February 2024 arXiv papers — page 122

Francis Nier, Xingfeng Sang, Francis White

In this article we reconsider the proof of subelliptic estimates for Geometric Kramers-Fokker-Planck operators, a class which includes Bismut's hypoelliptic Laplacian, when the base manifold is closed (no boundary). The method is significantly different from the ones proposed by Bismut-Lebeau in [BiLe] and Lebeau in [Leb1] and [Leb2]. As a new result we are

Sumeet Ramesh Motwani, Mikhail Baranchuk, Martin Strohmeier, Vijay Bolina

Recent capability increases in large language models (LLMs) open up applications in which groups of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information, or other unwanted forms of agent coordination. Modern steganographic techniques could render such dynamics hard

Anne-Laure Basdevant, Jean-Baptiste Gouéré, Marie Théret

Consider $\Xi$ a homogeneous Poisson point process on $\mathbb{R}^d$ ($d\geq 2$) with unit intensity with respect to the Lebesgue measure. For $\varepsilon\geq 0$, we define the Boolean model $\Sigma_{p, \varepsilon}$ as the union of the balls of volume $\varepsilon$ for the $p$-norm ($p\in [1,\infty]$) and centered at the points of $\Xi$. We define a random

Gastón Vergara-Hermosilla

In this work we study the 3D Navier-Stokes equations, under the action of an external force and with the fractional Laplacian operator $(-\Delta)^{\alpha}$ in the diffusion term, from the point of view of variable Lebesgue spaces. Based on decay estimates of the fractional heat kernel we prove the existence and uniqueness of mild solutions on this functional

Sarah Almeida Carneiro, Giovanni Chierchia, Aurelie Pirayre, Laurent Najman

A persistent challenge in the field of Intelligent Transportation Systems is to extract accurate traffic insights from geographic regions with scarce or no data coverage. To this end, we propose solutions for speed prediction using sparse GPS data points and their associated topographical and road design features. Our goal is to investigate whether we can us

Xabier Echeberria-Barrio, Mikel Gorricho, Selene Valencia, Francesco Zola

The usage of Artificial Intelligence (AI) systems has increased exponentially, thanks to their ability to reduce the amount of data to be analyzed, the user efforts and preserving a high rate of accuracy. However, introducing this new element in the loop has converted them into attacked points that can compromise the reliability of the systems. This new scen

Mainak Pal, William D. Baez, Pushan Majumdar, Arnab Sen

We investigate the dynamical phases and phase transitions arising in a classical two-dimensional anisotropic $XY$ model under the influence of a periodically driven temporal external magnetic field in the form of a symmetric square wave. We use a combination of finite temperature classical Monte Carlo simulation, implemented within a CPU + GPU paradigm, util

Chaja Baruch, P. Bryan Changala, Yuval Shagam, Yotam Soreq

The searches for CP violating effects in diatomic molecules, such as $\text{HfF}^+$ and ThO, are typically interpreted as a probe of the electron's electric dipole moment ($e\text{EDM}$), a new electron-nucleon interaction, and a new electron-electron interaction. However, in the case of a nonvanishing nuclear spin, a new CP violating nucleon-nucleon long ra

Michał Barski, Rafał Łochowski

We characterize affine term structure models of non-negative short rate $R$ which may be obtained as solutions of autonomous SDEs driven by independent, one-dimensional L\'evy martingales, that is equations of the form $$ dR(r)=F(R(t))dt+\sum_{i=1}^{d}G_i(R(t-))dZ_i(t), \quad R(0)=r_0\geq 0, \quad t>0, \quad (1)$$ with deterministic real functions $F,G_1,...

Marek Polewczyk, Marco Spinaci

We present a novel deep-learning-based method to cluster words in documents which we apply to detect and recognize tables given the OCR output. We interpret table structure bottom-up as a graph of relations between pairs of words (belonging to the same row, column, header, as well as to the same table) and use a transformer encoder model to predict its adjac

Haozhen Zhang, Xi Xiao, Le Yu, Qing Li

As network security receives widespread attention, encrypted traffic classification has become the current research focus. However, existing methods conduct traffic classification without sufficiently considering the common characteristics between data samples, leading to suboptimal performance. Moreover, they train the packet-level and flow-level classifica

R. V. Todorov, E. V. Kravchenko, I. N. Pashchenko, A. B. Pushkarev

The latest results of the most detailed analysis of multi-epoch polarization-sensitive observations of active galactic nuclei (AGN) jets at parsecs scales by very long baseline interferometry (VLBI) reveal several characteristic patterns of linear polarization distribution and its variability (Pushkarev et al., 2023; Zobnina et al., 2023). Some of the observ

