May 2023 arXiv papers — page 187

Jiaxin Shi, Ke Alexander Wang, Emily B. Fox

Efficiently capturing the long-range patterns in sequential data sources salient to a given task -- such as classification and generative modeling -- poses a fundamental challenge. Popular approaches in the space tradeoff between the memory burden of brute-force enumeration and comparison, as in transformers, the computational burden of complicated sequentia

Hao Zhang, Meng Yu, Dong Yu

Hybrid meetings have become increasingly necessary during the post-COVID period and also brought new challenges for solving audio-related problems. In particular, the interplay between acoustic echo and acoustic howling in a hybrid meeting makes the joint suppression of them difficult. This paper proposes a deep learning approach to tackle this problem by fo

Gabriela Jeronimo, Daniel Perrucci

Let $g_1,\dots, g_s \in \mathbb{R}[X_1,\dots, X_n,Y]$ and $S = \{(\bar{x},y)\in \mathbb{R}^{n+1} \mid g_1(\bar{x},y) \ge 0, \dots, g_s(\bar{x}, y) \ge 0\}$ be a non-empty, possibly unbounded, subset of a cylinder in $\mathbb{R}^{n+1}$. Let $f \in \mathbb{R}[X_1, \dots, X_n, Y]$ be a polynomial which is positive on $S$. We prove that, under certain additional

Anna De Mase

We give a characterization of finitely ramified $ω$-pseudo complete valued fields of mixed characteristic $(0, p)$, with fixed residue field $k$ and value group $G$ of cardinality $\aleph_{1}$, in terms of a Hahn-like construction over the Cohen field $C(k)$, modulo the Continuum Hypothesis. This is a generalization of results due to Ax and Kochen in '65

Balakrishna Phani Kommanaboina, Aditya Goverdhana, Naveena Kanderi, Jagadeesh Karri

This project aims to build an elastic web application that can automatically scale out and scale in on-demand and cost-effectively by utilizing cloud resources, specifically from Amazon Web Services (AWS). The application is an image classification service exposed as a RESTful web service for clients to access. The infrastructure is divided into two tiers, t

Anya Belz, Craig Thomson, Ehud Reiter, Gavin Abercrombie

We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible. We present our results and findings, which include that just 13\% of papers had (i) sufficiently low barriers to reproduction, and (ii) enough obtainable informati

Kim H. Pham, Kiarash Gordiz, Natan A. Spear, Amy K. Lin

Ionic conduction in solids that exceeds 1 mS/cm is predicted to involve coupled phonon-ion interactions in the crystal lattice. Here, we use theory and experiment to measure the possible contribution of coupled phonon-ion hopping modes which enhance Li+ migration in Li0.5La0.5TiO3 (LLTO). The ab initio calculations predict that the targeted excitation of ind

Jesse Wolfson

Simplicial objects $\mathsf{sC}$ in descent categories $\mathsf{C}$, as introduced by Behrend and Getzler, provide a context in which to study higher stacks. In this note, we extend the construction of the canonical cocycle of a smooth principal $G$-bundle to the context of principal $G$-bundles in $\mathsf{sC}_{/X}$. As an application, we show how this spec

Anatolii I. Lotkov, Denis V. Kurlov, Aleksey K. Fedorov, Nikita A. Nemkov

Conformal symmetry governs the behavior of closed systems near second-order phase transitions, and is expected to emerge in open systems going through dissipative phase transitions. We propose a framework allowing for a manifest description of conformal symmetry in open Markovian systems. The key difference from the closed case is that both conformal algebra

Gašper Beguš, Thomas Lu, Zili Wang

Computational models of syntax are predominantly text-based. Here we propose that the most basic first step in the evolution of syntax can be modeled directly from raw speech in a fully unsupervised way. We focus on one of the most ubiquitous and elementary suboperations of syntax -- concatenation. We introduce \textit{spontaneous concatenation}: a phenomeno

Amanda Bertsch, Uri Alon, Graham Neubig, Matthew R. Gormley

Since the proposal of transformers, these models have been limited to bounded input lengths, because of their need to attend to every token in the input. In this work, we propose Unlimiformer: a general approach that wraps any existing pretrained encoder-decoder transformer, and offloads the cross-attention computation to a single k-nearest-neighbor (kNN) in

