October 2023 arXiv papers — page 33

Anas Al-lahham, Nurbek Tastan, Zaigham Zaheer, Karthik Nandakumar

Detection of anomalous events in videos is an important problem in applications such as surveillance. Video anomaly detection (VAD) is well-studied in the one-class classification (OCC) and weakly supervised (WS) settings. However, fully unsupervised (US) video anomaly detection methods, which learn a complete system without any annotation or human supervisi

Takuma Yoneda, Tianchong Jiang, Gregory Shakhnarovich, Matthew R. Walter

A core capability for robot manipulation is reasoning over where and how to stably place objects in cluttered environments. Traditionally, robots have relied on object-specific, hand-crafted heuristics in order to perform such reasoning, with limited generalizability beyond a small number of object instances and object interaction patterns. Recent approaches

Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song

Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI's impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control o

Andrea Zazzi, Jeremy Witzens

We propose and model an optical communication scheme for short distance datacom links based on the distribution of information across a wide comb spectrum. This modulation format, orthogonal delay division multiplexing, allows the multiplexing of data streams from multiple modulators, as well as the deserialization and equalization of the data in the optical

Yuchen Ma, Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel

Fairness in predictions is of direct importance in practice due to legal, ethical, and societal reasons. This is often accomplished through counterfactual fairness, which ensures that the prediction for an individual is the same as that in a counterfactual world under a different sensitive attribute. However, achieving counterfactual fairness is challenging

Diogo D Carvalho, Diogo R Ferreira, Luis O Silva

We explore the possibility of fully replacing a plasma physics kinetic simulator with a graph neural network-based simulator. We focus on this class of surrogate models given the similarity between their message-passing update mechanism and the traditional physics solver update, and the possibility of enforcing known physical priors into the graph constructi

Chawin Sitawarin, Jaewon Chang, David Huang, Wesson Altoyan

Adversarial attacks have been a looming and unaddressed threat in the industry. However, through a decade-long history of the robustness evaluation literature, we have learned that mounting a strong or optimal attack is challenging. It requires both machine learning and domain expertise. In other words, the white-box threat model, religiously assumed by a la

Yoshitomo Matsubara

Reproducibility in scientific work has been becoming increasingly important in research communities such as machine learning, natural language processing, and computer vision communities due to the rapid development of the research domains supported by recent advances in deep learning. In this work, we present a significantly upgraded version of torchdistill

Nina Wiedemann, Ourania Kounadi, Martin Raubal, Krzysztof Janowicz

Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity patterns and interests of users, e.g., by categorizing visited locations based on nearby points of interest (POI). On

Tsun-Hsuan Wang, Alaa Maalouf, Wei Xiao, Yutong Ban

As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning. However, existing systems grapple with challenges such as unexpected open set environments and the complexity of black-box models. At the same time, the evolution of deep learning

Yikang Zhang, Thomas Barthel

The dynamics of Markovian open quantum systems are described by Lindblad master equations, generating a quantum dynamical semigroup. An important concept for such systems is (Davies) irreducibility, i.e., the question whether there exist non-trivial invariant subspaces. Steady states of irreducible systems are unique and faithful, i.e., they have full rank.

Yu Jia, Zhewen Mo, Xiaonu Xiong

We carry out a comprehensive study of the quark-to-meson fragmentation function in the 't Hooft model, i.e., the two-dimensional Quantum Chromodynamics (QCD) in $N_c\to \infty$ limit, following the operator definition pioneered by Collins and Soper. We apply the Hamiltonian approach as well as the diagrammatic approach to construct the functional form of the

Eric J. Bigelow, Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka

Large language models (LLMs) trained on huge corpora of text datasets demonstrate intriguing capabilities, achieving state-of-the-art performance on tasks they were not explicitly trained for. The precise nature of LLM capabilities is often mysterious, and different prompts can elicit different capabilities through in-context learning. We propose a framework

Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel

We introduce the first continuous-time score-based generative model that leverages fractional diffusion processes for its underlying dynamics. Although diffusion models have excelled at capturing data distributions, they still suffer from various limitations such as slow convergence, mode-collapse on imbalanced data, and lack of diversity. These issues are p

