May 2023 arXiv papers — page 20

Alex Cohen, Cosmin Pohoata, Dmitrii Zakharov

For sufficiently large $n$, we show that in every configuration of $n$ points chosen inside the unit square there exists a triangle of area less than $n^{-8/7-1/2000}$. This improves upon a result of Koml\'os, Pintz and Szemer\'edi from 1982. Our approach establishes new connections between the Heilbronn triangle problem and various themes in incidence geome

Igor Balla, Oliver Janzer, Benny Sudakov

In this short note we prove a lower bound for the MaxCut of a graph in terms of the Lov\'asz theta function of its complement. We combine this with known bounds on the Lov\'asz theta function of complements of $H$-free graphs to recover many known results on the MaxCut of $H$-free graphs. In particular, we give a new, very short proof of a conjecture of Alon

Smik Patel, Tzu-Ching Yen, Artur F. Izmaylov

Exactly-solvable Hamiltonians that can be diagonalized using relatively simple unitary transformations are of great use in quantum computing. They can be employed for decomposition of interacting Hamiltonians either in Trotter-Suzuki approximations of the evolution operator for the quantum phase estimation algorithm, or in the quantum measurement problem for

Thaïs Baudon, Carsten Fuhs, Laure Gonnord

We revisit parallel-innermost term rewriting as a model of parallel computation on inductive data structures and provide a corresponding notion of runtime complexity parametric in the size of the start term. We propose automatic techniques to derive both upper and lower bounds on parallel complexity of rewriting that enable a direct reuse of existing techniq

Zuobin Zhang, Honghui Liu, Divya Rawat, Cosimo Bambi

We conduct a spectral and timing analysis of GX 339-4 and EXO 1846-031 with the aim of studying the evolution of Type-C QPOs with spectral parameters. The high cadence data from Insight-HXMT and NICER allow us to track them. Type-C QPOs appear at the end of low-hard state and/or hard-intermediate state. The results reveal that the QPO frequency is closely re

Ayush Agrawal, Mirac Suzgun, Lester Mackey, Adam Tauman Kalai

State-of-the-art language models (LMs) are notoriously susceptible to generating hallucinated information. Such inaccurate outputs not only undermine the reliability of these models but also limit their use and raise serious concerns about misinformation and propaganda. In this work, we focus on hallucinated book and article references and present them as th

Yuan Gong, Youxin Pang, Xiaodong Cun, Menghan Xia

Accurate Story visualization requires several necessary elements, such as identity consistency across frames, the alignment between plain text and visual content, and a reasonable layout of objects in images. Most previous works endeavor to meet these requirements by fitting a text-to-image (T2I) model on a set of videos in the same style and with the same c

Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood

We present a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning (RL). One of the key shortcomings of existing Thompson sampling algorithms is the need to perform a Gaussian approximation of the posterior distribution, which is not a good surrogate in most practical settings. We instead directly sample the Q func

Malavika Mukundan

Given any postsingularly finite exponential function $p_\lambda(z) = \lambda \exp(z)$ where $\lambda \in \C^*$, we construct a sequence of postcritically finite unicritical polynomials $p_{d,\lambda_d}(z) = \lambda_d(1+\frac{z}{d})^d$ that converge to $p_\lambda$ locally uniformly in $\C$, with the same postsingular portrait as that of $p_\lambda$. We descri

Andrew R. McCluskey, Samuel W. Coles, Benjamin J. Morgan

Self-diffusion coefficients, $D^*$, are routinely estimated from molecular dynamics simulations by fitting a linear model to the observed mean-squared displacements (MSDs) of mobile species. MSDs derived from simulation exhibit statistical noise that causes uncertainty in the resulting estimate of $D^*$. An optimal scheme for estimating $D^*$ minimises this

Florentin Guth, Brice Ménard, Gaspar Rochette, Stéphane Mallat

A central question in deep learning is to understand the functions learned by deep networks. What is their approximation class? Do the learned weights and representations depend on initialization? Previous empirical work has evidenced that kernels defined by network activations are similar across initializations. For shallow networks, this has been theoretic

R. Beiranvand, S. H. Sangani

A family of interleaved current-fed high step-up dc-dc converters are introduced and analyzed here by combining a voltage multiplier (VM) and an active clamp circuit for high-voltage high-power applications. Low input currents and output voltages ripples values and high voltage-gains characteristics of these converters make them suitable for lots of dc-dc ap