Deming Ye, Yankai Lin, Zhengyan Zhang, Maosong Sun

Recent research demonstrates that external knowledge injection can advance pre-trained language models (PLMs) in a variety of downstream NLP tasks. However, existing knowledge injection methods are either applicable to structured knowledge or unstructured knowledge, lacking a unified usage. In this paper, we propose a UNified knowledge inTERface, UNTER, to p

Wenchao Ding, Jieru Zhao, Yubin Chu, Haihui Huang

There is extensive literature on perceiving road structures by fusing various sensor inputs such as lidar point clouds and camera images using deep neural nets. Leveraging the latest advance of neural architects (such as transformers) and bird-eye-view (BEV) representation, the road cognition accuracy keeps improving. However, how to cognize the ``road'&

Siddhant Arora, Hayato Futami, Shih-Lun Wu, Jessica Huynh

Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing. In this paper, we describe our proposed spoken semantic parsing system for the quality track (Track 1) in Spoken Language Understanding Grand Challenge which is part of ICASSP Signal Processing Grand Challenge 2023. We experiment

Durmus Demir

The ultraviolet (UV) cutoff on a quantum field theory (QFT) can explicitly break or conserve the Poincare (translation) symmetry. And the very same cutoff can explicitly break or conserve the gauge symmetry. In the present work, we perform a systematic study of the UV cutoff in regard to its gauge and Poincare properties, and construct UV completions restori

Zehao Zhu, Jiashun Wang, Yuzhe Qin, Deqing Sun

We propose a new dataset and a novel approach to learning hand-object interaction priors for hand and articulated object pose estimation. We first collect a dataset using visual teleoperation, where the human operator can directly play within a physical simulator to manipulate the articulated objects. We record the data and obtain free and accurate annotatio

Benito A. Juárez-Aubry

It is common folklore that semiclassical arguments suggest that in black hole evaporation an initially pure state can become mixed. This is known as the \emph{information loss puzzle} (or {\it paradox}). Here we argue that, if taken at face value, semiclassical gravity suggests the formation of a final singularity instead of information loss. A quantum stron

Hans L. Bodlaender, Matthew Johnson, Barnaby Martin, Jelle J. Oostveen

We study Steiner Forest on $H$-subgraph-free graphs, that is, graphs that do not contain some fixed graph $H$ as a (not necessarily induced) subgraph. We are motivated by a recent framework that completely characterizes the complexity of many problems on $H$-subgraph-free graphs. However, in contrast to e.g. the related Steiner Tree problem, Steiner Forest f

Anatolii Puhalskii

This paper obtains logarithmic asymptotics of moderate deviations of the stochastic process of the number of customers in a many--server queue with generally distributed interarrival and service times in the Halfin--Whitt heavy traffic regime. The deviation function is expressed in terms of the solution to a Fredholm equation of the second kind. The proof us

Yicheng Zhan, Koray Kavaklı, Hakan Urey, Qi Sun

Multi-color holograms rely on simultaneous illumination from multiple light sources. These multi-color holograms could utilize light sources better than conventional single-color holograms and can improve the dynamic range of holographic displays. In this letter, we introduce AutoColor , the first learned method for estimating the optimal light source powers

Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey

Despite rapid adoption and deployment of large language models (LLMs), the internal computations of these models remain opaque and poorly understood. In this work, we seek to understand how high-level human-interpretable features are represented within the internal neuron activations of LLMs. We train $k$-sparse linear classifiers (probes) on these internal

Javier Chagoya, I. Díaz-Saldaña, J. C. López-Domínguez, M. Sabido

In this paper we study the viability of an entropic cosmological model. The effects of entropic gravity are derived from a modified entropy-area relationship with a volumetric entropy term. This model describes a late time limit cosmic acceleration, whose origin is related to a volumetric term in the entropy. Moreover, we analyze the phenomenological implica

Mingyang Gong, Zhi-Zhong Chen, Guohui Lin, Lusheng Wang

We contribute the first randomized algorithm that is an integration of arbitrarily many deterministic algorithms for the fully online multiprocessor scheduling with testing problem. When there are two machines, we show that with two component algorithms its expected competitive ratio is already strictly smaller than the best proven deterministic competitive

Jialin Mao, Itay Griniasty, Han Kheng Teoh, Rahul Ramesh

We develop information-geometric techniques to analyze the trajectories of the predictions of deep networks during training. By examining the underlying high-dimensional probabilistic models, we reveal that the training process explores an effectively low-dimensional manifold. Networks with a wide range of architectures, sizes, trained using different optimi