Eric J. Ching, Ryan F. Johnson, Sarah Burrows, Jacklyn Higgs

This article concerns the development of a fully conservative, positivity-preserving, and entropy-bounded discontinuous Galerkin scheme for the multicomponent, chemically reacting, compressible Navier-Stokes equations with complex thermodynamics. In particular, we extend to viscous flows the fully conservative, positivity-preserving, and entropy-bounded disc

Michael Müller, Matias Rodrigues, Jörn Beyer, Martin Loidl

The precision of existing decay data of radionuclides for activity determination is often a limitation for actual applications in science, society, and industry. For this reason, the EMPIR project PrimA-LTD aims to introduce an advanced primary activity standardization technique that is based on magnetic microcalorimeters (MMCs) and that will offer very low

Ashwin Sah, Julian Sahasrabudhe, Mehtaab Sawhney

Let $A$ be an $n\times n$ matrix with iid entries where $A_{ij} \sim \mathrm{Ber}(p)$ is a Bernoulli random variable with parameter $p = d/n$. We show that the empirical measure of the eigenvalues converges, in probability, to a deterministic distribution as $n \rightarrow \infty$. This essentially resolves a long line of work to determine the spectral laws

Laura Smith, Yunhao Cao, Sergey Levine

Deep reinforcement learning (RL) can enable robots to autonomously acquire complex behaviors, such as legged locomotion. However, RL in the real world is complicated by constraints on efficiency, safety, and overall training stability, which limits its practical applicability. We present APRL, a policy regularization framework that modulates the robot's expl

Montie Avery

We study growth of solid tumors in a partial differential equation model introduced by Hillen et al for the interaction between tumor cells (TCs) and cancer stem cells (CSCs). We find that invasion into the cancer-free state may be separated into two regimes, depending on the death rate of tumor cells. In the first, staged invasion regime, invasion into the

Kohei Yamashita, Shohei Nobuhara, Ko Nishino

Geometry reconstruction of textureless, non-Lambertian objects under unknown natural illumination (i.e., in the wild) remains challenging as correspondences cannot be established and the reflectance cannot be expressed in simple analytical forms. We derive a novel multi-view method, DeepShaRM, that achieves state-of-the-art accuracy on this challenging task.

Lianghui Zhu, Xinggang Wang, Xinlong Wang

Evaluating Large Language Models (LLMs) in open-ended scenarios is challenging because existing benchmarks and metrics can not measure them comprehensively. To address this problem, we propose to fine-tune LLMs as scalable judges (JudgeLM) to evaluate LLMs efficiently and effectively in open-ended benchmarks. We first propose a comprehensive, large-scale, hi

Heng Yang, Ke Li

Instruction-based language modeling has received significant attention in pretrained language models. However, the efficiency of instruction engineering remains low and hinders the development of instruction studies. Recent studies have focused on automating instruction generation, but they primarily aim to improve performance without considering other cruci

Arnab Auddy, Haolin Zou, Kamiar Rahnama Rad, Arian Maleki

The out-of-sample error (OO) is the main quantity of interest in risk estimation and model selection. Leave-one-out cross validation (LO) offers a (nearly) distribution-free yet computationally demanding approach to estimate OO. Recent theoretical work showed that approximate leave-one-out cross validation (ALO) is a computationally efficient and statistical

Richard A. P. Birkett

In this article, we develop a dynamical theory for what shall be called a skew product on the Berkovich projective line, $\phi_*: \mathbb{P}^1_{\text{an}}(K) \to \mathbb{P}^1_{\text{an}}(K)$ over a non-Archimedean field $K$. These functions are defined algebraically yet strictly generalise the notion of a rational map on $\mathbb{P}^1_{\text{an}}$. We descri

Pavel Malý, Dita Strachotová, Aleš Holoubek, Petr Heřman

Fluorescence lifetime imaging microscopy (FLIM) is a well-established technique with numerous imaging applications. Yet, one of the limitations of FLIM is that it provides information about the emitting state only. Here, we present an extension of FLIM by interferometric measurement of fluorescence excitation spectra. Interferometric Excitation Fluorescence

Jindong Gu, Xiaojun Jia, Pau de Jorge, Wenqain Yu

The emergence of Deep Neural Networks (DNNs) has revolutionized various domains by enabling the resolution of complex tasks spanning image recognition, natural language processing, and scientific problem-solving. However, this progress has also brought to light a concerning vulnerability: adversarial examples. These crafted inputs, imperceptible to humans, c

