May 2023 arXiv papers — page 41

Federico Lelli

I review methods and techniques to build mass models of disk galaxies from gas dynamics. I focus on two key steps: (1) the derivation of rotation curves using 3D emission-line datacubes from HI, CO, and/or H-alpha observations, and (2) the calculation of the gravitational field from near-infrared images and emission-line maps, tracing the stellar and gas mas

Attila Szabó, Gøran J. Nilsen

Motivated by the classical spin-nematic state observed in the breathing pyrochlore spinel LiGa$_{0.95}$In$_{0.05}$Cr$_4$O$_8$, we theoretically discuss spin dynamics in models of spin-lattice coupling in these materials. Semiclassical dynamical simulations successfully recover the key features of inelastic neutron-scattering experiments on LiGa$_{0.95}$In$_{

Fedor K. Popov, Grigory Tarnopolsky

We consider a configuration of three stacked graphene monolayers with commensurate twist angles $\theta_{12}/\theta_{23}=p/q$, where $p$ and $q$ are coprime integers with $0<p<|q|$ and $q$ can be positive or negative. We study this system using the continuum model in the chiral limit when interlayer coupling terms between $\textrm{AA}_{12}$ and $\textrm{AA}_

Bartlomiej Czech, Jan de Boer, Ricardo Espíndola, Bahman Najian

We present a new perspective on bulk reconstruction using Berry phases in the boundary CFT. Our parallel transport of modular Hamiltonians is associated to a trajectory in the space of states, which we obtain from the insertion of a source in the Euclidean path integral. Using a modular version of the extrapolate dictionary and the equivalence between modula

Bhaskar Dutta, Doojin Kim, Hyunyong Kim

We propose a novel scheme for performing a beam-dump-like experiment with the general-purpose detectors (ATLAS and CMS) at the LHC. Collisions of high-energy protons result in jets containing a number of energetic hadrons and electromagnetic objects that are essentially "dumped" to hadronic and electromagnetic calorimeters, respectively, and induce the produ

Christopher T. Olund, Norman Y. Yao, Jack Kemp

Strong zero modes are edge-localized degrees of freedom capable of storing information at infinite temperature, even in systems with no disorder. To date, their stability has only been systematically explored at the physical edge of a system. Here, we extend the notion of strong zero modes to the boundary between two systems, and present a unifying framework

Shihao Zhao, Dongdong Chen, Yen-Chun Chen, Jianmin Bao

Text-to-Image diffusion models have made tremendous progress over the past two years, enabling the generation of highly realistic images based on open-domain text descriptions. However, despite their success, text descriptions often struggle to adequately convey detailed controls, even when composed of long and complex texts. Moreover, recent studies have al

Dor Verbin, Ben Mildenhall, Peter Hedman, Jonathan T. Barron

Decomposing an object's appearance into representations of its materials and the surrounding illumination is difficult, even when the object's 3D shape is known beforehand. This problem is especially challenging for diffuse objects: it is ill-conditioned because diffuse materials severely blur incoming light, and it is ill-posed because diffuse materials und

Yunpeng Ji, Jianyi Chen, Grant L. Schumacher, Gabriel G. T. Assumpção

We report the observation of the quantum Joule-Thomson (JT) effect in ideal and unitary Fermi gases. We study the temperature dynamics of these systems while they undergo an energy-per-particle conserving rarefaction. For scale-invariant systems, whose equations of state satisfy the relation $U\propto PV$, this rarefaction conserves the specific enthalpy, wh

Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu

This paper introduces a novel mathematical property applicable to diverse images, referred to as FINOLA (First-Order Norm+Linear Autoregressive). FINOLA represents each image in the latent space as a first-order autoregressive process, in which each regression step simply applies a shared linear model on the normalized value of its immediate neighbor. This i

Xizhou Zhu, Yuntao Chen, Hao Tian, Chenxin Tao

The captivating realm of Minecraft has attracted substantial research interest in recent years, serving as a rich platform for developing intelligent agents capable of functioning in open-world environments. However, the current research landscape predominantly focuses on specific objectives, such as the popular "ObtainDiamond" task, and has not yet shown ef

