October 2023 arXiv papers — page 24

Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong

In unsupervised causal representation learning for sequential data with time-delayed latent causal influences, strong identifiability results for the disentanglement of causally-related latent variables have been established in stationary settings by leveraging temporal structure. However, in nonstationary setting, existing work only partially addressed the

Jiatai Wang, Zhiwei Xu, Xuewen Yang, Xin Wang

Multi-view clustering (MVC) can explore common semantics from unsupervised views generated by different sources, and thus has been extensively used in applications of practical computer vision. Due to the spatio-temporal asynchronism, multi-view data often suffer from view missing and are unaligned in real-world applications, which makes it difficult to lear

Dennis Nguyen

A notion of vector field cobordism for oriented manifolds was defined by B\"okstedt and Svane. We extend this notion to define complex section cobordism for almost complex manifolds. We then determine the complex section cobordism groups and a relevant cobordism category. We describe an obstruction which tells us when a cobordism class contains a manifold, w

Saad Qadeer, Andrew Engel, Amanda Howard, Adam Tsou

Despite their immense promise in performing a variety of learning tasks, a theoretical understanding of the limitations of Deep Neural Networks (DNNs) has so far eluded practitioners. This is partly due to the inability to determine the closed forms of the learned functions, making it harder to study their generalization properties on unseen datasets. Recent

Hanmo Li, Yuedong Wang, Mengyang Gu

This article introduces the sequential Kalman filter, a computationally scalable approach for online changepoint detection with temporally correlated data. The temporal correlation was not considered in the Bayesian online changepoint detection approach due to the large computational cost. Motivated by detecting COVID-19 infections for dialysis patients from

Sankar Davuluri, Greeshma Gopinath, Matt J. Woolley

The quantum illumination technique requires joint measurement between the idler and the probe reflected from the low-reflective target present in a noisy environment. The joint measurement is only possible with prior knowledge about the target's location. The technique in this article overcomes this limitation by using entanglement and a cross-correlated hom

Ying Zang, Chenglong Fu, Tianrun Chen, Yuanqi Hu

As 3D models become critical in today's manufacturing and product design, conventional 3D modeling approaches based on Computer-Aided Design (CAD) are labor-intensive, time-consuming, and have high demands on the creators. This work aims to introduce an alternative approach to 3D modeling by utilizing free-hand sketches to obtain desired 3D models. We introd

Maolin Wang, Xinjian Zhao, Wanyu Wang, Sheng Zhang

Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that convert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors, which can enhance the recommendation performance. Embedding techniqu

Nguyen V. H. Viet, Alaa Al Masri, Masaharu Nomachi, Marc-Andre Tétrault

The Light only Liquid Xenon (LoLX) experiment is a prototype detector aimed to study liquid xenon (LXe) light properties and various photodetection technologies. LoLX is also aimed to quantify LXe's time resolution as a potential scintillator for 10~ps time-of-flight (TOF) PET. Another key goal of LoLX is to perform a time-based separation of Cerenkov and sc

Kunlin Cai, Jinghuai Zhang, Zhiqing Hong, Will Shand

As location-based services (LBS) have grown in popularity, more human mobility data has been collected. The collected data can be used to build machine learning (ML) models for LBS to enhance their performance and improve overall experience for users. However, the convenience comes with the risk of privacy leakage since this type of data might contain sensit

Zhengyang Geng, J. Zico Kolter

Deep Equilibrium (DEQ) Models, an emerging class of implicit models that maps inputs to fixed points of neural networks, are of growing interest in the deep learning community. However, training and applying DEQ models is currently done in an ad-hoc fashion, with various techniques spread across the literature. In this work, we systematically revisit DEQs an

Chonggang Lu, Richong Zhang, Kai Sun, Jaein Kim

Document-level relation extraction (DocRE) involves identifying relations between entities distributed in multiple sentences within a document. Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities. However, there are two drawbacks in existing methods. On o

Wencong You, Zayd Hammoudeh, Daniel Lowd

Backdoor attacks manipulate model predictions by inserting innocuous triggers into training and test data. We focus on more realistic and more challenging clean-label attacks where the adversarial training examples are correctly labeled. Our attack, LLMBkd, leverages language models to automatically insert diverse style-based triggers into texts. We also pro

