February 2024 arXiv papers — page 111

Fabio Coppini, Anna De Crescenzo, Huyen Pham

We address a system of weakly interacting particles where the heterogenous connections among the particles are described by a graph sequence and the number of particles grows to infinity. Our results extend the existing law of large numbers and propagation of chaos results to the case where the interaction between one particle and its neighbors is expressed

M. Gabriella Kuhn, N. Silvio Riccobon

We prove the following Theorem: Given any three distinct points on a straight line r, there exist an equilateral triangle, whose circumcenter lies on r, such that the projections of its vertices on r are exactly the three given points.

Anne-Katherine Burns, Venus Keus, Marc Sher, Tim M. P. Tait

Recently, the EMPRESS collaboration has included new data in the extraction of the primordial $^4$He abundance from Big Bang Nucleosynthesis (BBN), resulting in a determination that differs from the previous value and from theoretical expectations. There have been several studies attempting to explain this anomaly which involve variation of fundamental const

Ievgen Bondarenko, Kate Juschenko

The zero divisor conjecture is sufficient to prove for certain class of finitely presented groups where the relations are given by a pairing of generators. We associate Mealy automata to such pairings, and prove that the zero divisor conjecture holds for groups corresponding to invertible automata with three states. In particular, there cannot be zero diviso

Rokas Elijošius, Fabian Zills, Ilyes Batatia, Sam Walton Norwood

Generative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties. Recently, score-based, or diffusion, generative models have significantly outperformed previous approaches. Key to their success is the close relationship between the score and physical force, allowing the use of powerful equiv

Ziyuan Yin, Austin Hinkel

As the number of known Galactic structures mounts thanks to the Gaia Space Telescope, it is now pertinent to study methods for disentangling structures occupying the same regions of the Milky Way. Indeed, understanding the precise form of each individual structure and the interactions between structures may aid in understanding their origins and chronology.

Cameron Foreman, Richie Yeung, Alec Edgington, Florian J. Curchod

We present Cryptomite, a Python library of randomness extractor implementations. The library offers a range of two-source, seeded and deterministic randomness extractors, together with parameter calculation modules, making it easy to use and suitable for a variety of applications. We also present theoretical results, including new extractor constructions and

P. Yu. Minaev, A. S. Pozanenko, S. A. Grebenev, I. V. Chelovekov

The results of a study of the short gamma-ray burst GRB 231115A in the X-ray and gamma-ray ranges are presented, based on data from the INTEGRAL and Fermi space observatories. The source of the burst is localized by the IBIS/ISGRI telescope of INTEGRAL observatory with an accuracy of $\leq$ 1.5 arcmin, it is located in the Cigar Galaxy (M 82). Early follow-u

Michael Fischer, Zhengqin Li, Thu Nguyen-Phuoc, Aljaz Bozic

A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene. We here ask the question whether we can transfer the appearance from a source NeRF onto a target 3D geometry in a semantically meaningful way, such that the resulting new NeRF retains the target geometry but has an appearance that is an analogy to the sourc

Mohammad Pedramfar, Vaneet Aggarwal

In this paper, we analyze the problem of online convex optimization in different settings, including different feedback types (full-information/semi-bandit/bandit/etc) in either stochastic or non-stochastic setting and different notions of regret (static adversarial regret/dynamic regret/adaptive regret). This is done through a framework which allows us to s

Juan C. Muñoz-Sánchez, J. Tomás Lázaro, Julia Hillung, María J. Olmo-Uceda

It is well known that, during replication, RNA viruses spontaneously generate defective viral genomes (DVGs). DVGs are unable to complete an infectious cycle autonomously, and depend on coinfection with a helper wild-type virus (HV) for their replication and/or transmission. The study of the dynamics arising from a HV and its DVGs has been a longstanding que

Nathan Huynh, Majbah Uddin, Chu Cong Minh

With the growth of intermodal freight transportation, it is important that transportation planners and decision makers are knowledgeable about freight flow data to make informed decisions. This is particularly true with Intelligent Transportation Systems (ITS) offering new capabilities to intermodal freight transportation. Specifically, ITS enables access to

