Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Deep Neural Network Approximation for Korobov Functions with respect to Sobolev Norms

This paper establishes the nearly optimal rate of approximation for deep neural networks (DNNs) when applied to Korobov functions, effectively overcoming the curse of dimensionality. The approximation results presented in this paper are measured with respect to $L_p$ norms and $H^1$ norms. Our achieved approximation rate demonstrates a remarkable"super-convergence"rate, outperforming traditional methods and any continuous function approximator. These results are non-asymptotic, providing error bounds that consider both the width and depth of the networks simultaneously.
Bar-driven Gas Dynamics of M31

The large-scale gaseous shocks in the bulge of M31 can be naturally explained by a rotating stellar bar. We use gas dynamical models to provide an independent measurement of the bar pattern speed in M31. The gravitational potentials of our simulations are from a set of made-to-measure models constrained by stellar photometry and kinematics. If the inclination of the gas disk is fixed at i = 77°, we find that a low pattern speed of 16–20 km s−1 kpc−1 is needed to match the observed position and amplitude of the shock features, as shock positions are too close to the bar major axis in high Ω b models. The pattern speed can increase to 20–30 km s−1 kpc−1 if the inner gas disk has a slightly smaller inclination angle compared with the outer one. Including subgrid physics such as star formation and stellar feedback has minor effects on the shock amplitude, and does not change the shock position significantly. If the inner gas disk is allowed to follow a varying inclination similar to the H i and ionized gas observations, the gas models with a pattern speed of 38 km s−1 kpc−1, which is consistent with stellar-dynamical models, can match both the shock features and the central gas features.
Estimation of particulate matter from satellite- and ground-based observations over Hyderabad, India

Long-term trends in surface-level particulate matter of dynamic diameter ≤2 µm (PM2) in regard to air quality observations over Greater Hyderabad Region (GHR), India are estimated by the synergy of ground-based measurements and satellite observations during the period 2001–2013 (satellite) and July 2009–Dec 2013 (ground-based). Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical thickness (AOT) (MODIS-AOTs) was validated against that measured from Microtops-II Sunphotometer (MTS) AOTs (MTS-AOTs) and then utilized to estimate surface-level PM2 concentrations over GHR using regression analysis between MODIS-AOTs, MTS-AOTs, and measured PM2. In general, the MODIS-estimated PM2 concentrations fell within the uncertainty of the measurements, thus allowing the estimate of PM2 from MODIS, although in some cases they differed significantly due to vertical heterogeneity in aerosol distribution and the presence of distinct elevated aerosol layers of different origin and characteristics. Furthermore, significant spatial and temporal heterogeneity in the AOT and PM2 estimates is observed in urban environments, especially during the pre-monsoon and monsoon seasons, which reduces the accuracy of the PM2 estimates from MODIS. The estimates of PM2 using MTS or MODIS-AOT exhibit a root mean square deference (RMSD) of about 8–16% against measured PM2 on a seasonal basis. Furthermore, a tendency of increasing PM2 concentrations is observed, which however is difficult to quantify for urban areas due to uncertainties in PM2 estimations and gaps in the data set. Examination of surface and columnar aerosol concentrations, along with meteorological parameters from radiosonde observations on certain days, reveals that changes in local emissions and boundary-layer dynamics, and the presence or arrival of distinct aerosol plumes aloft, are major concerns in the accurate estimation of PM2 from MODIS, while the large spatial distribution of aerosol and pollutants in the urban environment makes such estimates a considerable challenge.
Cosmic Vine: A z=3.44 large-scale structure hosting massive quiescent galaxies

We report the discovery of a large-scale structure at $z=3.44$ revealed by JWST data in the Extended Groth Strip (EGS) field. This structure, called the Cosmic Vine, consists of 20 galaxies with spectroscopic redshifts at $3.43<z<3.45$ and six galaxy overdensities ($4-7 with consistent photometric redshifts, making up a vine-like structure extending over a $ pMpc$^2$ area. The two most massive galaxies ($M_* M_ of the Cosmic Vine are found to be quiescent with bulge-dominated morphologies ($B/T>70<! PCT! >$). Comparisons with simulations suggest that the Cosmic Vine would form a cluster with halo mass halo M_ at $z=0$, and the two massive galaxies are likely forming the brightest cluster galaxies (BCGs). The results unambiguously reveal that massive quiescent galaxies can form in growing large-scale structures at $z>3$, thus disfavoring the environmental quenching mechanisms that require a virialized cluster core. Instead, as suggested by the interacting and bulge-dominated morphologies, the two galaxies are likely quenched by merger-triggered starburst or active galactic nucleus (AGN) feedback before falling into a cluster core. Moreover, we found that the observed specific star formation rates of massive quiescent galaxies in $z>3$ dense environments are one to two orders of magnitude lower than that of the BCGs in the TNG300 simulation. This discrepancy potentially poses a challenge to the models of massive cluster galaxy formation. Future studies comparing a large sample with dedicated cluster simulations are required to solve the problem.
The length spectrum of random hyperbolic 3-manifolds