Shilin Yan, Renrui Zhang, Ziyu Guo, Wenchao Chen

Recently, video object segmentation (VOS) referred by multi-modal signals, e.g., language and audio, has evoked increasing attention in both industry and academia. It is challenging for exploring the semantic alignment within modalities and the visual correspondence across frames. However, existing methods adopt separate network architectures for different m

Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh

Diffusion models are powerful generative models but suffer from slow sampling, often taking 1000 sequential denoising steps for one sample. As a result, considerable efforts have been directed toward reducing the number of denoising steps, but these methods hurt sample quality. Instead of reducing the number of denoising steps (trading quality for speed), in

Renan A. Rojas-Gomez, Teck-Yian Lim, Minh N. Do, Raymond A. Yeh

For computer vision, Vision Transformers (ViTs) have become one of the go-to deep net architectures. Despite being inspired by Convolutional Neural Networks (CNNs), ViTs' output remains sensitive to small spatial shifts in the input, i.e., not shift invariant. To address this shortcoming, we introduce novel data-adaptive designs for each of the modules in Vi

Jiahui Lei, Congyue Deng, Bokui Shen, Leonidas Guibas

We propose Neural 3D Articulation Prior (NAP), the first 3D deep generative model to synthesize 3D articulated object models. Despite the extensive research on generating 3D objects, compositions, or scenes, there remains a lack of focus on capturing the distribution of articulated objects, a common object category for human and robot interaction. To generat

Congyue Deng, Jiahui Lei, Bokui Shen, Kostas Daniilidis

Equivariance has gained strong interest as a desirable network property that inherently ensures robust generalization. However, when dealing with complex systems such as articulated objects or multi-object scenes, effectively capturing inter-part transformations poses a challenge, as it becomes entangled with the overall structure and local transformations.

Sanjib Kumar Das, Bitan Roy

Traditional topological materials belong to different Altland-Zirnbauer symmetry classes (AZSCs) depending on their non-spatial symmetries. Here we introduce the notion of hybrid symmetry class topological insulators (HSCTIs): A fusion of two different AZSC topological insulators (TIs) such that they occupy orthogonal Cartesian hyperplanes and their universa

Carlos Rodriguez-Pardo, Henar Dominguez-Elvira, David Pascual-Hernandez, Elena Garces

We propose a learning-based method to recover normals, specularity, and roughness from a single diffuse image of a material, using microgeometry appearance as our primary cue. Previous methods that work on single images tend to produce over-smooth outputs with artifacts, operate at limited resolution, or train one model per class with little room for general

Omri Avrahami, Kfir Aberman, Ohad Fried, Daniel Cohen-Or

Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on the case of learning a single concept from multiple images with variations in backgrounds and poses, and struggle when adapted to a different scenario. In this work, we introduce t

Yu Zeng, Mo Zhou, Yuan Xue, Vishal M. Patel

Recent advances in deep generative models have led to the development of methods capable of synthesizing high-quality, realistic images. These models pose threats to society due to their potential misuse. Prior research attempted to mitigate these threats by detecting generated images, but the varying traces left by different generative models make it challe

Murtaza Dalal, Ajay Mandlekar, Caelan Garrett, Ankur Handa

Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error. However, common methods of data collection, such as human supervision, scale poorly, as they are time-consuming and labor-intensive. In contrast, Task and Motion Planning (TAMP) can a

Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik

It is well-known that real-world changes constituting distribution shift adversely affect model performance. How to characterize those changes in an interpretable manner is poorly understood. Existing techniques to address this problem take the form of shift explanations that elucidate how to map samples from the original distribution toward the shifted one

Jay Gala, Pranjal A. Chitale, Raghavan AK, Varun Gumma

India has a rich linguistic landscape with languages from 4 major language families spoken by over a billion people. 22 of these languages are listed in the Constitution of India (referred to as scheduled languages) are the focus of this work. Given the linguistic diversity, high-quality and accessible Machine Translation (MT) systems are essential in a coun

Nick Rekuski

Using techniques from Bridgeland stability, we show the kernel sheaf associated to sufficiently positive Gieseker stable sheaf on a Del Pezzo surface is slope stable. This is the first effective stability result for kernel sheaves associated to higher rank sheaves on surfaces and the first stability result for kernel sheaves associated to Gieseker stable she