Bruno Buonomo, Eleonora Messina, Claudia Panico, Antonia Vecchio

We propose an integral model describing an epidemic of an infectious disease. The model is behavioural in the sense that the constitutive law for the force of infection includes a distributed delay, called "information index", that describes the opinion-driven human behavioural changes. The information index, in turn, contains a memory kernel to mimic how th

Parikshit Pareek, Abhijith Jayakumar, Carleton Coffrin, Sidhant Misra

Quantum computers hold promise for solving problems intractable for classical computers, especially those with high time or space complexity. Practical quantum advantage can be said to exist for such problems when the end-to-end time for solving such a problem using a classical algorithm exceeds that required by a quantum algorithm. Reducing the power flow (

Leonard Henckel, Theo Würtzen, Sebastian Weichwald

Evaluating graphs learned by causal discovery algorithms is difficult: The number of edges that differ between two graphs does not reflect how the graphs differ with respect to the identifying formulas they suggest for causal effects. We introduce a framework for developing causal distances between graphs which includes the structural intervention distance f

Marvin Wyrich, Marvin Muñoz Barón, Justus Bogner

Software engineering (SE) is full of abstract concepts that are crucial for both researchers and practitioners, such as programming experience, team productivity, code comprehension, and system security. Secondary studies aimed at summarizing research on the influences and consequences of such concepts would therefore be of great value. However, the inabilit

Carey Lisse, James Bauer, Yaeji Kim

The upcoming NASA SPHEREx (Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer) all-sky 0.7 to 5.0 um spectral survey, to be conducted from 2025 to 2027, provides a unique space-based opportunity to detect, spectrally categorize, and catalog hundreds of thousands of solar system objects at WISE/NEOWISE sensitivities.

Sikha Pentyala, Mayana Pereira, Martine De Cock

Data is the lifeblood of the modern world, forming a fundamental part of AI, decision-making, and research advances. With increase in interest in data, governments have taken important steps towards a regulated data world, drastically impacting data sharing and data usability and resulting in massive amounts of data confined within the walls of organizations

Che Shen

We study the action of the quantum group $U_q(\widehat{\mathfrak{gl}_n})$ on the equivariant K-theory of affine Laumon spaces. We show that, at any highest weight away from the critical level, this can be identified with the contragredient dual Verma module of $U_q(\widehat{\mathfrak{gl}_n})$, improving earlier results of Braverman-Finkelberg and Negu{\c{t}}

Baptiste Bermond, Adolfo G. Grushin, David Carpentier

The Casimir effect is a macroscopic evidence of the quantum nature of the vacuum. On a ring, it leads to a finite size correction to the vacuum energy. In this work, we show that this vacuum's energy and pressure acquire additional, sizable corrections, when the ring's radius is increased fast enough, an experimentally accessible model of an expanding univer

Johan Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle

The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size. Analogous scaling laws remain elusive for reinforcement learning domains, however, where increasing the parameter count of a model often hurts its final performance. In this paper, we de

Marvin Wyrich, Sven Apel

Valid empirical studies build confidence in scientific findings. Fortunately, it is now common for software engineering researchers to consider threats to validity when designing their studies and to discuss them as part of their publication. Yet, in complex experiments with human participants, there is often an overwhelming number of intuitively plausible t

Gianluca Ceruti, Lukas Einkemmer, Jonas Kusch, Christian Lubich

Dynamical low-rank approximation has become a valuable tool to perform an on-the-fly model order reduction for prohibitively large matrix differential equations. A core ingredient is the construction of integrators that are robust to the presence of small singular values and the resulting large time derivatives of the orthogonal factors in the low-rank matri

Eric R. Anschuetz, Xun Gao

Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability; as a corollary of these results, practical exponential separations in expressive power over classical machine learning models are believed to be infeasible as such QNNs take a time

Eliot Heinrich, Xiao Chen

We present a quantum circuit model which emulates the interface growth of the classical raise-and-peel model. Our model consists of Clifford unitary gates interspersed with projective measurements, applied according to prescribed feedback rules. We numerically find via large-scale simulations that, depending on the feedback rules, the system may undergo seve