Prathamesh Joshi, Leo Tsukada, Chad Hanna

To evaluate the probability of a gravitational-wave candidate originating from noise, GstLAL collects noise statistics from the data it analyzes. Gravitational-wave signals of astrophysical origin get added to the noise statistics, harming the sensitivity of the search. We present the Background Filter, a novel tool to prevent this by removing noise statisti

Andrew Snowden

Given a semi-simple pre-Tannakian category over a finite field, we show that (a slight modification of) its linearization over a field of characteristic 0 is also semi-simple and pre-Tannakian. The key input is a result of Kuhn on the generic representation theory of finite fields. The resulting pre-Tannakian categories have substantially faster growth than

Jethro van Ekeren

We encapsulate the basic notions of the theory of vertex algebras into the construction of a comonad on an appropriate category of formal distributions. Vertex algebras are recovered as coalgebras over this comonad.

Bin Wang, Hongyi Pan, Armstrong Aboah, Zheyuan Zhang

Eye tracking research is important in computer vision because it can help us understand how humans interact with the visual world. Specifically for high-risk applications, such as in medical imaging, eye tracking can help us to comprehend how radiologists and other medical professionals search, analyze, and interpret images for diagnostic and clinical purpos

Xinbu Cheng, Zixiang Xu

The Euclidean Gallai-Ramsey problem, which investigates the existence of monochromatic or rainbow configurations in a colored $n$-dimensional Euclidean space $\mathbb{E}^{n}$, was introduced and studied recently. We further explore this problem for various configurations including triangles, squares, lines, and the structures with specific properties, such a

Roman Kessler, Kiran Raja, Juan Tapia, Christoph Busch

Face Morphing Attacks pose a threat to the security of identity documents, especially with respect to a subsequent access control process, because it enables both individuals involved to exploit the same document. In this study, face embeddings serve two purposes: pre-selecting images for large-scale Morphing Attack generation and detecting potential Morphin

Miguel Onorato, Yuri V. Lvov, Giovanni Dematteis, Sergio Chibbaro

One-dimensional chains are used as a fundamental model of condensed matter, and have constituted the starting point for key developments in nonlinear physics and complex systems. The pioneering work in this field was proposed by Fermi, Pasta, Ulam and Tsingou in the 50s in Los Alamos. An intense and fruitful mathematical and physical research followed during

Komarov S. O., Gorbatsievich A. K., Vereshchagin G.

We consider a test charged particle falling onto a Schwarzschild black hole and evaluate its electromagnetic field. The Regge-Wheeler equation is solved analytically by approximating the potential barrier with Dirac delta function and rectangular barrier. We show that for asymptotically large time measured by a distant observer the electromagnetic field appr

Zi Wang, Alexander Ku, Jason Baldridge, Thomas L. Griffiths

Understanding which concepts models can and cannot represent has been fundamental to many tasks: from effective and responsible use of models to detecting out of distribution data. We introduce Gaussian process probes (GPP), a unified and simple framework for probing and measuring uncertainty about concepts represented by models. As a Bayesian extension of l

Yael Vinker, Andrey Voynov, Daniel Cohen-Or, Ariel Shamir

A creative idea is often born from transforming, combining, and modifying ideas from existing visual examples capturing various concepts. However, one cannot simply copy the concept as a whole, and inspiration is achieved by examining certain aspects of the concept. Hence, it is often necessary to separate a concept into different aspects to provide new pers

Aykut Alkın, Dionyssios Mantzavinos, Türker Özsarı

We establish local well-posedness for the higher-order nonlinear Schr\"odinger equation, formulated on the half-line. We consider the scenario of associated coefficients such that only one boundary condition is required, which is assumed to be Dirichlet type. Our functional framework centers around fractional Sobolev spaces. We treat both high regularity and

Fangyuan Xu, Yixiao Song, Mohit Iyyer, Eunsol Choi

Long-form question answering (LFQA) enables answering a wide range of questions, but its flexibility poses enormous challenges for evaluation. We perform the first targeted study of the evaluation of long-form answers, covering both human and automatic evaluation practices. We hire domain experts in seven areas to provide preference judgments over pairs of a

Wen Zheng, Natasa Milic-Frayling, Ke Zhou

Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses. The context, comprising utterances from previous dialogue exchanges, is used as a source of content for response generation and as a means of selecting external knowledge. However, to avoid introducing ir

Jordan Budhu, Sean V. Hum, Steven Ellingson, R. Michael Buehrer

Radio telescopes are susceptible to interference arriving through its sidelobes. If a reflector antenna could be retrofitted with an adaptive null steering system, it could potentially mitigate this interference. The design of a reflectarray which can be used to reconfigure a radio telescopes radiation pattern by driving a null to the angle of incoming inter

