July 2023 arXiv papers — page 22

Alexander Kolpakov, Aidan Rocke

The present work explores the theoretical limits of Machine Learning (ML) within the framework of Kolmogorov's theory of Algorithmic Probability, which clarifies the notion of entropy as Expected Kolmogorov Complexity and formalizes other fundamental concepts such as Occam's razor via Levin's Universal Distribution. As a fundamental application, we develop M

Francesco Giordano, Stefano Negro, Roberto Tateo

In this study, we introduce and investigate a family of quantum mechanical models in 0+1 dimensions, known as generalized Born quantum oscillators. These models represent a one-parameter deformation of a specific system obtained by reducing the Nambu-Goto theory to 0+1 dimensions. Despite these systems showing significant similarities with $\mathrm{T}\overli

David A. Tovar

The process of conducting literature reviews is often time-consuming and labor-intensive. To streamline this process, I present an AI Literature Review Suite that integrates several functionalities to provide a comprehensive literature review. This tool leverages the power of open access science, large language models (LLMs) and natural language processing t

Keighley E. Rockcliffe, Elisabeth R. Newton, Allison Youngblood, Girish M. Duvvuri

Photoevaporation is a potential explanation for several features within exoplanet demographics. Atmospheric escape observed in young Neptune-sized exoplanets can provide insight into and characterize which mechanisms drive this evolution and at what times they dominate. AU Mic b is one such exoplanet, slightly larger than Neptune (4.19 Earth radii). It close

Chongjun Ouyang, Yuanwei Liu, Xingqi Zhang

This letter proposes advanced beamforming design and analyzes its influence on the sensing and communications (S&C) performance for a multiple-antenna integrated S&C (ISAC) system with a single communication user and a single target. Novel closed-form beamformers are derived for three typical scenarios, including the sensing-centric design, communications-ce

Indranil Mukherjee, P. K. Mohanty

Renormalization group theory allows continuous variation of critical exponents along a marginal direction (when there is one), keeping the scaling relations invariant. We propose a super universality hypothesis (SUH) suggesting that, up to constant scale factors, the scaling functions along the critical line must be identical to that of the base universality

Ben Elias, Hankyung Ko, Nicolas Libedinsky, Leonardo Patimo

For any Coxeter system, and any double coset for two standard parabolic subgroups, we introduce a Demazure operator. These operators form a basis for morphism spaces in a category we call the nilCoxeter category, and we also present this category by generators and relations. We prove a generalization to this context of Demazure's celebrated theorem on Froben

Liang Xu, Anqi Li, Lei Zhu, Hang Xue

Large language models (LLMs) have shown the potential to be integrated into human daily lives. Therefore, user preference is the most critical criterion for assessing LLMs' performance in real-world scenarios. However, existing benchmarks mainly focus on measuring models' accuracy using multi-choice questions, which limits the understanding of their capabili

Aminollah Khormali, Jiann-Shiun Yuan

Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset, where training and testing take place on the in-distribution dataset. However, their performance deteriorates significantly when presented with unseen samples. As a result, a reliable deepfake detection system must remain impartial to forgery types, appea

Kunal Talwar, Shan Wang, Audra McMillan, Vojta Jina

We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private algorithms require little trust but are (provably) limited in their utility. Centrally differentially private algorithms can allow significantly better utility but require a trust

Haotong Qin, Ge-Peng Ji, Salman Khan, Deng-Ping Fan

Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI. Notably, Bard has recently been updated to handle visual inputs alongside text prompts during conversations. Given Bard's impressive track record in handling textual inputs, we explore its capabilities in understanding and interpreting visual data (ima

Anurag Kaushal, Naveen S. Prabhakar, Spenta R. Wadia

We consider $\text{SU}(2)$ quantum chromodynamics in $1+1$ dimensions with a single quark in the spin $J$ representation of the gauge group and study the theory in the large $J$ limit where the gauge coupling $g^2 \to 0$ and $J \to \infty$ with $\lambda = g^2 J^2$ fixed. We work with a Dirac spinor field for arbitrary $J$, and with a Majorana spinor for inte

Ryan Abbott, William Detmold, Fernando Romero-López, Zohreh Davoudi

We present an algorithm to compute correlation functions for systems with the quantum numbers of many identical mesons from lattice quantum chromodynamics (QCD). The algorithm is numerically stable and allows for the computation of $n$-pion correlation functions for $n \in \{ 1, \dots, N\}$ using a single $N \times N$ matrix decomposition, improving on previ

