November 2023 arXiv papers — page 3

Mario Stepanik

This paper introduces a novel unsupervised learning paradigm inspired by Gerald Edelman's theory of neuronal group selection ("Neural Darwinism"). The presented automaton learns to recognize arbitrary symbols (e.g., letters of an alphabet) when they are presented repeatedly, as they are when children learn to read. On a second hierarchical level, the model c

Jing Wang, Xiaofeng Liu, Fangyun Wang, Lin Zheng

Congenital heart disease (CHD) is the most common birth defect and the leading cause of neonate death in China. Clinical diagnosis can be based on the selected 2D key-frames from five views. Limited by the availability of multi-view data, most methods have to rely on the insufficient single view analysis. This study proposes to automatically analyze the mult

He Wang, Yuejie Chi

In large-scale federated and decentralized learning, communication efficiency is one of the most challenging bottlenecks. While gossip communication -- where agents can exchange information with their connected neighbors -- is more cost-effective than communicating with the remote server, it often requires a greater number of communication rounds, especially

David Fabian, Patrick Morris, Tibor Szabó

For a graph $H$ and an $n$-vertex graph $G$, the $H$-bootstrap process on $G$ is the process which starts with $G$ and, at every time step, adds any missing edges on the vertices of $G$ that complete a copy of $H$. This process eventually stabilises and we are interested in the extremal question raised by Bollob\'as of determining the maximum running time (n

Viggo Moro, Charlotte Loh, Rumen Dangovski, Ali Ghorashi

Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials. Recently, publicly available material data repositories have grown rapidly. This growth encompasses not only more materials but also a greater variety and quantity of their associated pro

Giorgos Lysandrou, Roma English Owen, Vanja Popovic, Grant Le Brun

It is essential that healthcare professionals and members of the healthcare community can access and easily understand patient experiences in the real world, so that care standards can be improved and driven towards personalised drug treatment. Social media platforms and message boards are deemed suitable sources of patient experience information, as patient

Ying-Jie Wei, Zheng-Cheng Gu

In recent years, tensor network renormalization (TNR) has emerged as an efficient and accurate method for studying (1+1)D quantum systems or 2D classical systems using real-space renormalization group (RG) techniques. One notable application of TNR is its ability to extract central charge and conformal scaling dimensions for critical systems. In this paper,

Yifan Yao, Alina Kononov, Arne Metzlaff, Andreas Wucher

Characteristic properties of secondary electrons emitted from irradiated two-dimensional materials arise from multi-length and time-scale relaxation processes that connect the initial non-equilibrium excited electron distribution with their eventual emission. To understand these processes, which are critical for using secondary electrons as high-resolution t

Niall Bootland, Victorita Dolean, Frédéric Nataf, Pierre-Henri Tournier

In this paper we design, analyse and test domain decomposition methods for linear systems of equations arising from conforming finite element discretisations of positive Maxwell-type equations, namely for $\mathbf{H}(\mathbf{curl})$ problems. It is well known that convergence of domain decomposition methods rely heavily on the efficiency of the coarse space

Phillip Howard, Avinash Madasu, Tiep Le, Gustavo Lujan Moreno

While vision-language models (VLMs) have achieved remarkable performance improvements recently, there is growing evidence that these models also posses harmful biases with respect to social attributes such as gender and race. Prior studies have primarily focused on probing such bias attributes individually while ignoring biases associated with intersections

Alex Turzillo, Minyoung You

We study the interplay of duality and stacking of bosonic and fermionic symmetry-protected topological phases in one spatial dimension. In general the classifications of bosonic and fermionic phases have different group structures under the operation of stacking, but we argue that they are often isomorphic and give an explicit isomorphism when it exists. Thi

Astra Kolomatskaia, Michael Shulman

We introduce Displayed Type Theory (dTT), a multi-modal homotopy type theory with discrete and simplicial modes. In the intended semantics, the discrete mode is interpreted by a model for an arbitrary $\infty$-topos, while the simplicial mode is interpreted by Reedy fibrant augmented semi-simplicial diagrams in that model. This simplicial structure is repres

Linfeng Du, Ji Xin, Alex Labach, Saba Zuberi

Transformer-based models have greatly pushed the boundaries of time series forecasting recently. Existing methods typically encode time series data into $\textit{patches}$ using one or a fixed set of patch lengths. This, however, could result in a lack of ability to capture the variety of intricate temporal dependencies present in real-world multi-periodic t