Florian Schulze, Lorenzo Valbusa Dall'Armi, Julien Lesgourgues, Angelo Ricciardone

The anisotropies of the Cosmological Gravitational Wave Background (CGWB) retain information about the primordial mechanisms that source the gravitational waves and about the geometry and the particle content of the universe at early times. In this work, we discuss in detail the computation of the angular power spectra of CGWB anisotropies and of their cross

Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé

Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance. In practice, this may lead to sub-optimal or discriminatory decisions against minority cases. In this work we model such changes in depth and breadth of knowledge as a partitioning of the problem space into regions of differing expertise. We p

Anirudh Khatry, Joyce Cahoon, Jordan Henkel, Shaleen Deep

Creating programs to correctly manipulate data is a difficult task, as the underlying programming languages and APIs can be challenging to learn for many users who are not skilled programmers. Large language models (LLMs) demonstrate remarkable potential for generating code from natural language, but in the data manipulation domain, apart from the natural la

Vasilis Chasiotis, Dimitris Karlis

The demand of computational resources for the modeling process increases as the scale of the datasets does, since traditional approaches for regression involve inverting huge data matrices. The main problem relies on the large data size, and so a standard approach is subsampling that aims at obtaining the most informative portion of the big data. In the curr

Tong Lu, Yuan Zhang, Peter Kochunov, Elliot Hong

Brain connectome analysis commonly compresses high-resolution brain scans (typically composed of millions of voxels) down to only hundreds of regions of interest (ROIs) by averaging within-ROI signals. This huge dimension reduction improves computational speed and the morphological properties of anatomical structures; however, it also comes at the cost of su

Anantha Rao, Dhiraj Madan, Anupama Ray, Dhinakaran Vinayagamurthy

An important task in quantum generative machine learning is to model the probability distribution of measurements of many-body quantum systems. Classical generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), can model the distributions of product states with high fidelity, but fail or require an exponential nu

A. O. Lopes, E. R. Oliveira, W. de S. Pedra, V. Vargas

We consider here a dynamic model for a gas in which a variable number of particles $N \in \mathbb{N}_0 := \mathbb{N} \cup \{0\}$ can be located at a site. This point of view leads us to the grand-canonical framework and the need for a chemical potential. The dynamics is played by the shift acting on the set of sequences $\Omega := \mathcal{A}^\mathbb{N}$, wh

Mordehai Milgrom

I present a class of theories that generalize quasilinear MOND (QUMOND). Like QUMOND, these GQUMOND theories require solving only the linear Poisson equation (twice). Unlike QUMOND, their Lagrangian depends on higher derivatives of the Newtonian potential. They thus dictate different "phantom" densities as virtual sources in the Poisson equation for

Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich

Gradient clipping is a popular modification to standard (stochastic) gradient descent, at every iteration limiting the gradient norm to a certain value $c >0$. It is widely used for example for stabilizing the training of deep learning models (Goodfellow et al., 2016), or for enforcing differential privacy (Abadi et al., 2016). Despite popularity and simplic

Weixuan Sun, Zheyuan Liu, Yanhao Zhang, Yiran Zhong

The Segment Anything Model (SAM) has demonstrated exceptional performance and versatility, making it a promising tool for various related tasks. In this report, we explore the application of SAM in Weakly-Supervised Semantic Segmentation (WSSS). Particularly, we adapt SAM as the pseudo-label generation pipeline given only the image-level class labels. While

Vsevolod Chestnov, Saiei J. Matsubara-Heo, Henrik J. Munch, Nobuki Takayama

This work studies limits of Pfaffian systems, a class of first-order PDEs appearing in the Feynman integral calculus. Such limits appear naturally in the context of scattering amplitudes when there is a separation of scale in a given set of kinematic variables. We model these limits, which are often singular, via restrictions of D-modules. We thereby develop

Ken Osato, Takahiro Nishimichi, Atsushi Taruya, Francis Bernardeau

Constraining cosmological parameters from large-scale structure observations requires precise and accurate tools to compute its properties. While perturbation theory (PT) approaches can serve this purpose, exploration of large parameter space is challenging due to the potentially large computational cost of such calculations. In this study, we show that a re

Sam Tertooy

A group $G$ is twisted conjugacy separable if for every automorphism $\varphi$, distinct $\varphi$-twisted conjugacy classes can be separated in a finite quotient. Likewise, $G$ is completely twisted conjugacy separable if for any group $H$ and any two homomorphisms $\varphi,\psi$ from $H$ to $G$, distinct $(\varphi,\psi)$-twisted conjugacy classes can be se