DinhDuy Vu, Sankar Das Sarma

We obtain the numerical ground state of a one-dimensional ladder model with the upper and lower chains occupied by spatially-separated electrons and holes, respectively. Under charge neutrality, we find that the excitonic bound states are always formed, i.e., no finite regime of decoupled electron and hole plasma exists at zero temperature. The system either

Zheyuan Liu, Weixuan Sun, Damien Teney, Stephen Gould

Composed image retrieval aims to find an image that best matches a given multi-modal user query consisting of a reference image and text pair. Existing methods commonly pre-compute image embeddings over the entire corpus and compare these to a reference image embedding modified by the query text at test time. Such a pipeline is very efficient at test time si

Tzvika Geft

Multi-Agent Path Finding (MAPF) is a fundamental motion coordination problem arising in multi-agent systems with a wide range of applications. The problem's intractability has led to extensive research on improving the scalability of solvers for it. Since optimal solvers can struggle to scale, a major challenge that arises is understanding what makes MAPF ha

Shivanshu Gupta, Yoshitomo Matsubara, Ankit Chadha, Alessandro Moschitti

While impressive performance has been achieved on the task of Answer Sentence Selection (AS2) for English, the same does not hold for languages that lack large labeled datasets. In this work, we propose Cross-Lingual Knowledge Distillation (CLKD) from a strong English AS2 teacher as a method to train AS2 models for low-resource languages in the tasks without

Matthew Chang, Aditya Prakash, Saurabh Gupta

The analysis and use of egocentric videos for robotic tasks is made challenging by occlusion due to the hand and the visual mismatch between the human hand and a robot end-effector. In this sense, the human hand presents a nuisance. However, often hands also provide a valuable signal, e.g. the hand pose may suggest what kind of object is being held. In this

Amirkeivan Mohtashami, Martin Jaggi

While Transformers have shown remarkable success in natural language processing, their attention mechanism's large memory requirements have limited their ability to handle longer contexts. Prior approaches, such as recurrent memory or retrieval-based augmentation, have either compromised the random-access flexibility of attention (i.e., the capability to sel

Yehor Avdieiev, Alexander Tsymbaliuk

We generalize an algorithm of Leclerc describing explicitly the bijection of Lalonde-Ram from finite to affine Lie algebras. In type $A_n^{(1)}$, we compute all affine standard Lyndon words for any order of the simple roots, and establish some properties of the induced orders on the positive affine roots.

Abdul Zalloum

Relying on work of Caprace and Sageev \cite{capracesageev:rank}, we provide an effective form of rank rigidity in the context of groups virtually acting freely cocompactly on a CAT(0) cube complex with a factor system. We accomplish this by exhibiting a special pair of hyperplanes that can be skewered uniformly quickly. Furthermore, for virtually special com

Yutong He, Xinmeng Huang, Kun Yuan

Communication compression is a common technique in distributed optimization that can alleviate communication overhead by transmitting compressed gradients and model parameters. However, compression can introduce information distortion, which slows down convergence and incurs more communication rounds to achieve desired solutions. Given the trade-off between

Yury Demidovich, Grigory Malinovsky, Igor Sokolov, Peter Richtárik

Stochastic Gradient Descent (SGD) is arguably the most important single algorithm in modern machine learning. Although SGD with unbiased gradient estimators has been studied extensively over at least half a century, SGD variants relying on biased estimators are rare. Nevertheless, there has been an increased interest in this topic in recent years. However, e

Chia-Wen Kuo, Zsolt Kira

A great deal of progress has been made in image captioning, driven by research into how to encode the image using pre-trained models. This includes visual encodings (e.g. image grid features or detected objects) and more recently textual encodings (e.g. image tags or text descriptions of image regions). As more advanced encodings are available and incorporat

Johannes Alt, Raphael Ducatez, Antti Knowles

We analyse the eigenvectors of the adjacency matrix of the Erd\H{o}s-R\'enyi graph $\mathbb G(N,d/N)$ for $\sqrt{\log N} \ll d \lesssim \log N$. We show the existence of a localized phase, where each eigenvector is exponentially localized around a single vertex of the graph. This complements the completely delocalized phase previously established in [arXiv:2