Kevin Turbang, Max Lalleman, Thomas A. Callister, Nick van Remortel

The advent of gravitational-wave astronomy is now allowing for the study of compact binary merger demographics throughout the Universe. This information can be leveraged as tools for understanding massive stars, their environments, and their evolution. One active question is the nature of compact binary formation: the environmental and chemical conditions re

Ask Ellingsen, Douglas Lundholm, Jean-Pierre Magnot

We propose here selected actual features of measurement problems based on our concerns in our respective fields of research. Their technical similarity in apparently disconnected fields motivate this common communication. Problems of coherence and consistency, correlation, randomness and uncertainty are exposed in various fields including physics, decision t

Yonatan Oren, Nicole Meister, Niladri Chatterji, Faisal Ladhak

Large language models are trained on vast amounts of internet data, prompting concerns and speculation that they have memorized public benchmarks. Going from speculation to proof of contamination is challenging, as the pretraining data used by proprietary models are often not publicly accessible. We show that it is possible to provide provable guarantees of

Zhihan Zhou, Jiangchao Yao, Feng Hong, Ya Zhang

Self-supervised learning (SSL) as an effective paradigm of representation learning has achieved tremendous success on various curated datasets in diverse scenarios. Nevertheless, when facing the long-tailed distribution in real-world applications, it is still hard for existing methods to capture transferable and robust representation. Conventional SSL method

Jens Visbech, Allan Peter Engsig-Karup, Mario Ricchiuto

We present a new high-order accurate spectral element solution to the two-dimensional scalar Poisson equation subject to a general Robin boundary condition. The solution is based on a simplified version of the shifted boundary method employing a continuous arbitrary order $hp$-Galerkin spectral element method as the numerical discretization procedure. The si

Timothy Overbye, Srikanth Saripalli

Off-road robotics have traditionally utilized lidar for local navigation due to its accuracy and high resolution. However, the limitations of lidar, such as reduced performance in harsh environmental conditions and limited range, have prompted the exploration of alternative sensing technologies. This paper investigates the potential of radar for off-road loc

Anvy Moly Tom, Rathul Nath Raveendran

It is well known that damped superimposed oscillations at large scales in the primordial power spectrum can be generated in both single field and two field models. In single field inflationary models, these features typically arise due to deviations from the slow roll regime. On the other hand, in two field models, these features are generated due to a turn

Jean-François Paquet

Heavy-ion collisions provide a window into the properties of many-body systems of deconfined quarks and gluons. Understanding the collective properties of quarks and gluons is possible by comparing models of heavy-ion collisions to measurements of the distribution of particles produced at the end of the collisions. These model-to-data comparisons are extreme

J. P. Marquez

This contribution explores the ability to probe BSM physics by using the experimental prospects for measuring the forward-backward asymmetry ($A_{FB}$) in $e^{+}e^{-}\rightarrow b\bar{b}$ and $e^{+}e^{-}\rightarrow c\bar{c}$ processes at the baseline energy points of ILC: 250 and 500 GeV. The studies are based on the full simulation samples and reconstructio

Zhongye Wang, Qinxiang Cao, Yichen Tao

Many foundational program verification tools have been developed to build machine-checked program correctness proofs, a majority of which are based on Hoare logic. Their program logics, their assertion languages, and their underlying programming languages can be formalized by either a shallow embedding or a deep embedding. Tools like Iris and early versions

Theresa C. Anderson, Elisa Bellah, Zoe Markman, Teresa Pollard

Using a wide array of machinery from diverse fields across mathematics, we provide a construction of a measure on the real line which is doubling on all $n$-adic intervals for any finite list of $n\in\mathbb{N}$, yet not doubling overall. In particular, we extend previous results in the area, where only two coprime numbers $n$ were allowed, by using substant

Rahul Soni, Harini Radhakrishnan, Bernd Rosenow, Gonzalo Alvarez

We investigate the effects of electronic correlations on the Bernevig-Hughes-Zhang model using the real-space density matrix renormalization group (DMRG) algorithm. We introduce a method to probe topological phase transitions in systems with strong correlations using DMRG, substantiated by an unsupervised machine learning methodology that analyzes the orbita

Praise Adeyemo

We study a family $\mathcal{B}$ of pseudo-multipartite graphs indexed by staircase partitions. They are realised from the reduced words of certain class of permutations. We investigate the vertex proper colouring of these graphs and give the general chromatic polynomial. For each member $B_{\lambda}$, we construct an affine toric ideal $\mathcal{I}_{B_{\lamb