We study the length spectrum of a model of random hyperbolic 3-manifolds introduced by Petri and Raimbault. These are compact manifolds with boundary constructed by randomly gluing truncated tetrahedra along their faces. We prove that, as the volume tends to infinity, their length spectrum converge in distribution to a Poisson point process on $\mathbb{R}_{\geq0}$, with computable intensity $\lambda$.
Mechanical strain induced topological phase changes of few layer ZrTe$_5$

Understanding the topological aspects of the band structure of solids has fundamentally changed our appreciation of their properties. The layered, van der Waals transition-metal pentatelluride ZrTe$_5$ has proven on numerous occasions to be an excellent candidate for the study of controllable topological phase transitions. Here, we investigate the topological phase diagrams of monolayer and bilayer forms of ZrTe$_5$, under mechanical deformations using \textit{ab initio} techniques. We find that mechanical deformation can close the monolayer's topological gap, while the bilayer exhibits richer phase diagram, including both topological insulating, trivial metallic and insulating phases. The bilayer is predicted to be on the topological phase boundary. We also address the preparation of monolayers, using \emph{ab initio} simulations and experimental scanning tunneling microscopy measurements. We confirm that while monolayer ZrTe$_5$ is difficult to exfoliate without compromising its crystalline structure, bilayers offer a more stable alternative, revealing the complexities and limitations of using gold substrates for monolayer exfoliation.
Sparcle: Boosting the Accuracy of Data Cleaning Systems through Spatial Awareness

Though data cleaning systems have earned great success and wide spread in both academia and industry, they fall short when trying to clean spatial data. The main reason is that state-of-the-art data cleaning systems mainly rely on functional dependency rules where there is sufficient co-occurrence of value pairs to learn that a certain value of an attribute leads to a corresponding value of another attribute. However, for spatial attributes that represent locations on the form of, there is very little chance that two records would have the same exact coordinates, and hence co-occurrence would unlikely to exist. This paper presents Sparcle~(SPatially-AwaRe CLEaning); a novel framework that injects spatial awareness into the core engine of rule-based data cleaning systems as a means of boosting their accuracy. Sparcle injects two main spatial concepts into the core engine of data cleaning systems: (1) Spatial Neighborhood, where co-occurrence is relaxed to be within a certain spatial proximity rather than same exact value, and (2) Distance Weighting, where records are given different weights of whether they satisfy a dependency rule, based on their relative distance. Experimental results using a real deployment of Sparcle inside a state-of-the-art data cleaning system, and real and synthetic datasets, show that Sparcle significantly boosts the accuracy of data cleaning systems when dealing with spatial data.
Spatial distribution of Angular Momentum Inside a Quark State Dressed with a Gluon

We investigate the different decompositions of the angular momentum in QCD for a relativistic spin $1/2$ composite state, namely a quark dressed with a gluon. We use light-front Hamiltonian perturbation theory, and in the light-front gauge, use the two-component framework by eliminating the constrained degrees of freedom. We also investigate the different decompositions of the angular momentum at the level of two-dimensional densities in the front form, including the effect of the so-called potential term. In this work, we consider the contribution coming from the quark part of the energy-momentum tensor. We contrast the different decompositions and also compare with other calculations in the literature. We also present the gravitational form factor related to the antisymmetric part of the energy-momentum tensor.
Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition

The rapid growth of wearable sensor technologies holds substantial promise for the field of personalized and context-aware Human Activity Recognition. Given the inherently decentralized nature of data sources within this domain, the utilization of multi-agent systems with their inherent decentralization capabilities presents an opportunity to facilitate the development of scalable, adaptable, and privacy-conscious methodologies. This paper introduces a collaborative distributed learning approach rooted in multi-agent principles, wherein individual users of sensor-equipped devices function as agents within a distributed network, collectively contributing to the comprehensive process of learning and classifying human ac-tivities. In this proposed methodology, not only is the privacy of activity monitoring data upheld for each individual, eliminating the need for an external server to oversee the learning process, but the system also exhibits the potential to surmount the limitations of conventional centralized models and adapt to the unique attributes of each user. The proposed approach has been empirically tested on two publicly accessible human activity recognition datasets, specifically PAMAP2 and HARTH, across varying settings. The provided empirical results conclusively highlight the efficacy of inter-individual collaborative learning when contrasted with centralized configurations, both in terms of local and gdobal generalization.
Evaluating Diverse Large Language Models for Automatic and General Bug Reproduction