Cristian Armendariz-Picon, Alberto Diez-Tejedor

We revisit the cosmic evolution of the energy density of a quantized free scalar field and assess under what conditions the particle production and classical field approximations reproduce its correct value. Because the unrenormalized energy-momentum tensor diverges in the ultraviolet, it is necessary to frame our discussion within an appropriate regularizat

Maksym Andriushchenko, Dara Bahri, Hossein Mobahi, Nicolas Flammarion

Sharpness-aware minimization (SAM) is a recently proposed method that minimizes the sharpness of the training loss of a neural network. While its generalization improvement is well-known and is the primary motivation, we uncover an additional intriguing effect of SAM: reduction of the feature rank which happens at different layers of a neural network. We sho

Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar

We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code fo

Lisa Dunlap, Alyssa Umino, Han Zhang, Jiezhi Yang

Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen in training data can be used with large v

Simon Haeger, Akram Najjar, Caner Bektas, Dien Lessy

The 6G standard aims to be an integral part of the future economy by providing high-performance communication and sensing services. At terahertz (THz) frequencies, indoor campus networks can offer the highest sensing quality. Health monitoring in hospitals is expected to be an application site for these. This work outlines a monostatic phase-based system for

Yu-Tao Chen, Hai-Jun Tian, Min Fang, Xiao-Xiong Zuo

GW~Ori is a young hierarchical triple system located in $\lambda$ Orionis, consisting of a binary (GW~Ori\,A and B), a tertiary star (GW~Ori\,C) and a rare circumtriple disk. Due to the limited data with poor accuracy, several short-period signals were detected in this system, but the values from different studies are not fully consistent. As one of the most

Wangyou Zhang, Yanmin Qian

Self-supervised learning (SSL) based speech pre-training has attracted much attention for its capability of extracting rich representations learned from massive unlabeled data. On the other hand, the use of weakly-supervised data is less explored for speech pre-training. To fill this gap, we propose a weakly-supervised speech pre-training method based on spe

Pascal Häbig, Daniel Dittler, Maximilian Fey, Timo Müller

Power-to-X (PtX) products constitute a promising solution component in the defossilisation of hard-to-abate sectors. Where direct electrification is not possible, they can find application as chemical feedstock, easy to transport energy carriers and storage. In this contribution, a modular system architecture for a highly automated operation of an offshore o

Ahmed Khaled, Konstantin Mishchenko, Chi Jin

This paper proposes a new easy-to-implement parameter-free gradient-based optimizer: DoWG (Distance over Weighted Gradients). We prove that DoWG is efficient -- matching the convergence rate of optimally tuned gradient descent in convex optimization up to a logarithmic factor without tuning any parameters, and universal -- automatically adapting to both smoo

Guangyao Zhai, Evin Pınar Örnek, Shun-Cheng Wu, Yan Di

Controllable scene synthesis aims to create interactive environments for various industrial use cases. Scene graphs provide a highly suitable interface to facilitate these applications by abstracting the scene context in a compact manner. Existing methods, reliant on retrieval from extensive databases or pre-trained shape embeddings, often overlook scene-obj

Yi-Hsuan Lin, Philipp Zimmermann

The main purpose of this article is the study of an inverse problem for nonlocal porous medium equations (NPMEs) with a linear absorption term. More concretely, we show that under certain assumptions on the time-independent coefficients $\rho,q$ and the time-independent kernel $K$ of the nonlocal operator $L_K$, the (partial) Dirichlet-to-Neumann map uniquel

Georgios Chara-Lambous

It has long been said that the theories of Galois and Tannakian categories over a field $k$ are just ``formally similar''. With this note I will argue that this is in fact not the case: not only do Tannakian categories generalize Galois categories, but in fact the latter sit inside the former in a rather structured way.