A. V. Belitsky, A. A. Kokosinskaya, A. V. Smirnov, V. V. Voevodin

Feynman integral reduction by means of integration-by-parts identities is a major power gadget in a theorist toolbox indispensable for calculation of multiloop quantum effects relevant for particle phenomenology and formal theory alike. An algorithmic approach consists of solving a large sparse non-square system of homogeneous linear equations with polynomia

Devansh Bhardwaj, Kshitiz Kaushik, Sarthak Gupta

Randomized smoothing has emerged as a potent certifiable defense against adversarial attacks by employing smoothing noises from specific distributions to ensure the robustness of a smoothed classifier. However, the utilization of Monte Carlo sampling in this process introduces a compute-intensive element, which constrains the practicality of randomized smoot

Sarah Zaidan, Syamsudin Abdillah, Nur Arfian, Wawaimuli Arozal

The purpose of this study was to obtain natural drugs from brown seaweed (Sargassum crassifolium) as antiatherosclerosis candidates through the study of hypolipidemic mechanisms of action. Modeling of dyslipidemia rats was carried out by feeding high-fat (HFF) and doses of crude fucoidan 100. 200. 400mg / KgBB. in both treatments measured blood lipid profile

Xabier Echeberria-Barrio, Amaia Gil-Lerchundi, Jon Egana-Zubia, Raul Orduna-Urrutia

In recent years, Deep Neural Network models have been developed in different fields, where they have brought many advances. However, they have also started to be used in tasks where risk is critical. A misdiagnosis of these models can lead to serious accidents or even death. This concern has led to an interest among researchers to study possible attacks on t

N. Sahakyan, D. Bégué, A. Casotto, H. Dereli-Bégué

In the context of modeling spectral energy distributions (SEDs) for blazars, we extend the method that uses a convolutional neural network (CNN) to include external inverse Compton processes. The model assumes that relativistic electrons within the emitting region can interact and up-scatter external photon originating from the accretion disk, the broad-line

Ievgen Bondarenko

A finitely generated group $G$ is called poly-context-free if its word problem $\mathrm{WP}(G)$ is an intersection of finitely many context-free languages. We consider the quaternionic lattices $\Gamma_\tau$ over the field $\mathbb{F}_{q}(t)$ constructed by Stix-Vdovina (2017), and prove that they are not poly-context-free. As a corollary, since all the grou

Simone Floreani, Sabine Jansen, Stefan Wagner

We construct three representations of the $su(1,1)$ current algebra: in extended Fock space, with Gamma random measures, and with negative binomial (Pascal) point processes. For the second and third representations, the lowering and neutral operators are generators of measure-valued branching processes (Dawson-Watanabe superprocesses) and spatial birth-death

Ondřej Zelenka, Bernd Brügmann, Frank Ohme

Searching the data of gravitational-wave detectors for signals from compact binary mergers is a computationally demanding task. Recently, machine learning algorithms have been proposed to address current and future challenges. However, the results of these publications often differ greatly due to differing choices in the evaluation procedure. The Machine Lea

Linze Li, Luyu Wang, Tianyu Long, Zhouze Zhang

We present InP-based evanescently-coupled waveguide modified uni-traveling carrier photodiodes (MUTC-PDs) exhibiting a breakthrough in bandwidth. The optimization of carrier transport and optical coupling is achieved through a detailed discussion on the design of the cliff layer and waveguide layer. Addressing the parasitic capacitance challenge, we introduc

Amartya Pal, Paramita Dutta, Arijit Saha

The emergence of gapless surface states, known as Fermi arcs (FAs), is one of the unique properties of the novel topological Weyl semimetal (WSM). However, extracting the signatures of FAs from the bulk states has always been a challenge as both of them are gapless in nature and connected to each other. We capture the signatures of FAs via transport in an in

Zhaofang Bai, Shuanping Du

Generating a long-distance quantum state with genuine quantum correlation (GQC) is one of the most essential functions of quantum networks to support quantum communication. Here, we provide a deterministic scheme for generating multimode Gaussian states with certain GQC (including genuine entanglement). Efficient algorithms of generating multimode states are