Miles Cranmer

PySR is an open-source library for practical symbolic regression, a type of machine learning which aims to discover human-interpretable symbolic models. PySR was developed to democratize and popularize symbolic regression for the sciences, and is built on a high-performance distributed back-end, a flexible search algorithm, and interfaces with several deep l

Giwon Hong, Jeonghwan Kim, Junmo Kang, Sung-Hyon Myaeng

Most existing retrieval-augmented language models (LMs) assume a naive dichotomy within a retrieved document set: query-relevance and irrelevance. Our work investigates a more challenging scenario in which even the "relevant" documents may contain misleading or incorrect information, causing conflict among the retrieved documents and thereby negative

Károly Bezdek, Zsolt Lángi

Let $\mathcal{P}$ be a packing of circular disks of radius $\rho>0$ in the Euclidean, spherical, or hyperbolic plane. Let $0\leq\lambda\leq\rho$. We say that $\mathcal{P}$ is a $\lambda$-separable packing of circular disks of radius $\rho$ if the family $\mathcal{P'}$ of disks concentric to the disks of $\mathcal{P}$ having radius $\lambda$ form a totally se

Sean Monahan

We introduce a combinatorial theory of horospherical stacks which is motivated by the work of Geraschenko and Satriano on toric stacks. A horospherical stack corresponds to a combinatorial object called a stacky coloured fan. We give many concrete examples, including a class of easy-to-draw examples called coloured fantastacks. The main results in this paper

Georg Grasegger, Jan Legerský

A framework, which is a (possibly infinite) graph with a realization of its vertices in the plane, is called flexible if it can be continuously deformed while preserving the edge lengths. We focus on flexibility of frameworks in which 4-cycles form parallelograms. For the class of frameworks considered in this paper (allowing triangles), we prove that the fo

Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana

The ability to collect a large dataset of human preferences from text-to-image users is usually limited to companies, making such datasets inaccessible to the public. To address this issue, we create a web app that enables text-to-image users to generate images and specify their preferences. Using this web app we build Pick-a-Pic, a large, open dataset of te

Xin Xu, Yuqi Zhu, Xiaohan Wang, Ningyu Zhang

Scaling language models have revolutionized widespread NLP tasks, yet little comprehensively explored few-shot relation extraction with large language models. In this paper, we investigate principal methodologies, in-context learning and data generation, for few-shot relation extraction via GPT-3.5 through exhaustive experiments. To enhance few-shot performa

Teddy Lazebnik, Amit Yaniv-Rosenfeld

Family members' satisfaction with one another is central to creating healthy and supportive family environments. In this work, we propose and implement a novel computational technique aimed at exploring the possible relationship between the topology of a given family tree graph and its members' satisfaction with one another. Through an extensive empi

Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum

In conversational question answering, users express their information needs through a series of utterances with incomplete context. Typical ConvQA methods rely on a single source (a knowledge base (KB), or a text corpus, or a set of tables), thus being unable to benefit from increased answer coverage and redundancy of multiple sources. Our method EXPLAIGNN o

Steven E. Boggs, Sean N. Pike

In an ideal germanium detector, fully-absorbed monoenergetic gamma-rays will appear in the measured spectrum as a narrow peak, broadened into a Gaussian of width determined only by the statistical properties of charge cloud generation and the electronic noise of the readout electronics. Multielectrode detectors complicate this picture. Broadening of the char

Shivam Barwey, Venkat Raman

This work introduces Jacobian-scaled K-means (JSK-means) clustering, which is a physics-informed clustering strategy centered on the K-means framework. The method allows for the injection of underlying physical knowledge into the clustering procedure through a distance function modification: instead of leveraging conventional Euclidean distance vectors, the

Liwei Ji, Vassilios Mewes, Yosef Zlochower, Lorenzo Ennoggi

Numerical simulations of merging compact objects and their remnants form the theoretical foundation for gravitational wave and multi-messenger astronomy. While Cartesian-coordinate-based adaptive mesh refinement is commonly used for simulations, spherical-like coordinates are more suitable for nearly spherical remnants and azimuthal flows due to lower numeri