Joe Davighi, Ben A. Stefanek

The flavour puzzle is one of the greatest mysteries in particle physics. A `flavour deconstruction' of the electroweak gauge symmetry, by promoting at least part of it to the product of a third family factor (under which the Higgs is charged) times a light family factor, allows one to address the flavour puzzle at a low scale due to accidentally realised $U(

Frank J. Masci, Russ R. Laher, Benjamin Rusholme, David Shupe

We describe the Zwicky Transient Facility (ZTF) Forced Photometry Service (ZFPS) as developed and maintained by the ZTF Science Data System Team at IPAC/Caltech. The service is open for public use following a subscription. The ZFPS has been operational since early 2020 and has been used to generate publication quality lightcurves for a myriad of science prog

Sukhpal Singh Gill, Minxian Xu, Panos Patros, Huaming Wu

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector

Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu

Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and then improve the models based on the learned reward function. Even though relatively simple approaches (e.g., rejection sampling based on reward scores) have been investigated, fine-t

Alvise Raccanelli, Zvonimir Vlah

Forthcoming galaxy surveys will provide measurements of galaxy clustering with an unprecedented level of precision, that will require comparably good accuracy. Current models for galaxy correlations rely on approximations and idealizations that might be inadequate for ultra precise measurements. On the other hand, exact calculations have proven to be computa

Jacopo Bassi

Given an action of a discrete countable group $G$ on a countable set $\mathfrak{X}$, it is studied the relationship between properties of the associated Calkin representation and the dynamics of the group action on the boundary of the Stone-\v{C}ech compactification of $\mathfrak{X}$. The first section contains results about amenability properties of actions

Kevin Uhl, Daniel Hackenbeck, Janis Peter, Reinhold Kleiner

Nonlinear microwave circuits are key elements for many groundbreaking research directions and technologies, such as quantum computation and quantum sensing. The majority of microwave circuits with Josephson nonlinearities to date is based on aluminum thin films, and therefore they are severely restricted in their operation range regarding temperatures and ex

Chetan Joshi, Lawry Sorenson, Ammon Wolfert, Mark Clement

Progress in Automated Handwriting Recognition has been hampered by the lack of large training datasets. Nearly all research uses a set of small datasets that often cause models to overfit. We present CENSUS-HWR, a new dataset consisting of full English handwritten words in 1,812,014 gray scale images. A total of 1,865,134 handwritten texts from a vocabulary

Zacharia Issa, Blanka Horvath, Maud Lemercier, Cristopher Salvi

Neural SDEs are continuous-time generative models for sequential data. State-of-the-art performance for irregular time series generation has been previously obtained by training these models adversarially as GANs. However, as typical for GAN architectures, training is notoriously unstable, often suffers from mode collapse, and requires specialised techniques

Hongtao Hu, Tobias Flöry, Vinzenz Stummer, Audrius Pugzlys

We present a novel approach to achieve hyper spectral resolution, high sensitive detection, and high speed data acquisition Stimulated Raman Spectroscopy by employing amplified offset-phase controlled fs-pulse bursts. In this approach, the Raman-shift spectrum is obtained through the direct mapping between the bursts offset phase and the Raman-shift frequenc

Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin Vechev

Collaborative learning techniques have the potential to enable training machine learning models that are superior to models trained on a single entity's data. However, in many cases, potential participants in such collaborative schemes are competitors on a downstream task, such as firms that each aim to attract customers by providing the best recommendations

Jonathan Hanselman

To a nullhomologous knot $K$ in a 3-manifold $Y$, knot Floer homology associates a bigraded chain complex over $\mathbb{F}[U,V]$ as well as a collection of flip maps; we show that this data can be interpretted as a collection of decorated immersed curves in the marked torus. This is inspired by earlier work of the author with Rasmussen and Watson, showing th

Uzu Lim

We characterise high-dimensional topology that arises from a random Cech complex constructed on the circle. Expected Euler characteristic curve is computed, where we observe limiting spikes. The spikes correspond to expected Betti numbers growing arbitrarily large over shrinking intervals of filtration radii. Using the fact that the homotopy type of the rand

Shady Abu-Hussein, Raja Giryes

Denoising Diffusion Probabilistic Models (DDPM) have recently gained significant attention. DDPMs compose a Markovian process that begins in the data domain and gradually adds noise until reaching pure white noise. DDPMs generate high-quality samples from complex data distributions by defining an inverse process and training a deep neural network to learn th

Dan Comănescu

The steady states of an antitone electric system are described by an antitone function with respect to the componentwise order. When this function is bounded from below by a positive vector, it has only one fixed point. This fixed point is attractive for the fixed point iteration method. In the general case, we find existence and uniqueness results of fixed

Robert Haslhofer

In this paper, we introduce a new method to establish existence of geometric flows with surgery. In contrast to all prior constructions of flows with surgery in the literature our new approach does not require any a priori estimates in the smooth setting. Instead, our approach is based on a hybrid compactness theorem, which takes smooth limits near the surge