Marc Mars

We introduce and study the notion of null manifold. This is a smooth manifold ${\mathcal N}$ endowed with a degenerate metric $\gamma$ with one-dimensional radical at every point. We also define the notion of ruled null manifold, which is a special case of null manifolds. We prove that ruled null manifolds are in one-to-one correspondence with equivalence cl

Wenpin Tang, Hanyang Zhao

This is an expository article on the score-based diffusion models, with a particular focus on the formulation via stochastic differential equations (SDE). After a gentle introduction, we discuss the two pillars in the diffusion modeling -- sampling and score matching, which encompass the SDE/ODE sampling, score matching efficiency, the consistency models, an

S. A. R. Horsley, J. B. Pendry

We show that a grating amplitude stationary in space but oscillating in time can be accurately modelled as a set of independent gratings travelling in opposite directions, interacting almost exclusively with waves travelling in the same direction. This structure reproduces the key features of travelling gratings: amplification of a wave at points where the l

Hang Zhao, Yifei Xin, Zhesong Yu, Bilei Zhu

In the realm of audio-language pre-training (ALP), the challenge of achieving cross-modal alignment is significant. Moreover, the integration of audio inputs with diverse distributions and task variations poses challenges in developing generic audio-language models. In this study, we present MINT, a novel ALP framework boosting audio-language models through

Dejun Luo, Bin Tang, Guohuan Zhao

We investigate the mixing properties of solutions to the stochastic transport equation $d u= \circ d W \cdot\nabla u$, where the driving noise $W(t,x)$ is white in time, colored and divergence-free in space. Furthermore, we prove the dissipation enhancement in the presence of a small viscous term. Applying our results, we also derive the mixing properties fo

Masoomali Fatehkia, Ji Kim Lucas, Sanjay Chawla

Large Language Models (LLM) have shown remarkable language capabilities fueling attempts to integrate them into applications across a wide range of domains. An important application area is question answering over private enterprise documents where the main considerations are data security, which necessitates applications that can be deployed on-prem, limite

Marc Mars

The notions of (metric) hypersurface data were introduced in [Mars,2013] as a tool to analyze, from an abstract viewpoint, hypersurfaces of arbitrary signature in pseudo-riemannian manifolds. In this paper, general geometric properties of these notions are studied. In particular, the properties of the gauge group inherent to the geometric construction are an

Toufik Zaimi

We show that for any natural number $n$ satisfying $n\equiv 4 \mod 8$ and $n\not\equiv 0 \mod 5$, and for any odd integer $t\geq \frac{n+6}{2}$ there are infinitely many Salem numbers ${\alpha}$ of degree $2t$ such that ${\alpha}^n-1$ is a unit. This result, obtained using a generalization of a construction due to Gross and McMullen [5], partially completes

Xabier Echeberria-Barrio, Amaia Gil-Lerchundi, Iñigo Mendialdua, Raul Orduna-Urrutia

In the last years, Deep Learning technology has been proposed in different fields, bringing many advances in each of them, but identifying new threats in these solutions regarding cybersecurity. Those implemented models have brought several vulnerabilities associated with Deep Learning technology. Moreover, those allow taking advantage of the implemented mod

Hannah M. Christensen, Salah Kouhen, Greta Miller, Raghul Parthipan

Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the sub-grid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale-separation in

Volodymyr M. Lashkin, Oleg K. Cheremnykh

We present a two-dimensional nonlinear equation to govern the dynamics of disturbances in a rotating self-gravitating fluid. The nonlinear term of the equation has the form of a Poisson bracket (Jacobian), and the linear part contains, along with the Laplacian, a biharmonic operator. A solution was found in the form of a dipole vortex (modon). The solution a

Alexander Schnurr, Angelika Silbernagel

We describe and analyze different approaches to represent ordinal patterns. All of these can be found in the literature. The most important representations (plus sub-classes) are compared in terms of their applicability from different angles. Namely we consider digital implementation, inverse patterns and ties between values. At the end we provide a guidelin

Ali Rostami, Ramesh Jain, Amir M. Rahmani

State-of-the-art rule-based and classification-based food recommendation systems face significant challenges in becoming practical and useful. This difficulty arises primarily because most machine learning models struggle with problems characterized by an almost infinite number of classes and a limited number of samples within an unbalanced dataset. Converse

Irit Dinur, Ting-Chun Lin, Thomas Vidick

We introduce a high-dimensional cubical complex, for any dimension t>0, and apply it to the design of quantum locally testable codes. Our complex is a natural generalization of the constructions by Panteleev and Kalachev and by Dinur et. al of a square complex (case t=2), which have been applied to the design of classical locally testable codes (LTC) and qua

Bobo Hua, Yong Lin, Tao Wang

In this paper, we study the Bakry-\'Emery Ricci flow on finite graphs. Our main result is the local existence and uniqueness of solutions to the Ricci flow. We prove the long-time convergence or finite-time blow up for the Bakry-\'Emery Ricci flow on finite trees and circles.