Ulrik Enstad, Gabriel Favre, Sven Raum

We show that any free action of a connected Lie group of polynomial growth on a finite dimensional locally compact space has finite tube dimension. This is shown to imply that the associated crossed product C*-algebra has finite nuclear dimension. As an application we show that C*-algebras associated with certain aperiodic point sets in connected Lie groups

Junyang Zhang

Divide a deck of $kn$ cards into $k$ equal piles and place them from left to right. The standard shuffle $\sigma$ is performed by picking up the top cards one by one from left to right and repeating until all cards have been picked up. For every permutation $\tau$ of the $k$ piles, use $\rho_{\tau}$ to denote the induced permutation on the $kn$ cards. The sh

Douglas R. Anderson, Masakazu Onitsuka, Donal O'Regan

This study uses an associated Riccati equation to study the Ulam stability of non-autonomous linear differential vector equations that model the damped linear oscillator. In particular, the best (minimal) Ulam constants for these non-autonomous linear differential vector equations are derived. These robust results apply to vector equations with solutions tha

Matteo Ippoliti, Vedika Khemani

Monitored quantum dynamics -- unitary evolution interspersed with measurements -- has recently emerged as a rich domain for phase structure in quantum many-body systems away from equilibrium. Here we study monitored dynamics from the point of view of an eavesdropper who has access to the classical measurement outcomes, but not to the quantum many-body system

Soon-Tae Hong

In order to investigate the gravitational wave (GW) radiation, without appealing to the tensorial formalism of the linearized general relativity, we formulate the so-called modified linearized general relativity (MLGR). As an application of the MLGR, we construct a novel paradigm of measuring the GW radiation from a binary system of compact objects, to theor

Xiangzun Wang, Frank Cichos

The processing of information is an indispensable property of living systems realized by networks of active processes with enormous complexity. They have inspired many variants of modern machine learning one of them being reservoir computing, in which stimulating a network of nodes with fading memory enables computations and complex predictions. Reservoirs a

Ange A. Maurice, Alberto E. Quintero, Marcos Vera

This paper presents an exhaustive how-to guide on measuring the total vanadium concentration and state of charge of vanadium electrolytes using UV-Visible spectroscopy. The study is provided with an open-access database (https://github.com/AngeAM/SOC_Vanadium_Spectra_2023.git) that supports the methods and procedures and facilitates access to the calibration

Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju

With the increased deployment of machine learning models in various real-world applications, researchers and practitioners alike have emphasized the need for explanations of model behaviour. To this end, two broad strategies have been outlined in prior literature to explain models. Post hoc explanation methods explain the behaviour of complex black-box model

Douglas R. Anderson, Masakazu Onitsuka, Donal O'Regan

This study deals with the Ulam stability of non-autonomous linear differential systems without assuming the condition that they admit an exponential dichotomy. In particular, the best (minimal) Ulam constants for two-dimensional non-autonomous linear differential systems with generalized Jordan normal forms are derived. The obtained results are applicable no

Roman Vetter, Steve V. M. Runser, Dagmar Iber

We present PolyHoop, a lightweight standalone C++ implementation of a mechanical model to simulate the dynamics of soft particles and cellular tissues in two dimensions. With only few geometrical and physical parameters, PolyHoop is capable of simulating a wide range of particulate soft matter systems: from biological cells and tissues to vesicles, bubbles,

Jin Heo, Christopher Phillips, Ada Gavrilovska

Light detection and ranging (LiDAR) sensors are becoming available on modern mobile devices and provide a 3D sensing capability. This new capability is beneficial for perceptions in various use cases, but it is challenging for resource-constrained mobile devices to use the perceptions in real-time because of their high computational complexity. In this conte

Jingliang Li, Qiang Zhou, Chaohui Yu, Zhengda Lu

Since the advent of Neural Radiance Fields, novel view synthesis has received tremendous attention. The existing approach for the generalization of radiance field reconstruction primarily constructs an encoding volume from nearby source images as additional inputs. However, these approaches cannot efficiently encode the geometric information of real scenes w