Roberto Barrio, Santiago Ibáñez, Lucía Pérez

Inspecting a $p$-dimensional parameter space by means of $(p-1)$-dimensional slices, changes can be detected that are only determined by the geometry of the manifolds that compose the bifurcation set. We refer to these changes as geometric bifurcations. They can be understood within the framework of the theory of singularities for differentiable mappings and

Wuwei Lan, Zhiguo Wang, Anuj Chauhan, Henghui Zhu

A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures. To comprehensively evaluate text-to-SQL systems, we introduce a UNIfied benchmark for Text-to-SQL Evaluation (UNITE). It is composed of publicly available text-to-SQL datasets, containing natural lang

Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao

The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we ru

Lingwei Meng, Jiawen Kang, Mingyu Cui, Haibin Wu

Multi-talker overlapped speech poses a significant challenge for speech recognition and diarization. Recent research indicated that these two tasks are inter-dependent and complementary, motivating us to explore a unified modeling method to address them in the context of overlapped speech. A recent study proposed a cost-effective method to convert a single-t

Xinran Zhu, Bodong Chen

In Computer-Supported Collaborative Learning, ideas generated through collaborative discourse are informative indicators of students' learning and collaboration. Idea creation is a product of emergent and interactive socio-cognitive endeavors. Therefore, analyzing ideas requires capturing contextual information in addition to the ideas themselves. In this pa

Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli

We propose a new class of generative models that naturally handle data of varying dimensionality by jointly modeling the state and dimension of each datapoint. The generative process is formulated as a jump diffusion process that makes jumps between different dimensional spaces. We first define a dimension destroying forward noising process, before deriving

Colin Wilkins, Vassilis Angelopoulos, Andrei Runov, Anton Artemyev

Utilizing observations from the ELFIN satellites, we present a statistical study of $\sim$2000 events in 2019-2020 characterizing the occurrence in magnetic local time (MLT) and latitude of $\geq$50 keV electron isotropy boundaries (IBs) at Earth, and the dependence of associated precipitation on geomagnetic activity. The isotropy boundary for an electron of

Dimitrios Tsirmpas, Ioannis Gkionis, Georgios Th. Papadopoulos, Ioannis Mademlis

The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural Language Processing (NLP) during the past decade. However, the demands of long document analysis are quite different from those of shorter texts, while the ever increasing size of documents uploaded online renders automated understanding of lengthy texts a critical issue. Relevant appl

Tara Abrishami, Bogdan Alecu, Maria Chudnovsky, Sepehr Hajebi

The tree-independence number tree-$\alpha$, first defined and studied by Dallard, Milani\v{c} and \v{S}torgel, is a variant of treewidth tailored to solving the maximum independent set problem. Over a series of papers, Abrishami et al. developed the so-called central bag method to study induced obstructions to bounded treewidth. Among others, they showed tha

Baojian Zhou, Yifan Sun, Reza Babanezhad

This paper studies the online node classification problem under a transductive learning setting. Current methods either invert a graph kernel matrix with $\mathcal{O}(n^3)$ runtime and $\mathcal{O}(n^2)$ space complexity or sample a large volume of random spanning trees, thus are difficult to scale to large graphs. In this work, we propose an improvement bas

Garrison Koch, Nathan Shank

Given a graph $G=(V,E)$, the dominating number of a graph is the minimum size of a vertex set, $V' \subseteq V$, so that every vertex in the graph is either in $V'$ or is adjacent to a vertex in $V'$. A Roman Dominating function of $G$ is defined as $f:V \rightarrow \{0,1,2\}$ such that every vertex with a label of 0 in $G$ is adjacent to a vertex with a lab

Paul Ghelasi, Florian Ziel

Aggregated curves are common structures in economics and finance, and the most prominent examples are supply and demand curves. In this study, we exploit the fact that all aggregated curves have an intrinsic hierarchical structure, and thus hierarchical reconciliation methods can be used to improve the forecast accuracy. We provide an in-depth theory on how