Juyang Weng

This is a theoretical paper on "Deep Learning" misconduct in particular and Post-Selection in general. As far as the author knows, the first peer-reviewed papers on Deep Learning misconduct are [32], [37], [36]. Regardless of learning modes, e.g., supervised, reinforcement, adversarial, and evolutional, almost all machine learning methods (except for a few m

Homin K. Lee, Charles Masson

Large, distributed data streams are now ubiquitous. High-accuracy sketches with low memory overhead have become the de facto method for analyzing this data. For instance, if we wish to group data by some label and report the largest counts using fixed memory, we need to turn to mergeable heavy hitter sketches that can provide highly accurate approximate coun

Kh. Jafarzade, B. Eslam Panah, M. E. Rodrigues

Motivated by high interest in Lorentz invariant massive gravity models known as dRGT massive gravity, we present an exact phantom black hole solution in this theory of gravity and discuss the thermodynamic structure of the black hole in the canonical ensemble. Calculating the conserved and thermodynamic quantities, we check the validity of the first law of t

Federico Paolucci, Federica Bianco, Francesco Giazotto, Stefano Roddaro

In the emergent field of quantum technology, the ability to manage heat at the nanoscale and in cryogenic conditions is crucial for enhancing device performance in terms of noise, coherence, and sensitivity. Here, we demonstrate the active cooling and refrigeration of the electron gas in a graphene thermal transistor, by taking advantage of nanoscale superco

Xiaoou Li, Hongru Zhao

Motivated by modern applications such as computerized adaptive testing, sequential rank aggregation, and heterogeneous data source selection, we study the problem of active sequential estimation, which involves adaptively selecting experiments for sequentially collected data. The goal is to design experiment selection rules for more accurate model estimation

Yunji Jung, Seokju Lee, Tair Djanibekov, Hyunjung Shim

Text-guided non-rigid editing involves complex edits for input images, such as changing motion or compositions within their surroundings. Since it requires manipulating the input structure, existing methods often struggle with preserving object identity and background, particularly when combined with Stable Diffusion. In this work, we propose a training-free

R. R. Hartmann, M. E. Portnoi

We show that the (2+1)-dimensional massless Dirac equation, which includes a tilt term, can be reduced to the biconfluent Heun equation for a broad range of scalar confining potentials, including the well-known Morse potential. Applying these solutions, we investigate a bipolar electron waveguide in 8-$Pmmn$ borophene, formed by a well and barrier, both desc

Naihuan Jing, Yibo Liu, Jiacheng Sun, Chengrui Zhao

We study an optimization problem originated from the Grothendieck constant. A generalized normal equation is proposed and analyzed. We establish a correspondence between solutions of the general normal equation and its dual equation. Explicit solutions are described for the two-dimensional case.

Siarhei Finski

The main goal of this paper is to generalize a part of the relationship between mean curvature and Harder-Narasimhan filtrations of holomorphic vector bundles to arbitrary polarized fibrations. More precisely, for a polarized family of complex projective manifolds, we establish lower bounds on a fibered version of Yang-Mills functionals in terms of the Harde

Zhiwei Yang, Ao Chen, Xiaohang Xie, Stephan W. Anderson

Noise pollution is a persistent environmental concern with severe implications for human health and resources. Acoustic metamaterials offer the potential for ultrathin silencing devices; however, existing designs often lack practical openness and are thereby limited by their functional bandwidths. This paper introduces a novel approach utilizing a phase grad

A. Y. Ibrahim, A. Borghese, F. Coti Zelati, E. Parent

Recently, the Galactic magnetar SGR J1935+2154 has garnered attention due to its emission of an extremely luminous radio burst, reminiscent of Fast Radio Bursts (FRBs). SGR J1935+2154 is one of the most active magnetars, displaying flaring events nearly every year, including outbursts as well as short and intermediate bursts. Here, we present our results on

Emily Jin, Michael Bronstein, İsmail İlkan Ceylan, Matthias Lanzinger

A large body of work has investigated the properties of graph neural networks and identified several limitations, particularly pertaining to their expressive power. Their inability to count certain patterns (e.g., cycles) in a graph lies at the heart of such limitations, since many functions to be learned rely on the ability of counting such patterns. Two pr