Hai Wu, Xu Chen, Kaibin Huang

Foundation models (FoMos), referring to large-scale AI models, possess human-like capabilities and are able to perform competitively in the domain of human intelligence. The breakthrough in FoMos has inspired researchers to deploy such models in the sixth-generation (6G) mobile networks for automating a broad range of tasks in next-generation mobile applicat

Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar

Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior: At each time, the algorithm observes an action chosen by a fallible agent, and decides whether to *accept* that agent's decision, *intervene* with an alternative, or *request* the expert's opinion. For instance, in clini

Debtanu Datta, Shubham Soni, Rajdeep Mukherjee, Saptarshi Ghosh

Automatic summarization of legal case judgments is a practically important problem that has attracted substantial research efforts in many countries. In the context of the Indian judiciary, there is an additional complexity -- Indian legal case judgments are mostly written in complex English, but a significant portion of India's population lacks command of t

Kaito Kayo

A statistical manifold is a pseudo-Riemannian manifold endowed with a Codazzi structure. This structure plays an important role in Information Geometry and its related fields, e.g., a statistical model admits this structure with the Fisher-Rao metric. In practical application, however, the metric may be degenerate, and then this geometric structure is not fu

Toan Nguyen, Kien Do, Bao Duong, Thin Nguyen

We propose a novel approach for domain generalisation (DG) leveraging risk distributions to characterise domains, thereby achieving domain invariance. In our findings, risk distributions effectively highlight differences between training domains and reveal their inherent complexities. In testing, we may observe similar, or potentially intensifying in magnitu

Daniil V. Smirnov, Aleksandr V. Mosenkov, Vladimir P. Reshetnikov

Polar-ring galaxies (PRGs) are an outstanding example of galaxies with misaligned kinematics where a typically red central galaxy is surrounded by a large-scale ring or disk of stars, gas and dust oriented almost perpendicular to the main body. It is believed that polar structures are formed in a secondary event after the assembly of a central galaxy, but du

Chao Li, Balaji Palanisamy, Runhua Xu, Li Duan

Delegated-Proof-of-Stake (DPoS) blockchains, such as EOSIO, Steem and TRON, are governed by a committee of block producers elected via a coin-based voting system. We recently witnessed the first de facto blockchain takeover that happened between Steem and TRON. Within one hour of this incident, TRON founder took over the entire Steem committee, forcing the o

Bharath Antarvedi Goda, David Labonte, Mattia Bacca

Cutting mechanics in soft solids have been a subject of study for several decades, an interest fuelled by the multitude of its applications, including material testing, manufacturing, and biomedical technology. Wire cutting is the simplest model system to analyze the cutting resistance of a soft material. However, even for this simple system, the complex fai

Ronit Karmakar, Umananda Dev Goswami

We introduce a modification in the energy-momentum conservation violating Rastall's theory of gravity and obtain a Reissner-Nordstr\"om-type black hole solution in spacetime surrounded by a cloud of strings and charge fields. We examine the horizons of the black hole along with the influence of the parameters of the model on it. The scalar quasinormal modes

Junghyun Lee, Hanseul Cho, Se-Young Yun, Chulhee Yun

Fair Principal Component Analysis (PCA) is a problem setting where we aim to perform PCA while making the resulting representation fair in that the projected distributions, conditional on the sensitive attributes, match one another. However, existing approaches to fair PCA have two main problems: theoretically, there has been no statistical foundation of fai

J. Griff-McMahon, S. Malko, V. Valenzuela-Villaseca, C. Walsh

Magnetic fields generated from a laser-foil interaction are measured with high fidelity using a proton radiography scheme with in situ x-ray fiducials. In contrast to prior findings under similar experimental conditions, this technique reveals the self-generated, Biermann-battery fields extend beyond the edge of the expanding plasma plume to a radius of over

Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar

Decision analysis deals with modeling and enhancing decision processes. A principal challenge in improving behavior is in obtaining a transparent description of existing behavior in the first place. In this paper, we develop an expressive, unifying perspective on inverse decision modeling: a framework for learning parameterized representations of sequential

Rishabh Tiwari, Durga Sivasubramanian, Anmol Mekala, Ganesh Ramakrishnan

Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks. This vulnerability is aggravated in distillation, where a student model may have lesser representational capacity than the corresponding teacher model. Often, knowledge of specific spurious correlations is used to reweight instances & rebalance the learni

Chiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia Rudin

We present ProtoConcepts, a method for interpretable image classification combining deep learning and case-based reasoning using prototypical parts. Existing work in prototype-based image classification uses a ``this looks like that'' reasoning process, which dissects a test image by finding prototypical parts and combining evidence from these prototypes to