Nicholas J. Harmon, James P. Ashton, Patrick M. Lenahan, Michael E. Flatté

Electrically detected magnetic resonance (EDMR) and near-zero field magnetoresistance (NZFMR) are techniques that probe defect states at dielectric interfaces critical for metal-oxide-semiconductor (MOS) electronic devices such as the Si/SiO$_2$ MOS field effect transistor (MOSFET). A comprehensive theory, adapted from the trap-assisted recombination theory

Shiyu Zhang, Xi Zhang

In this paper, we establish a "pseudo-effective" version of the holonomy principle for compact K\"{a}hler manifolds with nonnegative holomorphic sectional curvature. As applications, we prove that if a compact complex manifold $M$ admits a K\"{a}hler metric $\omega$ with nonnegative holomorphic sectional curvature and $(M,\omega)$ has no nonzero truly flat t

Saurabh Page, Sudeep Mangalvedhekar, Kshitij Deshpande, Tanmay Chavan

This paper presents our work for the Violence Inciting Text Detection shared task in the First Workshop on Bangla Language Processing. Social media has accelerated the propagation of hate and violence-inciting speech in society. It is essential to develop efficient mechanisms to detect and curb the propagation of such texts. The problem of detecting violence

Simone Carano, Domenico Mucci

We deal with the relaxed area functional in the strict $BV$-convergence of non-smooth maps defined in domains of generic dimension and taking values into the unit circle. In case of Sobolev maps, a complete explicit formula is obtained. Our proof is based on tools from Geometric Measure Theory and Cartesian currents. We then discuss the possible extension to

Ioana Petkova

In this paper the operator $A = u(z)\frac{d}{dz}$ is considered, where $u$ is an entire or meromorphic function in the complex plane. The expansion of $A^{k}$ ($k\geq1$) with the help of the powers of the differential operator $D=\frac{d}{dz}$ is obtained, and it is shown that this expansion depends on special numbers. Connections between these numbers and k

Zineng Tang, Ziyi Yang, Mahmoud Khademi, Yang Liu

We present CoDi-2, a versatile and interactive Multimodal Large Language Model (MLLM) that can follow complex multimodal interleaved instructions, conduct in-context learning (ICL), reason, chat, edit, etc., in an any-to-any input-output modality paradigm. By aligning modalities with language for both encoding and generation, CoDi-2 empowers Large Language M

Jonas Roth, Domenic Keller, Oscar Castañeda, Christoph Studer

Analog subtractive synthesizers are generally considered to provide superior sound quality compared to digital emulations. However, analog circuitry requires calibration and suffers from aging, temperature instability, and limited flexibility in generating a wide variety of waveforms. Digital synthesis can mitigate many of these drawbacks, but generating arb

Rémi Kazmierczak, Eloïse Berthier, Goran Frehse, Gianni Franchi

In this paper, we introduce an explainable algorithm designed from a multi-modal foundation model, that performs fast and explainable image classification. Drawing inspiration from CLIP-based Concept Bottleneck Models (CBMs), our method creates a latent space where each neuron is linked to a specific word. Observing that this latent space can be modeled with

Zitian Tang, Rohan Myer Krishnan, Zhiqiu Yu, Chen Sun

Learning from (procedural) videos has increasingly served as a pathway for embodied agents to acquire skills from human demonstrations. To do this, video understanding models must be able to obtain structured understandings, such as the temporal segmentation of a demonstration into sequences of actions and skills, and to generalize the understandings to nove

Vladimir Gurvich, Artem Parfenov, Michael Vyalyi

We compare to different extensions of the ancient game of nim: Moore's nim$(n, \leq k)$ and exact nim$(n, = k)$. Given integers $n$ and $k$ such that $0 < k \leq n$, we consider $n$ piles of stones. Two players alternate turns. By one move it is allowed to choose and reduce any (i) at most $k$ or (ii) exactly $k$ piles of stones in games nim$(n, \leq k)$ and

Ilijas Farah, Ilan Hirshberg

We prove that the central sequence algebra of a separable C*-algebra is either subhomogeneous or non-exact, confirming a conjecture of Enders and Shulman. We also prove analogous dichotomy for other massive C*-algebras.