Phyllis Wan, Chen Zhou

In this paper, we estimate the sparse dependence structure in the tail region of a multivariate random vector, potentially of high dimension. The tail dependence is modeled via a graphical model for extremes embedded in the H\"usler-Reiss distribution. We propose the extreme graphical lasso procedure to estimate the sparsity in the tail dependence, similar t

Kuantay Boshkayev, Talgar Konysbayev, Yergali Kurmanov, Orlando Luongo

We consider thin accretion disks in the field of a class of rotating regular black holes. For this purpose, we obtain the radius of the innermost stable circular orbit, $r_{ISCO}$ and efficiency of accretion disk in converting matter into radiation $\eta$ with the aim of modeling the disk's emission spectrum. We consider a simple model for the disk's radiati

Juri Opitz

The effectiveness of compression in text classification ('gzip') has recently garnered lots of attention. In this note we show that `bag-of-words' approaches can achieve similar or better results, and are more efficient.

Amrendra Kumar, C. Kamal

We predict energetically and dynamically stable ternary Carbon-Phosphorous-Arsenic (CPAs2) monolayers in buckled geometric structure by employing density functional theory based calculations. We consider three different symmetric configurations, namely, inversion (i), mirror (m) and rotational (r). The low-energy dispersions in electronic band structure and

Oscar Braun-White, Nigel Glover, Christian T Preuss

The antenna-subtraction technique has demonstrated remarkable effectiveness in providing next-to-next-to-leading order in $\alpha_s$ (NNLO) predictions for a wide range of processes relevant for the Large Hadron Collider. In a previous paper [1], we demonstrated how to build real-radiation antenna functions for any number of real emissions directly from a sp

Jiuqi Yan, Yingxian Chen, W. W. T. Fok

The safety of children in children home has become an increasing social concern, and the purpose of this experiment is to use machine learning applied to detect the scenarios of child abuse to increase the safety of children. This experiment uses machine learning to classify and recognize a child's voice and predict whether the current sound made by the chil

Natalia Nottingham, Michael A. Perlin, Dhirpal Shah, Ryan White

Despite major ongoing advancements in neutral atom hardware technology, there remains limited work in systems-level software tailored to overcoming the challenges of neutral atom quantum computers. In particular, most current neutral atom architectures do not natively support local addressing of single-qubit rotations about an axis in the xy-plane of the Blo

Zhen Qin, Dong Li, Weigao Sun, Weixuan Sun

We present TransNormerLLM, the first linear attention-based Large Language Model (LLM) that outperforms conventional softmax attention-based models in terms of both accuracy and efficiency. TransNormerLLM evolves from the previous linear attention architecture TransNormer by making advanced modifications that include positional embedding, linear attention ac

Massimo Bilancioni, Massimiliano Esposito, Nahuel Freitas

We study an autonomous model of a Maxwell demon that works by rectifying thermal fluctuations of chemical reactions. It constitutes the chemical analog of a recently studied electronic demon. We characterize its scaling behavior in the macroscopic limit, its performances, and the impact of potential internal delays. We obtain analytical expressions for all q

Stephen Chung, Ivan Anokhin, David Krueger

We propose the Thinker algorithm, a novel approach that enables reinforcement learning agents to autonomously interact with and utilize a learned world model. The Thinker algorithm wraps the environment with a world model and introduces new actions designed for interacting with the world model. These model-interaction actions enable agents to perform plannin

Yen-Tsung Chen, Ryotaro Harada

In the present paper, we study linear equations on tensor powers of the Carlitz module using the theory of Anderson dual $t$-motives and a detailed analysis of a specific Frobenius difference equation. As an application, we derive some explicit sufficient conditions for the linear independence for Carlitz polylogarithms at algebraic points in both $\infty$-a

Jiyang Zhang, Pengyu Nie, Junyi Jessy Li, Milos Gligoric

Many software projects implement APIs and algorithms in multiple programming languages. Maintaining such projects is tiresome, as developers have to ensure that any change (e.g., a bug fix or a new feature) is being propagated, timely and without errors, to implementations in other programming languages. In the world of ever-changing software, using rule-bas

Feng Xu, Jialong Chen, Yong Hou, Juan Cheng

Widefield quantum diamond microscopy (WQDM) based on Kohler-illumination has been widely adopted in the field of quantum sensing, however, practical applications are still limited by issues such as unavoidable photodamage and unsatisfied spatial-resolution. Here, we design and develop a super-resolution enabled WQDM using a digital micromirror device (DMD)-b