Jan Gregorovič, David Sykes

We study CR hypersurfaces in $\mathbb{C}^4$ that are Levi degenerate with constant rank Levi form, and moreover finitely nondegenerate. Each of these can be described as a deformation of a model CR hypersurface by adding terms of higher natural weighted order to the model's defining equation. We obtain a complete normal form for models of real analytic unifo

Guang Yang, Yu Zhou, Xiangyu Zhang, Xiang Chen

Context: Pre-trained models (PTMs) have demonstrated significant potential in automatic code translation. However, the vulnerability of these models in translation tasks, particularly in terms of syntax, has not been extensively investigated. Objective: To fill this gap, our study aims to propose a novel approach CoTR to assess and improve the syntactic adve

Jung Hun Oh, Rena Elkin, Anish Kumar Simhal, Jiening Zhu

The Wasserstein distance from optimal mass transport (OMT) is a powerful mathematical tool with numerous applications that provides a natural measure of the distance between two probability distributions. Several methods to incorporate OMT into widely used probabilistic models, such as Gaussian or Gaussian mixture, have been developed to enhance the capabili

Oren Barkan, Yehonatan Elisha, Yuval Asher, Amit Eshel

We introduce Iterated Integrated Attributions (IIA) - a generic method for explaining the predictions of vision models. IIA employs iterative integration across the input image, the internal representations generated by the model, and their gradients, yielding precise and focused explanation maps. We demonstrate the effectiveness of IIA through comprehensive

P. Thongkonsing, P. Chainakun, T. Worrakitpoonpon, A. J. Young

Symbolic regression (SR) is a regression analysis based on genetic algorithms to search for mathematical expressions that best fit a given data set, by allowing the expressions themselves to mutate. We use the SR to analyze the parameter relations of the X-ray reverberating Active Galactic Nuclei (AGN) where the soft Fe-L lags were observed by XMM-Newton. Fi

Hao Wang, Euijoon Ahn, Lei Bi, Jinman Kim

The clinical diagnosis of skin lesion involves the analysis of dermoscopic and clinical modalities. Dermoscopic images provide a detailed view of the surface structures whereas clinical images offer a complementary macroscopic information. The visual diagnosis of melanoma is also based on seven-point checklist which involves identifying different visual attr

Pei Ge, Zhongqiang Zhang, Huan Lei

We present a data-driven method to learn stochastic reduced models of complex systems that retain a state-dependent memory beyond the standard generalized Langevin equation (GLE) with a homogeneous kernel. The constructed model naturally encodes the heterogeneous energy dissipation by jointly learning a set of state features and the non-Markovian coupling am

Neeraj Varshney, Agneet Chatterjee, Mihir Parmar, Chitta Baral

Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks; however, their large size makes their inference slow and computationally expensive. Focusing on this problem, we propose to instruction tune LLMs with additional explicit losses from the intermediate layers (LITE) and show that it enables these

V. A. Dzuba, V. V. Flambaum

We calculate hyperfine structure of $^{229}$Th and its ions (Th~IV, Th~III, Th~II, Th~I) to reveal the dependence of the nuclear clock frequency on the hyperfine interaction (hfi). We calculate first and second-order hfi shifts and demonstrate that due to the differences in the hyperfine structure for different ions and for the ground and isomeric nuclear st

Jiamin Liu, Guo Li, Xinxu Zhang, Jiahao Wei

The two-dimensional transition metal carbide/nitride family (MXenes) has garnered significant attention due to their highly customizable surface functional groups. Leveraging modern material science techniques, the customizability of MXenes can be enhanced further through the construction of associated heterostructures. As indicated by recent research, the M

Liujia Min, Aigen Li, Xianghong Chen, Yonghui Li

Cyclocarbon molecules are critical in understanding the carbon structure formation and the nature of the interaction between carbon atoms. In cyclocarbons, light elements such as H, O and N may interplay with rings to form doped cyclocarbon molecules. Such molecules show unique optical properties that have never been reported before. In this study, density f

Mingcheng Nie, Deepak Mishra, Azzam Al-nahari, Jinhong Yuan

This paper focuses on secure backscatter transmission in the presence of a passive multi-antenna eavesdropper through a symbiotic radio (SR) network. Specifically, a single-antenna backscatter device (BD) aims to transmit confidential information to a primary receiver (PR) by using a multi-antenna primary transmitter's (PT) signal, where the received symbols

Biswajit Bhowmick, Rohit Mahendra Shinde, Bhabani Prasad Mandal

We consider a modified version of the well-known 2d vdP oscillator with a new non-Hermitian interaction. The usual perturbative approach fails to provide the classical dynamics of the system as the classical solutions become divergent in the long time limit. These kinds of divergences are similar to what occurs in quantum field theory and critical phenomena.