William W. Howard, Samuel R. Shebert, Anthony F. Martone, R. Michael Buehrer

Cognitive Radar Networks, which were popularized by Simon Haykin in 2006, have been proposed to address limitations with legacy radar installations. These limitations include large physical size, power consumption, fixed operating parameters, and single point vulnerabilities. Cognitive radar solves part of this problem through adaptability, using biologicall

Juan M. Z. Pretel, Takol Tangphati, Ayan Banerjee, Anirudh Pradhan

Perturbative Quantum Chromodynamics (pQCD) corrections and color superconductivity predict that strongly interacting matter can reveal new physical phenomena under extreme conditions. Taking into account these interaction effects, we investigate the role of anisotropic pressure in quark stars composed of interacting quark matter. Adopting two physically well

Lei Xin, George Chiu, Shreyas Sundaram

The problem of online change point detection is to detect abrupt changes in properties of time series, ideally as soon as possible after those changes occur. Existing work on online change point detection either assumes i.i.d data, focuses on asymptotic analysis, does not present theoretical guarantees on the trade-off between detection accuracy and detectio

Mohammad Hossein Amini, Shervin Naseri, Shiva Nejati

Simulators are widely used to test Autonomous Driving Systems (ADS), but their potential flakiness can lead to inconsistent test results. We investigate test flakiness in simulation-based testing of ADS by addressing two key questions: (1) How do flaky ADS simulations impact automated testing that relies on randomized algorithms? and (2) Can machine learning

Martin Bridgeman, Kenneth Bromberg, Franco Vargas Pallete, Yilin Wang

The universal Liouville action (also known as the Loewner energy for Jordan curves) is a K\"ahler potential on the Weil-Petersson universal Teichm\"uller space, which is identified with the family of Weil-Petersson quasicircles via conformal welding. Our main result shows that, under regularity assumptions, the universal Liouville action equals the renormali

Sergey Malev, Anastasiia Zhilina

In this paper an algebraic proof of Christoph's theorem is provided. This theorem from algebraic-geometry is about the existence of a finite automaton for computing coefficient of a series for an algebraic function.

Yanqing Liu, Kai Wang, Wenqi Shao, Ping Luo

Visual-language pre-training has achieved remarkable success in many multi-modal tasks, largely attributed to the availability of large-scale image-text datasets. In this work, we demonstrate that Multi-modal Large Language Models (MLLMs) can enhance visual-language representation learning by establishing richer image-text associations for image-text dataset

Alexander Bruce Johnson, Stefan Steinerberger

We study optimal transport plans from $m$ equally weighted points (with weights $1/m$) to $n$ equally weighted points (with weights $1/n$). The Birkhoff-von Neumann Theorem implies that if $m=n$, then the optimal transport plan can be realized by a bijective map: the mass from each $x_i$ is sent to a unique $y_j$. This is impossible when $m \neq n$, however,

James Seale Smith, Yen-Chang Hsu, Zsolt Kira, Yilin Shen

Recent work has demonstrated a remarkable ability to customize text-to-image diffusion models to multiple, fine-grained concepts in a sequential (i.e., continual) manner while only providing a few example images for each concept. This setting is known as continual diffusion. Here, we ask the question: Can we scale these methods to longer concept sequences wi

Shuaishuai Han, Mohammad Ahmad Al-Jarrah, Emad Alsusa

This paper introduces a system model called pilot-aided simultaneous communication and localisation (PASCAL) and illustrates its performance in the presence of practical gain and phase imperfections. Specifically, we consider the scenario where multiple single-antenna unmanned aerial vehicles (UAVs) transmit data packets to a multi-antenna base station (BS)

Aryaman Chobey, Oliver Smith, Anzi Wang, Grusha Prasad

The use of neural language models to model human behavior has met with mixed success. While some work has found that the surprisal estimates from these models can be used to predict a wide range of human neural and behavioral responses, other work studying more complex syntactic phenomena has found that these surprisal estimates generate incorrect behavioral

Yongliang Shen, Kaitao Song, Xu Tan, Wenqi Zhang

In recent years, the remarkable progress of large language models (LLMs) has sparked interest in task automation, which involves decomposing complex tasks described by user instructions into sub-tasks and invoking external tools to execute them, playing a central role in autonomous agents. However, there is a lack of systematic and standardized benchmarks to

Wenzheng Gao, Zhenting Sun

The partially linear binary choice model can be used for estimating structural equations where nonlinearity may appear due to diminishing marginal returns, different life cycle regimes, or hectic physical phenomena. The inference procedure for this model based on the analytic asymptotic approximation could be unreliable in finite samples if the sample size i

Daoan Zhang, Yunhao Luo, Jianguo Zhang

We bring a new perspective to semi-supervised semantic segmentation by providing an analysis on the labeled and unlabeled distributions in training datasets. We first figure out that the distribution gap between labeled and unlabeled datasets cannot be ignored, even though the two datasets are sampled from the same distribution. To address this issue, we the

