter assays and chromatin immunoprecipitation assays in combination with mRNA sequencing in HSCs revealed that cAMP response element-binding protein-like 2 (Crebl2), a novel regulator in HSCs treated by PTH that interacted with mothers against decapentaplegic homolog 3 (SMAD3) and increased the transcription of TGFβ in activating HSCs and collagen deposition. In agreement, HSCs-specific Crebl2 deletion ameliorated PTH-induced liver fibrosis in CCl4-treated mice. Conclusions: In both mouse and human models, we found that PTH1R was highly expressed in activated HSCs and fibrotic liver. PTH1R signaling regulated collagen production in the HSCs through Crebl2/SMAD3/TGFβ regulatory circuits. Blockade of PTH1R signaling in HSCs might help mitigate the development of liver fibrosis.
Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning

We consider the estimation problem in high-dimensional semi-supervised learning. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of the fact that such linear models may be misspecified in data analysis. We first establish the minimax lower bound for parameter estimation in the semi-supervised setting, and show that this lower bound cannot be achieved by supervised estimators using the labeled data only. We propose an optimal semi-supervised estimator that can attain this lower bound and therefore improves the supervised estimators, provided that the conditional mean function can be consistently estimated with a proper rate. We further propose a safe semi-supervised estimator. We view it safe, because this estimator is always at least as good as the supervised estimators. We also extend our idea to the aggregation of multiple semi-supervised estimators caused by different misspecifications of the conditional mean function. Extensive numerical simulations and a real data analysis are conducted to illustrate our theoretical results.
Time modulation to manage and increase the power harvested from external vibrations

We investigate how a single resonator with a time-modulated component extracts power from an external ambient source. The collected power is largely dependent on the precise modulation signal frequency choice. We focus on the power absorbed from external vibration using a mechanical resonator and how time modulation of the damper can make a significant difference in the amount of harvested power, leading to more than 10 times enhancement compared to an analogous system without time modulation. We also find that a narrow band pair of peak and dip in the spectrum of the absorbed power occurs because of the presence of an exceptional point of degeneracy (EPD). In this narrow frequency range, the delay between the damper modulating signal and the external vibrating signal largely affects the collected power. The high frequency-selectivity of EPD-induced power management could potentially be used in sensing and spectrometer applications.
Rethinking Event-based Optical Flow: Iterative Deblurring as an Alternative to Correlation Volumes

Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit construction of correlation volumes, which are expensive to compute and store, at the same time prohibiting them from estimating high-resolution flow. We observe that the spatiotemporally continuous traces of events provide a natural search direction for seeking pixel correspondences, obviating the need to rely on gradients of explicit correlation volumes as such search directions. We introduce IDNet (Iterative Deblurring Network), a lightweight yet high-performing event-based optical flow network directly estimating flow from event traces without using correlation volumes. We further propose two iterative update schemes:"ID"which iterates over the same batch of events, and"TID"which iterates over time with streaming events in an online fashion. Benchmark results show the"ID"scheme outperforms previous state-of-the-art by 9% of EPE with 52% fewer parameters and 77% savings in memory footprint, while the"TID"scheme is even more efficient promising 80% of compute savings and 18 times less latency at the cost of only 6% of performance drop.
Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surro-gate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed.
High resistivity annealed low-temperature GaAs with 100 fs lifetimes

We demonstrate the critical effect of postgrowth annealing temperature on the properties of low-temperature-grown GaAs. By using annealing temperatures substantially below the 500–600 °C commonly reported, GaAs with high resistivity and with carrier lifetimes as short as 100 fs can be routinely obtained. We discuss the optimum, but different, anneal conditions required for terahertz photoconductive emitters and detectors, and illustrate their use in a continuous-wave system.
Fixed Design Analysis of Regularization-Based Continual Learning

We consider a continual learning (CL) problem with two linear regression tasks in the fixed design setting, where the feature vectors are assumed fixed and the labels are assumed to be random variables. We consider an $\ell_2$-regularized CL algorithm, which computes an Ordinary Least Squares parameter to fit the first dataset, then computes another parameter that fits the second dataset under an $\ell_2$-regularization penalizing its deviation from the first parameter, and outputs the second parameter. For this algorithm, we provide tight bounds on the average risk over the two tasks. Our risk bounds reveal a provable trade-off between forgetting and intransigence of the $\ell_2$-regularized CL algorithm: with a large regularization parameter, the algorithm output forgets less information about the first task but is intransigent to extract new information from the second task; and vice versa. Our results suggest that catastrophic forgetting could happen for CL with dissimilar tasks (under a precise similarity measurement) and that a well-tuned $\ell_2$-regularization can partially mitigate this issue by introducing intransigence.
Constructing Bayesian Pseudo-Coresets using Contrastive Divergence