Xu-Dong Dai, Zijian Wang, He-Ran Wang, Zhong Wang

We investigate a generalization of topological order from closed systems to open systems, for which the steady states take the place of ground states. We construct typical lattice models with steady-state topological order, and characterize them by complementary approaches based on topological degeneracy of steady states, topological entropy, and dissipative

Yibo Jiang, Bryon Aragam, Victor Veitch

Machine learning tools often rely on embedding text as vectors of real numbers. In this paper, we study how the semantic structure of language is encoded in the algebraic structure of such embeddings. Specifically, we look at a notion of ``semantic independence'' capturing the idea that, e.g., ``eggplant'' and ``tomato'' are independent given ``vegetable''.

Jonathan W. Siegel, Stephan Wojtowytsch

We consider gradient flow/gradient descent and heavy ball/accelerated gradient descent optimization for convex objective functions. In the gradient flow case, we prove the following: 1. If $f$ does not have a minimizer, the convergence $f(x_t)\to \inf f$ can be arbitrarily slow. 2. If $f$ does have a minimizer, the excess energy $f(x_t) - \inf f$ is integrab

Haitao Li, Yunqiu Shao, Yueyue Wu, Qingyao Ai

As an important component of intelligent legal systems, legal case retrieval plays a critical role in ensuring judicial justice and fairness. However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candid

Sean Timothy Paul, Song Sun, Junsheng Zhang

In this note, we give examples that demonstrate a negative answer to the generalized numerical criterion problem for pairs.

K. Castillo, G. Filipuk, D. Mbouna

In [J. Phys. A: Math. Theor. 45 (2012)], while looking for spin chains that admit perfect state transfer, Vinet and Zhedanov found an apparently new sequence of orthogonal polynomials, that they called para-Krawtchouk polynomials, defined on a bilinear lattice. In this note we present necessary and sufficient conditions for the regularity of solutions of the

Kristian Tyn Kai Chung, Michel J. P. Gingras

We introduce a novel geometrically frustrated classical Ising model, dubbed the "spin vorticity model", whose ground state manifold is a novel classical spin liquid, a "2-form Coulomb phase". We study the thermodynamics of this model both analytically and numerically, exposing the presence of algebraically decaying correlations and demonstrating an extensive

Owen Henkel, Hannah Horne-Robinson, Libby Hills, Bill Roberts

This paper reports on a set of three recent experiments utilizing large-scale speech models to evaluate the oral reading fluency (ORF) of students in Ghana. While ORF is a well-established measure of foundational literacy, assessing it typically requires one-on-one sessions between a student and a trained evaluator, a process that is time-consuming and costl

Alec E. Sigurðarson, Yorick L. A. Schmerwitz, Dagrún K. V. Tveiten, Gianluca Levi

Density functional calculations of Rydberg excited states up to high energy are carried out for several molecules using an approach where the orbitals are variationally optimized by converging on saddle points on the electronic energy surface within a real space grid representation. Remarkably good agreement with experimental estimates of the excitation ener

G. A. Carvalho, S. Pilling, S. Gerasimenko

In this work, we focus on the study of radiation induced desorption processes that occurred in acetonitrile ice irradiated by broadband X-rays (6 eV to 2 keV) monitored by FTIR spectroscopy at different radiation fluences. In a previous work, we used the PROCODA code to derive the chemical evolution of the ice. Here, we have obtained that the acetonitrile de

A. Gibbs, S. Langdon

For problems of time-harmonic scattering by rational polygonal obstacles, embedding formulae express the far-field pattern induced by any incident plane wave in terms of the far-field patterns for a relatively small (frequency-independent) set of canonical incident angles. Although these remarkable formulae are exact in theory, here we demonstrate that: (i)

Xiaosheng Zhao, Yi Mao, Shifan Zuo, Benjamin D. Wandelt

The information regarding how the intergalactic medium is reionized by astrophysical sources is contained in the tomographic three-dimensional 21 cm images from the epoch of reionization. In Zhao et al. (2022a) ("Paper I"), we demonstrated for the first time that density estimation likelihood-free inference (DELFI) can be applied efficiently to perform a Bay

