Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting Stereotypes: Race and Running

The athletic achievements of African athletes in global running championships have long been subject to scientific and sociological inquiry. During the 1990s, a popular narrative emerged, suggesting that West African lineage conferred inherent sprinting advantages, and that North, South and East African's are specialized for longer distances. Part and parcel to this narrative was the enthusiastic belief that it would very soon be substantiated by a genotyping revolution that would enable prognostication of individual athletic potential. We revisit this hypothesis in the post-genomic era. First, we compare the global running records used to generate the racialist hypotheses with performances over the last twenty years (2004- 2023). Focusing on the 100m reveals intriguing trends, including the ascendancy of Jamaica as a sprint powerhouse and the elevation of South African and East Asian sprinters to the global stage, a direct challenge to the racialist paradigm. In line with an in-depth analysis of the influences on elite runners, we build a regression model to predict 100m performance based on environmental and psychological factors. Next, we direct our attention to 1500m, where the last two British champions have been part of a European resurgence that has not been seen in decades. Examining three different time periods, we identify a thirty year national slowdown (1989-2018). Adapting our model to this time period reveals striking evidence that racial perception has greater impact on performance than racial physiology. Synthesizing these findings, we introduce a psychocultural hypothesis, positing that interactions between racial perceptions and social dynamics shape the global distribution of running performance. We contrast this hypothesis with the racialist paradigm and propose extending it beyond sport where it offers insight across many domains.
AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling, which leverages limited labeled data along with larger amounts of unlabeled data. Current state-of-the-art methods train the labeled data with ground truths and unlabeled data with pseudo labels. However, the two training flows are separate, which allows labeled data to dominate the training process, resulting in low-quality pseudo labels and, consequently, sub-optimal results. To alleviate this issue, we present AllSpark, which reborns the labeled features from unlabeled ones with the channel-wise cross-attention mechanism. We further introduce a Semantic Memory along with a Channel Semantic Grouping strategy to ensure that unlabeled features adequately represent labeled features. The AllSpark shed new light on the architecture level designs of SSSS rather than framework level, which avoids increasingly complicated training pipeline designs. It can also be regarded as a flexible bottleneck module that can be seamlessly integrated into a general transformer-based segmentation model. The proposed AllSpark outperforms existing methods across all evaluation protocols on Pascal, Cityscapes and COCO benchmarks without bells-and-whistles. Code and model weights are available at: https://github.com/xmed-lab/AllSpark.
Strong-field quantum control by pulse shaping in the extreme ultraviolet domain

Tailored light-matter interactions in the strong coupling regime enable the manipulation and control of quantum systems with up to unit efficiency, with applications ranging from quantum information to photochemistry. While strong light-matter interactions are readily induced at the valence electron level using long-wavelength radiation, comparable phenomena have been only recently observed with short wavelengths, accessing highly-excited multi-electron and inner-shell electron states. However, the quantum control of strong-field processes at short wavelengths has not been possible, so far, due to the lack of pulse shaping technologies in the extreme ultraviolet (XUV) and X-ray domain. Here, exploiting pulse shaping of the seeded free-electron laser (FEL) FERMI, we demonstrate the strong-field quantum control of ultrafast Rabi dynamics in helium atoms with high fidelity. Our approach unravels the strong-field induced bound-continuum coupling of the electronic states in helium and enables substantial suppression of the ionization rate, with prospective applications in many XUV and X-ray experiments. Leveraging recent advances in intense few-femtosecond to attosecond XUV to X-ray light sources, our results open an avenue to the efficient manipulation and selective control of core electron processes and electron correlation phenomena in real time
Efficient Action Counting with Dynamic Queries

Temporal repetition counting aims to quantify the repeated action cycles within a video. The majority of existing methods rely on the similarity correlation matrix to characterize the repetitiveness of actions, but their scalability is hindered due to the quadratic computational complexity. In this work, we introduce a novel approach that employs an action query representation to localize repeated action cycles with linear computational complexity. Based on this representation, we further develop two key components to tackle the essential challenges of temporal repetition counting. Firstly, to facilitate open-set action counting, we propose the dynamic update scheme on action queries. Unlike static action queries, this approach dynamically embeds video features into action queries, offering a more flexible and generalizable representation. Secondly, to distinguish between actions of interest and background noise actions, we incorporate inter-query contrastive learning to regularize the video representations corresponding to different action queries. As a result, our method significantly outperforms previous works, particularly in terms of long video sequences, unseen actions, and actions at various speeds. On the challenging RepCountA benchmark, we outperform the state-of-the-art method TransRAC by 26.5% in OBO accuracy, with a 22.7% mean error decrease and 94.1% computational burden reduction. Code is available at https://github.com/lizishi/DeTRC.
Critical windows: non-asymptotic theory for feature emergence in diffusion models