Mohammad Musavinezhad, Jan Renger, Johannes Zirkelbach, Tobias Utikal

We perform laser spectroscopy at liquid helium temperatures (T=2 K) to investigate single dibenzoterrylene (DBT) molecules doped in anthracene crystals of nanoscopic height fabricated by electrohydrodynamic dripping. Using high-resolution fluorescence excitation spectroscopy, we show that zero-phonon lines of single molecules in printed nanocrystals are near

Dan Mao, Liangyun Chen

This paper mainly studies the ResLieDer pair in characteristic 2, that is, a restricted Lie algebra with a restricted derivation. We define the restricted representation of a ResLieDer pair and the corresponding cohomology complex. We show that a ResLieDer pair is rigid if the second cohomology group is trivial and a deformation of order $n$ is extensible if

Bingqing Cheng

Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modelling in material science and chemistry. Many potentials use atomic cluster expansion or equivariant message passing frameworks. Such frameworks typically use spherical harmonics as angular basis functions, and then use Clebsch-Gordan contraction to maintain rotat

Edwige Cyffers, Aurélien Bellet, Jalaj Upadhyay

The popularity of federated learning comes from the possibility of better scalability and the ability for participants to keep control of their data, improving data security and sovereignty. Unfortunately, sharing model updates also creates a new privacy attack surface. In this work, we characterize the privacy guarantees of decentralized learning with rando

David Ruhe, Jonathan Heek, Tim Salimans, Emiel Hoogeboom

Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data. These methods generally treat subsequent frames equally regarding the amount of noise in the diffusion process. This paper explores Rolling Diffusion: a new approach that uses a sliding window denoising process. It ensures th

Yazhou Zhang, Mengyao Wang, Chenyu Ren, Qiuchi Li

The value of text classification's future research has encountered challenges and uncertainties, due to the extraordinary efficacy demonstrated by large language models (LLMs) across numerous downstream NLP tasks. In this era of open-ended language modeling, where task boundaries are gradually fading, an urgent question emerges: have we made significant adva

Sangjoon Lee, Philip S. Marcus

The transient dynamics of a wake vortex, modelled as a strong swirling $q$-vortex, are investigated with a focus on optimal transient growth driven by continuous eigenmodes associated with continuous spectra. The pivotal contribution of viscous critical-layer eigenmodes (Lee & Marcus, J. Fluid Mech., vol. 967) amongst the entire eigenmode families to optimal

A. Antuñano, T. del Río-Gaztelurrutia, A. Sánchez-Lavega, R. Hueso

We analyze data retrieved by the Imaging Science System onboard the Cassini spacecraft to study the horizontal velocity and vorticity fields of Saturn's Polar Regions (latitudes 60-90$^\circ$N in June-December 2013 and 60-90$^\circ$S in October 2006 and July-December 2008), including the Northern region where the hexagonal wave is prominent. With the aid of

Rose Alaslani, Levina Perzhilla, Muhammad Mahboob Ur Rahman, Taous-Meriem Laleg-Kirati

This work proposes for the first time to utilize the regular smartphone -- a popular assistive gadget -- to design a novel, non-invasive method for self-monitoring of one's hydration level on a scale of 1 to 4. The proposed method involves recording a small video of a fingertip using the smartphone camera. Subsequently, a photoplethysmography (PPG) signal is

Oron Nir, Idan Vidra, Avi Neeman, Barak Kinarti

Streamlining content discovery within media archives requires integrating advanced data representations and effective visualization techniques for clear communication of video topics to users. The proposed system addresses the challenge of efficiently navigating large video collections by exploiting a fusion of visual, audio, and textual features to accurate

Zheyuan Hu, Zhongqiang Zhang, George Em Karniadakis, Kenji Kawaguchi

The Fokker-Planck (FP) equation is a foundational PDE in stochastic processes. However, curse of dimensionality (CoD) poses challenge when dealing with high-dimensional FP PDEs. Although Monte Carlo and vanilla Physics-Informed Neural Networks (PINNs) have shown the potential to tackle CoD, both methods exhibit numerical errors in high dimensions when dealin