Antonio deMarti iOlius, Patricio Fuentes, Román Orús, Pedro M. Crespo

Quantum technologies have the potential to solve certain computationally hard problems with polynomial or super-polynomial speedups when compared to classical methods. Unfortunately, the unstable nature of quantum information makes it prone to errors. For this reason, quantum error correction is an invaluable tool to make quantum information reliable and ena

Or Sharir, Anima Anandkumar

Deep learning often faces the challenge of efficiently processing dynamic inputs, such as sensor data or user inputs. For example, an AI writing assistant is required to update its suggestions in real time as a document is edited. Re-running the model each time is expensive, even with compression techniques like knowledge distillation, pruning, or quantizati

Victor E. Ambruş, Maxim N. Chernodub

An ensemble of massless fermions can be characterized by its total helicity charge given by the sum of axial charges of particles minus the sum of axial charges of antiparticles. We show that charged massless fermions develop a dissipationless flow of helicity along the background magnetic field. We dub this transport phenomenon as the Helical Separation Eff

M. Griesemer, V. Kußmaul

Kato's well known distributional inequality for the magnetic Laplacian holds equally in the more general setting of non-relativistic quantum electrodynamics (QED), where the wave function is vector-valued and the vector potential is quantized. We give two new applications of this result: First, we show that eigenstates satisfy a subsolution estimate. Second,

Chen Gao, Xiaochong Lan, Zhihong Lu, Jinzhu Mao

Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanation, and policy-making support, among others. In this work, we harness the formidable human-like capabilities exhibited by large language models (LLMs) in sensing, reasoning, and b

A. Banik, H. Hinrichsen, W. Porod

We present on shell-scheme for the 2PI formalism with a particular focus on the renormalized equations of motion. We first revisit the so-called Hartree approximation where we give the counterterms for both the broken and unbroken phase. Moreover, we give explicit formulas for the renormalized three- and four-point functions in the broken phase. We then turn

Andrea Marino, Martin Sundermann, Denise S. Christovam, Andrea Amorese

Using high resolution tender-x-ray resonant inelastic scattering and hard-x-ray non-resonant inelastic scattering beyond the dipole limit we were able to detect electronic excitations in intermetallic UGa$_2$ that are highly atomic in nature. Analysis of the spectral lineshape reveals that the local $5f^2$ configuration characterizes the correlated nature of

Jiaheng Li, Jonathan R. Stewart

We propose a novel class of separable multilayer network models to capture cross-layer dependencies in multilayer networks, enabling the analysis of how interactions in one or more layers may influence interactions in other layers. Our approach separates the network formation process from the layer formation process, and is able to extend existing single-lay

Chenming Wu, Jiadai Sun, Zhelun Shen, Liangjun Zhang

Simulating camera sensors is a crucial task in autonomous driving. Although neural radiance fields are exceptional at synthesizing photorealistic views in driving simulations, they still fail to generate extrapolated views. This paper proposes to incorporate map priors into neural radiance fields to synthesize out-of-trajectory driving views with semantic ro

Carlos Barroso-Fernández, Jorge Martín-Pérez, Constantine Ayimba, Antonio de la Oliva

Industry 4.0 applications impose the challenging demand of delivering packets with bounded latencies via a wireless network. This is further complicated if the network is not dedicated to the time critical application. In this paper we use network calculus analysis to derive closed form expressions of latency bounds for time critical traffic when 802.11 Targ

Maurizio Fagotti

A quantum spin-$\frac{1}{2}$ chain with an axial symmetry is normally described by quasiparticles associated with the spins oriented along the axis of rotation. Kinetic constraints can enrich such a description by setting apart different species of quasiparticles, which can get stuck at high enough density, realising the quantum analogue of jamming. We ident

Paul C. W. Davies, Philip Tee

There is increasing interest in discrete or "pixelated" spacetime models as a foundation for a satisfactory theory of quantum gravity. If spacetime possesses a cellular structure, there should be observable consequences: for example, the vacuum becomes a dispersive medium. Of obvious interest are the implications for the thermodynamic properties of quantum b