Andrea Lucchini, Luca Sabatini, Mima Stanojkovski

Let $d$ be a positive integer. A finite group is called $d$-maximal if it can be generated by precisely $d$ elements, while its proper subgroups have smaller generating sets. For $d\in\{1,2\}$, the $d$-maximal groups have been classified up to isomorphism and only partial results have been proven for larger $d$. In this work, we prove that a $d$-maximal grou

Yan Liu, Yan Gao, Zhe Su, Xiaokang Chen

Content Warning: This work contains examples that potentially implicate stereotypes, associations, and other harms that could be offensive to individuals in certain social groups.} Large pre-trained language models are acknowledged to carry social biases towards different demographics, which can further amplify existing stereotypes in our society and cause e

Genta Indra Winata, Lingjue Xie, Karthik Radhakrishnan, Shijie Wu

Real-life multilingual systems should be able to efficiently incorporate new languages as data distributions fed to the system evolve and shift over time. To do this, systems need to handle the issue of catastrophic forgetting, where the model performance drops for languages or tasks seen further in its past. In this paper, we study catastrophic forgetting,

Nikhilesh Singh, Vinod Ganesan, Chester Rebeiro

In the last two decades, the evolving cyber-threat landscape has brought to center stage the contentious tradeoffs between the security and performance of modern microprocessors. The guarantees provided by the hardware to ensure no violation of process boundaries have been shown to be breached in several real-world scenarios. While modern CPU features such a

Manas Debnath, Ritesh Ghosh, Najmul Haque

Gribov-Zwanziger prescription in Yang-Mills theory improves the infrared dynamics. In this work, we study the static potential of a heavy quark-antiquark pair with the HTL resummed perturbation method within the Gribov-Zwanziger approach at finite temperature. The real and imaginary parts of the heavy quark complex potential are obtained from the one-loop ef

MicroBooNE collaboration, P. Abratenko, O. Alterkait, D. Andrade Aldana

We present a measurement of $\eta$ production from neutrino interactions on argon with the MicroBooNE detector. The modeling of resonant neutrino interactions on argon is a critical aspect of the neutrino oscillation physics program being carried out by the DUNE and Short Baseline Neutrino programs. $\eta$ production in neutrino interactions provides a power

Jie Cai, Sagnik Chowdhury, Hongyang Zhou, Donghee Yvette Wohn

Online harassment and content moderation have been well-documented in online communities. However, new contexts and systems always bring new ways of harassment and need new moderation mechanisms. This study focuses on hate raids, a form of group attack in real-time in live streaming communities. Through a qualitative analysis of hate raids discussion in the

Howard E. Bond, Klaus Werner, Gregory R. Zeimann, Jonathan Talbot

Fr 2-30 = PN? G126.8-15.5 is a faint emission nebula, hosting a 14th-mag central star that we identify here for the first time. Deep Halpha and [O III] images reveal a roughly elliptical nebula with dimensions of at least 22'x14', fading into a surrounding network of even fainter emission. Optical spectrograms of the central star show it to have a subdwarf O

Rui Liu, Alex Olshevsky

We provide a new non-asymptotic analysis of distributed temporal difference learning with linear function approximation. Our approach relies on ``one-shot averaging,'' where $N$ agents run identical local copies of the TD(0) method and average the outcomes only once at the very end. We demonstrate a version of the linear time speedup phenomenon, where the co

Sanjukta Kundu, Jerzy Szuniewicz, Grzegorz Firlik, Alexander Krupinski-Ptaszek

Efficient measurement of high-dimensional quantum correlations, especially spatial ones, is essential for quantum technologies, given their inherent high dimensionality and easy manipulation with basic optical elements. We propose and demonstrate an adaptively-gated hybrid intensified camera (HIC) that combines the information from a high spatial resolution

Natalia Masalaeva, Helmut Ritsch, Farokh Mivehvar

Ultracold atoms trapped in laser-generated optical lattices serve as a versatile platform for quantum simulations. However, as these lattices are infinitely stiff, they do not allow to emulate phonon degrees of freedom. This restriction can be lifted in emerged optical lattices inside multimode cavities. Motivated by recent experimental progress in multimode

Ehsan Doostmohammadi, Tobias Norlund, Marco Kuhlmann, Richard Johansson

Augmenting language models with a retrieval mechanism has been shown to significantly improve their performance while keeping the number of parameters low. Retrieval-augmented models commonly rely on a semantic retrieval mechanism based on the similarity between dense representations of the query chunk and potential neighbors. In this paper, we study the sta