Charles Batty, Alexander Gomilko, Dominik Kobos, Yuri Tomilov

This paper investigates when analytic Besov functions of $n$ variables act on the generators of $n$ commuting $C_0$-semigroups on a Banach space. The theory for $n=1$ has already been published, and the present paper uses a different approach to that case as well as extending to the cases when $n\ge2$. It also clarifies some spectral mapping properties and p

Harry Liu, Siddhartha Sarkar, A. Nafis Arafat, Ethan Stanifer

Topological mechanical metamaterials have demonstrated exotic and robust mechanical properties which led to promising engineering applications. One of such properties is the focusing of stress at the interface connecting domains of topological Maxwell lattices of opposite topological polarizations, which protects the bulk of the material against fracturing.

Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli

Neural rendering methods have significantly advanced photo-realistic 3D scene rendering in various academic and industrial applications. The recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed combining the benefits of both primitive-based representations and volumetric representations. However, it often leads to

En Yu, Liang Zhao, Yana Wei, Jinrong Yang

Humans possess the remarkable ability to foresee the future to a certain extent based on present observations, a skill we term as foresight minds. However, this capability remains largely under explored within existing Multimodal Large Language Models (MLLMs), hindering their capacity to learn the fundamental principles of how things operate and the intentio

S. K. Rajput, M. K. Panda

In this study, we investigate the impact of heating or cooling in a suspension experiencing phototactic bioconvection. The suspension is illuminated by collimated irradiation from the top and subjected to heating or cooling from the bottom. The governing equations include the Navier Stokes equations with the Boussinesq approximation, the diffusion equation f

Stefano Marini, Nicoletta Tardini, Michela Zedda

Motivated by a conjecture in [9] we prove that the K\"ahler cone over a regular complete Sasakian manifold is Ricci-flat and projectively induced if and only if it is flat. We also obtain that, up to $\mathcal D_a$-homothetic transformations, K\"ahler cones over homogeneous compact Sasakian manifolds are projectively induced. As main tool we provide a relati

Xizhi Liu, Oleg Pikhurko

For given positive integers $r\ge 3$, $n$ and $e\le \binom{n}{2}$, the famous Erd\H os--Rademacher problem asks for the minimum number of $r$-cliques in a graph with $n$ vertices and $e$ edges. A conjecture of Lov\'asz and Simonovits from the 1970s states that, for every $r\ge 3$, if $n$ is sufficiently large then, for every $e\le \binom{n}{2}$, at least one

Giuseppe De Laurentis, Harald Ita, Vasily Sotnikov

We complete the computation of two-loop helicity amplitudes required to obtain next-to-next-to-leading order QCD corrections for three-jet production at hadron colliders, including all contributions beyond the leading-color approximation. The analytic expressions are reconstructed from finite-field samples obtained with the numerical unitarity method. We fin

Hiroki Furuta, Yutaka Matsuo, Aleksandra Faust, Izzeddin Gur

Language model agents (LMA) recently emerged as a promising paradigm on muti-step decision making tasks, often outperforming humans and other reinforcement learning agents. Despite the promise, their performance on real-world applications that often involve combinations of tasks is still underexplored. In this work, we introduce a new benchmark, called CompW

Oliver Diekmann, Dmitry O. Krimer, Stefan Rotter

The strong coupling of quantum emitters to a cavity mode has been of paramount importance in the development of quantum optics. Recently, also the strong coupling to more than a single mode of an electromagnetic resonator has drawn considerable interest. We investigate how this multimode strong coupling regime can be harnessed to coherently control quantum s

Jie Shi, Arno P. J. M. Siebes, Siamak Mehrkanoon

This paper proposes an interpretable two-stream transformer CORAL networks (TransCORALNet) for supply chain credit assessment under the segment industry and cold start problem. The model aims to provide accurate credit assessment prediction for new supply chain borrowers with limited historical data. Here, the two-stream domain adaptation architecture with c

Jesús Suárez

We give a universal formula describing derivation operators on a Hilbert space for a large class of interpolation methods. It is based on a simple new technique on ``critical points" where all the derivations attain the maximum. We deduce from this a version of Kalton uniqueness theorem for such methods, in particular, for the real method. As an application

Pegah Azizi, Siddhartha Sarkar, Kai Sun, Stefano Gonella

So-called fragile topological states of matter challenge our conventional notion of topology by lacking the robustness typically associated with topological protection, thereby displaying elusive manifestations that are difficult to harness for wave control. In this Letter, we leverage the recent discovery of fragile topological states in special classes of