Haeju Lee, Minchan Jeong, Se-Young Yun, Kee-Eung Kim

Prompt tuning, in which prompts are optimized to adapt large-scale pre-trained language models to downstream tasks instead of fine-tuning the full model parameters, has been shown to be particularly effective when the prompts are trained in a multi-task transfer learning setting. These methods generally involve individually training prompts for each source t

Xiangru Tang, Howard Dai, Elizabeth Knight, Fang Wu

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development

Jovan Blanuša, Maximo Cravero Baraja, Andreea Anghel, Luc von Niederhäusern

In this paper, we present "Graph Feature Preprocessor", a software library for detecting typical money laundering patterns in financial transaction graphs in real time. These patterns are used to produce a rich set of transaction features for downstream machine learning training and inference tasks such as detection of fraudulent financial transactions. We s

Reza Sarshar, Mohammad Heydari, Elham Akhondzadeh Noughabi

Facial analysis has emerged as a prominent area of research with diverse applications, including cosmetic surgery programs, the beauty industry, photography, and entertainment. Manipulating patient images often necessitates professional image processing software. This study contributes by providing a model that facilitates the detection of blemishes and skin

L. A. Cañizares, S. T. Badman, S. A. Maloney, M. J. Owens

Solar radio bursts (SRBs), are emitted by electrons propagating through the corona and interplanetary space. Tracking such bursts is key to understanding the properties of accelerated electrons and radio wave propagation as well as the local plasma environment that they propagate through. Here, we present a novel multilateration algorithm called BayEsian Loc

Bendik Nyheim, Signe Riemer-Sørensen, Rodrigo Parra, Claudia Cicone

Radio, sub-millimeter and millimeter ground-based telescopes are powerful instruments for studying the gas and dust-rich regions of the Universe that are invisible at optical wavelengths, but the pointing accuracy is crucial for obtaining high-quality data. Pointing errors are small deviations of the telescope's orientation from its desired direction. The te

Moritz Otto

We prove a Poisson process approximation result for stabilizing functionals of a determinantal point process. Our results use concrete couplings of determinantal processes with different Palm measures and exploit their association properties. Second, we focus on the Ginibre process and show in the asymptotic scenario of an increasing window size that the pro

Zsolt Páles, Mahmood Kamil Shihab

The main goal of this paper is to show that if a real valued function defined on a groupoid satisfies a certain Levi--Civita-type functional equation, then it also fulfills a Cauchy--Schwarz-type functional inequality. In particular, if the groupoid is the multiplicative structure of commutative ring, then we can establish the existence of nontrivial additiv

Lorenzo Cascioli, Laurens Devos, Ondřej Kuželka, Jesse Davis

Tree ensembles are one of the most widely used model classes. However, these models are susceptible to adversarial examples, i.e., slightly perturbed examples that elicit a misprediction. There has been significant research on designing approaches to construct such examples for tree ensembles. But this is a computationally challenging problem that often must

Claudio Gorodski, Andreas Kollross, Alberto Rodríguez-Vázquez

We classify totally geodesic submanifolds of the real Stiefel manifolds of orthogonal two-frames. We also classify polar actions on these Stiefel manifolds, specifically, we prove that the orbits of polar actions are lifts of polar actions on the corresponding Grassmannian. In the case of cohomogeneity-one actions we are able to obtain a classification for a

Yongchao Chen, Jacob Arkin, Yilun Hao, Yang Zhang

Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task. LLMs have been successfully used to help find and improve prompt candidates for single-step tasks. However, realistic tasks for agents are multi-step and introduce new challenges: (1) Prompt content is likely to be more extensive and complex, making it more dif

Danijel Grahovac, Peter Kevei

In this paper we consider sample path growth of superpositions of Ornstein--Uhlenbeck type processes (supOU). SupOU processes are stationary infinitely divisible processes defined as integrals with respect to a random measure. They allow marginal distributions and correlations to be modeled independently. Our results show that the almost sure behavior is pri

Li Ma, Haoyu Han, Juanhui Li, Harry Shomer

Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning. Heuristic methods, leveraging a range of different pairwise measures such as common neighbors and shortest paths, often rival the performance of vanilla Graph Neural Networks (GNNs). Therefore, recent advancements in GNNs for link prediction