Wenhao Wu, Jan Hückelheim, Paul D. Hovland, Ziqing Luo

Many parallel programming models guarantee that if all sequentially consistent (SC) executions of a program are free of data races, then all executions of the program will appear to be sequentially consistent. This greatly simplifies reasoning about the program, but leaves open the question of how to verify that all SC executions are race-free. In this paper

Shrinkhala Sharma, Hong Li, Zheng Ren, Wilber Alfaro Castro

Antiferromagnetic order, being a ground state of a number of exotic quantum materials, is of immense interest both from the fundamental physics perspective and for driving potential technological applications. For a complete understanding of antiferromagnetism in materials, nanoscale visualization of antiferromagnetic domains, domain walls and their robustne

Adam Denchfield, Hyowon Park, Russell J. Hemley

First-principles density functional theory (DFT) calculations of Lu-H-N compounds reveal low-energy configurations of Fm$\overline{3}$m Lu$_{8}$H$_{23-x}$N structures that exhibit novel electronic properties such as flat bands, sharply peaked densities of states (van Hove singularities, vHs), and intersecting Dirac cones near the Fermi energy (E$_F$). These

Tomas Lundquist, Andrew Winters, Jan Nordström

We extend the construction of so-called encapsulated global summation-by-parts operators to the general case of a mesh which is not boundary conforming. Owing to this development, energy stable discretizations of nonlinear and variable coefficient initial boundary value problems can be formulated in simple and straightforward ways using high-order accurate o

Fridolin Melong, Mahouton Norbert Hounkonnou

In this paper, we characterize the multivariate uniform probability distribution of the first and second kinds in the framework of the $\mathcal{R}(p,q)$-deformed quantum algebras. Their bivariate distributions and related properties, namely ($\mathcal{R}(p,q)$-mean, $\mathcal{R}(p,q)$-variance and $\mathcal{R}(p,q)$-covariance) are computed and discussed. P

Igor Spasojevic, Xu Liu, Alejandro Ribeiro, George J. Pappas

Accurate and robust state estimation is critical for autonomous navigation of robot teams. This task is especially challenging for large groups of size, weight, and power (SWAP) constrained aerial robots operating in perceptually-degraded GPS-denied environments. We can, however, actively increase the amount of perceptual information available to such robots

Angel Garcia-Chung, Matthew F. Carney, James B. Mertens, Aliasghar Parvizi

We present the first empirical constraints on the polymer scale describing polymer quantized GWs propagating on a classical background. These constraints are determined from the polymer-induced deviation from the classically predicted propagation speed of GWs. We leverage posterior information on the propagation speed of GWs from two previously reported sour

Franco Giovenzana, Luca Giovenzana, Michele Graffeo, Paolo Lella

Let $[Z]\in\text{Hilb}^d \mathbb A^3$ be a zero-dimensional subscheme of the affine three-dimensional complex space of length $d>0$. Okounkov and Pandharipande have conjectured that the dimension of the tangent space of $\text{Hilb}^d \mathbb A^3$ at $[Z]$ and $d$ have the same parity. The conjecture was proven by Maulik, Nekrasov, Okounkov and Pandharipande

Su-Qi Zhang, Jian-Song Hong, Yuan Xue, Xun-Jiang Luo

The realization of quantum gates in topological quantum computation still confronts significant challenges in both fundamental and practical aspects. Here, we propose a deterministic and fully topologically protected measurement-based scheme to realize the issue of implementing Clifford quantum gates on the Majorana qubits. Our scheme is based on rigorous pr

Myra Cheng, Esin Durmus, Dan Jurafsky

To recognize and mitigate harms from large language models (LLMs), we need to understand the prevalence and nuances of stereotypes in LLM outputs. Toward this end, we present Marked Personas, a prompt-based method to measure stereotypes in LLMs for intersectional demographic groups without any lexicon or data labeling. Grounded in the sociolinguistic concept

Francesco Innocenti, Ryan Singh, Christopher L. Buckley

Predictive coding (PC) is a brain-inspired local learning algorithm that has recently been suggested to provide advantages over backpropagation (BP) in biologically relevant scenarios. While theoretical work has mainly focused on showing how PC can approximate BP in various limits, the putative benefits of "natural" PC are less understood. Here we develop a

Sang-Bum Yoo

We describe the moduli space of Higgs pairs on an irreducible nodal curve of arithmetic genus one and its geometric structures in terms of the Hitchin map and a flat degeneration of the moduli space of Higgs bundles on an elliptic curve.