Uchechukwu F. Njoku, Alberto Abelló, Besim Bilalli, Gianluca Bontempi

Many-Objective Feature Selection (MOFS) approaches use four or more objectives to determine the relevance of a subset of features in a supervised learning task. As a consequence, MOFS typically returns a large set of non-dominated solutions, which have to be assessed by the data scientist in order to proceed with the final choice. Given the multi-variate nat

Zhongtian Dong, Marçal Comajoan Cara, Gopal Ramesh Dahale, Roy T. Forestano

This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENN) and Deep Neural Networks (DNN). We evaluate the performance of each network with two toy examples for a binary classific

Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang

Alignment has become a critical step for instruction-tuned Large Language Models (LLMs) to become helpful assistants. However, the effective evaluation of alignment for emerging Chinese LLMs is still largely unexplored. To fill in this gap, we introduce AlignBench, a comprehensive multi-dimensional benchmark for evaluating LLMs' alignment in Chinese. We desi

Lucas Aragão, Jonathan Chapman, Miquel Ortega, Victor Souza

The following question was asked by Prendiville: given an $r$-colouring of the interval $\{2, \dotsc, N\}$, what is the minimum number of monochromatic solutions of the equation $xy = z$? For $r=2$, we show that there are always asymptotically at least $(1/2\sqrt{2}) N^{1/2} \log N$ monochromatic solutions, and that the leading constant is sharp. For $r=3$ a

Luca Ballotta, Nicolò Dal Fabbro, Giovanni Perin, Luca Schenato

Assisted and autonomous driving are rapidly gaining momentum and will soon become a reality. Artificial intelligence and machine learning are regarded as key enablers thanks to the massive amount of data that smart vehicles will collect from onboard sensors. Federated learning is one of the most promising techniques for training global machine learning model

Eugenio P Balanzario, Daniel Eduardo Cardenas Romero

We present an explicit formula for a weighted sum over the zeros of the Riemann zeta function. This weighted sum is evaluated in terms of a sum over the prime numbers, weighted with help of the Hermite polynomials. From the explicit formula presented in this note, it follows that prime numbers determine the distribution of the zeros of the Riemann zeta funct

Jan Dreier, Ioannis Eleftheriadis, Nikolas Mählmann, Rose McCarty

A graph class $\mathscr{C}$ is called monadically stable if one cannot interpret, in first-order logic, arbitrary large linear orders in colored graphs from $\mathscr{C}$. We prove that the model checking problem for first-order logic is fixed-parameter tractable on every monadically stable graph class. This extends the results of [Grohe, Kreutzer, and Siebe

Vedant Deshpande, Yash Patwardhan, Kshitij Deshpande, Sudeep Mangalvedhekar

In this paper, we present our approach for the "Nuanced Arabic Dialect Identification (NADI) Shared Task 2023". We highlight our methodology for subtask 1 which deals with country-level dialect identification. Recognizing dialects plays an instrumental role in enhancing the performance of various downstream NLP tasks such as speech recognition and translatio

Benjamin Horowitz, Peter Melchior

We reconstruct the dark matter density field from spatially overlapping spectroscopic and photometric redshift catalogs through a forward modelling approach. Instead of directly inferring the underlying density field, we find the best fitting initial Gaussian fluctuations that will evolve into the observed cosmic volume. To account for the substantial uncert

Natalie Hilliard, Juha Vierinen, Philip J. Erickson

The incoherent scatter radar (ISR) technique is a powerful remote sensing tool for ionosphere and thermosphere dynamics in the near-Earth space environment. Weak ISR scatter from naturally occurring Langmuir oscillations, or plasma lines, contain high precision information on the altitude-dependent thermal ionospheric electron density. However, analyzing thi

Xiangyuan Zhang, Weichao Mao, Saviz Mowlavi, Mouhacine Benosman

We introduce controlgym, a library of thirty-six industrial control settings, and ten infinite-dimensional partial differential equation (PDE)-based control problems. Integrated within the OpenAI Gym/Gymnasium (Gym) framework, controlgym allows direct applications of standard reinforcement learning (RL) algorithms like stable-baselines3. Our control environm

Andrea Bressan, Alen Kushova, Gabriele Loli, Monica Montardini

This work investigates diagonalization-based methods for efficiently solving linear evolution problems, with a particular focus on the heat equation. The plain diagonalization of the differential operator, though effective for elliptic problems where fast diagonalization can be used, exhibits instability when applied to the heat equation. To address this dif