Andrea Kunder, Zdenek Prudil, Kevin Covey, Joanne Hughes

The Milky Way Bulge extra-tidal star survey (MWBest) is a spectroscopic survey with the goal of identifying stripped globular cluster stars from inner Galaxy clusters. In this way, an indication of the fraction of metal-poor bulge stars that originated from globular clusters can be determined. We observed and analyzed stars in and around BH 261, an understud

Zheyuan Liu, Guangyao Dou, Yijun Tian, Chunhui Zhang

Machine Unlearning (MU) algorithms have become increasingly critical due to the imperative adherence to data privacy regulations. The primary objective of MU is to erase the influence of specific data samples on a given model without the need to retrain it from scratch. Accordingly, existing methods focus on maximizing user privacy protection. However, there

Mingcheng Nie, Shuangyang Li, Deepak Mishra

Orthogonal time frequency space (OTFS) has been widely acknowledged as a promising wireless technology for challenging transmission scenarios, including high-mobility channels. In this paper, we investigate the pilot design for the multi-user OTFS system based on the a priori statistical channel state information (CSI), where the practical threshold-based es

Xuelin Zhu, Yu Sun, Yumin Zeng, Cong Xu

This study aims to analyze the service and return landing areas in badminton men's double, based on data extracted from 20 badminton matches. We find that most services land near the center-line, while returns tend to land in the crossing areas of the serving team's court. Using generalized logit models, we are able to predict the return landing area based o

Gaurav Dhruv Goel

The space of all pencils of conics in the plane $\mathbb{P} V$ (where $\dim V = 3$) is a projective Grassmannian $\mathbb{G} (1, \mathbb{P} \mathrm{Sym}^2 V^*)$ and admits a natural $\mathrm{PGL}(V)$ action. It is a classical theorem that this action has exactly eight orbits, and in fact that the orbit of a pencil $\ell \subset \mathbb{P} \mathrm{Sym}^2 V^*$

B. V. Sorin

The Roelcke precompactness of transformation groups of discrete spaces and chains in the permutation topology and LOTS in the topology of pointwise convergence is studied. For ultratransitive actions compactifications of transformation groups using the Ellis construction are built.

Xiao Hu, Xiangsheng Chen

Grasping algorithms have evolved from planar depth grasping to utilizing point cloud information, allowing for application in a wider range of scenarios. However, data-driven grasps based on models trained on basic open-source datasets may not perform well on novel objects, which are often required in different scenarios, necessitating fine-tuning using new

Yuying Man, Nian Li, Zejun Xiang, Xiangyong Zeng

Boukerrou et al. (IACR Trans. Symmetric Cryptol. 2020(1), 331-362) introduced the notion of Feistel Boomerang Connectivity Table (FBCT), the Feistel counterpart of the Boomerang Connectivity Table (BCT), and the Feistel boomerang uniformity (which is the same as the second-order zero differential uniformity in even characteristic). FBCT is a crucial table fo

Shi-Shun Chen, Xiao-Yang Li, Wenrui Xie

A reasonable description of the degradation process is essential for credible reliability assessment in accelerated degradation testing. Existing methods usually use Markovian stochastic processes to describe the degradation process. However, degradation processes of some products are non-Markovian due to the interaction with environments. Misinterpretation

Dmitrii V. Prokhorov

Characterizations of the associated spaces and second associated spaces of the Hardy space on $\mathbb{R}^n$ are given. Some results on the associated spaces of the $\textrm{BMO}(\mathbb{R}^n)$ space are proved also.