Isao Kishimoto, Shigenori Seki, Tomohiko Takahashi

Two-point tree-level amplitudes in bosonic closed string theory are described by a correlation function within the BRST formalism, which respects manifest Lorentz and conformal invariance. In the derivation of the two-point amplitudes, we use the mostly BRST exact operator, which has been introduced for two-point open string amplitudes, and a closed string v

Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele

The dynamic mode decomposition (DMD) is a simple and powerful data-driven modeling technique that is capable of revealing coherent spatiotemporal patterns from data. The method's linear algebra-based formulation additionally allows for a variety of optimizations and extensions that make the algorithm practical and viable for real-world data analysis. As a re

Nathan I. N. Henry, Mangor Pedersen, Matt Williams, Jamin L. B. Martin

The value-loading problem is a significant challenge for researchers aiming to create artificial intelligence (AI) systems that align with human values and preferences. This problem requires a method to define and regulate safe and optimal limits of AI behaviors. In this work, we propose HALO (Hormetic ALignment via Opponent processes), a regulatory paradigm

G. -X. Wang, Y. -K. Wu, R. Yao, W. -Q. Lian

The spin-boson model is a prototypical model for open quantum dynamics. Here we simulate the spin-boson model using a chain of trapped ions where a spin is coupled to a structured reservoir of bosonic modes. We engineer the spectral density of the reservoir by adjusting the ion number, the target ion location, the laser detuning to the phonon sidebands, and

Elena Masserini, Davide Ginelli, Daniela Micucci, Daniela Briola

Failure data collected from the field (e.g., failure traces, bug reports, and memory dumps) represent an invaluable source of information for developers who need to reproduce and analyze failures. Unfortunately, field data may include sensitive information and thus cannot be collected indiscriminately. Privacy-preserving techniques can address this problem a

Aditya Dwivedi, Siddharth Dwivedi, Vivek Kumar Singh, Pichai Ramadevi

We compute the $\text{PSL}(2,\mathbb{C})$ Chern-Simons partition function of a closed 3-manifold obtained from Dehn fillings of the link complement $\mathbf S^3\backslash {\mathcal{L}}$, where $\mathcal{L}=\mathcal{K}# H$ is the connected sum of the knot $\mathcal {K}$ with the Hopf link $H$. Motivated by our earlier work on topological entanglement and the

Mingda Qiao, Letian Zheng

We study a sequential binary prediction setting where the forecaster is evaluated in terms of the calibration distance, which is defined as the $L_1$ distance between the predicted values and the set of predictions that are perfectly calibrated in hindsight. This is analogous to a calibration measure recently proposed by B{\l}asiok, Gopalan, Hu and Nakkiran

Sahil Gehlawat, Aakanksha Jain, Amar Deep Sarkar

For a planar domain $\Omega$, we consider the Dirichlet spaces with respect to a base point $\zeta\in\Omega$ and the corresponding kernel functions. It is not known how these kernel functions behave as we vary the base point. In this note, we prove that these kernel functions vary smoothly. As an application of the smoothness result, we prove a Ramadanov-typ

Zhiyong Wu, Chengcheng Han, Zichen Ding, Zhenmin Weng

Autonomous interaction with the computer has been a longstanding challenge with great potential, and the recent proliferation of large language models (LLMs) has markedly accelerated progress in building digital agents. However, most of these agents are designed to interact with a narrow domain, such as a specific software or website. This narrow focus const

Matteo Gaibotti, Sonia G. Mogilevskaya, Andrea Piccolroaz, Davide Bigoni

An analytical solution is derived for the bifurcations of an elastic disc that is constrained on the boundary with an isoperimetric Cosserat coating. The latter is treated as an elastic circular rod, either perfectly or partially bonded (with a slip interface in the latter case) and is subjected to three different types of uniformly distributed radial loads

Ajith Ramachandran, Alexander Eisfeld, Sebastian Wüster, Jan-Michael Rost

Quantum transport of charge or energy in networks with discrete sites is central to diverse quantum technologies, from molecular electronics to light harvesting and quantum opto-mechanical metamaterials. A one dimensional network can be viewed as waveguide. We show that if such waveguide is hybridised with a control unit that contains a few sites, then trans

Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran

Consider the domain of multiclass classification within the adversarial online setting. What is the price of relying on bandit feedback as opposed to full information? To what extent can an adaptive adversary amplify the loss compared to an oblivious one? To what extent can a randomized learner reduce the loss compared to a deterministic one? We study these