Suman Sapkota, Binod Bhattarai

The recent success of multiple neural architectures like CNNs, Transformers, and MLP-Mixers motivated us to look for similarities and differences between them. We found that these architectures can be interpreted through the lens of a general concept of dimension mixing. Research on coupling flows and the butterfly transform shows that partial and hierarchic

Janos Engländer, Giulio Iacobelli, Gábor Pete, Rodrigo Ribeiro

We study the Tree Builder Random Walk: a randomly growing tree, built by a walker as she is walking around the tree. Namely, at each time $n$, she adds a leaf to her current vertex with probability $p_n \asymp n^{-\gamma}$, $\gamma\in (2/3,1]$, then moves to a uniform random neighbor on the possibly modified tree. We show that the tree process at its growth

Wei Tang, Frank Verstraete, Jutho Haegeman

The contraction of tensor networks is a central task in the application of tensor network methods to the study of quantum and classical many body systems. In this paper, we investigate the impact of gauge degrees of freedom in the virtual indices of the tensor network on the contraction process, specifically focusing on boundary matrix product state methods

Marco Limongi, Lorenzo Roberti, Alessandro Chieffi, Ken'ichi Nomoto

According to a standard initial mass function, stars in the range 7-12 Msun constitute ~50% (by number) of the stars more massive than ~7 Msun, but, in spite of this, their evolutionary properties, and in particular their final fate, are still scarcely studied. In this paper we present a detailed study of the evolutionary properties of solar metallicity, non

Anish Shastri, Andres Garcia-Saavedra, Paolo Casari

We consider the localization of a mobile millimeter-wave client in a large indoor environment using multilayer perceptron neural networks (NNs). Instead of training and deploying a single deep model, we proceed by choosing among multiple tiny NNs trained in a self-supervised manner. The main challenge then becomes to determine and switch to the best NN among

Jan Scholtz, Roberto Maiolino, Francesco D'Eugenio, Emma Curtis-Lake

We present the identification of 42 narrow-line active galactic nuclei (type-2 AGN) candidates in the two deepest observations of the JADES spectroscopic survey with JWST/NIRSpec. The spectral coverage and the depth of our observations allow us to select narrow-line AGNs based on both rest-frame optical and UV emission lines up to z=10. Due to the metallicit

Sudeep Mangalvedhekar, Kshitij Deshpande, Yash Patwardhan, Vedant Deshpande

In this paper, we highlight our approach for the "Arabic AI Tasks Evaluation (ArAiEval) Shared Task 2023". We present our approaches for task 1-A and task 2-A of the shared task which focus on persuasion technique detection and disinformation detection respectively. Detection of persuasion techniques and disinformation has become imperative to avoid distorti

Yu Deng, Duomin Wang, Xiaohang Ren, Xingyu Chen

Existing one-shot 4D head synthesis methods usually learn from monocular videos with the aid of 3DMM reconstruction, yet the latter is evenly challenging which restricts them from reasonable 4D head synthesis. We present a method to learn one-shot 4D head synthesis via large-scale synthetic data. The key is to first learn a part-wise 4D generative model from

Erik Gustafson, Michael Hite, Jay Hubisz, Bharath Sambasivam

The 1+1D Ising model is an ideal benchmark for quantum algorithms, as it is very well understood theoretically. This is true even when expanding the model to include complex coupling constants. In this work, we implement quantum algorithms designed for the simulation of open or complex coupling quantum field theories on IBM devices with a focus on the measur

Min Lin

We extend JAX with the capability to automatically differentiate higher-order functions (functionals and operators). By representing functions as a generalization of arrays, we seamlessly use JAX's existing primitive system to implement higher-order functions. We present a set of primitive operators that serve as foundational building blocks for constructing

Bartholomew Andrews

The Hofstadter model successfully describes the behavior of non-interacting quantum particles hopping on a lattice coupled to a gauge field, and hence is ubiquitous in many fields of research, including condensed matter, optical, and atomic physics. Motivated by this, we introduce HofstadterTools (https://hofstadter.tools), a Python package that can be used

Yuhan Li, Hongtao Zhang, Keaven Anderson, Songzi Li

In the pharmaceutical industry, the use of artificial intelligence (AI) has seen consistent growth over the past decade. This rise is attributed to major advancements in statistical machine learning methodologies, computational capabilities and the increased availability of large datasets. AI techniques are applied throughout different stages of drug develop

Nikita Borisov, Thomas Brazelton, Frenly Espino, Thomas Hagedorn

We describe the Macaulay2 package "A1BrouwerDegrees" for computing local and global $\mathbb{A}^1$-Brouwer degrees and studying symmetric bilinear forms over the complex numbers, the real numbers, the rational numbers, and finite fields of characteristic not equal to 2.