Niels Linnemann, James Read, Nicholas Teh

Most contemporary physicists hold that the local validity of special relativity (SR) within general relativity (GR) is expressed by means of an interdependent cluster of mathematical concepts, one of which is the existence of normal coordinate systems. Nonetheless, there remains conceptual work to be done with regard to this `standard story' on the local

Siddharth Mahesh, Sean T. McWilliams, Michal Pirog

We present an analytical and numerical study of a system composed of a stellar binary pair and a massless, locally isothermal viscous accretion disk that is coplanar to the binary orbital plane. Analytically, we study the effect of the binary's gravitational potential over short timescales through the study of stability for epicyclic orbits, and over lon

Markus Faulhuber, Stefan Steinerberger

We prove that among all 1-periodic configurations $\Gamma$ of points on the real line $\mathbb{R}$ the quantities $$ \min_{x \in \mathbb{R}} \sum_{\gamma \in \Gamma} e^{- \pi \alpha (x - \gamma)^2} \quad \text{and} \quad \max_{x \in \mathbb{R}} \sum_{\gamma \in \Gamma} e^{- \pi \alpha (x - \gamma)^2}$$ are maximized and minimized, respectively, if and only i

Przemysław Talar

In the present note we study combinatorial and algebraic properties of cubic-line arrangements in the complex projective plane admitting nodes, ordinary triple and $A_{5}$ singular points. We deliver a Hirzebruch-type inequality for such arrangement and we study the freeness of such arrangements providing an almost complete classification.

Andrew Zhu, Karmanya Aggarwal, Alexander Feng, Lara J. Martin

Dungeons & Dragons (D&D) is a tabletop roleplaying game with complex natural language interactions between players and hidden state information. Recent work has shown that large language models (LLMs) that have access to state information can generate higher quality game turns than LLMs that use dialog history alone. However, previous work used game state in

Hassan Firouzjahi

We study the one-loop corrections in power spectrum of long gravitational waves induced from small scale modes in the models of single field inflation undergoing a phase of ultra-slow-roll (USR). We show that the spectrum of long tensor perturbations are largely unaffected by the loop corrections from the short scalar modes. In particular, the spectrum of lo

DongNyeong Heo, Heeyoul Choi

Non-autoregressive neural machine translation (NAT) offers substantial translation speed up compared to autoregressive neural machine translation (AT) at the cost of translation quality. Latent variable modeling has emerged as a promising approach to bridge this quality gap, particularly for addressing the chronic multimodality problem in NAT. In the previou

Konstantinos Kanellis, Badrish Chandramouli, Ted Hart, Shivaram Venkataraman

Modern large-scale services such as search engines, messaging platforms, and serverless functions, rely on key-value (KV) stores to maintain high performance at scale. When such services are deployed in constrained memory environments, they present challenging requirements: point operations requiring high throughput, working sets much larger than main memory

Alessandro Borghi, Tobias Breiten

Optimal model reduction for large-scale linear dynamical systems is studied. In contrast to most existing works, the systems under consideration are not required to be stable, neither in discrete nor in continuous time. As a consequence, the underlying rational transfer functions are allowed to have poles in general domains in the complex plane. In particula

Kenichi N. Komagata, Alexandre Parriaux, Mathieu Bertrand, Johannes Hillbrand

We demonstrate the use of a low power near-infrared laser illuminating the front facet of a quantum cascade laser (QCL) as an optical actuator for the coherent control of a mid-infrared frequency comb. We show that by appropriate current control of the QCL comb and intensity modulation of the near-infrared laser, a tight phase lock of a comb line to a distri

Wangseok Shin

We study the local and global well-posedness for the coupled system of Schrödinger and Kawahara equations on the real line. The Sobolev space $L^{2} \times H^{-2}$ is the space where the lowest regularity local solutions are obtained. The energy space is $H^1 \times H^2$. We apply the Colliander-Holmer-Tzirakis method [7] to prove the global well-posedness i

K. Oreszczuk, A. Rodek, M. Goryca, T. Kazimierczuk

Employing the original, all-optical method, we quantify the magnetic susceptibility of a two-dimensional electron gas (2DEG) confined in the MoSe$_2$ monolayer in the range of low and moderate carrier densities. The impact of electron-electron interactions on the 2DEG magnetic susceptibility is found to be particularly strong in the limit of, studied in deta

Miao Li, Eduard Hovy, Jey Han Lau

We present PeerSum, a novel dataset for generating meta-reviews of scientific papers. The meta-reviews can be interpreted as abstractive summaries of reviews, multi-turn discussions and the paper abstract. These source documents have rich inter-document relationships with an explicit hierarchical conversational structure, cross-references and (occasionally)

Matías Morales, Claudio Muñoz

We consider the long time behavior of solutions to scalar field models appearing in the theory of cosmological inflation (oscillons) and cold dark matter, in presence or absence of the cosmological constant. These models are not included in standard mathematical literature due to their unusual nonlinearities, which model different features to classical ones.