Yinyu Ye, Shijing Chen, Dong Ni, Ruobing Huang

Different diseases, such as histological subtypes of breast lesions, have severely varying incidence rates. Even trained with substantial amount of in-distribution (ID) data, models often encounter out-of-distribution (OOD) samples belonging to unseen classes in clinical reality. To address this, we propose a novel framework built upon a long-tailed OOD dete

Ryan A. Rubenzahl, Fei Dai, Andrew W. Howard, Jack J. Lissauer

The extreme environments of ultra-short-period planets (USPs) make excellent laboratories to study how exoplanets obtain, lose, retain, and/or regain gaseous atmospheres. We present the confirmation and characterization of the USP TOI-1347 b, a $1.8 \pm 0.1$ R$_\oplus$ planet on a 0.85 day orbit that was detected with photometry from the TESS mission. We mea

Jangho Kim, Giovanni Pederiva, Andrea Shindler

We discuss a machine learning (ML) regression model to reduce the computational cost of disconnected diagrams in lattice QCD calculations. This method creates a mapping between the results of fermionic loops computed at different quark masses and flow times. The ML mapping, trained with just a small fraction of the complete data set, makes use of translation

Jie-Ran Xue, Fa Wang

The recent discovery of possible high temperature superconductivity in single crystals of $La_3Ni_2O_7$ under pressure renews the interest in research on nickelates. The DFT calculations reveal that both $d_{z^2}$ and $d_{x^2-y^2}$ orbitals are active, which suggests a minimal two-orbital model to capture the low-energy physics of this system. In this work,

Ahmed Heakl, Youssef Mohamed, Ahmed B. Zaky

This study presents AraSpider, the first Arabic version of the Spider dataset, aimed at improving natural language processing (NLP) in the Arabic-speaking community. Four multilingual translation models were tested for their effectiveness in translating English to Arabic. Additionally, two models were assessed for their ability to generate SQL queries from A

Pascal Marquet, Max Planck

This is an English (annotated) translation of the German paper by Max Planck (1943) about "The history of the discovery of the physical quantum of action"

Genqian Liu

In this note, we shall show that the two-term spectral asymptotics for the operator of linear elasticity with mixed boundary conditions which were given by Capoferri and Mann in \cite{CaMa-24} essentially are old well-known results due to T. Branson, P. Gilkey, B. {\O}rsted and A. Pierzchalski in \cite{BGOP}. In addition, we further point out that the so-cal

Surangika Ranathunga, Nisansa de Silva, Menan Velayuthan, Aloka Fernando

We conducted a detailed analysis on the quality of web-mined corpora for two low-resource languages (making three language pairs, English-Sinhala, English-Tamil and Sinhala-Tamil). We ranked each corpus according to a similarity measure and carried out an intrinsic and extrinsic evaluation on different portions of this ranked corpus. We show that there are s

Pascal Marquet, Max Planck

This is an English (annotated) translation of the German paper by Max Planck (1887) about "The principle of the conservation of energy"

Yuepeng Yang, Antares Chen, Lorenzo Orecchia, Cong Ma

In this paper, we address the top-$K$ ranking problem with a monotone adversary. We consider the scenario where a comparison graph is randomly generated and the adversary is allowed to add arbitrary edges. The statistician's goal is then to accurately identify the top-$K$ preferred items based on pairwise comparisons derived from this semi-random comparison

Yuxiao Wen, Yanjun Han, Zhengyuan Zhou

We consider contextual bandits with graph feedback, a class of interactive learning problems with richer structures than vanilla contextual bandits, where taking an action reveals the rewards for all neighboring actions in the feedback graph under all contexts. Unlike the multi-armed bandits setting where a growing literature has painted a near-complete unde

S. Halder, M. Bewong, A. Mahboubi, Y. Jiang

Protecting software supply chains from malicious packages is paramount in the evolving landscape of software development. Attacks on the software supply chain involve attackers injecting harmful software into commonly used packages or libraries in a software repository. For instance, JavaScript uses Node Package Manager (NPM), and Python uses Python Package

Pascal Marquet, Max Planck

This is an English (annotated) translation of the Habilitation-Thesis report (in German) of Max Planck at the University of Munich (1880)

