Miracle-dz/UniFAR-FTUs huggingface dataset:
Inter-layer valence bonds and two-component theory for high-$T_c$ superconductivity of La$_{3}$Ni$_{2}$O$_{7}$ under pressure

The recent discovery of high-$T_{c}$ superconductivity in bilayer nickelate La$_{3}$Ni$_{2}$O$_{7}$ under high pressure has stimulated great interest concerning its pairing mechanism. We argue that the weak coupling model from the almost fully-filled $d_{z^{2}}$ bonding band cannot give rise to its high $T_{c}$, and thus propose a strong coupling model based on local inter-layer spin singlets of Ni-$d_{z^{2}}$ electrons due to their strong on-site Coulomb repulsion. This leads to a minimal effective model that contains local pairing of $d_{z^{2}}$ electrons and a considerable hybridization with near quarter-filled itinerant $d_{x^{2}-y^{2}}$ electrons on nearest-neighbor sites. Their strong coupling provides a unique two-component scenario to achieve high-$T_{c}$ superconductivity. Our theory highlights the importance of the bilayer structure of superconducting La$_{3}$Ni$_{2}$O$_{7}$ and points out a potential route for the exploration of more high-$T_{c}$ superconductors.
FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving

Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such data from multiple sensors to jointly optimize the prediction and planning tasks remains largely unexplored. In this paper, we present FusionAD, to the best of our knowledge, the first unified framework that fuse the information from two most critical sensors, camera and LiDAR, goes beyond perception task. Concretely, we first build a transformer based multi-modality fusion network to effectively produce fusion based features. In constrast to camera-based end-to-end method UniAD, we then establish a fusion aided modality-aware prediction and status-aware planning modules, dubbed FMSPnP that take advantages of multi-modality features. We conduct extensive experiments on commonly used benchmark nuScenes dataset, our FusionAD achieves state-of-the-art performance and surpassing baselines on average 15% on perception tasks like detection and tracking, 10% on occupancy prediction accuracy, reducing prediction error from 0.708 to 0.389 in ADE score and reduces the collision rate from 0.31% to only 0.12%.
Regularity of oscillatory integral operators

In this paper, we establish the global boundedness of oscillatory integral operators on Besov-Lipschitz and Triebel-Lizorkin spaces, with amplitudes in general $S^m_{\rho,\delta}(\mathbb{R}^n)$-classes and non-degenerate phase functions in the class $\textart F^k$. Our results hold for a wide range of parameters $0\leq\rho\leq1$, $0\leq\delta<1$, $00$. We also provide a sufficient condition for the boundedness of operators with amplitudes in the forbidden class $S^m_{1,1}(\mathbb{R}^n)$ in Triebel-Lizorkin spaces.
Strongly convergent unitary representations of right-angled Artin groups

We prove using a novel random matrix model that all right-angled Artin groups have a sequence of finite dimensional unitary representations that strongly converge to the regular representation. We deduce that this result applies also to: the fundamental group of a closed hyperbolic manifold that is either three dimensional or standard arithmetic type, any Coxeter group, and any word-hyperbolic cubulated group. One strong consequence of these results is that any closed hyperbolic three-manifold has a sequence of finite dimensional flat Hermitian vector bundles with bottom of the spectrum of the Laplacian asymptotically at least 1.
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation

—Segment anything model (SAM) has emerged as the leading approach for zero-shot learning in segmentation, offering the advantage of avoiding pixel-wise annotation. It is particularly appealing in medical image segmentation where annotation is laborious and expertise-demanding. However, the direct application of SAM often yields inferior results compared to conventional fully supervised segmentation networks. An alternative approach is to use SAM to generate pseudo labels for fully supervised segmentation. However, the performance is limited by the quality of pseudo labels. In this paper, we propose a novel label corruption to push the boundary of SAM-based segmentation. Our model utilizes a novel noise detection module to distinguish between noisy labels from clean labels. This enables to correct the noisy labels using an uncertainty-based self-correction module, thereby enriching the clean training set. Finally, we retrain the network with updated labels to optimize its weights for future predictions. One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a subset of expert-level annotations, while attaining good segmentation performance. We demonstrate the effectiveness of our proposed model on both X-ray and lung CT datasets, indicating its ability to improve segmentation accuracy and outperform baseline methods in label correction.
Deep Learning Approaches in Pavement Distress Identification: A Review