Masashi Aiko, Johannes Braathen, Shinya Kanemura

Leading two-loop contributions to the di-photon decay of the Higgs boson are evaluated for the first time in the Inert Doublet Model (IDM). We employ for this calculation the Higgs low-energy theorem, meaning that we obtain corrections to the Higgs decay process by taking Higgs-field derivatives of the leading two-loop contributions to the photon self-energy

Gioia Carinci, Chiara Franceschini, Rouven Frassek, Cristian Giardinà

We consider the boundary driven harmonic model, i.e. the Markov process associated to the open integrable XXX chain with non-compact spins. Using the factorial moments we characterize the stationary measure as a mixture of product measures. For all spin values, we identify the law of the mixture in terms of the Dirichlet process. Next, by using the explicit

Yu-Feng Mao, Yu-Liang Tao, Jiong-Hao Wang, Qi-Bo Zeng

Quasicrystals allow for symmetries that are impossible in crystalline materials, such as eight-fold rotational symmetry, enabling the existence of novel higher-order topological insulators in two dimensions without crystalline counterparts. However, it remains an open question whether three-dimensional higher-order topological insulators and Weyl-like semime

Antoine Luciano, Christian P. Robert, Robin J. Ryder

In some applied scenarios, the availability of complete data is restricted, often due to privacy concerns; only aggregated, robust and inefficient statistics derived from the data are made accessible. These robust statistics are not sufficient, but they demonstrate reduced sensitivity to outliers and offer enhanced data protection due to their higher breakdo

Rusa Mandal, Tom Tong

Motivated by the dynamical reasons for the hierarchical structure of the Yukawa sector of the Standard Model (SM), we consider an extension of the SM with a complex scalar field, known as `flavon', based on the Froggatt-Nielsen mechanism. In an effective theory approach, the SM fermion masses and mixing patterns are generated in orders of the parameter relat

Ziyi Wang, Xumin Yu, Yongming Rao, Jie Zhou

With the overwhelming trend of mask image modeling led by MAE, generative pre-training has shown a remarkable potential to boost the performance of fundamental models in 2D vision. However, in 3D vision, the over-reliance on Transformer-based backbones and the unordered nature of point clouds have restricted the further development of generative pre-training

Dominik Sturm, Suryanarayana Maddu, Ivo F. Sbalzarini

We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in self-organized active particle systems. The self-organized emergence of macroscopic patterns from microscopic interactions between self-propelled particles can be widely observed na

F. Mandujano, E. Vázquez-Luis

Non-linear oscillations of an elliptical cylinder, that can rotate about an axis that passes through its symmetry axle due to a torsional spring and hydrodynamic torque produced by the flow of a Newtonian fluid, were analysed in terms of a single parameter that compares vortex shedding frequency with the torsional spring's natural frequency. The governing eq

Hongye Guo, Mohand O. Saed, Eugene M. Terentjev

Nematic liquid crystal elastomers (LCEs) have anomalously high vibration damping, and it has been assumed this is the cause of their anomalously high pressure-sensitive adhesion (PSA). Here we investigate the mechanism behind this enhanced PSA by first preparing thin adhesive tapes with LCE of varying crosslinking density, characterizing their material and s

ATLAS Collaboration

A search for quantum black holes in electron+jet and muon+jet invariant mass spectra is performed with 140 fb$^{-1}$ of data collected by the ATLAS detector in proton-proton collisions at $\sqrt{s}$ = 13 TeV at the Large Hadron Collider. The observed invariant mass spectrum of lepton+jet pairs is consistent with Standard Model expectations. Upper limits are

Jinfeng Zhang, Ting Lin, Ao Wang, Xiaochao Wang

Releasing the epitaxial oxide heterostructures from substrate constraints leads to the emergence of various correlated electronic phases and paves the way for integrations with advanced semiconductor technologies. Identifying a suitable water-soluble sacrificial layer, compatible with the high-quality epitaxial growth of oxide heterostructures, is currently

Chen Lu, Zhiming Pan, Fan Yang, Congjun Wu

The newly discovered high-temperature superconductivity in La$_3$Ni$_2$O$_7$ under pressure has attracted a great deal of attentions. The essential ingredient characterizing the electronic properties is the bilayer NiO$_2$ planes coupled by the interlayer bonding of $3d_{z^2}$ orbitals through the intermediate oxygen-atoms. In the strong coupling limit, the