Bayesian Pseudo-Coreset (BPC) and Dataset Condensation are two parallel streams of work that construct a synthetic set such that, a model trained independently on this synthetic set, yields the same performance as training on the original training set. While dataset condensation methods use non-bayesian, heuristic ways to construct such a synthetic set, BPC methods take a bayesian approach and formulate the problem as divergence minimization between posteriors associated with original data and synthetic data. However, BPC methods generally rely on distributional assumptions on these posteriors which makes them less flexible and hinders their performance. In this work, we propose to solve these issues by modeling the posterior associated with synthetic data by an energy-based distribution. We derive a contrastive-divergence-like loss function to learn the synthetic set and show a simple and efficient way to estimate this loss. Further, we perform rigorous experiments pertaining to the proposed method. Our experiments on multiple datasets show that the proposed method not only outperforms previous BPC methods but also gives performance comparable to dataset condensation counterparts.
EVA-02: A Visual Representation for Neon Genesis

We launch EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling. With an updated plain Transformer architecture as well as extensive pre-training from an open&accessible giant CLIP vision encoder, EVA-02 demonstrates superior performance compared to prior state-of-the-art approaches across various representative vision tasks, while utilizing significantly fewer parameters and compute budgets. Notably, using exclusively publicly accessible training data, EVA-02 with only 304M parameters achieves a phenomenal 90.0 fine-tuning top-1 accuracy on ImageNet-1K val set. Additionally, our EVA-02-CLIP can reach up to 80.4 zero-shot top-1 on ImageNet-1K, outperforming the previous largest&best open-sourced CLIP with only ~1/6 parameters and ~1/6 image-text training data. We offer four EVA-02 variants in various model sizes, ranging from 6M to 304M parameters, all with impressive performance. To facilitate open access and open research, we release the complete suite of EVA-02 to the community at https://github.com/baaivision/EVA/tree/master/EVA-02.
On Trivalent Logics, Compound Conditionals, and Probabilistic Deduction Theorems

In this paper we recall some results for conditional events, compound conditionals, conditional random quantities, p-consistency, and p-entailment. Then, we show the equivalence between bets on conditionals and conditional bets, by reviewing de Finetti's trivalent analysis of conditionals. But our approach goes beyond de Finetti's early trivalent logical analysis and is based on his later ideas, aiming to take his proposals to a higher level. We examine two recent articles that explore trivalent logics for conditionals and their definitions of logical validity and compare them with our approach to compound conditionals. We prove a Probabilistic Deduction Theorem for conditional events. After that, we study some probabilistic deduction theorems, by presenting several examples. We focus on iterated conditionals and the invalidity of the Import-Export principle in the light of our Probabilistic Deduction Theorem. We use the inference from a disjunction,"$A$ or $B$", to the conditional,"if not-$A$ then $B$", as an example to show the invalidity of the Import-Export principle. We also introduce a General Import-Export principle and we illustrate it by examining some p-valid inference rules of System P. Finally, we briefly discuss some related work relevant to AI.
Igneous Rim Accretion on Chondrules in Low-velocity Shock Waves

Shock-wave heating is a leading candidate for the mechanisms of chondrule formation. This mechanism forms chondrules when the shock velocity is in a certain range. If the shock velocity is lower than this range, dust particles smaller than chondrule precursors melt, while chondrule precursors do not. We focus on the low-velocity shock waves as the igneous rim accretion events. Using a semianalytical treatment of the shock-wave heating model, we found that the accretion of molten dust particles occurs when they are supercooling. The accreted igneous rims have two layers, which are the layers of the accreted supercooled droplets and crystallized dust particles. We suggest that chondrules experience multiple rim-forming shock events.
Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine

This report provides a preliminary evaluation of ChatGPT for machine translation, including translation prompt, multilingual translation, and translation robustness. We adopt the prompts advised by ChatGPT to trigger its translation ability and find that the candidate prompts generally work well with minor performance differences. By evaluating on a number of benchmark test sets, we find that ChatGPT performs competitively with commercial translation products (e.g., Google Translate) on high-resource European languages but lags behind significantly on low-resource or distant languages. As for the translation robustness, ChatGPT does not perform as well as the commercial systems on biomedical abstracts or Reddit comments but exhibits good results on spoken language. Further, we explore an interesting strategy named $\mathbf{pivot~prompting}$ for distant languages, which asks ChatGPT to translate the source sentence into a high-resource pivot language before into the target language, improving the translation performance noticeably. With the launch of the GPT-4 engine, the translation performance of ChatGPT is significantly boosted, becoming comparable to commercial translation products, even for distant languages. Human analysis on Google Translate and ChatGPT suggests that ChatGPT with GPT-3.5 tends to generate more hallucinations and mis-translation errors while that with GPT-4 makes the least errors. In other words, ChatGPT has already become a good translator. Please refer to our Github project for more details: https://github.com/wxjiao/Is-ChatGPT-A-Good-Translator
Energy equality for the compressible Primitive Equations with vacuum

We study the energy conservation for the weak solutions to the compressible Primitive Equations (CPE) system with degenerate viscosity. We give sufficient conditions on the regularity of weak solutions for the energy equality to hold, even for solutions that may include a vacuum. In this paper, we give two theorems, the first one gives regularity in the classical isotropic Sobolev and Besov spaces. The second one state result in anisotropic spaces. We get new regularity results in the second theorem because of the special structure of the CPE system, which is in contrast to compressible Navier-Stokes equations.
FullFormer: Generating Shapes Inside Shapes

Implicit generative models have been widely employed to model 3D data and have recently proven to be successful in encoding and generating high-quality 3D shapes. This work builds upon these models and alleviates current limitations by presenting the first implicit generative model that facilitates the generation of complex 3D shapes with rich internal geometric details. To achieve this, our model uses unsigned distance fields to represent nested 3D surfaces allowing learning from non-watertight mesh data. We propose a transformer-based autoregressive model for 3D shape generation that leverages context-rich tokens from vector quantized shape embeddings. The generated tokens are decoded into an unsigned distance field which is rendered into a novel 3D shape exhibiting a rich internal structure. We demonstrate that our model achieves state-of-the-art point cloud generation results on popular classes of 'Cars', 'Planes', and 'Chairs' of the ShapeNet dataset. Additionally, we curate a dataset that exclusively comprises shapes with realistic internal details from the `Cars' class of ShapeNet and demonstrate our method's efficacy in generating these shapes with internal geometry.
Entangled states dynamics of moving two-level atoms in a thermal field bath

We consider a two-level atom that follows a wordline of constant velocity, while interacting with a massless scalar field in a thermal state through: (i) an Unruh-DeWitt coupling, and (ii) a coupling that involves the time derivative of the field. We treat the atom as an open quantum system, with the field playing the role of the environment, and employ a master equation to describe its time evolution. We study the dynamics of entanglement between the moving atom and a (auxiliary) qubit at rest and isolated from the thermal field. We find that in the case of the standard Unruh-DeWitt coupling and for high temperatures of the environment the decay of entanglement is delayed due to the atom's motion. Instead, in the derivative coupling case, the atom's motion always causes the rapid death of entanglement.
Elastic Interaction Energy-Based Generative Model: Approximation in Feature Space

In this paper, we propose a novel approach to generative modeling using a loss function based on elastic interaction energy (EIE), which is inspired by the elastic interaction between defects in crystals. The utilization of the EIE-based metric presents several advantages, including its long range property that enables consideration of global information in the distribution. Moreover, its inclusion of a self-interaction term helps to prevent mode collapse and captures all modes of distribution. To overcome the difficulty of the relatively scattered distribution of high-dimensional data, we first map the data into a latent feature space and approximate the feature distribution instead of the data distribution. We adopt the GAN framework and replace the discriminator with a feature transformation network to map the data into a latent space. We also add a stabilizing term to the loss of the feature transformation network, which effectively addresses the issue of unstable training in GAN-based algorithms. Experimental results on popular datasets, such as MNIST, FashionMNIST, CIFAR-10, and CelebA, demonstrate that our EIEG GAN model can mitigate mode collapse, enhance stability, and improve model performance.