Bug reproduction is a critical developer activity that is also challenging to automate, as bug reports are often in natural language and thus can be difficult to transform to test cases consistently. As a result, existing techniques mostly focused on crash bugs, which are easier to automatically detect and verify. In this work, we overcome this limitation by using large language models (LLMs), which have been demonstrated to be adept at natural language processing and code generation. By prompting LLMs to generate bug-reproducing tests, and via a post-processing pipeline to automatically identify promising generated tests, our proposed technique LIBRO could successfully reproduce about one-third of all bugs in the widely used Defects4J benchmark. Furthermore, our extensive evaluation on 15 LLMs, including 11 open-source LLMs, suggests that open-source LLMs also demonstrate substantial potential, with the StarCoder LLM achieving 70% of the reproduction performance of the closed-source OpenAI LLM code-davinci-002 on the large Defects4J benchmark, and 90% of performance on a held-out bug dataset likely not part of any LLM's training data. In addition, our experiments on LLMs of different sizes show that bug reproduction using LIBRO improves as LLM size increases, providing information as to which LLMs can be used with the LIBRO pipeline.
Blurry Video Compression: A Trade-off between Visual Enhancement and Data Compression

Existing video compression (VC) methods primarily aim to reduce the spatial and temporal redundancies between consecutive frames in a video while preserving its quality. In this regard, previous works have achieved remarkable results on videos acquired under specific settings such as instant (known) exposure time and shutter speed which often result in sharp videos. However, when these methods are evaluated on videos captured under different temporal priors, which lead to degradations like motion blur and low frame rate, they fail to maintain the quality of the contents. In this work, we tackle the VC problem in a general scenario where a given video can be blurry due to predefined camera settings or dynamics in the scene. By exploiting the natural trade-off between visual enhancement and data compression, we formulate VC as a min-max optimization problem and propose an effective framework and training strategy to tackle the problem. Extensive experimental results on several benchmark datasets confirm the effectiveness of our method compared to several state-of-the-art VC approaches.
Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values

Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, the observed data may contain negative values due to noise even when the true underlying physical signal is strictly positive. Prior NMF work has not treated negative data in a statistically consistent manner, which becomes problematic for low signal-to-noise data with many negative values. In this paper we present two algorithms, Shift-NMF and Nearly-NMF, that can handle both the noisiness of the input data and also any introduced negativity. Both of these algorithms use the negative data space without clipping, and correctly recover non-negative signals without any introduced positive offset that occurs when clipping negative data. We demonstrate this numerically on both simple and more realistic examples, and prove that both algorithms have monotonically decreasing update rules.
Profiling Irony & Stereotype: Exploring Sentiment, Topic, and Lexical Features

Social media has become a very popular source of information. With this popularity comes an interest in systems that can classify the information produced. This study tries to create such a system detecting irony in Twitter users. Recent work emphasize the importance of lexical features, sentiment features and the contrast herein along with TF-IDF and topic models. Based on a thorough feature selection process, the resulting model contains specific sub-features from these areas. Our model reaches an F1-score of 0.84, which is above the baseline. We find that lexical features, especially TF-IDF, contribute the most to our models while sentiment and topic modeling features contribute less to overall performance. Lastly, we highlight multiple interesting and important paths for further exploration.
Learning to Control Under Uncertainty with Data-Based Iterative Linear Quadratic Regulator

This paper studies the learning-to-control problem under process and sensing uncertainties for dynamical systems. In our previous work, we developed a data-based generalization of the iterative linear quadratic regulator (iLQR) to design closed-loop feedback control for high-dimensional dynamical systems with partial state observation. This method required perfect simulation rollouts which are not realistic in real applications. In this work, we briefly introduce this method and explore its efficacy under process and sensing uncertainties. We prove that in the fully observed case where the system dynamics are corrupted with noise but the measurements are perfect, it still converges to the global minimum. However, in the partially observed case where both process and measurement noise exist in the system, this method converges to a biased “optimum”. Thus multiple rollouts need to be averaged to retrieve the true optimum. The analysis is verified in two nonlinear robotic examples simulated in the above cases.
Large GPT-like Models are Bad Babies: A Closer Look at the Relationship between Linguistic Competence and Psycholinguistic Measures

Research on the cognitive plausibility of language models (LMs) has so far mostly concentrated on modelling psycholinguistic response variables such as reading times, gaze durations and N400/P600 EEG signals, while mostly leaving out the dimension of what Mahowald et al. (2023) described as formal and functional linguistic competence, and developmental plausibility. We address this gap by training a series of GPT-like language models of different sizes on the strict version of the BabyLM pretraining corpus, evaluating on the challenge tasks (BLiMP, GLUE, MSGS) and an additional reading time prediction task. We find a positive correlation between LM size and performance on all three challenge tasks, with different preferences for model width and depth in each of the tasks. In contrast, a negative correlation was found between LM size and reading time fit of linear mixed-effects models using LM surprisal as a predictor, with the second-smallest LM achieving the largest log-likelihood reduction over a baseline model without surprisal. This suggests that modelling processing effort and linguistic competence may require an approach different from training GPT-like LMs on a developmentally plausible corpus.
Impact of Cosmic Rays on Atmospheric Ion Chemistry and Spectral Transmission Features of TRAPPIST-1e