Qiang Yue, Xiaoliang Xu, Yuxiang Wang, Yikun Tao

Given a vector dataset $\mathcal{X}$, a query vector $\vec{x}_q$, graph-based Approximate Nearest Neighbor Search (ANNS) aims to build a proximity graph (PG) as an index of $\mathcal{X}$ and approximately return vectors with minimum distances to $\vec{x}_q$ by searching over the PG index. It suffers from the large-scale $\mathcal{X}$ because a PG with full v

Zhiltsov N. S., Kurskiev G. S., Tolstyakov S. Yu., Solovey V. A.

The paper is devoted to the Thomson scattering (TS) diagnostics recently developed for the Globus-M2 spherical tokamak and prototyping the ITER divertor TS diagnostics. The distinctive features of the system are the use of spectrometers, acquisition system and lasers that meet the base requirements for ITER TS diagnostics. The paper describes the diagnostic

F Adersh, M Muhsin, M Sahoo

We study the motion of an inertial microswimmer in a non-Newtonian environment with a finite memory and present the theoretical realization of an unexpected transition from its random self-propulsion to rotational (circular or elliptical) motion. Further, the rotational motion of the swimmer is followed by spontaneous local direction reversals yet with a ste

Yuanyuan Yang, Heyang Long, Christopher M. Hirata

This paper investigates the distribution and implications of cosmic ray electrons within the intergalactic medium (IGM). Utilizing a synthesis model of the extragalactic background, we evolve the spectrum of Compton-included cosmic rays. The energy density distribution of cosmic ray electrons peaks at redshift $z \approx2$, and peaks in the $\sim$MeV range.

Rosalind Huang, Rituparno Mandal, Colin Scheibner, Vincenzo Vitelli

Odd elasticity describes the unusual elastic response of solids whose stress-strain relationship is not compatible with an elastic potential. Here, we present a study of odd elasticity in a driven granular matter system composed of grains with ratchet-like interparticle friction and activated by oscillatory shear. We find that the system permits a time-avera

Rodrigo Martínez-Peña, Miguel C. Soriano, Roberta Zambrini

Quantum kernel methods leverage a kernel function computed by embedding input information into the Hilbert space of a quantum system. However, large Hilbert spaces can hinder generalization capability, and the scalability of quantum kernels becomes an issue. To overcome these challenges, various strategies under the concept of inductive bias have been propos

Lénaïc Chizat, Praneeth Netrapalli

Deep learning succeeds by doing hierarchical feature learning, yet tuning hyper-parameters (HP) such as initialization scales, learning rates etc., only give indirect control over this behavior. In this paper, we introduce a key notion to predict and control feature learning: the angle $\theta_\ell$ between the feature updates and the backward pass (at layer

Brett Hemenway Falk, Gerry Tsoukalas, Niuniu Zhang

Existing studies on crypto wash trading often use indirect statistical methods or leaked private data, both with inherent limitations. This paper leverages public on-chain NFT data for a more direct and granular estimation. Analyzing three major exchanges, we find that ~38% (30-40%) of trades and ~60% (25-95%) of traded value likely involve manipulation, wit

J. C. Bellizotti Souza, N. P. Vizarim, C. J. O. Reichhardt, C. Reichhardt

Using atomistic simulations, we investigate the dynamical behavior of a single skyrmion interacting with an asymmetric linear protrusion array under external ac driving. When the ac drive is applied along the $x$ direction, the skyrmion moves along the hard direction of the substrate asymmetry in three phases: a pinned phase with localized skyrmion orbits, a

Saurabh Farkya, Aswin Raghavan, Avi Ziskind

Most real-world applications that employ deep neural networks (DNNs) quantize them to low precision to reduce the compute needs. We present a method to improve the robustness of quantized DNNs to white-box adversarial attacks. We first tackle the limitation of deterministic quantization to fixed ``bins'' by introducing a differentiable Stochastic Quantizer (

Seung Won Suh, Seung Whan Chung, Peer-Timo Bremer, Youngsoo Choi

We develop an on-the-fly reduced-order model (ROM) integrated with a flow simulation, gradually replacing a corresponding full-order model (FOM) of a physics solver. Unlike offline methods requiring a separate FOM-only simulation prior to model reduction, our approach constructs a ROM dynamically during the simulation, replacing the FOM when deemed credible.