Michael Hott, Alexander B. Watson, Mitchell Luskin

We give a complete and rigorous derivation of the mechanical energy for twisted 2D bilayer heterostructures without any approximation beyond the existence of an empirical many-body site energy. Our results apply to both the continuous and discontinuous continuum limit. Approximating the intralayer Cauchy-Born energy by linear elasticity theory and assuming a

Lindia Tjuatja, Emmy Liu, Lori Levin, Graham Neubig

Recent advances in large language models have prompted researchers to examine their abilities across a variety of linguistic tasks, but little has been done to investigate how models handle the interactions in meaning across words and larger syntactic forms -- i.e. phenomena at the intersection of syntax and semantics. We present the semantic notion of agent

Giacomo Galloni, Mario Ballardini, Nicola Bartolo, Alessandro Gruppuso

Since the very first observations, the Cosmic Microwave Background (CMB) has revealed on large-scales unexpected features known as anomalies, which challenge the standard $\Lambda$ cold dark matter ($\Lambda$CDM) cosmological model. One such anomaly is the "lack-of-correlation", where the measured two-point angular correlation function of CMB temperature ani

Abbavaram Gowtham Reddy, Saketh Bachu, Saloni Dash, Charchit Sharma

Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data. These biases, such as spurious correlations, arise due to various observed and unobserved confounding variables in the data generation process. In this paper, we formally analyze how confounding biases impact downstream classifiers and prese

Kyra Gan, Esmaeil Keyvanshokooh, Xueqing Liu, Susan Murphy

Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments. However, to ensure the effectiveness of the treatments, patients are often requested to take actions that have no immediate benefit to them, which we refer to as pro-treatment actions. In practice, clinicians have a limited budget to encourage patients to t

Noriyuki Aoyagi, Hiroaki Matsueda, Kunio Ishida

Herein, the dynamics of excitons coupled with optical phonons in a triangular system is numerically studied. By representing the excitons by quasi-spin states, the similarity between the chiral spin states and the exciton chiral states is discussed. In particular, the optical control of excitons is discussed, where photoirradiation causes the switching of th

Masaru Ito, Zhaosong Lu, Chuan He

In this paper we consider a composite optimization problem that minimizes the sum of a weakly smooth function and a convex function with either a bounded domain or a uniformly convex structure. In particular, we first present a parameter-dependent conditional gradient method for this problem, whose step sizes require prior knowledge of the parameters associa

Jean-Paul Allouche, Claude Morin

Inspired by a question asked on the list {\tt mathfun}, we revisit {\em Kempner-like series}, i.e., harmonic sums $\sum' 1/n$ where the integers $n$ in the summation have ``restricted'' digits. First we give a short proof that $\lim_{k \to \infty}(\sum_{s_2(n) = k} 1/n) = 2 \log 2$, where $s_2(n)$ is the sum of the binary digits of the integer $n$. Then we p

Jangho Kim, Thomas Luu, Wolfgang Unger

Lattice QCD in the strong coupling regime can be formulated in dual variables which are integer-valued. It can be efficiently simulated for modest finite temperatures and finite densities via the worm algorithm, circumventing the finite density sign problem in this regime. However, the low temperature regime is more expensive to address. As the partition fun

Riccardo Della Monica, Ivan de Martino, Mariafelicia de Laurentis

We developed a numerical methodology to compute the fully-relativistic propagation time of photons emitted by a pulsar in orbit around a massive compact object, like the supermassive black hole Sagittarius A* in the Galactic Center, whose gravitational field is described by a generic spherically symmetric space-time. Pulsars at the Galactic Center are usuall

Daniel Coelho, Miguel Oliveira, Vitor Santos

Current approaches of Reinforcement Learning (RL) applied in urban Autonomous Driving (AD) focus on decoupling the perception training from the driving policy training. The main reason is to avoid training a convolution encoder alongside a policy network, which is known to have issues related to sample efficiency, degenerated feature representations, and cat

Marco Coco

The effect of inclusion of the planar phonon anisotropy on thermo-electrical behavior of graphene is analyzed. Charge transport is simulated by means of Direct Simulation Monte Carlo technique coupled with numerical solution of the phonon Boltzmann equations based on deterministic methods. The definition of the crystal lattice local equilibrium temperature i