Qing-Dong Jiang

Based on a hybrid light-matter unitary transformation for cavity quantum electrodynamics, we investigate the spectral shift of an atom induced by quantum fluctuations in a chiral vacuum cavity. Remarkably, we find an intriguing angular momentum-dependent shift in the spectra of bound states. Our approach shows promise in going beyond traditional perturbative

Ryan Roussel, Auralee Edelen, Dylan Kennedy, Seongyeol Kim

Transverse beam emittance plays a key role in the performance of high brightness accelerators. Characterizing beam emittance is often done using a quadrupole scan, which fits beam matrix elements to experimental measurements using first order optics. Despite its simplicity at face value, this procedure is difficult to automate due to practical limitations. K

Hyungjin Kim, Alessandro Lenoci, Gilad Perez, Wolfram Ratzinger

The axion-gluon coupling is the defining feature of the QCD axion. This feature induces additional and qualitatively different interactions of the axion with standard model particles -- quadratic couplings. Previously, hadronic quadratic couplings have been studied and experimental implications have been explored especially in the context of atomic spectrosc

Peipei Lu, Roland Maier, Andreas Rupp

We formulate and analyze a multiscale method for an elliptic problem with an oscillatory coefficient based on a skeletal (hybrid) formulation. More precisely, we employ hybrid discontinuous Galerkin approaches and combine them with the localized orthogonal decomposition methodology to obtain a coarse-scale skeletal method that effectively includes fine-scale

Julian Legendre, Karyn Le Hur

We introduce a local spectroscopic method in real space to probe the topological properties of a circuit quantum electrodynamics (cQED) array generalizing previous approaches from one to two dimensions in the plane. As an application, we develop the theory of microwave light propagating in the local probe capacitively coupled to the cQED array associated to

Marawan Elbatel, Hualiang Wang, Robert Martí, Huazhu Fu

In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, f

Zhan Jiang, Rebecca R. G

We extend the notion of F-rationality to other closure operations, inspired by the work of Smith, Epstein and Schwede, and Ma and Schwede, which describe F-rationality in terms of the canonical module and top local cohomology module. We give conditions for a closure operation cl on a Cohen-Macaulay complete local ring under which cl-rationality is equivalent

Adam Schwimmer, Stefan Theisen

The structure of type A and B trace anomalies is reanalyzed in terms of the universal behaviour of dimension -2 invariant amplitudes. Based on it a general argument for trace anomaly matching between the unbroken and broken phases of a CFT is given. The structure of moduli trace anomalies and their transformations under source reparametrizations is discussed

Balázs Hidasi, Ádám Tibor Czapp

Reproducibility of recommender systems research has come under scrutiny during recent years. Along with works focusing on repeating experiments with certain algorithms, the research community has also started discussing various aspects of evaluation and how these affect reproducibility. We add a novel angle to this discussion by examining how unofficial thir

Giovanni Canepa, Alberto S. Cattaneo, Filippo Fila-Robattino, Manuel Tecchiolli

In this article a description of the reduced phase space of the standard model coupled to gravity is given. For space or time-like boundaries this is achieved as the reduction of a symplectic space with respect to a coisotropic submanifold and with the BFV formalism. For light-like boundaries the reduced phase space is described as the reduction of a symplec

Giulio Gasbarri, Matias Bilkis, Elisabet Roda-Salichs, John Calsamiglia

We consider a quantum system that is being continuously monitored, giving rise to a measurement signal. From such a stream of data, information needs to be inferred about the underlying system's dynamics. Here we focus on hypothesis testing problems and put forward the usage of sequential strategies where the signal is analyzed in real time, allowing the exp

Eduardo Fernandes Montesuma, Fred Ngolè Mboula, Antoine Souloumiac

This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to an unlabeled target domain. We propose a novel MSDA framework based on dictionary learning and optimal transport. We interpret each domain in MSDA as an empirical distribution. As

Connor Mclaughlin, Matthew Ding, Denis Edogmus, Lili Su

As the network scale increases, existing fully distributed solutions start to lag behind the real-world challenges such as (1) slow information propagation, (2) network communication failures, and (3) external adversarial attacks. In this paper, we focus on hierarchical system architecture and address the problem of non-Bayesian learning over networks that a