Adrian Jackson, Nicolau Manubens

This work in progress paper outlines research looking at the performance impact of using different storage interfaces to access the high performance object store DAOS. We demonstrate that using DAOS through a FUSE based filesystem interface can provide high performance, but there are impacts when choosing what I/O library or interface to utilises, with HDF5

M. D. Ragoza, N. V. Kozyrev, S. V. Nekrasov, B. R. Namozov

In a bulk GaAs crystal, an unusual magnetoresistance effect, which takes place when a spin-polarized current flows through the sample, was detected. Under conditions of optical pumping of electron spins, an external magnetic field directed along the electric current and perpendicular to the oriented spins decreases the resistance of the material. The phenome

Qing Wang, Haojie Jia, Wenfei Song, Qi Li

In this paper, we observe and address the challenges of the coordination recognition task. Most existing methods rely on syntactic parsers to identify the coordinators in a sentence and detect the coordination boundaries. However, state-of-the-art syntactic parsers are slow and suffer from errors, especially for long and complicated sentences. To better solv

Feilin Han, Zhaoxu Meng

In film production, metadata plays an important role in original raw video indexing and classification within the industrial post-production software. Inspired by deep visual-semantic methods, we propose an automated image information extraction process to extend the diversity of metadata entities for massive large-scale raw video searching and retrieval. In

Matúš Pikuliak, Andrea Hrckova, Stefan Oresko, Marián Šimko

We present GEST -- a new manually created dataset designed to measure gender-stereotypical reasoning in language models and machine translation systems. GEST contains samples for 16 gender stereotypes about men and women (e.g., Women are beautiful, Men are leaders) that are compatible with the English language and 9 Slavic languages. The definition of said s

Matthieu Terris, Thomas Moreau

Deep neural networks have become a foundational tool for addressing imaging inverse problems. They are typically trained for a specific task, with a supervised loss to learn a mapping from the observations to the image to recover. However, real-world imaging challenges often lack ground truth data, rendering traditional supervised approaches ineffective. Mor

Jessie L. Christiansen, Jon K. Zink, Kevin K. Hardegree-Ullman, Rachel B. Fernandes

The NASA K2 mission obtained high precision time-series photometry for four young clusters, including the near-twin 600-800 Myr-old Praesepe and Hyades clusters. Hot sub-Neptunes are highly prone to mass-loss mechanisms, given their proximity to the the host star and the weakly bound gaseous envelopes, and analyzing this population at young ages can provide

Hamza Fawzi, Omar Fawzi, Samuel O. Scalet

Predicting observables in equilibrium states is a central yet notoriously hard question in quantum many-body systems. In the physically relevant thermodynamic limit, certain mathematical formulations of this task have even been shown to result in undecidable problems. Using a finite-size scaling of algorithms devised for finite systems often fails due to the

Lena Mangold, Camille Roth

Network analysis is often enriched by including an examination of node metadata. In the context of understanding the mesoscale of networks it is often assumed that node groups based on metadata and node groups based on connectivity patterns are intrinsically linked. This assumption is increasingly being challenged, whereby metadata might be entirely unrelate

Andrea Shindler

We describe a procedure to determine moments of parton distribution functions of any order in lattice quantum chromodynamics (QCD). The procedure is based on the gradient flow for fermion and gauge fields. The flowed matrix elements of twist-2 operators renormalize multiplicatively, and the matching with the physical matrix elements can be obtained using con

Xiaolong Tu, Anik Mallik, Haoxin Wang, Jiang Xie

Climate change poses one of the most significant challenges to humanity. As a result of these climatic changes, the frequency of weather, climate, and water-related disasters has multiplied fivefold over the past 50 years, resulting in over 2 million deaths and losses exceeding $3.64 trillion USD. Leveraging AI-powered technologies for sustainable developmen

Daniel Jarne Ornia, Giannis Delimpaltadakis, Jens Kober, Javier Alonso-Mora

In Reinforcement Learning (RL), agents have no incentive to exhibit predictable behaviors, and are often pushed (through e.g. policy entropy regularisation) to randomise their actions in favor of exploration. This often makes it challenging for other agents and humans to predict an agent's behavior, triggering unsafe scenarios (e.g. in human-robot interactio

Pei Ke, Bosi Wen, Zhuoer Feng, Xiao Liu

Since the natural language processing (NLP) community started to make large language models (LLMs) act as a critic to evaluate the quality of generated texts, most of the existing works train a critique generation model on the evaluation data labeled by GPT-4's direct prompting. We observe that these models lack the ability to generate informative critiques