Beatriz Cabrero-Daniel, Andrea Sanagustín Cabrero

Natural Language Generation tools, such as chatbots that can generate human-like conversational text, are becoming more common both for personal and professional use. However, there are concerns about their trustworthiness and ethical implications. The paper addresses the problem of understanding how different users (e.g., linguists, engineers) perceive and

Stephen M. Wilkins, Christopher C. Lovell, Dimitrios Irodotou, Aswin P. Vijayan

With the successful launch and commissioning of JWST we are now able to routinely spectroscopically probe the rest-frame optical emission of galaxies at $z>6$ for the first time. Amongst the most useful spectral diagnostics used in the optical is the Balmer/4000~\AA\ break; this is, in principle, a diagnostic of the mean ages of composite stellar populations

Louise T. C. Seeyave, Stephen M. Wilkins, Jussi K. Kuusisto, Christopher C. Lovell

The history of reionisation is highly dependent on the ionising properties of high-redshift galaxies. It is therefore important to have a solid understanding of how the ionising properties of galaxies are linked to physical and observable quantities. In this paper, we use the First Light and Reionisation Epoch Simulations (FLARES) to study the Lyman-continuu

Chiara Razzetta, Valentina Candiani, Marco Crocco, Federico Benvenuto

Medical ultrasound imaging is the most widespread real-time non-invasive imaging system and its formulation comprises signal transmission, signal reception, and image formation. Ultrasound signal transmission modelling has been formalized over the years through different approaches by exploiting the physics of the associated wave problem. This work proposes

V. Chigarev, A. Kazakov, A. Pikovsky

We study the heterodimensional dynamics in a simple map on a three-dimensional torus. This map consists of a two-dimensional driving Anosov map and a one-dimensional driven M\"obius map, and demonstrates the collision of a chaotic attractor with a chaotic repeller if parameters are varied. We explore this collision by following tangent bifurcations of the pe

Sanghyuk Chun

Image-Text Matching (ITM) task, a fundamental vision-language (VL) task, suffers from the inherent ambiguity arising from multiplicity and imperfect annotations. Deterministic functions are not sufficiently powerful to capture ambiguity, prompting the exploration of probabilistic embeddings to tackle the challenge. However, the existing probabilistic ITM app

Zhanming Jie, Wei Lu

Chain-of-thought (CoT) prompting with large language models has proven effective in numerous natural language processing tasks, but designing prompts that generalize well to diverse problem types can be challenging, especially in the context of math word problem (MWP) solving. Additionally, it is common to have a large amount of training data that have a bet

Amirhossein Abaskohi, Sascha Rothe, Yadollah Yaghoobzadeh

In recent years, there has been significant progress in developing pre-trained language models for NLP. However, these models often struggle when fine-tuned on small datasets. To address this issue, researchers have proposed various adaptation approaches. Prompt-based tuning is arguably the most common way, especially for larger models. Previous research sho

Umpon Jairuk, Sikarin Yoo-Kong

We present a new type of integrable one-dimensional many-body systems called a one-parameter Calogero-Moser (CM) system. In the discrete level, the Lax pairs with a parameter are introduced and, of course, the discrete-time equations of motion are obtained as well as their corresponding discrete-time Lagrangian. The integrability feature of this new system c

Alessandro De Stefani, Jonathan Montaño, Luis Núñez-Betancourt, Lisa Seccia

In this article we show that the symbolic Rees algebra of a mixed ladder determinantal ideal is strongly $F$-regular. Furthermore, we prove that the symbolic associated graded algebra of a mixed ladder determinantal ideal is $F$-pure. The latter implies that mixed ladder determinantal rings are $F$-pure. We also show that ideals of the poset of minors of a g

Moreno Bevilacqua, Eloy Alvarado, Christian Caamaño-Carrillo

The Gaussian copula is a powerful tool that has been widely used to model spatial and/or temporal correlated data with arbitrary marginal distributions. However, this kind of model can potentially be too restrictive since it expresses a reflection symmetric dependence. In this paper, we propose a new spatial copula model that makes it possible to obtain rand

Yibo Guo, Jingyi Xue, Yingkang Zhang, Mingliang Xu

Rapid developments in artificial intelligence technology have led to unmanned systems replacing human beings in many fields requiring high-precision predictions and decisions. In modern operational environments, all job plans are affected by emergency events such as equipment failures and resource shortages, making a quick resolution critical. The use of unm