Ongoing observing projects like the James Webb Space Telescope and future missions offer the chance to characterize Earth-like exoplanetary atmospheres. Thereby, M dwarfs are preferred targets for transit observations, for example, due to their favorable planet–star contrast ratio. However, the radiation and particle environment of these cool stars could be far more extreme than what we know from the Sun. Thus, knowing the stellar radiation and particle environment and its possible influence on detectable biosignatures—in particular, signs of life like ozone and methane—is crucial to understanding upcoming transit spectra. In this study, with the help of our unique model suite INCREASE, we investigate the impact of a strong stellar energetic particle event on the atmospheric ionization, neutral and ion chemistry, and atmospheric biosignatures of TRAPPIST-1e. Therefore, transit spectra for six scenarios are simulated. We find that a Carrington-like event drastically increases atmospheric ionization and induces substantial changes in ion chemistry and spectral transmission features: all scenarios show high event-induced amounts of nitrogen dioxide (i.e., at 6.2 μm), a reduction of the atmospheric transit depth in all water bands (i.e., at 5.5–7.0 μm), a decrease of the methane bands (i.e., at 3.0–3.5 μm), and depletion of ozone (i.e., at ∼9.6 μm). Therefore, it is essential to include high-energy particle effects to correctly assign biosignature signals from, e.g., ozone and methane. We further show that the nitric acid feature at 11.0–12.0 μm, discussed as a proxy for stellar particle contamination, is absent in wet-dead atmospheres.
Non-Rigid Shape Registration via Deep Functional Maps Prior

In this paper, we propose a learning-based framework for non-rigid shape registration without correspondence supervision. Traditional shape registration techniques typically rely on correspondences induced by extrinsic proximity, therefore can fail in the presence of large intrinsic deformations. Spectral mapping methods overcome this challenge by embedding shapes into, geometric or learned, high-dimensional spaces, where shapes are easier to align. However, due to the dependency on abstract, non-linear embedding schemes, the latter can be vulnerable with respect to perturbed or alien input. In light of this, our framework takes the best of both worlds. Namely, we deform source mesh towards the target point cloud, guided by correspondences induced by high-dimensional embeddings learned from deep functional maps (DFM). In particular, the correspondences are dynamically updated according to the intermediate registrations and filtered by consistency prior, which prominently robustify the overall pipeline. Moreover, in order to alleviate the requirement of extrinsically aligned input, we train an orientation regressor on a set of aligned synthetic shapes independent of the training shapes for DFM. Empirical results show that, with as few as dozens of training shapes of limited variability, our pipeline achieves state-of-the-art results on several benchmarks of non-rigid point cloud matching, but also delivers high-quality correspondences between unseen challenging shape pairs that undergo both significant extrinsic and intrinsic deformations, in which case neither traditional registration methods nor intrinsic methods work. The code is available at https://github.com/rqhuang88/DFR.
Flexibly Estimating and Interpreting Heterogeneous Treatment Effects of Laparoscopic Surgery for Cholecystitis Patients

Laparoscopic surgery has been shown through a number of randomized trials to be an effective form of treatment for cholecystitis. Given this evidence, one natural question for clinical practice is: does the effectiveness of laparoscopic surgery vary among patients? It might be the case that, while the overall effect is positive, some patients treated with laparoscopic surgery may respond positively to the intervention while others do not or may be harmed. In our study, we focus on conditional average treatment effects to understand whether treatment effects vary systematically with patient characteristics. Recent methodological work has developed a meta-learner framework for flexible estimation of conditional causal effects. In this framework, nonparametric estimation methods can be used to avoid bias from model misspecification while preserving statistical efficiency. In addition, researchers can flexibly and effectively explore whether treatment effects vary with a large number of possible effect modifiers. However, these methods have certain limitations. For example, conducting inference can be challenging if black-box models are used. Further, interpreting and visualizing the effect estimates can be difficult when there are multi-valued effect modifiers. In this paper, we develop new methods that allow for interpretable results and inference from the meta-learner framework for heterogeneous treatment effects estimation. We also demonstrate methods that allow for an exploratory analysis to identify possible effect modifiers. We apply our methods to a large database for the use of laparoscopic surgery in treating cholecystitis. We also conduct a series of simulation studies to understand the relative performance of the methods we develop. Our study provides key guidelines for the interpretation of conditional causal effects from the meta-learner framework.