Simon Foucart

This note is concerned with deterministic constructions of $m \times N$ matrices satisfying a restricted isometry property from $\ell_2$ to $\ell_1$ on $s$-sparse vectors. Similarly to the standard ($\ell_2$ to $\ell_2$) restricted isometry property, such constructions can be found in the regime $m \asymp s^2$, at least in theory. With effectiveness of imple

Sophia Sanborn, Nina Miolane

We introduce a general method for achieving robust group-invariance in group-equivariant convolutional neural networks ($G$-CNNs), which we call the $G$-triple-correlation ($G$-TC) layer. The approach leverages the theory of the triple-correlation on groups, which is the unique, lowest-degree polynomial invariant map that is also complete. Many commonly used

Tymon Słoczyński, S. Derya Uysal, Jeffrey M. Wooldridge

How should researchers adjust for covariates? We show that if the propensity score is estimated using a specific covariate balancing approach, inverse probability weighting (IPW), augmented inverse probability weighting (AIPW), and inverse probability weighted regression adjustment (IPWRA) estimators are numerically equivalent for the average treatment effec

Shuoyuan Wang, Jindong Wang, HuaJun Xi, Bob Zhang

Human Activity Recognition (HAR) models often suffer from performance degradation in real-world applications due to distribution shifts in activity patterns across individuals. Test-Time Adaptation (TTA) is an emerging learning paradigm that aims to utilize the test stream to adjust predictions in real-time inference, which has not been explored in HAR befor

Yutaka Yoshii

Let $G$ be a simply connected and simple algebraic group defined and split over a finite prime field $\mathbb{F}_p$ of $p$ elements. In this paper, using an $\mathbb{F}_p$-linear map splitting Frobenius endomorphism on a hyperalgebra relative to $G$, we obtain some $\mathbb{F}_p$-linear isomorphisms induced by multiplication in the hyperalgebra.

Mario Pasquato, Piero Trevisan, Abbas Askar, Pablo Lemos

Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained on GC simulations can in principle predict IMBH host candidates based on observable features. This approach has two limitations: first, an accurate ML model is expected to be a black box due to complexity; second, despi

Xitong Xu, Jia-Xin Yin, Zhe Qu, Shuang Jia

Kagome magnet has been found to be a fertile ground for the search of exotic quantum states in condensed matter. Arising from the unusual geometry, the quantum interactions in the kagome lattice give rise to various quantum states, including the Chern-gapped Dirac fermion, Weyl fermion, flat band and van Hove singularity. Here we review recent advances in th

Braedon Jones, Christiana Z. Suggs, Elena Krivyakina, Daniel Phelan

We present a detailed study of the local atomic and magnetic structure of the type-I multiferroic perovskite system (Sr,Ba)(Mn,Ti)O$_3$ using x-ray and neutron pair distribution function (PDF) analysis, polarized neutron scattering, and muon spin relaxation ($\mu$SR) techniques. The atomic PDF analysis reveals widespread nanoscale tetragonal distortions of t

Sergei Dyda, Shane W. Davis, Daniel Proga

We study AGN line driven disc winds using time-dependent radiation hydrodynamics. The key criterion for determining wind launching is the coupling strength of the UV radiation field via the spectral lines of the gas. The strength of these lines in turn relies crucially on the gas ionization state, determined by the local X-ray intensity. We consider a suite

Siyu Heng, Jiawei Zhang, Yang Feng

Design-based causal inference, also known as randomization-based or finite-population causal inference, is one of the most widely used causal inference frameworks, largely due to the merit that its validity can be guaranteed by study design (e.g., randomized experiments) and does not require assuming specific outcome-generating distributions or super-populat

Christos Tsirigotis, Joao Monteiro, Pau Rodriguez, David Vazquez

Empirical risk minimization (ERM) is sensitive to spurious correlations in the training data, which poses a significant risk when deploying systems trained under this paradigm in high-stake applications. While the existing literature focuses on maximizing group-balanced or worst-group accuracy, estimating these accuracies is hindered by costly bias annotatio

Junghyun Lee, Se-Young Yun, Kwang-Sung Jun

Logistic bandit is a ubiquitous framework of modeling users' choices, e.g., click vs. no click for advertisement recommender system. We observe that the prior works overlook or neglect dependencies in $S \geq \lVert \theta_\star \rVert_2$, where $\theta_\star \in \mathbb{R}^d$ is the unknown parameter vector, which is particularly problematic when $S$ is lar

Kristina Lerman, Dan Feldman, Zihao He, Ashwin Rao

Members of different political groups not only disagree about issues but also dislike and distrust each other. While social media can amplify this emotional divide -- called affective polarization by political scientists -- there is a lack of agreement on its strength and prevalence. We measure affective polarization on social media by quantifying the emotio