This paper presents a comprehensive review of recent advancements in image processing and deep learning techniques for pavement distress detection and classification, a critical aspect in modern pavement management systems. The conventional manual inspection process conducted by human experts is gradually being superseded by automated solutions, leveraging machine learning and deep learning algorithms to enhance efficiency and accuracy. The ability of these algorithms to discern patterns and make predictions based on extensive datasets has revolutionized the domain of pavement distress identification. The paper investigates the integration of unmanned aerial vehicles (UAVs) for data collection, offering unique advantages such as aerial perspectives and efficient coverage of large areas. By capturing high-resolution images, UAVs provide valuable data that can be processed using deep learning algorithms to detect and classify various pavement distresses effectively. While the primary focus is on 2D image processing, the paper also acknowledges the challenges associated with 3D images, such as sensor limitations and computational requirements. Understanding these challenges is crucial for further advancements in the field. The findings of this review significantly contribute to the evolution of pavement distress detection, fostering the development of efficient pavement management systems. As automated approaches continue to mature, the implementation of deep learning techniques holds great promise in ensuring safer and more durable road infrastructure for the benefit of society.
Causal exposure-response curve estimation with surrogate confounders: a study of air pollution and children's health in Medicaid claims data

In this paper, we undertake a case study in which interest lies in estimating a causal exposure-response function (ERF) for long-term exposure to fine particulate matter (PM$_{2.5}$) and respiratory hospitalizations in socioeconomically disadvantaged children using nationwide Medicaid claims data. New methods are needed to address the specific challenges the Medicaid data present. First, Medicaid eligibility criteria, which are largely based on family income for children, differ by state, creating socioeconomically distinct populations and leading to clustered data, where zip codes (our units of analysis) are nested within states. Second, Medicaid enrollees' individual-level socioeconomic status, which is known to be a confounder and an effect modifier of the exposure-response relationships under study, is not available. However, two useful surrogates are available: median household income of each enrollee's zip code of residence and state-level Medicaid family income eligibility thresholds for children. In this paper, we introduce a customized approach, called \textit{MedMatch}, that builds on generalized propensity score matching methods for estimating causal ERFs, adapting these approaches to leverage our two surrogate variables to account for potential confounding and/or effect modification by socioeconomic status. We conduct extensive simulation studies, consistently demonstrating the strong performance of \textit{MedMatch} relative to conventional approaches to handling the surrogate variables. We apply \textit{MedMatch} to estimate the causal ERF between long-term PM$_{2.5}$ exposure and first respiratory hospitalization among children in Medicaid from 2000 to 2012. We find a positive association, with a steeper curve at PM$_{2.5} \le 8$ $\mu$g/m$^3$ that levels off at higher concentrations.
Do Multilingual Language Models Think Better in English?

Translate-test is a popular technique to improve the performance of multilingual language models. This approach works by translating the input into English using an external machine translation system, and running inference over the translated input. However, these improvements can be attributed to the use of a separate translation system, which is typically trained on large amounts of parallel data not seen by the language model. In this work, we introduce a new approach called self-translate, which overcomes the need of an external translation system by leveraging the few-shot translation capabilities of multilingual language models. Experiments over 5 tasks show that self-translate consistently outperforms direct inference, demonstrating that language models are unable to leverage their full multilingual potential when prompted in non-English languages. Our code is available at https://github.com/juletx/self-translate.
Tracing the Total Stellar Mass and Star Formation of High-redshift Protoclusters

As the progenitors of present-day galaxy clusters, protoclusters are excellent laboratories to study galaxy evolution. Since existing observations of protoclusters are limited to the detected constituent galaxies at UV and/or infrared wavelengths, the details of how typical galaxies grow in these young, pre-virialized structures remain uncertain. We measure the total stellar mass and star formation within protoclusters, including the contribution from faint undetected members by performing a stacking analysis of 211 z = 2–4 protoclusters selected as Planck cold sources. We stack Wide-field Infrared Survey Explorer and Herschel/SPIRE images to measure the angular size and the spectral energy distribution of the integrated light from the protoclusters. The fluxes of protoclusters selected as Planck cold sources can be contaminated by line-of-sight interlopers. Using the WebSky simulation, we estimate that a single protocluster contributes 33% ± 15% of the flux of a Planck cold source on average. After this correction, we obtain a total star formation rate of 7.3 ± 3.2 × 103 M ⊙yr−1 and a total stellar mass of 4.9 ± 2.2 × 1012 M ⊙. Our results indicate that protoclusters have, on average, 2× more star formation and 4× more stellar mass than the total contribution from individually detected galaxies in spectroscopically confirmed protoclusters. This suggests that much of the total flux within z = 2–4 protoclusters comes from galaxies with luminosities lower than the detection limit of SPIRE (L IR < 3 × 1012 L ⊙). Lastly, we find that protoclusters subtend a half-light radius of 2. ′8 (4.2–5.8 cMpc), which is consistent with simulations.
Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?

Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale. Although detection methods for such AI-generated text exist already, we investigate ChatGPT's performance as a detector on such AI-generated text, inspired by works that use ChatGPT as a data labeler or annotator. We evaluate the zero-shot performance of ChatGPT in the task of human-written vs. AI-generated text detection, and perform experiments on publicly available datasets. We empirically investigate if ChatGPT is symmetrically effective in detecting AI-generated or human-written text. Our findings provide insight on how ChatGPT and similar LLMs may be leveraged in automated detection pipelines by simply focusing on solving a specific aspect of the problem and deriving the rest from that solution. All code and data is available at https://github.com/AmritaBh/ChatGPT-as-Detector.
Embedding Capabilities of Neural ODEs

A class of neural networks that gained particular interest in the last years are neural ordinary differential equations (neural ODEs). We study input-output relations of neural ODEs using dynamical systems theory and prove several results about the exact embedding of maps in different neural ODE architectures in low and high dimension. The embedding capability of a neural ODE architecture can be increased by adding, for example, a linear layer, or augmenting the phase space. Yet, there is currently no systematic theory available and our work contributes towards this goal by developing various embedding results as well as identifying situations, where no embedding is possible. The mathematical techniques used include as main components iterative functional equations, Morse functions and suspension flows, as well as several further ideas from analysis. Although practically, mainly universal approximation theorems are used, our geometric dynamical systems viewpoint on universal embedding provides a fundamental understanding, why certain neural ODE architectures perform better than others.
Certified Multi-Fidelity Zeroth-Order Optimization

We consider the problem of multi-fidelity zeroth-order optimization, where one can evaluate a function $f$ at various approximation levels (of varying costs), and the goal is to optimize $f$ with the cheapest evaluations possible. In this paper, we study \emph{certified} algorithms, which are additionally required to output a data-driven upper bound on the optimization error. We first formalize the problem in terms of a min-max game between an algorithm and an evaluation environment. We then propose a certified variant of the MFDOO algorithm and derive a bound on its cost complexity for any Lipschitz function $f$. We also prove an $f$-dependent lower bound showing that this algorithm has a near-optimal cost complexity. We close the paper by addressing the special case of noisy (stochastic) evaluations as a direct example.
Continual Domain Adaptation on Aerial Images under Gradually Degrading Weather

Domain adaptation (DA) strives to mitigate the domain gap between the source domain where a model is trained, and the target domain where the model is deployed. When a deep learning model is deployed on an aerial platform, it may face gradually degrading weather conditions during operation, leading to widening domain gaps between the training data and the encountered evaluation data. We synthesize two such gradually worsening weather conditions on real images from two existing aerial imagery datasets, generating a total of four benchmark datasets. Under the continual, or test-time adaptation setting, we evaluate three DA models on our datasets: a baseline standard DA model and two continual DA models. In such setting, the models can access only one small portion, or one batch of the target data at a time, and adaptation takes place continually, and over only one epoch of the data. The combination of the constraints of continual adaptation, and gradually deteriorating weather conditions provide the practical DA scenario for aerial deployment. Among the evaluated models, we consider both convolutional and transformer architectures for comparison. We discover stability issues during adaptation for existing buffer-fed continual DA methods, and offer gradient normalization as a simple solution to curb training instability.
A Backend Platform for Supporting the Reproducibility of Computational Experiments

In recent years, the research community has raised serious questions about the reproducibility of scientific work. In particular, since many studies include some kind of computing work, reproducibility is also a technological challenge, not only in computer science, but in most research domains. Replicability and computational reproducibility are not easy to achieve, not only because researchers have diverse proficiency in computing technologies, but also because of the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment using the same frameworks, code, data sources, programming languages, dependencies, and so on. In this work, we propose an Integrated Development Environment allowing the share, configuration, packaging and execution of an experiment by setting the code and data used and defining the programming languages, code, dependencies, databases, or commands to execute to achieve consistent results for each experiment. After the initial creation and configuration, the experiment can be executed any number of times, always producing exactly the same results. Furthermore, it allows the execution of the experiment by using a different associated dataset, and it can be possible to verify the reproducibility and replicability of the results. This allows the creation of a reproducible pack that can be re-executed by anyone on any other computer. Our platform aims to allow researchers in any field to create a reproducibility package for their science that can be re-executed on any other computer. To evaluate our platform, we used it to reproduce 25 experiments extracted from published papers. We have been able to successfully reproduce 20 (80%) of these experiments achieving the results reported in such works with minimum effort, thus showing that our approach is effective.
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion

Time Series Classification (TSC) has received much attention in the past two decades and is still a crucial and challenging problem in data science and knowledge engineering. Indeed, along with the increasing availability of time series data, many TSC algorithms have been suggested by the research community in the literature. Besides state-of-the-art methods based on similarity measures, intervals, shapelets, dictionaries, deep learning methods or hybrid ensemble methods, several tools for extracting unsupervised informative summary statistics, aka features, from time series have been designed in the recent years. Originally designed for descriptive analysis and visualization of time series with informative and interpretable features, very few of these feature engineering tools have been benchmarked for TSC problems and compared with state-of-the-art TSC algorithms in terms of predictive performance. In this article, we aim at filling this gap and propose a simple TSC process to evaluate the potential predictive performance of the feature sets obtained with existing feature engineering tools. Thus, we present an empirical study of 11 feature engineering tools branched with 9 supervised classifiers over 112 time series data sets. The analysis of the results of more than 10000 learning experiments indicate that feature-based methods perform as accurately as current state-of-the-art TSC algorithms, and thus should rightfully be considered further in the TSC literature.
Teaching Smaller Language Models To Generalise To Unseen Compositional Questions

We equip a smaller Language Model to generalise to answering challenging compositional questions that have not been seen in training. To do so we propose a combination of multitask supervised pretraining on up to 93 tasks designed to instill diverse reasoning abilities, and a dense retrieval system that aims to retrieve a set of evidential paragraph fragments. Recent progress in question-answering has been achieved either through prompting methods against very large pretrained Language Models in zero or few-shot fashion, or by fine-tuning smaller models, sometimes in conjunction with information retrieval. We focus on the less explored question of the extent to which zero-shot generalisation can be enabled in smaller models with retrieval against a corpus within which sufficient information to answer a particular question may not exist. We establish strong baselines in this setting for diverse evaluation datasets (StrategyQA, CommonsenseQA, IIRC, DROP, Musique and ARC-DA), and show that performance can be significantly improved by adding retrieval-augmented training datasets which are designed to expose our models to a variety of heuristic reasoning strategies such as weighing partial evidence or ignoring an irrelevant context.
Occlusion-Resistant LiDAR Fiducial Marker Detection

The LiDAR fiducial marker, akin to the well-known AprilTag used in camera applications, serves as a convenient resource to impart artificial features to the LiDAR sensor, facilitating robotics applications. Unfortunately, current LiDAR fiducial marker detection methods are limited to occlusion-free point clouds. In this work, we present a novel approach for occlusion-resistant LiDAR fiducial marker detection. We first extract 3D points potentially corresponding to the markers, leveraging the 3D intensity gradients. Afterward, we analyze the 3D spatial distribution of the extracted points through clustering. Subsequently, we determine the potential marker locations by examining the geometric characteristics of these clusters. We then successively transfer the 3D points that fall within the candidate locations from the raw point cloud onto a designed intermediate plane. Finally, using the intermediate plane, we validate each location for the presence of a fiducial marker and compute the marker's pose if found. We conduct both qualitative and quantitative experiments to demonstrate that our approach is the first LiDAR fiducial marker detection method applicable to point clouds with occlusion while achieving better accuracy.
Combined Multiphysics Model of Switched Flux PM Machines Under Fault Operations

In this paper, the transient thermal response of a conventional double-layer switched-flux permanent-magnet machine is studied for both healthy and fault conditions such as inter-turn short-circuit. A highly optimized and accurate cosimulation model for different operating conditions is developed requiring low computation and time resources. The electromechanical models for both healthy and faulty operation are implemented in MATLAB/Simulink while the thermal model is implemented using three-dimensional (3-D) finite-element model (FEM) software. Both models are dynamically coupled to enable the influence of temperature rise on the electromagnetic performance and vice versa to be predicted. Operation under various conditions are investigated, and it is found that the temperature rise under fault conditions and high speed can lead to irreversible demagnetization of the permanent magnets. The superposition principle is used to accurately estimate the impact of short-circuit currents on the temperature rise. A series of dynamic tests are carried out to validate the transient thermal response prediction when operating during both the healthy and fault conditions.