Raphaël Clouâtre, Hridoyananda Saikia

Motivated by Arveson's conjecture, we introduce a notion of hyperrigidity for a partial order on the state space of a $C^*$-algebra $B$. We show how this property is equivalent to the existence of a boundary: a subset of the pure states which completely encodes maximality in the given order. In the classical case where $B$ is commutative, such boundaries are

Ashwin Sah, Julian Sahasrabudhe, Mehtaab Sawhney

Let $A_n$ be an $n\times n$ matrix with iid entries distributed as Bernoulli random variables with parameter $p = p_n$. Rudelson and Tikhomirov, in a beautiful and celebrated paper, show that the distribution of eigenvalues of $A_n \cdot (pn)^{-1/2}$ is approximately uniform on the unit disk as $n\rightarrow \infty$ as long as $pn \rightarrow \infty$, which

Jörg Nick, Selina Burkhard, Christian Lubich

This paper studies time-dependent electromagnetic scattering from metamaterials that are described by dispersive material laws. We consider the numerical treatment of a scattering problem in which a dispersive material law, for a causal and passive homogeneous material, determines the wave-material interaction in the scatterer. The resulting problem is nonlo

Federica Devoto, Kirill Melnikov, Raoul Röntsch, Chiara Signorile-Signorile

We describe how the nested soft-collinear subtraction scheme [1] can be used to compute the next-to-next-to-leading order (NNLO) QCD corrections to the production of an arbitrary number of gluonic jets in hadron collisions. We show that the infrared subtraction terms can be combined into recurring structures that in many cases are simple iterations of those

Wallace Jaffray, Farhan Ali, Sven Stengel, Ziheng Guo

Novel photonic nanowires were fabricated using low-index materials and tested in the near-infrared spectrum to assess their nonlinear optical properties. In this work, we argue the need to redefine the standard nonlinear figure of merit in terms of nonlinear phase shift and optical transmission for a given propagation distance. According to this new metric,

Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola

Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents. However, the demonstrations can be extremely costly and time-consuming to collect. We introduce MimicGen, a system for automatically synthesizing large-scale, rich datasets from only a small number of human demonstrations by a

Christian d'Elbée, Isabel Müller, Nicholas Ramsey, Daoud Siniora

We give a systematic study of the model theory of generic nilpotent groups and Lie algebras. We show that the Fra\"iss\'e limit of 2-nilpotent groups of exponent $p$ studied by Baudisch is 2-dependent and NSOP$_{1}$. We prove that the class of $c$-nilpotent Lie algebras over an arbitrary field, in a language with predicates for a Lazard series, is closed und

Zhiqing Xiao, Haobo Wang, Ying Jin, Lei Feng

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In thi

Md Sariful Sheikh, Ryan Jacobs, Dane Morgan, John Booske

Thermionic electron emission cathodes are critical components of various high power and high frequency vacuum electronic devices, electron microscopes, e-beam lithographic devices, and thermionic energy converters, which all demand an efficient and long-lasting low work function cathode. Single phase, polycrystalline perovskite oxide SrVO$_3$, with its intri

Jerson Caro, Natalia Garcia-Fritz

For an elliptic curve $E$ defined over the field $\mathbb{C}$ of complex numbers, we classify all translates of elliptic curves in $E^3$ such that the $x$-coordinates satisfy a linear equation. This classification enables us to establish a relation between the rank of finite rank subgroups of $E$ and triples in $E$ whose $x$-coordinates are linearly related.

Venkata S Govindarajan, Juan Diego Rodriguez, Kaj Bostrom, Kyle Mahowald

We present Lil-Bevo, our submission to the BabyLM Challenge. We pretrained our masked language models with three ingredients: an initial pretraining with music data, training on shorter sequences before training on longer ones, and masking specific tokens to target some of the BLiMP subtasks. Overall, our baseline models performed above chance, but far below

Oren Katzir, Or Patashnik, Daniel Cohen-Or, Dani Lischinski

Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains. In this paper, we reexamine the SDS process and introduce a straightforward interpretation that demystifies the necessity for large Classifier-Free Guidance (CFG) scales, rooted in the distillation of an undesired noise term. Building u

Yucheng Li, Frank Guerin, Chenghua Lin

Data contamination in model evaluation has become increasingly prevalent with the growing popularity of large language models. It allows models to "cheat" via memorisation instead of displaying true capabilities. Therefore, contamination analysis has become an crucial part of reliable model evaluation to validate results. However, existing contamination anal