Xiangyun Lei, Weike Ye, Joseph Montoya, Tim Mueller

This paper introduces the Chemical Environment Modeling Theory (CEMT), a novel, generalized framework designed to overcome the limitations inherent in traditional atom-centered Machine Learning Force Field (MLFF) models, widely used in atomistic simulations of chemical systems. CEMT demonstrated enhanced flexibility and adaptability by allowing reference poi

Ummara Mumtaz, Awais Ahmed, Summaya Mumtaz

We aim to present a comprehensive overview of the latest advancements in utilizing Large Language Models (LLMs) within the healthcare sector, emphasizing their transformative impact across various medical domains. LLMs have become pivotal in supporting healthcare, including physicians, healthcare providers, and patients. Our review provides insight into the

Hao Wang

Human culture has evolved for thousands of years and thrived in the era of Internet. Due to the availability of big data, we could do research on human culture by analyzing its representation such as user item rating values on websites like MovieLens and Douban. Industrial workers have applied recommender systems in big data to predict user behavior and prom

Zhenyuan Zhang, Shaswat Mohanty, Jose Blanchet, Wei Cai

Recent studies have established a connection between the macroscopic mechanical response of polymeric materials and the statistics of the shortest path (SP) length between distant nodes in the polymer network. Since these statistics can be costly to compute and difficult to study theoretically, we introduce a branching random walk (BRW) model to describe the

Zhiqiang Gong, Xian Zhou, Wen Yao

Due to the powerful ability in capturing the global information, Transformer has become an alternative architecture of CNNs for hyperspectral image classification. However, general Transformer mainly considers the global spectral information while ignores the multiscale spatial information of the hyperspectral image. In this paper, we propose a multiscale sp

Zhiqiang Gong, Xian Zhou, Wen Yao

Convolutional neural networks (CNNs) have been demonstrated their powerful ability to extract discriminative features for hyperspectral image classification. However, general deep learning methods for CNNs ignore the influence of complex environmental factor which enlarges the intra-class variance and decreases the inter-class variance. This multiplies the d

Ana Rosalía Huamán Reyna, Alex Josué Flórez Farfán, Geraldo Pereira Rocha Filho, Sandra Sampaio

Currently, there are computer vision systems that help us with tasks that would be dull for humans, such as surveillance and vehicle tracking. An important part of this analysis is to identify traffic anomalies. An anomaly tells us that something unusual has happened, in this case on the highway. This paper aims to model vehicle tracking using computer visio

Lequn Chen, Zihao Ye, Yongji Wu, Danyang Zhuo

Low-rank adaptation (LoRA) has become an important and popular method to adapt pre-trained models to specific domains. We present Punica, a system to serve multiple LoRA models in a shared GPU cluster. Punica contains a new CUDA kernel design that allows batching of GPU operations for different LoRA models. This allows a GPU to hold only a single copy of the

K. R. Fast, J. E. Losby, G. Hajisalem, P. E. Barclay

The Einstein-de Haas (EdH) effect is a fundamental, mechanical consequence of any temporal change of magnetism in an object. EdH torque results from conserving the object's total angular momentum: the angular momenta of all the specimen's magnetic moments, together with its mechanical angular momentum. Although the EdH effect is usually small and difficult t

Yuanyuan Lei, Ruihong Huang

Conspiracy theories, as a type of misinformation, are narratives that explains an event or situation in an irrational or malicious manner. While most previous work examined conspiracy theory in social media short texts, limited attention was put on such misinformation in long news documents. In this paper, we aim to identify whether a news article contains c

Hoang Ky Nguyen, Francisco S. N. Lobo

The recently obtained $\textit{special}$ Buchdahl-inspired metric [Phys. Rev. D 107, 104008 (2023)] describes asymptotically flat spacetimes in pure Ricci-squared gravity. The metric depends on a new (Buchdahl) parameter $\tilde{k}$ of higher-derivative characteristic, and reduces to the Schwarzschild metric, for $\tilde{k}=0$. For the case $\tilde{k}\in(-1,

Yuanyuan Lei, Ruihong Huang

Propaganda is a form of deceptive narratives that instigate or mislead the public, usually with a political purpose. In this paper, we aim to identify propaganda in political news at two fine-grained levels: sentence-level and token-level. We observe that propaganda content is more likely to be embedded in sentences that attribute causality or assert contras