Balázs Hidasi, Ádám Tibor Czapp

Even though offline evaluation is just an imperfect proxy of online performance -- due to the interactive nature of recommenders -- it will probably remain the primary way of evaluation in recommender systems research for the foreseeable future, since the proprietary nature of production recommenders prevents independent validation of A/B test setups and ver

Tanmoy Bhattacharya, Scott Lawrence, Jun-Sik Yoo

In most lattice field theories, correlators are plagued by a signal-to-noise problem of exponential difficulty in the time separation. We propose a method for improving the signal-to-noise ratio, in which control variates are systematically constructed from lattice Schwinger-Dyson relations. The method is demonstrated on various two-dimensional lattices in s

Alexander Straub, Nikolaos Karadimitriou, Guido Reina, Steffen Frey

We developed a new approach comprised of different visualizations for the comparative spatio-temporal analysis of displacement processes in porous media. We aim to analyze and compare ensemble datasets from experiments to gain insight into the influence of different parameters on fluid flow. To capture the displacement of a defending fluid by an invading flu

David Meretzky, Anand Pillay

Let K be differential field with algebraically closed field of constants. Let K^diff be a differential closure of K, and L the (iterated) Picard-Vessiot closure of K inside K^diff. Let G be a linear differential algebraic group over K and X a differential algebraic torsor for G over K. We prove that X(L) is Kolchin-dense in X. When G is finite-dimensional we

Surangkhana Rukdee, Sagi Ben-Ami, Mercedes López-Morales, Andrew Szentgyorgyi

The upcoming Extremely Large Telescopes (ELTs) are expected to have the collecting area required to detect potential biosignature gases such as molecular oxygen, $\mathrm{O_2}$, in the atmosphere of terrestrial planets around nearby stars. One of the most promising detection methods is transmission spectroscopy. To maximize our capability to detect $\mathrm{

Joel C. Allred, Graham S. Kerr, Meriem Alaoui, Juan Camilo Buitrago-Casas

Solar flares and coronal mass ejections are interrelated phenomena that together are known as solar eruptive events. These are the main drivers of space weather and understanding their origins is a primary goal of Heliophysics. In this white paper, we advocate for the allocation of sufficient resources to bring together experts in observations and modeling t

Aarsh Chotalia, Shiva Dixit, P. Parmananda

We propose a minimal yet intriguing model for a relationship between two individuals. The feeling of an individual is modeled by a complex variable and hence has two degrees of freedom. The effect of memory of other individual's behavior in the past has now been incorporated via a conjugate coupling between each other's feelings. A region of parameter space

Federico Gerbino, Igor Lesanovsky, Gabriele Perfetto

We consider the quantum reaction-diffusion dynamics in $d$ spatial dimensions of a Fermi gas subject to binary annihilation reactions $A+A \to \emptyset$. These systems display collective nonequilibrium long-time behavior, which is signalled by an algebraic decay of the particle density. Building on the Keldysh formalism, we devise a field theoretical approa

ATLAS Collaboration

New particles with large masses that decay into hadronically interacting particles are predicted by many models of physics beyond the Standard Model. A search for a massive resonance that decays into pairs of dijet resonances is performed using 140 fb$^{-1}$ of proton$-$proton collisions at $\sqrt{s}=13$ TeV recorded by the ATLAS detector during Run 2 of the

Sebastian Benthall, David Shekman

A fiduciary is a trusted agent that has the legal duty to act with loyalty and care towards a principal that employs them. When fiduciary organizations interact with users through a digital interface, or otherwise automate their operations with artificial intelligence, they will need to design these AI systems to be compliant with their duties. This article

Sebastiano Raffa, Gianmario Merisio, Francesco Topputo

Trajectory design in highly-perturbed environments like binary asteroids is challenging. It typically requires using realistic, non-autonomous dynamical models in which periodic solutions derived in autonomous systems vanish. In this work, Lagrangian descriptors are employed in the perturbed planar bi-elliptic restricted four-body problem to find regions of

Suhail M. Shah, Raghu Bollapragada

Decentralized optimization is typically studied under the assumption of noise-free transmission. However, real-world scenarios often involve the presence of noise due to factors such as additive white Gaussian noise channels or probabilistic quantization of transmitted data. These sources of noise have the potential to degrade the performance of decentralize

Jimmy He, Dominik Schmid

We study the speed of convergence to equilibrium for the asymmetric simple exclusion process (ASEP) on a finite interval with one open boundary. We provide sharp estimates on the total-variation distance from equilibrium and verify that the limit profile undergoes a phase transition from a Gaussian to a KPZ profile.