Guangliang Liu, Zhiyu Xue, Xitong Zhang, Kristen Marie Johnson

Fine-tuning pretrained language models (PLMs) for downstream tasks is a large-scale optimization problem, in which the choice of the training algorithm critically determines how well the trained model can generalize to unseen test data, especially in the context of few-shot learning. To achieve good generalization performance and avoid overfitting, technique

B. Adorjani, A. Libal, C. Reichhardt, C. J. O. Reichhardt

We examine run and tumble disks in two-dimensional systems where the particles also have a Magnus component to their dynamics. For increased activity, we find that the system forms a motility-induced phase-separated (MIPS) state with chiral edge flow around the clusters, where the direction of the current is correlated with the sign of the Magnus term. The s

Anjishnu Mukherjee, Chahat Raj, Ziwei Zhu, Antonios Anastasopoulos

Human biases are ubiquitous but not uniform: disparities exist across linguistic, cultural, and societal borders. As large amounts of recent literature suggest, language models (LMs) trained on human data can reflect and often amplify the effects of these social biases. However, the vast majority of existing studies on bias are heavily skewed towards Western

Mattheus Burkhard, Onur Pusuluk, Tristan Farrow

The resource theory of quantum thermodynamics has emerged as a powerful tool for exploring the out-of-equilibrium dynamics of microscopic and highly correlated systems. Recently, it has been employed in photoisomerization, a mechanism facilitating vision through the isomerism of the photo receptor protein rhodopsin, to elucidate the fundamental limits of eff

Cristina Cipriani, Alessandro Scagliotti, Tobias Wöhrer

In this paper, we address the adversarial training of neural ODEs from a robust control perspective. This is an alternative to the classical training via empirical risk minimization, and it is widely used to enforce reliable outcomes for input perturbations. Neural ODEs allow the interpretation of deep neural networks as discretizations of control systems, u

Gerd Röpke, Tobias Dornheim, Jan Vorberger, David Blaschke

The properties of plasmas in the low-density limit are described by virial expansions. Analytical expressions are known from Green's function approaches only for the first three virial coefficients. Accurate path integral Monte Carlo (PIMC) simulations have recently been performed for the uniform electron gas, allowing the virial expansions to be analyzed an

Xiuyuan Cheng, Jianfeng Lu, Yixin Tan, Yao Xie

Flow-based generative models enjoy certain advantages in computing the data generation and the likelihood, and have recently shown competitive empirical performance. Compared to the accumulating theoretical studies on related score-based diffusion models, analysis of flow-based models, which are deterministic in both forward (data-to-noise) and reverse (nois

Zhen-Lai Wang

We present a spin-induced none-geodesic effect of Dirac wave packets in a static uniform gravitational field. Our approach is based on the Foldy-Wouthuysen transformation of Dirac equation in a curved spacetime, which predicts the gravitational spin-orbit coupling. Due to this coupling, we find that the dynamics of the free-fall Dirac wave packets with oppos

Owen Henkel, Libby Hills, Bill Roberts, Joshua McGrane

Open-ended questions, which require students to produce multi-word, nontrivial responses, are a popular tool for formative assessment as they provide more specific insights into what students do and don't know. However, grading open-ended questions can be time-consuming leading teachers to resort to simpler question formats or conduct fewer formative assessm

Eric W. Hester, Sean P. Carney, Vishwesh Shah, Alyssa Arnheim

Liquid-liquid phase separation is key to understanding aqueous two-phase systems (ATPS) arising throughout cell biology, medical science, and the pharmaceutical industry. Controlling the detailed morphology of phase-separating compound droplets leads to new technologies for efficient single-cell analysis, targeted drug delivery, and effective cell scaffolds

Charles Xu, Laney Goldman, Valentina Guo, Benjamin Hollander-Bodie

Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. However, much less work has been done on signal classification, where the data consists of many functions (referred to as signals) defined on the vertices of a single graph. These tasks require networks designed differently from those

Jinhui Hou, Zhiyu Zhu, Junhui Hou, Hui Liu

This paper studies a diffusion-based framework to address the low-light image enhancement problem. To harness the capabilities of diffusion models, we delve into this intricate process and advocate for the regularization of its inherent ODE-trajectory. To be specific, inspired by the recent research that low curvature ODE-trajectory results in a stable and e