Taha Ameen, Bruce Hajek

Two models are introduced to investigate graph matching in the presence of corrupt nodes. The weak model, inspired by biological networks, allows one or both networks to have a positive fraction of molecular entities interact randomly with their network. For this model, it is shown that no estimator can correctly recover a positive fraction of the corrupt no

Shibal Ibrahim, Kayhan Behdin, Rahul Mazumder

We propose a new optimization-based approach for feature selection in tree ensembles, an important problem in statistics and machine learning. Popular tree ensemble toolkits e.g., Gradient Boosted Trees and Random Forests support feature selection post-training based on feature importance scores, while very popular, they are known to have drawbacks. We propo

Suiyao Chen, Jing Wu, Naira Hovakimyan, Handong Yao

Representation learning stands as one of the critical machine learning techniques across various domains. Through the acquisition of high-quality features, pre-trained embeddings significantly reduce input space redundancy, benefiting downstream pattern recognition tasks such as classification, regression, or detection. Nonetheless, in the domain of tabular

David Baraglia

The mapping class group $M(X)$ of a smooth manifold $X$ is the group of smooth isotopy classes of orientation preserving diffeomorphisms of $X$. We prove a number of results about the mapping class groups of compact, simply-connected, smooth $4$-manifolds. We prove that $M(X)$ is non-finitely generated for $X = 2n \mathbb{CP}^2 # 10n \overline{\mathbb{CP}^2}

Lana L. Blaschke, Da Nian, Bathiany, Maya Ben-Yami

The Amazon rainforest (ARF) is threatened by deforestation and climate change, which could trigger a regime shift to a savanna-like state. Previous work suggesting declining resilience in recent decades was based only on local resilience indicators. Moreover, previous results are potentially biased by the employed multi-sensor and optical satellite data and

Wu-Rong Jian, Mian Xiao, WaiChing Sun, Wei Cai

A yield surface of a material is a set of critical stress conditions beyond which macroscopic plastic deformation begins. For crystalline solids, plastic deformation occurs through the motion of dislocations, which can be captured by discrete dislocation dynamics (DDD) simulations. In this paper, we predict the yield surfaces and strain-hardening behaviors u

Mohammadreza Pourreza, Davood Rafiei

Text-to-SQL benchmarks play a crucial role in evaluating the progress made in the field and the ranking of different models. However, accurately matching a model-generated SQL query to a reference SQL query in a benchmark fails for various reasons, such as underspecified natural language queries, inherent assumptions in both model-generated and reference que

Subhajit Sahu

PageRank is a widely used algorithm for ranking webpages and plays a significant role in determining web traffic. This study employs the Gini coefficient, a measure of income/wealth inequality, to assess the inequality in PageRank distributions and explores six deterministic methods for reducing inequality. Our findings indicate that a combination of two dis

Zhengxin Wang, Daniel B. Rowe, Xinyi Li, D. Andrew Brown

Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. In contrast, research suggests that analyzing both real and imaginary components of the complex-valued fMRI (cv-fMRI) sig

Yifan Hu, Jie Wang, Yao Xie, Andreas Krause

We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on some contextual information and the upper-level decision variable. This framework extends classical stochastic bilevel optimization when the lower-level decision maker responds opt

Vaisakh Shaj, Saleh Gholam Zadeh, Ozan Demir, Luiz Ricardo Douat

Intelligent agents use internal world models to reason and make predictions about different courses of their actions at many scales. Devising learning paradigms and architectures that allow machines to learn world models that operate at multiple levels of temporal abstractions while dealing with complex uncertainty predictions is a major technical hurdle. In

Tong Lu, Yuan Zhang, Vince Lyzinski, Chuan Bi

The joint analysis of multimodal neuroimaging data is critical in the field of brain research because it reveals complex interactive relationships between neurobiological structures and functions. In this study, we focus on investigating the effects of structural imaging (SI) features, including white matter micro-structure integrity (WMMI) and cortical thic

Mohammad Mahdi Mohajer, Reem Aleithan, Nima Shiri Harzevili, Moshi Wei

We introduce SkipAnalyzer, a large language model (LLM)-powered tool for static code analysis. SkipAnalyzer has three components: 1) an LLM-based static bug detector that scans source code and reports specific types of bugs, 2) an LLM-based false-positive filter that can identify false-positive bugs in the results of static bug detectors (e.g., the result of