Barna Saha, Christopher Ye

Self-attention is at the heart of the popular Transformer architecture, yet suffers from quadratic time and memory complexity. The breakthrough FlashAttention algorithm revealed I/O complexity as the true bottleneck in scaling Transformers. Given two levels of memory hierarchy, a fast cache (e.g. GPU on-chip SRAM) and a slow memory (e.g. GPU high-bandwidth m

Ray Ito, Junichiro Takahashi

Several attempts have been made to implement text command control for game agents. However, current technologies are limited to processing predefined format commands. This paper proposes a pioneering text command control system for a game agent that can understand natural language commands expressed in free-form. The proposed system uses a large language mod

Sujoy Bhore, Timothy M. Chan

In this work, we study two fundamental graph optimization problems, minimum vertex cover (MVC) and maximum-cardinality matching (MCM), for intersection graphs of geometric objects, e.g., disks, rectangles, hypercubes, etc., in $d$-dimensional Euclidean space. We consider the problems in fully dynamic settings, allowing insertions and deletions of objects. We

Jon Saad-Falcon, Daniel Y. Fu, Simran Arora, Neel Guha

Retrieval pipelines-an integral component of many machine learning systems-perform poorly in domains where documents are long (e.g., 10K tokens or more) and where identifying the relevant document requires synthesizing information across the entire text. Developing long-context retrieval encoders suitable for these domains raises three challenges: (1) how to

Oscar Oelrich, Mattias Villani

A multi-output Gaussian process (GP) is introduced as a model for the joint posterior distribution of the local predictive ability of set of models and/or experts, conditional on a vector of covariates, from historical predictions in the form of log predictive scores. Following a power transformation of the log scores, a GP with Gaussian noise can be used, w

Sangbum Cho, Yuya Koda, Jung Hoon Lee

The Powell Conjecture states that the Goeritz group of the Heegaard splitting of the $3$-sphere is finitely generated; furthermore, four specific elements suffice to generate the group. Zupan demonstrated that the conjecture holds if and only if the reducing sphere complexes are all connected. In this work, we establish the connectivity of the reducing spher

Pascal Marquet, Max Planck

This is an English (annotated) translation of the Thesis report (in German) of Max Planck at the University of Munich (1879)

Qiwen Cui, Maryam Fazel, Simon S. Du

In multiplayer games, self-interested behavior among the players can harm the social welfare. Tax mechanisms are a common method to alleviate this issue and induce socially optimal behavior. In this work, we take the initial step of learning the optimal tax that can maximize social welfare with limited feedback in congestion games. We propose a new type of f

Haruhisa Oda, Mayuko Kida, Yoichi Nakata, Hiroki Kurihara

While branching network structures abound in nature, their objective analysis is more difficult than expected because existing quantitative methods often rely on the subjective judgment of branch structures. This problem is particularly pronounced when dealing with images comprising discrete particles. Here we propose an objective framework for quantitative

Narayan Tondapu

In this study, we examine the fluctuation in the value of the Great Britain Pound (GBP). We focus particularly on its relationship with the United States Dollar (USD) and the Euro (EUR) currency pairs. Utilizing data from June 15, 2018, to June 15, 2023, we apply various mathematical models to assess their effectiveness in predicting the 20-day variation in

Masahiro Tanaka

Orthogonal matrices play an important role in probability and statistics, particularly in high-dimensional statistical models. Parameterizing these models using orthogonal matrices facilitates dimension reduction and parameter identification. However, establishing the theoretical validity of statistical inference in these models from a frequentist perspectiv

Logan Kenwright, Partha Roop, Nathan Allen, Sanjay Lall

Kahn Process Networks (KPNs) are a deterministic Model of Computation (MoC) for distributed systems. KPNs supports non-blocking writes and blocking reads, with the consequent assumption of unbounded buffers between processes. Variants such as Finite FIFO Platforms (FFP) have been developed, which enforce boundedness. One issue with existing models is that th

Guanyi Chen, Fahime Same, Kees van Deemter

Recently, a human evaluation study of Referring Expression Generation (REG) models had an unexpected conclusion: on \textsc{webnlg}, Referring Expressions (REs) generated by the state-of-the-art neural models were not only indistinguishable from the REs in \textsc{webnlg} but also from the REs generated by a simple rule-based system. Here, we argue that this

Ragib Amin Nihal, Tran Dong Huu Quoc, Lin Zirui, Xu Yimimg

SALAD is an AI-driven language-learning application designed to help foreigners learn Japanese. It offers translations in Kanji-Kana-Romaji, speech recognition, translated audio, vocabulary tracking, grammar explanations, and songs generated from newly learned words. The app targets beginners and intermediate learners, aiming to make language acquisition mor