We develop theory to understand an intriguing property of diffusion models for image generation that we term critical windows. Empirically, it has been observed that there are narrow time intervals in sampling during which particular features of the final image emerge, e.g. the image class or background color (Ho et al., 2020b; Georgiev et al., 2023; Raya&Ambrogioni, 2023; Sclocchi et al., 2024; Biroli et al., 2024). While this is advantageous for interpretability as it implies one can localize properties of the generation to a small segment of the trajectory, it seems at odds with the continuous nature of the diffusion. We propose a formal framework for studying these windows and show that for data coming from a mixture of strongly log-concave densities, these windows can be provably bounded in terms of certain measures of inter- and intra-group separation. We also instantiate these bounds for concrete examples like well-conditioned Gaussian mixtures. Finally, we use our bounds to give a rigorous interpretation of diffusion models as hierarchical samplers that progressively"decide"output features over a discrete sequence of times. We validate our bounds with synthetic experiments. Additionally, preliminary experiments on Stable Diffusion suggest critical windows may serve as a useful tool for diagnosing fairness and privacy violations in real-world diffusion models.
Hydrogen is not necessary for superconductivity in topotactically reduced nickelates

A key open question in the study of layered superconducting nickelate films is the role that hydrogen incorporation into the lattice plays in the appearance of the superconducting state. Due to the challenges of stabilizing highly crystalline square planar nickelate films, films are prepared by the deposition of a more stable parent compound which is then transformed into the target phase via a topotactic reaction with a strongly reducing agent such as CaH$_2$. Recent studies, both experimental and theoretical, have introduced the possibility that the incorporation of hydrogen from the reducing agent into the nickelate lattice may be critical for the superconductivity. In this work, we use secondary ion mass spectrometry to examine superconducting La$_{1-x}$X$_x$NiO$_2$ / SrTiO$_3$ (X = Ca and Sr) and Nd$_6$Ni$_5$O$_{12}$ / NdGaO$_3$ films, along with non-superconducting NdNiO$_2$ / SrTiO$_3$ and (Nd,Sr)NiO$_2$ / SrTiO$_3$. We find no evidence for extensive hydrogen incorporation across a broad range of samples, including both superconducting and non-superconducting films. Theoretical calculations indicate that hydrogen incorporation is broadly energetically unfavorable in these systems, supporting our conclusion that hydrogen incorporation is not generally required to achieve a superconducting state in layered square-planar nickelates.
Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives

Exercise-based rehabilitation programs have proven to be effective in enhancing the quality of life and reducing mortality and rehospitalization rates. AI-driven virtual rehabilitation, which allows patients to independently complete exercises at home, utilizes AI algorithms to analyze exercise data, providing feedback to patients and updating clinicians on their progress. These programs commonly prescribe a variety of exercise types, leading to a distinct challenge in rehabilitation exercise assessment datasets: while abundant in overall training samples, these datasets often have a limited number of samples for each individual exercise type. This disparity hampers the ability of existing approaches to train generalizable models with such a small sample size per exercise. Addressing this issue, our paper introduces a novel supervised contrastive learning framework with hard and soft negative samples that effectively utilizes the entire dataset to train a single model applicable to all exercise types. This model, with a Spatial-Temporal Graph Convolutional Network (ST-GCN) architecture, demonstrated enhanced generalizability across exercises and a decrease in overall complexity. Through extensive experiments on three publicly available rehabilitation exercise assessment datasets, the University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD), IntelliRehabDS (IRDS), and KInematic assessment of MOvement and clinical scores for remote monitoring of physical REhabilitation (KIMORE), our method has shown to surpass existing methods, setting a new benchmark in rehabilitation exercise assessment accuracy.
MagicClay: Sculpting Meshes With Generative Neural Fields

The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial properties, such as incremental control - a fundamental requirement for artistic work. Triangular meshes, on the other hand, are the representation of choice for most geometry related tasks, offering efficiency and intuitive control, but do not lend themselves to neural optimization. To support downstream tasks, previous art typically proposes a two-step approach, where first a shape is generated using neural fields, and then a mesh is extracted for further processing. Instead, in this paper we introduce a hybrid approach that maintains both a mesh and a Signed Distance Field (SDF) representations consistently. Using this representation, we introduce MagicClay - an artist friendly tool for sculpting regions of a mesh according to textual prompts while keeping other regions untouched. Our framework carefully and efficiently balances consistency between the representations and regularizations in every step of the shape optimization; Relying on the mesh representation, we show how to render the SDF at higher resolutions and faster. In addition, we employ recent work in differentiable mesh reconstruction to adaptively allocate triangles in the mesh where required, as indicated by the SDF. Using an implemented prototype, we demonstrate superior generated geometry compared to the state-of-the-art, and novel consistent control, allowing sequential prompt-based edits to the same mesh for the first time.
Heterodyne dispersive cavity ring-down spectroscopy exploiting eigenmode frequencies for high-fidelity measurements