Charulkumar Chodvadiya, Navyansh Mahla, Kinshuk Gaurav Singh, Kshitij Sharad Jadhav

Medical image segmentation is a critical process in the field of medical imaging, playing a pivotal role in diagnosis, treatment, and research. It involves partitioning of an image into multiple regions, representing distinct anatomical or pathological structures. Conventional methods often grapple with the challenge of balancing spatial precision and compre

Yiyang Li, Lei Li, Dingxin Hu, Xueyi Hao

Improving factual consistency in abstractive summarization has been a focus of current research. One promising approach is the post-editing method. However, previous works have yet to make sufficient use of factual factors in summaries and suffers from the negative effect of the training datasets. In this paper, we first propose a novel factual error correct

Noah Schnitzer, Berit H. Goodge, Gregory Powers, Jaewook Kim

Charge order pervades the phase diagrams of quantum materials where it competes with superconducting and magnetic phases, hosts electronic phase transitions and topological defects, and couples to the lattice generating intricate structural distortions. Incommensurate charge order is readily stabilized in manganese oxides where it is associated with anomalou

Qingshan Wang, Clara C. Wanjura, Florian Marquardt

Given the rapidly growing scale and resource requirements of machine learning applications, the idea of building more efficient learning machines much closer to the laws of physics is an attractive proposition. One central question for identifying promising candidates for such neuromorphic platforms is whether not only inference but also training can exploit

Yongzhe Jia, Xuyun Zhang, Amin Beheshti, Wanchun Dou

Federated Learning (FL) has emerged as a promising solution in Edge Computing (EC) environments to process the proliferation of data generated by edge devices. By collaboratively optimizing the global machine learning models on distributed edge devices, FL circumvents the need for transmitting raw data and enhances user privacy. Despite practical successes,

Dong Lu, Tianyu Pang, Chao Du, Qian Liu

Backdoor attacks are commonly executed by contaminating training data, such that a trigger can activate predetermined harmful effects during the test phase. In this work, we present AnyDoor, a test-time backdoor attack against multimodal large language models (MLLMs), which involves injecting the backdoor into the textual modality using adversarial test imag

Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan

Algorithms for playing in Stackelberg games have been deployed in real-world domains including airport security, anti-poaching efforts, and cyber-crime prevention. However, these algorithms often fail to take into consideration the additional information available to each player (e.g. traffic patterns, weather conditions, network congestion), which may signi

Jackson Bunting, Paul Diegert, Arnaud Maurel

We provide identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: known heterogeneity, initially unknown heterogeneity that may be revealed over time, and transitory uncertainty. We consider a common environment where the researcher only has access to a short panel on choices and reali

Rasmus Kjær Høier, Christopher Zach

The search for ``biologically plausible'' learning algorithms has converged on the idea of representing gradients as activity differences. However, most approaches require a high degree of synchronization (distinct phases during learning) and introduce substantial computational overhead, which raises doubts regarding their biological plausibility as well as

Anthony Zhou, Linnia Hawkins, Pierre Gentine

Earth system models (ESMs) are vital for understanding past, present, and future climate, but they suffer from legacy technical infrastructure. ESMs are primarily implemented in Fortran, a language that poses a high barrier of entry for early career scientists and lacks a GPU runtime, which has become essential for continued advancement as GPU power increase

Murad Özkoç, Büşra Köstel

The main purpose of this paper is to introduce and study two new operators $(\cdot)_R^{\diamond}$ and $cl_R^{\diamond}(\cdot)$ via primal which is a new notion. We also show that the operator $cl_R^{\diamond}(\cdot)$ is a Kuratowski closure operator, while the operator $(\cdot)_R^{\diamond}$ is not. In addition, we prove that the topology on $X$, shown as $\

Zhiyu Xu, Qingliang Chen

Glass-like objects can be seen everywhere in our daily life which are very hard for existing methods to segment them. The properties of transparencies pose great challenges of detecting them from the chaotic background and the vague separation boundaries further impede the acquisition of their exact contours. Moving machines which ignore glasses have great r