Ethan Weinberger, Ian Covert, Su-In Lee

Contrastive analysis (CA) refers to the exploration of variations uniquely enriched in a target dataset as compared to a corresponding background dataset generated from sources of variation that are irrelevant to a given task. For example, a biomedical data analyst may wish to find a small set of genes to use as a proxy for variations in genomic data only pr

Thijmen B. Kroeze, David Fernandez Rivas, Miguel A. Quetzeri-Santiago

Studies of liquid jet impacts onto a deep liquid pool are of great significance for a multitude of engineering and environmental applications. During jet impact, the free surface of the pool deforms and a cavity is generated. Simultaneously, the free surface of the cavity extends radially outward and forms a rim. Eventually the cavity collapses by means of g

Haowen Zhou, Brandon Y. Feng, Haiyun Guo, Siyu Lin

Image stacks provide invaluable 3D information in various biological and pathological imaging applications. Fourier ptychographic microscopy (FPM) enables reconstructing high-resolution, wide field-of-view image stacks without z-stack scanning, thus significantly accelerating image acquisition. However, existing FPM methods take tens of minutes to reconstruc

Maksim Tomchenko

A nonuniform condensate is usually described by the Gross-Pitaevskii (GP) equation, which is derived with the help of the c-number ansatz $\hat{ \Psi}(\mathbf{r},t)=\Psi (\mathbf{r},t)$. Proceeding from a more accurate operator ansatz $\hat{\Psi}(\mathbf{r},t)=\hat{a}_{0}\Psi (\mathbf{r},t) \sqrt{N}$, we find the equation $i\hbar \frac{\partial \Psi (\mathbf

Tong Lu, Chixiang Chen, Hsin-Hsiung Huang, Peter Kochunov

Missingness is a common issue for neuroimaging data, and neglecting it in downstream statistical analysis can introduce bias and lead to misguided inferential conclusions. It is therefore crucial to conduct appropriate statistical methods to address this issue. While multiple imputation is a popular technique for handling missing data, its application to neu

Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar

We propose a general class of sample based explanations of machine learning models, which we term generalized representers. To measure the effect of a training sample on a model's test prediction, generalized representers use two components: a global sample importance that quantifies the importance of the training point to the model and is invariant to test

Nicolás Adrián Nuñez Barreto, Cecilia Cormick, Christian Tomás Schmiegelow

We present experimental results and a theoretical model that illustrate how competing eigenbases can determine the dynamics of a fluorescing atom. In the absence of a magnetic field, the atom can get trapped in a dark state, which inhibits fluorescence. In general, this will happen when the magnetic degeneracy of the ground state is greater than the one of t

Gastón Blatter, Martín Sirena, Yeonkyu Lee, Jeehoon Kim

We report on the impact of the magnetic domain stripe configuration on the critical velocity of vortices in superconducting/ferromagnetic bilayers. Using a 23 nm thick Mo$_2$N film, covered by a 48 nm FePt layer with tunable nanosized striped domains, we demonstrate that flux instability at low magnetic fields depends on the orientation of the stripes. When

Lukas Fuchs, Tom Kirstein, Christoph Mahr, Orkun Furat

The structural characterization of hetero-aggregates in 3D is of great interest, e.g., for deriving process-structure or structure-property relationships. However, since 3D imaging techniques are often difficult to perform as well as time and cost intensive, a characterization of hetero-aggregates based on 2D image data is desirable, but often non-trivial. T

Igor Arrieta

There are a number of localic separation axioms which are roughly analogous to the $T_1$-axiom from classical topology. For instance, besides the well-known subfitness and fitness, there are also Rosicky-Smarda's $T_1$-locales, totally unordered locales and, more categorically, the recently introduced $\mathcal{F}$-separated locales (i.e., those with a fitte

Vassilis Papadopoulos

In this thesis, I study Interface Conformal Field Theories (ICFT) and their holographic dual, which is composed of two asymptotically Anti-de-Sitter (AdS) spaces glued through a thin gravitating membrane. I restrict the study to simple minimal models, which allow for analytic control while providing universally applicable results. The analysis is set in 2D I

Paul Musial, Valentin A. Skvortsov, Piotr Sworowski, Francesco Tulone

It is proved that any function of a Lusin-type class, the class of $ACG_r$-functions, is differentiable almost everywhere in the sense of a derivative defined in the space~$L^r$, $1\le r<\infty$. This leads to obtaining a full descriptive characterization of a Henstock-Kurzweil-type integral, the $HK_r$-integral, which serves to recover functions from their