Measuring low light absorption with combined uncertainty<1 permille is crucial in a wide range of applications. Popular cavity ring-down spectroscopy can provide ultra-high precision, below 0.01 permille, but its accuracy is strongly dependent on the measurement capabilities of the detection system and typically is about 10 permille. Here, we exploit the optical frequency information carried by the ring-down cavity electromagnetic field, not explored in conventional CRDS, for high-fidelity spectroscopy. Instead of measuring only the decaying light intensity, we perform heterodyne detection of ring-downs followed by Fourier analysis to provide exact frequencies of optical cavity modes and a dispersive spectrum of a gas sample from them. This approach is insensitive to inaccuracies in light intensity measurements and eliminates the problem of detector band nonlinearity, the main cause of measurement error in traditional CRDS. Using the CO and HD line intensities as examples, we demonstrate the sub-permille accuracy of our method, confirmed by the best ab initio results, and the long-term repeatability of our dispersion measurements at 10^(-4) level. Such results have not been achieved in optical spectroscopy before. The high accuracy of the presented method indicates its potential in atmospheric studies, isotope ratio metrology, thermometry, and the establishment of primary gas standards.
Spatio-temporal optical vortices: principles of description and basic properties

These notes appeared as a summary during the initial acquaintance with the structure and properties of spatio-temporal optical vortices (STOV), as these are presented in the current literature. The general approach to the STOV description and characterization is based on the model of scalar paraxial Gaussian wave packet. On this ground, the STOV structures of arbitrary orders are considered as superpositions of spatio-temporal Hermite-Gaussian modes. This representation enables the direct and physically meaningful characterization of the STOV amplitude and phase distributions, their evolution upon free propagation and in optical systems, internal energy flows and the orbital angular momentum. The topologically determined asymmetry and the difference between the"energy center"and"probability center"[Phys. Rev. A 107, L031501 (2023)] is discussed and qualitatively interpreted. Methods for the STOV generation and diagnostics are outlined, and the main properties of non-Gaussian (Bessel-type) STOVs are briefly described. The notes may be useful as initial introduction to the STOV-associated ideas and their extraordinary properties.
A Laboratory Experiment on Using Different Financial-Incentivization Schemes in Software-Engineering Experimentation

In software-engineering research, many empirical studies are conducted with open-source or industry developers. However, in contrast to other research communities like economics or psychology, only few experiments use financial incentives (i.e., paying money) as a strategy to motivate participants' behavior and reward their performance. The most recent version of the SIGSOFT Empirical Standards mentions payouts only for increasing participation in surveys, but not for mimicking real-world motivations and behavior in experiments. Within this article, we report a controlled experiment in which we tackled this gap by studying how different financial incentivization schemes impact developers. For this purpose, we first conducted a survey on financial incentives used in the real-world, based on which we designed three incentivization schemes: (1) a performance-dependent scheme that employees prefer, (2) a scheme that is performance-independent, and (3) a scheme that mimics open-source development. Then, using a between-subject experimental design, we explored how these three schemes impact participants' performance. Our findings indicate that the different schemes can impact participants' performance in software-engineering experiments. Due to the small sample sizes, our results are not statistically significant, but we can still observe clear tendencies. Our contributions help understand the impact of financial incentives on participants in experiments as well as real-world scenarios, guiding researchers in designing experiments and organizations in compensating developers.
WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations

Enhancing the attribution in large language models (LLMs) is a crucial task. One feasible approach is to enable LLMs to cite external sources that support their generations. However, existing datasets and evaluation methods in this domain still exhibit notable limitations. In this work, we formulate the task of attributed query-focused summarization (AQFS) and present WebCiteS, a Chinese dataset featuring 7k human-annotated summaries with citations. WebCiteS derives from real-world user queries and web search results, offering a valuable resource for model training and evaluation. Prior works in attribution evaluation do not differentiate between groundedness errors and citation errors. They also fall short in automatically verifying sentences that draw partial support from multiple sources. We tackle these issues by developing detailed metrics and enabling the automatic evaluator to decompose the sentences into sub-claims for fine-grained verification. Our comprehensive evaluation of both open-source and proprietary models on WebCiteS highlights the challenge LLMs face in correctly citing sources, underscoring the necessity for further improvement. The dataset and code will be open-sourced to facilitate further research in this crucial field.
TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts

Accurate traffic forecasting is challenging due to the complex dependency on road networks, various types of roads, and the abrupt speed change due to the events. Recent works mainly focus on dynamic spatial modeling with adaptive graph embedding or graph attention having less consideration for temporal characteristics and in-situ modeling. In this paper, we propose a novel deep learning model named TESTAM, which individually models recurring and non-recurring traffic patterns by a mixture-of-experts model with three experts on temporal modeling, spatio-temporal modeling with static graph, and dynamic spatio-temporal dependency modeling with dynamic graph. By introducing different experts and properly routing them, TESTAM could better model various circumstances, including spatially isolated nodes, highly related nodes, and recurring and non-recurring events. For the proper routing, we reformulate a gating problem into a classification problem with pseudo labels. Experimental results on three public traffic network datasets, METR-LA, PEMS-BAY, and EXPY-TKY, demonstrate that TESTAM achieves a better indication and modeling of recurring and non-recurring traffic. We published the official code at https://github.com/HyunWookL/TESTAM
Multimessenger observations and the science enabled: Continuous waves and their progenitors, equation of state of dense matter

Rotating and oscillating neutron stars can give rise to long-lived Continuous Gravitational Waves (CGWs). Despite many years of searching, the detection of such a CGW signal remains elusive. In this article we describe the main astrophysical uncertainties regarding such emission, and their relation to the behaviour of matter at extremely high density. We describe the main challenges in searching for CGWs, and the prospects of detecting them using third-generation gravitational wave detectors. We end by describing some pressing issues in the field, whose resolution would help turn the detection and exploitation of CGWs into reality.
Projected Gradient Descent Algorithm for Low-Rank Matrix Estimation

Most existing methodologies of estimating low-rank matrices rely on Burer-Monteiro factorization, but these approaches can suffer from slow convergence, especially when dealing with solutions characterized by a large condition number, defined by the ratio of the largest to the $r$-th singular values, where $r$ is the search rank. While methods such as Scaled Gradient Descent have been proposed to address this issue, such methods are more complicated and sometimes have weaker theoretical guarantees, for example, in the rank-deficient setting. In contrast, this paper demonstrates the effectiveness of the projected gradient descent algorithm. Firstly, its local convergence rate is independent of the condition number. Secondly, under conditions where the objective function is rank-$2r$ restricted $L$-smooth and $\mu$-strongly convex, with $L/\mu<3$, projected gradient descent with appropriate step size converges linearly to the solution. Moreover, a perturbed version of this algorithm effectively navigates away from saddle points, converging to an approximate solution or a second-order local minimizer across a wide range of step sizes. Furthermore, we establish that there are no spurious local minimizers in estimating asymmetric low-rank matrices when the objective function satisfies $L/\mu<3. $
Vanilla Transformers are Transfer Capability Teachers

Recently, Mixture of Experts (MoE) Transformers have garnered increasing attention due to their advantages in model capacity and computational efficiency. However, studies have indicated that MoE Transformers underperform vanilla Transformers in many downstream tasks, significantly diminishing the practical value of MoE models. To explain this issue, we propose that the pre-training performance and transfer capability of a model are joint determinants of its downstream task performance. MoE models, in comparison to vanilla models, have poorer transfer capability, leading to their subpar performance in downstream tasks. To address this issue, we introduce the concept of transfer capability distillation, positing that although vanilla models have weaker performance, they are effective teachers of transfer capability. The MoE models guided by vanilla models can achieve both strong pre-training performance and transfer capability, ultimately enhancing their performance in downstream tasks. We design a specific distillation method and conduct experiments on the BERT architecture. Experimental results show a significant improvement in downstream performance of MoE models, and many further evidences also strongly support the concept of transfer capability distillation. Finally, we attempt to interpret transfer capability distillation and provide some insights from the perspective of model feature.
Local weak convergence and its applications

Motivated in part by understanding average case analysis of fundamental algorithms in computer science, and in part by the wide array of network data available over the last decade, a variety of random graph models, with corresponding processes on these objects, have been proposed over the last few years. The main goal of this paper is to give an overview of local weak convergence, which has emerged as a major technique for understanding large network asymptotics for a wide array of functionals and models. As opposed to a survey, the main goal is to try to explain some of the major concepts and their use to junior researchers in the field and indicate potential resources for further reading.