Denni Currin-Ross, Sami C. Al-Izzi, Ivar Noordstra, Alpha S. Yap

We propose and analyse an active hydrodynamic theory that characterises the effects of the scaffold protein anillin. Anillin is found at major sites of cortical activity, such as adherens junctions and the cytokinetic furrow, where the canonical regulator of actomyosin remodelling is the small GTPase, RhoA. RhoA acts via intermediary 'effectors' to increase

Serdar Kadioglu, Filip Michalsky

The acceleration in the adoption of AI-based automated decision-making systems poses a challenge for evaluating the fairness of algorithmic decisions, especially in the absence of ground truth. When designing interventions, uplift modeling is used extensively to identify candidates that are likely to benefit from treatment. However, these models remain parti

Tianrui Liu, Changxin Xu, Yuxin Qiao, Chufeng Jiang

Simultaneous Localization and Mapping (SLAM) presents a formidable challenge in robotics, involving the dynamic construction of a map while concurrently determining the precise location of the robotic agent within an unfamiliar environment. This intricate task is further compounded by the inherent "chicken-and-egg" dilemma, where accurate mapping relies on a

Samuel Cordova, Alexandre Moreira, Miguel Heleno

In the face of a substantial and uncertain growth of behind-the-meter Distributed Energy Resources (DERs), utilities and regulators are currently in the search for new network planning strategies for facilitating an efficient Transmission & Distribution (T&D) coordination. In this context, here we propose a novel distribution system planning methodology to f

Gautham Vadlamudi, Akhil Aravind, Saini Jatin Rao, Saptarshi Basu

The study comprehensively investigates the response of a combusting droplet during its interaction with a high-speed transient flow that is imposed by a coaxially propagating blast wave. The blast wave is generated using a specially designed unique miniature shock generation apparatus that generates blast waves using the wire-explosion technique which facili

Kunhe Yang, Hanrui Zhang

We study Bayesian persuasion under approximate best response, where the receiver may choose any action that is not too much suboptimal given their posterior belief upon receiving the signal. We focus on the computational aspects of the problem, aiming to design algorithms that efficiently compute (almost) optimal strategies for the sender. Despite the absenc

Wentao Ning, Reynold Cheng, Xiao Yan, Ben Kao

Global popularity (GP) bias is the phenomenon that popular items are recommended much more frequently than they should be, which goes against the goal of providing personalized recommendations and harms user experience and recommendation accuracy. Many methods have been proposed to reduce GP bias but they fail to notice the fundamental problem of GP, i.e., i

Taikei Fujii, Takahiko Nobukawa, Tatsushi Shimazaki

We give some special values of Grothendieck polynomials and an explicit formula for the number of set-valued tableaux. For Young diagrams consisting of a single row or a single column, both the value and number are written by the Gauss' hypergeometric function ${}_2F_1$. For general Young diagrams, the Holman hypergeometric function $F^{(n)}$ is used to repr

Mohammed Saad Qadri

Let $F$ be a non-archimedean local field. Let $\pi_1$ and $\pi_2$ be irreducible Arthur type representations of $\mathrm{GL}_n(F)$ and $\mathrm{GL}_{n-1}(F)$ respectively. We study Ext branching laws when $\pi_1$ and $\pi_2$ are products of discrete series representations and their Aubert-Zelevinsky duals. We obtain an Ext analogue of the local non-tempered

Tianrui Liu, Changxin Xu, Yuxin Qiao, Chufeng Jiang

This paper explores the area of news recommendation, a key component of online information sharing. Initially, we provide a clear introduction to news recommendation, defining the core problem and summarizing current methods and notable recent algorithms. We then present our work on implementing the NRAM (News Recommendation with Attention Mechanism), an att

Chenyang Li, Yingyu Liang, Zhenmei Shi, Zhao Song

In the evolving landscape of machine learning, a pivotal challenge lies in deciphering the internal representations harnessed by neural networks and Transformers. Building on recent progress toward comprehending how networks execute distinct target functions, our study embarks on an exploration of the underlying reasons behind networks adopting specific comp

Leonid Kitchatinov

Helioseismology has revealed an increase in the rotation rate with depth in a thin ($\sim$30 Mm) near-surface layer. The normalized rotational shear in this layer is independent of latitude. This rotational state is shown to be a consequence of the short characteristic time of near-surface convection compared to the rotation period, and the radial anisotropy