Po-han Li, Oyku Selin Toprak, Aditya Narayanan, Ufuk Topcu

Foundation models have recently expanded into robotics after excelling in computer vision and natural language processing. The models are accessible in two ways: open-source or paid, closed-source options. Users with access to both face a problem when deciding between effective yet costly closed-source models and free but less powerful open-source alternativ

Diana P. Ovalle-Muñoz, M. Dolores Ruiz-Medina

This paper considers the problem of manifold functional multiple regression with functional response, time--varying scalar regressors, and functional error term displaying Long Range Dependence (LRD) in time. Specifically, the error term is given by a manifold multifractionally integrated functional time series (see, e.g., Ovalle--Mu\~noz \& Ruiz--Medina, 20

Malena I. Español, Gabriela Jeronimo

Variable projection methods prove highly efficient in solving separable nonlinear least squares problems by transforming them into a reduced nonlinear least squares problem, typically solvable via the Gauss-Newton method. When solving large-scale separable nonlinear inverse problems with general-form Tikhonov regularization, the computational demand for comp

Xiangming Gu, Xiaosen Zheng, Tianyu Pang, Chao Du

A multimodal large language model (MLLM) agent can receive instructions, capture images, retrieve histories from memory, and decide which tools to use. Nonetheless, red-teaming efforts have revealed that adversarial images/prompts can jailbreak an MLLM and cause unaligned behaviors. In this work, we report an even more severe safety issue in multi-agent envi

Syed S. Ahmed, Mohammed A. Shalaby, Charles C. Cossette, Jerome Le Ny

Multi-robot systems must have the ability to accurately estimate relative states between robots in order to perform collaborative tasks, possibly with no external aiding. Three-dimensional relative pose estimation using range measurements oftentimes suffers from a finite number of non-unique solutions, or ambiguities. This paper: 1) identifies and accurately

Francisco Bolanos, Angelo Salatino, Francesco Osborne, Enrico Motta

This manuscript presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates previous research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in thi

Yotam Gafni, Aviv Yaish

To allocate transactions to blocks, cryptocurrencies use an auction-like transaction fee mechanism (TFM). A conjecture of Roughgarden [44] asks whether there is a TFM that is incentive compatible for both the users and the miner, and is also resistant to off-chain agreements (OCAs) between these parties, a collusion notion that captures the ability of users

Amartya Mukherjee, Melissa M. Stadt, Lena Podina, Mohammad Kohandel

Diffusion models have emerged as a promising class of generative models that map noisy inputs to realistic images. More recently, they have been employed to generate solutions to partial differential equations (PDEs). However, they still struggle with inverse problems in the Laplacian operator, for instance, the Poisson equation, because the eigenvalues that

Chongyang Gao, Kezhen Chen, Jinmeng Rao, Baochen Sun

Parameter-efficient tuning (PEFT) techniques like low-rank adaptation (LoRA) offer training efficiency on Large Language Models, but their impact on model performance remains limited. Recent efforts integrate LoRA and Mixture-of-Experts (MoE) to improve the performance of PEFT methods. Despite promising results, research on improving the efficiency of LoRA w

N. Carey, L. Zanisi, S. Pamela, V. Gopakumar

Simulation-based plasma scenario development, optimization and control are crucial elements towards the successful deployment of next-generation experimental tokamaks and Fusion power plants. Current simulation codes require extremely intensive use of HPC resources that make them unsuitable for iterative or real time applications. Neural network based surrog

Guixiang Hong, Éric Ricard

In this paper, we provide a counterexample to show that in sharp contrast to the classical case, the almost uniform convergence may not happen for truly noncommutative $L_p$-martingales when $1\leq p<2$. The same happens to ergodic averages. The proof consists of some sharp estimates of the distributional function of a sequence of matrices and some non stand

Chunyu Huang, Yu Luo, Yule Zhao, Xiaofei Ma

Integrated nonlinear optical devices play an important role in modern optical communications. However, conventional on-chip optical devices with homogeneous or periodic translation dimensions generally have limited bandwidth when applied to nonlinear optical applications. Up today, there lacks a general method to design compact nonlinear optical devices over