Peiling Jiang, Li Feng, Fuling Sun, Parakrant Sarkar

Existing text selection techniques on touchscreen focus on improving the control for moving the carets. Coarse-grained text selection on word and phrase levels has not received much support beyond word-snapping and entity recognition. We introduce 1D-Touch, a novel text selection method that complements the carets-based sub-word selection by facilitating the

Dušan Marčeta, Darryl Z. Seligman

The discovery of two interstellar objects passing through the Solar System, 1I/`Oumuamua and 2I/Borisov, implies that a galactic population exists with a spatial number density of order $\sim0.1$ au$^{-3}$. The forthcoming Rubin Observatory Legacy Survey of Space and Time (LSST) has been predicted to detect more asteroidal interstellar objects like 1I/`Oumua

Jingwen Ding, Giuseppe Spallitta, Roberto Sebastiani

This paper investigates novel techniques to solve prime factorization by quantum annealing (QA). Our contribution is twofold. First, we present a novel and very compact modular encoding of a binary multiplier circuit into the Pegasus architecture of current D-Wave QA devices. The key contribution is a compact encoding of a controlled full-adder into an 8-qub

Nitin Yadav, Rony Keppens

We investigate the viability of MHD waves, in particular acoustic p-modes, in causing strong current accumulation at the null points. We begin with a three-dimensional numerical setup incorporating a gravitationally stratifed solar atmosphere and an axially symmetric magnetic feld including a coronal magnetic null point. To excite waves, we employ wave drive

Amir Dembo, Kevin Yang

We study a stochastic Laplacian growth model, where a set $\mathbf{U}\subseteq\mathbb{R}^{\mathrm{d}}$ grows according to a reflecting Brownian motion in $\mathbf{U}$ stopped at level sets of its boundary local time. We derive a scaling limit for the leading-order behavior of the growing boundary (i.e. "interface"). It is given by a geometric flow-type PDE.

Seulki Chung

Long short-term memory (LSTM) and gated recurrent unit (GRU) are used to model US recessions from 1967 to 2021. Their predictive performances are compared to those of the traditional linear models. The out-of-sample performance suggests the application of LSTM and GRU in recession forecasting, especially for longer-term forecasts. The Shapley additive explan

Yongxin Zhu, Zhujin Gao, Xinyuan Zhou, Zhongyi Ye

While Diffusion Generative Models have achieved great success on image generation tasks, how to efficiently and effectively incorporate them into speech generation especially translation tasks remains a non-trivial problem. Specifically, due to the low information density of speech data, the transformed discrete speech unit sequence is much longer than the c

Xinghui Li, Jingyi Lu, Kai Han, Victor Prisacariu

In this paper, we address the challenge of matching semantically similar keypoints across image pairs. Existing research indicates that the intermediate output of the UNet within the Stable Diffusion (SD) can serve as robust image feature maps for such a matching task. We demonstrate that by employing a basic prompt tuning technique, the inherent potential o

Stephanie M. Lukin, Kimberly A. Pollard, Claire Bonial, Taylor Hudson

Human-guided robotic exploration is a useful approach to gathering information at remote locations, especially those that might be too risky, inhospitable, or inaccessible for humans. Maintaining common ground between the remotely-located partners is a challenge, one that can be facilitated by multi-modal communication. In this paper, we explore how particip

Dingli Yu, Simran Kaur, Arushi Gupta, Jonah Brown-Cohen

With LLMs shifting their role from statistical modeling of language to serving as general-purpose AI agents, how should LLM evaluations change? Arguably, a key ability of an AI agent is to flexibly combine, as needed, the basic skills it has learned. The capability to combine skills plays an important role in (human) pedagogy and also in a paper on emergence

Kazem Zafari, Atefeh Ashrafian, Mohammad Pasdari-Kia, Hamed Saghaei

In this study, novel low-loss waveguides and power dividers for ultra-broadband surface plasmon polaritons (SPPs) are introduced. This article uses complementary metasurfaces in place of traditional SPP, which are typically produced as metasurface unit cells on the dielectric sublayer. It has been demonstrated that the use of complementary metasurfaces consi

Ipsita Sahin, Mehrnoosh Ayazi, Caio Mucchiani, Jared Dube

This paper focuses on the design and systematic evaluation of fabric-based, bellow-type soft pneumatic actuators to assist with flexion and extension of the elbow, intended for use in infant wearable devices. Initially, the performance of a range of actuator variants was explored via simulation. The actuator variants were parameterized based on the shape, nu