Luca Mesiti

We generalize to dimension 2 the well-known fact that a colimit in a 1-dimensional slice is precisely the map from the colimit of the domains of the diagram that is induced by the universal property. For this, we find the need to reduce weighted 2-colimits to cartesian-marked oplax conical ones, and as a consequence the need to consider lax slices. We prove

Yinhao Wu, Clément Baruteau, Sergei Nayakshin

ALMA has spatially resolved over 200 annular structures in protoplanetary discs, many of which are suggestive of the presence of planets. Constraining the mass of these putative planets is quite degenerate for it depends on the disc physical properties, and for simplicity a steady-state is often assumed whereby the planet position is kept fixed and there is

Simon Baker, Henna Koivusalo

In this paper we study quantitative recurrence and the shrinking target problem for dynamical systems coming from overlapping iterated function systems. Such iterated function systems have the important property that a point often has several distinct choices of forward orbit. As is demonstrated in this paper, this non-uniqueness leads to different behaviour

Florian Allmann-Rahn, Simon Lautenbach, Magnus Deisenhofer, Rainer Grauer

Collsionless astrophysical and space plasmas cover regions that typically display a separation of scales that exceeds any code's capabilities. To help address this problem, the muphyII code utilizes a hierarchy of models with different inherent scales, unified in an adaptive framework that allows stand-alone use of models as well as a model-based dynamic

Azmine Toushik Wasi, Karlo Šerbetar, Raima Islam, Taki Hasan Rafi

In this paper, we introduce a framework ARBEx, a novel attentive feature extraction framework driven by Vision Transformer with reliability balancing to cope against poor class distributions, bias, and uncertainty in the facial expression learning (FEL) task. We reinforce several data pre-processing and refinement methods along with a window-based cross-atte

Matteo Gardini, Edoardo Santilli

In this article we discuss the application of the Heath-Jarrow-Morton framework Heath et al. [26] to energy markets. The goal of the article is to give a detailed overview of the topic, focusing on practical aspects rather than on theory, which has been widely studied in literature. This work aims to be a guide for practitioners and for all those who deal wi

Urban Senica, Alexander Dikopoltsev, Andres Forrer, Sara Cibella

Frequency-modulated (FM) combs feature flat intensity spectra with a linear frequency chirp, useful for metrology and sensing applications. Generating FM combs in semiconductor lasers generally requires a fast saturable gain, usually limited by the intrinsic gain medium properties. Here, we show how a spatial modulation of the laser gain medium can enhance t

Anees Kazi, Jocelyn Mora, Bruce Fischl, Adrian V. Dalca

We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design

Ke Wan, Alain Kornhauser

This paper investigates Gaussian copula mixture models (GCMM), which are an extension of Gaussian mixture models (GMM) that incorporate copula concepts. The paper presents the mathematical definition of GCMM and explores the properties of its likelihood function. Additionally, the paper proposes extended Expectation Maximum algorithms to estimate parameters

Jacopo Emmenegger, Luca Mesiti, Giuseppe Rosolini, Thomas Streicher

We prove that cloven Grothendieck fibrations over a fixed base $\ct{B}$ are the pseudo-coalgebras for a lax idempotent 2-comonad on $\ct{Cat}/\ct{B}$. We show this via an original observation that the known colax idempotent 2-monad for fibrations over a fixed base has a right 2-adjoint. As an important consequence, we obtain an original cofree construction o

Hiroaki Nishikawa, Jeffery A. White

In this paper, we propose an efficient quadratic interpolation formula utilizing solution gradients computed and stored at nodes and demonstrate its application to a third-order cell-centered finite-volume discretization on tetrahedral grids. The proposed quadratic formula is constructed based on an efficient formula of computing a projected derivative. It i

Kaustav Mukherjee, Sarah Chehade, Lorenzo Versini, Karim Alaa El-Din

Random ensembles of pure states have proven to be extremely important in various aspects of quantum physics such as benchmarking the performance of quantum circuits, testing for quantum advantage, providing novel insights for many-body thermalization and studying the black hole information paradox. Although generating a fully random ensemble is experimentall

Sung Hoon Choi, Donggyu Kim

In this paper, we develop a novel large volatility matrix estimation procedure for analyzing global financial markets. Practitioners often use lower-frequency data, such as weekly or monthly returns, to address the issue of different trading hours in the international financial market. However, this approach can lead to inefficiency due to information loss.