Félix Chénier, Etienne Marquis, Maude Fleury-Rousseau

Estimating the position of the whole-body centre of mass (CoM) based on skin markers and anthropometric tables requires tracking the pelvis and lower body, which is impossible for wheelchair users due to occlusion. In this work, we present a method to track the user's whole-body CoM using visible markers affixed to the user and wheelchair where the user rema

Shubham Innani, Prasad Dutande, Ujjwal Baid, Venu Pokuri

Skin cancer is a serious condition that requires accurate diagnosis and treatment. One way to assist clinicians in this task is using computer-aided diagnosis (CAD) tools that automatically segment skin lesions from dermoscopic images. We propose a novel adversarial learning-based framework called Efficient-GAN (EGAN) that uses an unsupervised generative net

Lingzhi Li, Zhongshu Wang, Zhen Shen, Li Shen

Reconstructing neural radiance fields with explicit volumetric representations, demonstrated by Plenoxels, has shown remarkable advantages on training and rendering efficiency, while grid-based representations typically induce considerable overhead for storage and transmission. In this work, we present a simple and effective framework for pursuing compact ra

Michele Coti Zelati, Michele Dolce, Chia-Chun Lo

In this note, we study the long-time dynamics of passive scalars driven by rotationally symmetric flows. We focus on identifying precise conditions on the velocity field in order to prove enhanced dissipation and Taylor dispersion in three-dimensional infinite pipes. As a byproduct of our analysis, we obtain an enhanced decay for circular flows on a disc of

Nicolae Cotfas

There exist many attempts to define a Wigner function for qudits, each of them coming with its advantages and limitations. The existing finite versions have simple definitions, but they are artificial in their construction and do not allow an intuitive state analysis. The continuous versions have more complicated definitions, but they are similar to the orig

Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko

In reinforcement learning, the advantage function is critical for policy improvement, but is often extracted from a learned Q-function. A natural question is: Why not learn the advantage function directly? In this work, we introduce VA-learning, which directly learns advantage function and value function using bootstrapping, without explicit reference to Q-f

Yifei Wang, Zhengyang Zhou, Liqin Wang, John Laurentiev

When using machine learning to aid decision-making, it is critical to ensure that an algorithmic decision is fair and does not discriminate against specific individuals/groups, particularly those from underprivileged populations. Existing group fairness methods aim to ensure equal outcomes (such as loan approval rates) across groups delineated by protected v

Kweku Kwegyir-Aggrey, Marissa Gerchick, Malika Mohan, Aaron Horowitz

When determining which machine learning model best performs some high impact risk assessment task, practitioners commonly use the Area under the Curve (AUC) to defend and validate their model choices. In this paper, we argue that the current use and understanding of AUC as a model performance metric misunderstands the way the metric was intended to be used.

Yu Wang, Pengchong Qiao, Chang Liu, Guoli Song

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framew

Vikram Giri, Hyunju Kwon, Matthew Novack

In this work, we prove the $L^3$-based strong Onsager conjecture for the three-dimensional Euler equations. Our main theorem states that there exist weak solutions which dissipate the total kinetic energy, satisfy the local energy inequality, and belong to $C^0_t (W^{\frac 13-, 3} \cap L^{\infty-})$. More precisely, for every $\beta<\frac 13$, we can constru

Yue Chen, Jian Li

As the second part of our study, in this paper, we proceed to refine the solar system model by incorporating the gravitational influence of Plutinos in Neptune's 2:3 resonance. We aim to develop the arc model to represent the global perturbation of Plutinos by taking into account their asymmetric spatial distribution resulting from the 2:3 resonance, and dem

Mengsay Loem, Masahiro Kaneko, Sho Takase, Naoaki Okazaki

Large-scale pre-trained language models such as GPT-3 have shown remarkable performance across various natural language processing tasks. However, applying prompt-based methods with GPT-3 for Grammatical Error Correction (GEC) tasks and their controllability remains underexplored. Controllability in GEC is crucial for real-world applications, particularly in

Malte Henkel, Stoimen Stoimenov

The dynamical scaling and ageing in the relaxational dynamics of the quenched directed spherical model is analysed. The exact two-time correlation and response functions display new regimes of ballistic or anisotropic ballistic scaling, at larger distances than probed in the usual regime of diffusive scaling. The r\^ole of long-ranged initial correlations on

Hong Huang

We show the following result: Let $(M,g_0)$ be a compact manifold of dimension $n\geq 12$ with positive isotropic curvature. Then $M$ is diffeomorphic to a spherical space form, or a quotient manifold of $\mathbb{S}^{n-1}\times \mathbb{R}$ by a cocompact discrete subgroup of the isometry group of the round cylinder $\mathbb{S}^{n-1}\times \mathbb{R}$, or a c

Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu

Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks. Current research focuses on enhancing their performance within their existing knowledge. Despite their vast knowledge, LLMs are still limited by the amount of information they can accommodate and comprehend. Therefore, the abi

Zhiyi Li, Shengjie Zhang, Yujie Song, Jungyeul Park

Biomedical named entity recognition (NER) is a critial task that aims to identify structured information in clinical text, which is often replete with complex, technical terms and a high degree of variability. Accurate and reliable NER can facilitate the extraction and analysis of important biomedical information, which can be used to improve downstream appl

Gil Kur, Eli Putterman, Alexander Rakhlin

It is well known that Empirical Risk Minimization (ERM) may attain minimax suboptimal rates in terms of the mean squared error (Birg\'e and Massart, 1993). In this paper, we prove that, under relatively mild assumptions, the suboptimality of ERM must be due to its large bias. Namely, the variance error term of ERM is bounded by the minimax rate. In the fixed

SayedHassan Khatoonabadi, Diego Elias Costa, Suhaib Mujahid, Emad Shihab

Pull Requests (PRs) that are neither progressed nor resolved clutter the list of PRs, making it difficult for the maintainers to manage and prioritize unresolved PRs. To automatically track, follow up, and close such inactive PRs, Stale bot was introduced by GitHub. Despite its increasing adoption, there are ongoing debates on whether using Stale bot allevia

Yuchuan Tian, Hanting Chen, Xutao Wang, Zheyuan Bai

Recent releases of Large Language Models (LLMs), e.g. ChatGPT, are astonishing at generating human-like texts, but they may impact the authenticity of texts. Previous works proposed methods to detect these AI-generated texts, including simple ML classifiers, pretrained-model-based zero-shot methods, and finetuned language classification models. However, main

Sizhong Zhou, Jiancheng Wu

Let $\mathcal{A}$ be a set of connected graphs. Then a spanning subgraph $A$ of $G$ is called an $\mathcal{A}$-factor if each component of $A$ is isomorphic to some member of $\mathcal{A}$. Especially, when every graph in $\mathcal{A}$ is a path, $A$ is a path factor. For a positive integer $d\geq2$, we write $\mathcal{P}_{\geq d}=\{P_i|i\geq d\}$. Then a $\

Belinda Damian, Jessy Jose, Beth Biller, KT Paul

Understanding the evolution and dissipation of protoplanetary disks are crucial in star and planet formation studies. We report the protoplanetary disk population in the nearby young $\sigma$ Orionis cluster (d$\sim$408 pc; age$\sim$1.8 Myr) and analyse the disk properties such as dependence on stellar mass and disk evolution. We utilise the comprehensive ce

Fu-An Chao, Tien-Hong Lo, Tzu-I Wu, Yao-Ting Sung

Automatic Pronunciation Assessment (APA) plays a vital role in Computer-assisted Pronunciation Training (CAPT) when evaluating a second language (L2) learner's speaking proficiency. However, an apparent downside of most de facto methods is that they parallelize the modeling process throughout different speech granularities without accounting for the hierarch

Christian Gourieroux, Quinlan Lee

The goal of this paper is to extend the nonparametric estimation of Impulse Response Functions (IRF) by means of local projections in the nonlinear dynamic framework. We discuss the existence of a nonlinear autoregressive representation for Markov processes and explain how their IRFs are directly linked to the Nonlinear Local Projection (NLP), as in the case

Jiaqi Bai, Hongcheng Guo, Jiaheng Liu, Jian Yang

Retrieval-enhanced text generation has shown remarkable progress on knowledge-intensive language tasks, such as open-domain question answering and knowledge-enhanced dialogue generation, by leveraging passages retrieved from a large passage corpus for delivering a proper answer given the input query. However, the retrieved passages are not ideal for guiding

Yi Hu, Haotong Yang, Zhouchen Lin, Muhan Zhang

Large language models (LLMs) have scaled up to unlock a wide range of complex reasoning tasks with the aid of various prompting methods. However, current prompting methods generate natural language intermediate steps to help reasoning, which can cause imperfect task reduction and confusion. To mitigate such limitations, we explore code prompting, a neural sy

Laura State, Salvatore Ruggieri, Franco Turini

Many high-performing machine learning models are not interpretable. As they are increasingly used in decision scenarios that can critically affect individuals, it is necessary to develop tools to better understand their outputs. Popular explanation methods include contrastive explanations. However, they suffer several shortcomings, among others an insufficie