Jeffrey Meier, Alexander Zupan

We exhibit infinitely many ribbon knots, each of which bounds infinitely many pairwise non-isotopic ribbon disks whose exteriors are diffeomorphic. This family provides a positive answer to a stronger version of an old question of Hitt and Sumners. The examples arise from our main result: a classification of fibered, homotopy-ribbon disks for each generalize

Jeremy J. Drake, Julián Alvarado Gomez, Costanza Argiroffi, Ettore Flaccomio

We outline some of the highlights of the scientific case for the advancement of stellar high energy physics using the Line Emission Mapper X-ray Probe ({\it LEM}). The key to advancements with LEM lie in its large effective area -- up to 100 times that of the {\it Chandra} MEG -- and 1~eV spectral resolution. The large effective area opens up for the first t

Jaroslav Bradík

We consider weighted harmonic Bergman spaces on upper half-space with weights depending only on the vertical coordinate. In these settings, we give full asymptotic expansion of weighted harmonic Bergman kernel as well as full asymptotic expansion of the harmonic Berezin transform for functions depending only on the vertical coordinate. Both expansions utiliz

Lukas Eisenmann, Zahra Monfared, Niclas Alexander Göring, Daniel Durstewitz

Recurrent neural networks (RNNs) are popular machine learning tools for modeling and forecasting sequential data and for inferring dynamical systems (DS) from observed time series. Concepts from DS theory (DST) have variously been used to further our understanding of both, how trained RNNs solve complex tasks, and the training process itself. Bifurcations ar

Luca Comisso, Brian Jiang

Radiation emitted by nonthermal particles accelerated during relativistic magnetic reconnection is critical for understanding the nonthermal emission in a variety of astrophysical systems, including blazar jets, black hole coronae, pulsars, and magnetars. By means of fully kinetic Particle-in-Cell (PIC) simulations, we demonstrate that reconnection-driven pa

Oliver Turnbull, George Cevora

Adversarial examples resulting from instability of current computer vision models are an extremely important topic due to their potential to compromise any application. In this paper we demonstrate that instability is inevitable due to a) symmetries (translational invariance) of the data, b) the categorical nature of the classification task, and c) the funda

Gene-Ping Yang, Hao Tang

Learning phone types from phone instances has been a long-standing problem, while still being open. In this work, we revisit this problem in the context of self-supervised learning, and pose it as the problem of matching cluster centroids to phone embeddings. We study two key properties that enable matching, namely, whether cluster centroids of self-supervis

M. Gatti, N. Jeffrey, L. Whiteway, J. Williamson

Beyond-two-point statistics contain additional information on cosmological as well as astrophysical and observational (systematics) parameters. In this methodology paper we provide an end-to-end simulation-based analysis of a set of Gaussian and non-Gaussian weak lensing statistics using detailed mock catalogues of the Dark Energy Survey. We implement: 1) se

Yixiao Chen, Hao Xie, Han Wang

We propose a new algorithm for efficiently solving the damped Fisher matrix in large-scale scenarios where the number of parameters significantly exceeds the number of available samples. This problem is fundamental for natural gradient descent and stochastic reconfiguration. Our algorithm is based on Cholesky decomposition and is generally applicable. Benchm

Huihan Liu, Alice Chen, Yuke Zhu, Adith Swaminathan

The ability to learn and refine behavior after deployment has become ever more important for robots as we design them to operate in unstructured environments like households. In this work, we design a new learning system based on large language model (LLM), OLAF, that allows everyday users to teach a robot using verbal corrections when the robot makes mistak

Pedro F. dos Santos, Carlos Florentino, Javier Orts

We obtain a characterization of Maximal and Galois-Maximal $C_2$-spaces (including real algebraic varieties) in terms of $\operatorname{RO}(C_2)$-graded cohomology with coefficients in the constant Mackey functor $\underline{\mathbf{F}}_2$, using the structure theorem of \cite{clover_may:structure_theorem}. Other known characterizations, for instance in term

Shaswata Chowdhury

In several classes of modified gravity theories, extra degrees of freedom are not completely screened in the interiors of stellar and substellar objects. In such theories, the hydrostatic equilibrium condition inside these objects is altered. Moreover, the interior structures of these objects might have a small pressure anisotropy induced by several physical