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The branching fraction $\mathcal{B}(B^0 \to D^{*-}τ^+ν_τ)$ is measured relative to that of the normalization mode $B^0 \to D^{*-}π^+π^-π^+$ using hadronic $τ^+ \to π^+π^-π^+(π^0)\overlineν_τ$ decays in proton-proton collision data at a center-of-mass energy of 13 TeV collected by the LHCb experiment, corresponding to an integrated luminosity of 2 fb$^{-1}$.

Robert E. Grimm

Constraints on the interior of the Moon have been derived from its inductive response, principally as measured by the magnetic transfer function (TF) between the distantly orbiting Explorer 35 satellite and the Apollo 12 surface station. The most successful prior studies used a dataset spanning 0.01-1 mHz, so the lunar response could be modeled as a simple d

Jinming Xu, Nasser Lashgarian Azad, Yuan Lin

Many optimal control problems require the simultaneous output of discrete and continuous control variables. These problems are usually formulated as mixed-integer optimal control (MIOC) problems, which are challenging to solve due to the complexity of the solution space. Numerical methods such as branch-and-bound are computationally expensive and undesirable

Uwe Naumann

A modification of Newton's method for solving systems of $n$ nonlinear equations is presented. The new matrix-free method relies on a given decomposition of the invertible Jacobian of the residual into invertible sparse local Jacobians according to the chain rule of differentiation. It is motivated in the context of local Jacobians with bandwidth $2m+1$

Giovanni Ballarin, Lyudmila Grigoryeva, Juan-Pablo Ortega

Numerical evaluations of the memory capacity (MC) of recurrent neural networks reported in the literature often contradict well-established theoretical bounds. In this paper, we study the case of linear echo state networks, for which the total memory capacity has been proven to be equal to the rank of the corresponding Kalman controllability matrix. We shed

Rakesh Arora, Phan Thành Nam, Phuoc-Tai Nguyen

We extend the Moser-Trudinger inequality of one function to systems of orthogonal functions. Our results are asymptotically sharp when applied to the collective behavior of eigenfunctions of Schrödinger operators on bounded domains.

Luca Benatti, Mattia Fogagnolo, Lorenzo Mazzieri

We deal with suitable nonlinear versions of Jauregui&#39;s isocapacitary mass in 3-manifolds with nonnegative scalar curvature and compact outermost minimal boundary. These masses, which depend on a parameter $1<p\leq 2$, interpolate between Jauregui&#39;s mass $p=2$ and Huisken&#39;s isoperimetric mass, as $p \to 1^+$. We derive positive mass theorems for t

Yang Yu, Sergei Iskakov, Emanuel Gull

We present a temperature extrapolation technique for self-consistent many-body methods, which provides a causal starting point for converging to a solution at a target temperature. The technique employs the Carath\'eodory formalism for interpolating causal matrix-valued functions and is applicable to various many-body methods, including dynamical mean field

Alexandru Dimca, Piotr Pokora, Gabriel Sticlaru

In the present paper we compute Alexander polynomials for certain classes of conic-line arrangements in the complex projective plane which are related to pencils. We prove two general results for curve arrangements coming from Halphen pencils of index $k\geq 2$. Then we apply them to the Hesse arrangement of conics and to some of its degenerations. The resul

JPAC Collaboration, D. Winney, C. Fernandez-Ramirez, A. Pilloni

The study of $J/ψ$ photoproduction at low energies has consequences for the understanding of multiple aspects of nonperturbative QCD, ranging from mechanical properties of the proton, to the binding inside nuclei, and the existence of hidden-charm pentaquarks. Factorization of the photon-$c \bar c$ and nucleon dynamics or Vector Meson Dominance are often inv

Marilena Crupi, Antonino Ficarra

A very well-covered graph is a well-covered graph without isolated vertices such that the size of its minimal vertex covers is half of the number of vertices. If $G$ is a Cohen-Macaulay very well-covered graph, we deeply investigate some algebraic properties of the cover ideal of $G$ via the Rees algebra associated to the ideal.