Jiseok Chae, Kyuwon Kim, Donghwan Kim

Minimax problems are notoriously challenging to optimize. However, we present that the two-timescale extragradient method can be a viable solution. By utilizing dynamical systems theory, we show that it converges to points that satisfy the second-order necessary condition of local minimax points, under mild conditions that the two-timescale gradient descent

Bianca Monachesi, Alessandro Grecucci, Parisa Ahmadi Ghomroudi, Irene Messina

In the emotion regulation literature, the amount of neuroimaging studies on cognitive reappraisal led the impression that the same top-down, control-related neural mechanisms characterize all emotion regulation strategies. However, top-down processes may coexist with more bottom-up and emotion-focused processes that partially bypass the recruitment of execut

Marco Carbone, Sonia Marin, Carsten Schürmann

Session types are types for specifying the protocols that communicating processes must follow in a concurrent system. When composing two or more well-typed processes, a session typing system must check whether such processes are multiparty compatible, a property that guarantees that all sent messages are eventually received and no deadlock ever occurs. Previ

Gokul Bhusal, Ekaterina Merkurjev, Guo-Wei Wei

The success of many machine learning (ML) methods depends crucially on having large amounts of labeled data. However, obtaining enough labeled data can be expensive, time-consuming, and subject to ethical constraints for many applications. One approach that has shown tremendous value in addressing this challenge is semi-supervised learning (SSL); this techni

Suchismita Banerjee

The kinetic exchange model has gained popularity in the field of statistical mechanics for investigating wealth interaction. Traditionally, kinetic exchange models have been studied without considering preferential interactions. However, in this study, we introduce two types of preferential interactions to explore wealth dynamics and its associated distribut

Andrew Li

Of the three space telescopes launched so far to survey transiting extrasolar planets, CoRoT is unique in that it was the only one with spectral resolution, allowing for an extraordinary opportunity to study the reflective properties of exoplanets at different wavelengths. In this work, I present a systematic lightcurve analysis of the white-light and chroma

Kristina Giesel, Andreas Leitherer, David Winnekens

We consider a reduced phase space quantisation of a model with $T^3$ Gowdy symmetry in which gravity has been coupled to Gaussian dust. We complete the quantisation programme in reduced loop quantum gravity (LQG) as well as algebraic quantum gravity (AQG) and derive a Schr\"odinger-like equation with a physical Hamiltonian operator encoding the dynamics. Due

Lingxuan Shao, Fang Yao

This paper examines robust functional data analysis for discretely observed data, where the underlying process encompasses various distributions, such as heavy tail, skewness, or contaminations. We propose a unified robust concept of functional mean, covariance, and principal component analysis, while existing methods and definitions often differ from one an

Davide Venturelli, Sarah A. M. Loos, Benjamin Walter, Édgar Roldán

We develop a framework for the stochastic thermodynamics of a probe coupled to a fluctuating medium with spatio-temporal correlations, described by a scalar field. For a Brownian particle dragged by a harmonic trap through a fluctuating Gaussian field, we show that near criticality (where the field displays long-range spatial correlations) the spatially-reso

Samiran Roy

Future long-baseline experiments will play an important role in exploring physics beyond the standard model. One such new physics concept is the large extra dimension (LED), which provides an elegant solution to the hierarchy problem. This model also explains the small neutrino mass in a natural way. The presence of LED modifies the standard neutrino oscilla

Xiaokang Chen, Jiaxiang Tang, Diwen Wan, Jingbo Wang

This paper investigates the potential of enhancing Neural Radiance Fields (NeRF) with semantics to expand their applications. Although NeRF has been proven useful in real-world applications like VR and digital creation, the lack of semantics hinders interaction with objects in complex scenes. We propose to imitate the backbone feature of off-the-shelf percep

Johan Asplund, Youngjin Bae, Orsola Capovilla-Searle, Marco Castronovo

Casals-Gorsky-Gorsky-Simental realized all positroid strata of the complex Grassmannian as augmentation varieties of Legendrians called positroid links. We prove that the partial order on strata induced by Zariski closure also has a symplectic interpretation, given by exact Lagrangian cobordism.