Vikram Giri, Hyunju Kwon, Matthew Novack

In this work, we develop a wavelet-inspired, $L^3$-based convex integration framework for constructing weak solutions to the three-dimensional incompressible Euler equations. The main innovations include a new multi-scale building block, which we call an intermittent Mikado bundle; a wavelet-inspired inductive set-up which includes assumptions on spatial and

Yiqiu Han, Xiao Chen

We study the entanglement dynamics of quantum automaton (QA) circuits in the presence of U(1) symmetry. We find that the second R\'enyi entropy grows diffusively with a logarithmic correction as $\sqrt{t\ln{t}}$, saturating the bound established by Huang [IOP SciNotes 1, 035205 (2020)]. Thanks to the special feature of QA circuits, we understand the entangle

Ryodai Kawaguchi, Tomohiro Fujita, Misao Sasaki

We study a single-field inflation model in which the inflaton potential has an upward step between two slow-roll regimes by taking into account the finite width of the step. We calculate the probability distribution function (PDF) of the curvature perturbation $P[{\cal{R}}]$ using the $\delta N$ formalism. The PDF has an exponential-tail only for positive ${

Zimo Hao, Mingyan Wu

For $\alpha \in (1,2)$, we study the following stochastic differential equation driven by a non-degenerate symmetric $\alpha$-stable process in $\mathbb{R}^d$: \begin{align*} {\rm d} X_t=b(t,X_t){\mathord{{\rm d}}} t+\sigma(t,X_{t-}){\mathord{{\rm d}}} L_t^{(\alpha)},\ \ X_0 =x \in \mathbb{R}^d, \end{align*} where $b$ belongs to $ L^\infty(\mathbb{R}_+;\math

Samuel G. G. Johnston

Let $X_1,\ldots,X_N$ be i.i.d.\ random variables distributed like $X$. Suppose that the first $k \geq 3$ moments $\{ \mathbb{E}[X^j] : j = 1,\ldots,k\}$ of $X$ agree with that of the standard Gaussian distribution, that $\mathbb{E}[|X|^{k+1}] < \infty$, and that there is a subinterval of $\mathbb{R}$ of width $w$ over which the law of $X$ has a density of at

Kotaro Fukushima, Shogo B. Kobayashi, Kyoko Matsushita

Chemical elements in the hot medium permeating early-type galaxies, groups, and clusters make them an excellent laboratory for studying metal enrichment and cycling processes in the largest scales of the universe. Here, we report the analysis by the XMM-Newton Reflection Grating Spectrometer of 14 early-type galaxies, including the well-known brightest clust

Yan Dolinsky, Or Zuk

The aim of this short note is to present a solution to the discrete time exponential utility maximization problem in a case where the underlying asset has a multivariate normal distribution. In addition to the usual setting considered in Mathematical Finance, we also consider an investor who is informed about the risky asset's price changes with a delay. Our

Steven Tel, Zongwei Wu, Yulun Zhang, Barthélémy Heyrman

High dynamic range (HDR) imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output. The essence is to leverage the contextual information, including both dynamic and static semantics, for better image generation. Existing methods often focus on the spatial misalignment across input frames caused by the foregroun

Jianfa Lai, Zixiong Yu, Songtao Tian, Qian Lin

In this paper, we study the generalization ability of the wide residual network on $\mathbb{S}^{d-1}$ with the ReLU activation function. We first show that as the width $m\rightarrow\infty$, the residual network kernel (RNK) uniformly converges to the residual neural tangent kernel (RNTK). This uniform convergence further guarantees that the generalization e

Stefano Baranzini, Alessandro Portaluri, Ran Yang

The classical theory of attractive central force problem on the standard (flat) Euclidean plane can be generalized to surfaces by reformulating the basic underlying physical principles by means of differential geometry. Attractive central force problems on state manifolds appear quite often and in several different context ranging from nonlinear control theo

Tianhan Gao, Wei Lu

This paper proposes physical-based, reduced-order electrochemical models that are much faster than the electrochemical pseudo 2D (P2D) model, while providing high accuracy even under the challenging conditions of high C-rate and strong polarization of lithium ion concentration and potential in a battery cell. In particular, an innovative weak form of equatio

Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee

Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories, rather than explicit reward signals. While PbRL has demonstrated practical success in fine-tuning language models, existing theoretical work focuses on regret minimization and fails to captur