G. Jeyakodi, Trisha Agarwal, P. Shanthi Bala

Vector-Borne Disease (VBD) is an infectious disease transmitted through the pathogenic female Aedes mosquito to humans and animals. It is important to control dengue disease by reducing the spread of Aedes mosquito vectors. Community awareness plays acrucial role to ensure Aedes control programmes and encourages the communities to involve active participatio

Anatole Gaudin

We aim to contribute to the folklore of function spaces on Lipschitz domains. We prove the boundedness of the trace operator for homogeneous Sobolev and Besov spaces on a special Lipschitz domain with sharp regularity. To achieve this, we provide appropriate definitions and properties, ensuring our construction of these spaces is suitable for non-linear part

Lucía Santamaría-Sanz

The quantum vacuum interaction energy between a pair of semitransparent two-dimensional plates represented by Dirac delta potentials and its first derivative, embedded in the topological background of a sine-Gordon kink, is studied through an extension of the TGTG-formula (developped by O. Kenneth and I. Klich in the scattering approach). Quantum vacuum osci

Zhen Zhang, Jialu Wang, Xin Eric Wang

Pre-trained vision and language models such as CLIP have witnessed remarkable success in connecting images and texts with a primary focus on English texts. Despite recent efforts to extend CLIP to support other languages, disparities in performance among different languages have been observed due to uneven resource availability. Additionally, current cross-l

Vyas Raina, Mark Gales

With the advent of deep learning methods, Neural Machine Translation (NMT) systems have become increasingly powerful. However, deep learning based systems are susceptible to adversarial attacks, where imperceptible changes to the input can cause undesirable changes at the output of the system. To date there has been little work investigating adversarial atta

Konrad Menzel

We consider the problem of extrapolating treatment effects across heterogeneous populations (``sites"/``contexts"). We consider an idealized scenario in which the researcher observes cross-sectional data for a large number of units across several ``experimental" sites in which an intervention has already been implemented to a new ``target" site for which a b

Julian Bernhardt, Christian S. Fischer

We investigate the quality of the extrapolation procedure employed in Ref. [1] to extract the crossover line at real chemical potential from lattice data at imaginary potential. To this end we employ a functional approach that does not suffer from the sign problem. We utilize a well-studied combination of lattice Yang--Mills theory with a truncated set of Dy

Pietro Conzinu, Giovanni Marozzi

We discuss the production of primordial black holes in an early matter dominated era, which typically takes place in string inspired early universe cosmological models. In particular, we consider a pre-big bang scenario (extending previous results regarding formation in the radiation dominated era) where the enhancement of curvature perturbations is induced

David Guijo-Rubio, Matthew Middlehurst, Guilherme Arcencio, Diego Furtado Silva

Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to the regressor series. The TSER archive for comparing algorithms was released in 2022 with 19 problems. We increase the size of this archive to 63 problems and reproduce the previous

Jiaoyang Huang, Horng-Tzer Yau

We consider the statistics of extreme eigenvalues of random $d$-regular graphs, with $N^{\mathfrak c}\leq d\leq N^{1/3-{\mathfrak c}}$ for arbitrarily small ${\mathfrak c}>0$. We prove that in this regime, the fluctuations of extreme eigenvalues are given by the Tracy-Widom distribution. As a consequence, about 69% of $d$-regular graphs have all nontrivial e

Mathieu Lehaut, Nir Piterman

We study an extension of Zielonka's (fixed) asynchronous automata called reconfigurable asynchronous automata where processes can dynamically change who they communicate with. We show that reconfigurable asynchronous automata are not more expressive than fixed asynchronous automata by giving translations from one to the other. However, going from reconfigura

Simon Kolker, Louise Dennis, Ramon Fraga Pereira, Mengwei Xu

We propose the use of the hypothetical retrospection argumentation procedure, developed by Sven Ove Hansson to improve existing approaches to machine ethical reasoning by accounting for probability and uncertainty from a position of Philosophy that resonates with humans. Actions are represented with a branching set of potential outcomes, each with a state, u

Josep Marti-Saumell, Joan Sola, Angel Santamaria-Navarro, Hugo Duarte

This paper introduces Borinot, an open-source flying robotic platform designed to perform hybrid agile locomotion and manipulation. This platform features a compact and powerful hexarotor that can be outfitted with torque-actuated extremities of diverse architecture, allowing for whole-body dynamic control. As a result, Borinot can perform agile tasks such a