C. Coelho, M. Fernanda P. Costa, L. L. Ferrás

The continuous dynamics of natural systems has been effectively modelled using Neural Ordinary Differential Equations (Neural ODEs). However, for accurate and meaningful predictions, it is crucial that the models follow the underlying rules or laws that govern these systems. In this work, we propose a self-adaptive penalty algorithm for Neural ODEs to enable

Matteo Pintonello, Pavel Shumyatsky

A group-word $w$ is called concise if the verbal subgroup $w(G)$ is finite whenever $w$ takes only finitely many values in a group $G$. It is known that there are words that are not concise. In particular, Olshanskii gave an example of such a word, which we denote by $w_o$. The problem whether every word is concise in the class of residually finite groups re

Saber Jafarpour, Akash Harapanahalli, Samuel Coogan

In this paper, we propose a computationally efficient framework for interval reachability of systems with neural network controllers. Our approach leverages inclusion functions for the open-loop system and the neural network controller to embed the closed-loop system into a larger-dimensional embedding system, where a single trajectory over-approximates the

Ralf Stannarius, Kirsten Harth

Disclinations or disclination clusters in smectic C freely suspended films with topological charges larger than one are unstable. They disintegrate, preferably in a spatially symmetric fashion, into single defects with individual charges +1, which is the smallest positive topological charge allowed in polar vector fields. While the opposite process of defect

Bo Shen, Jiaxin Zhang, Taihong Chen, Daoguang Zan

Large Language Models for Code (Code LLM) are flourishing. New and powerful models are released on a weekly basis, demonstrating remarkable performance on the code generation task. Various approaches have been proposed to boost the code generation performance of pre-trained Code LLMs, such as supervised fine-tuning, instruction tuning, reinforcement learning

George Chernishev, Michael Polyntsov, Anton Chizhov, Kirill Stupakov

Data profiling is an essential process in modern data-driven industries. One of its critical components is the discovery and validation of complex statistics, including functional dependencies, data constraints, association rules, and others. However, most existing data profiling systems that focus on complex statistics do not provide proper integration with

Valeriia Liakh, Rony Keppens

We present a 2.5-dimensional magnetohydrodynamic simulation of a systematically rotating prominence inside its coronal cavity using the open-source \texttt{MPI-AMRVAC} code. Our simulation starts from a non-adiabatic, gravitationally stratified corona, permeated with a sheared arcade magnetic structure. The flux rope (FR) is formed through converging and she

Dhrumil Patel, Mark M. Wilde

Density Matrix Exponentiation is a technique for simulating Hamiltonian dynamics when the Hamiltonian to be simulated is available as a quantum state. In this paper, we present a natural analogue to this technique, for simulating Markovian dynamics governed by the well known Lindblad master equation. For this purpose, we first propose an input model in which

Ilya Losev, Stanislav Smirnov

Diffusion Limited Aggregation and its generalization, Dielectric Breakdown model play an important role in physics, approximating a range of natural phenomena. Yet little is known about them, with the famous Kesten's estimate on the DLAs growth being perhaps the most important result. Using a different approach we prove a generalisation of this result for th

Diego Arroyuelo, Adrián Gómez-Brandón, Gonzalo Navarro

Regular Path Queries (RPQs), which are essentially regular expressions to be matched against the labels of paths in labeled graphs, are at the core of graph database query languages like SPARQL. A way to solve RPQs is to translate them into a sequence of operations on the adjacency matrices of each label. We design and implement a Boolean algebra on sparse m

Alexander W. Goodall, Francesco Belardinelli

Reinforcement learning (RL) has shown great potential for solving complex tasks in a variety of domains. However, applying RL to safety-critical systems in the real-world is not easy as many algorithms are sample-inefficient and maximising the standard RL objective comes with no guarantees on worst-case performance. In this paper we propose approximate model

Daniel A. Inafuku, Kay L. Kirkpatrick, Onyema Osuagwu, Qier An

Translation is one of the most fundamental processes in the biological cell. Because of the central role that translation plays across all domains of life, the enzyme that carries out this process, the ribosome, is required to process information with high accuracy. This accuracy often approaches values near unity experimentally. In this paper, we model the