Peter Kun, Amalia De Götzen, Miriam Bidoglia, Niels Jørgen Gommesen

While diversity has become a debated issue in design, very little research exists on positive use-cases for diversity beyond scholarly criticism. The current work addresses this gap through the case of a diversity-aware chatbot, exploring what benefits a diversity-aware chatbot could bring to people and how do people interpret diversity when being presented

Jan Skolimowski

The Hatsugai-Kohmoto interaction model has gained a lot of attention in recent years, due to the fact it is exactly solvable in momentum space in any dimension while capturing some key features of the Mott phase. Here a one-dimensional lattice model with this interaction is approached from the real-space perspective, to explore how breaking the translation i

Stefano Mangini, Marco Cattaneo, Daniel Cavalcanti, Sergei Filippov

Characterization of noise in current near-term quantum devices is of paramount importance to fully use their computational power. However, direct quantum process tomography becomes unfeasible for systems composed of tens of qubits. A promising alternative method based on tensor networks was recently proposed [Nat. Commun. 14, 2858 (2023)]. In this paper, we

Emma Albertini, Fay Dowker, Arad Nasiri, Stav Zalel

Causal set theory is an approach to quantum gravity in which spacetime is fundamentally discrete at the Planck scale and takes the form of a Lorentzian lattice, or "causal set", from which continuum spacetime emerges in a large-scale (low-energy) approximation. In this work, we present new developments in the framework of interacting quantum field theory on

Siarhei Finski

For a polarized family of complex projective manifolds, we study the asymptotic distribution of Harder-Narasimhan slopes of direct image sheaves associated with high tensor powers of the polarization. We establish a theorem of Mehta-Ramanathan type, showing that this asymptotic distribution can be recovered from the analogous asymptotic distributions associa

Lili Vajtai, Ferenc Simon, Maria del Puerto Morales, Kolos Molnár

Ferrofluids containing magnetic nanoparticles represent a special class of magnetic materials due to the added freedom of particle tumbling in the fluids. We studied this process, known as Brownian relaxation, and its effect on the magnetic properties of ferrofluids with controlled magnetite nanoparticle sizes. For small nanoparticles (below 10 nm diameter)

Ziyi Zhang, Sen Zhang, Yibing Zhan, Yong Luo

Bridging the gap between diffusion models and human preferences is crucial for their integration into practical generative workflows. While optimizing downstream reward models has emerged as a promising alignment strategy, concerns arise regarding the risk of excessive optimization with learned reward models, which potentially compromises ground-truth perfor

Gianluca Passarelli, Rosario Fazio, Procolo Lucignano

Typical measures of nonstabilizerness of a system of $N$ qubits require computing $4^N$ expectation values, one for each Pauli string in the Pauli group, over a state of dimension $2^N$. For permutationally invariant systems, this exponential overhead can be reduced to just $O(N^3)$ expectation values on a state with a dimension $O(N)$. We exploit this simpl

M. Akin Yilmaz, O. Ugur Ulas, Ahmet Bilican, A. Murat Tekalp

While the performance of recent learned intra and sequential video compression models exceed that of respective traditional codecs, the performance of learned B-frame compression models generally lag behind traditional B-frame coding. The performance gap is bigger for complex scenes with large motions. This is related to the fact that the distance between th

Matthew Buckland

We introduce and study branching interval partition diffusions in their natural generality. We let interval widths evolve independently according to a general real-valued diffusion subject only to conditions that ensure finite lifetimes of intervals and allow the continuous generation of new intervals. The latter is governed by the Pitman-Yor excursion measu

Simina Brânzei, MohammadTaghi Hajiaghayi, Reed Phillips, Suho Shin

We consider the setting of repeated fair division between two players, denoted Alice and Bob, with private valuations over a cake. In each round, a new cake arrives, which is identical to the ones in previous rounds. Alice cuts the cake at a point of her choice, while Bob chooses the left piece or the right piece, leaving the remainder for Alice. We consider

Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri

Planning algorithms decompose complex problems into intermediate steps that can be sequentially executed by robots to complete tasks. Recent works have employed Large Language Models (LLMs) for task planning, using natural language to generate robot policies in both simulation and real-world environments. LLMs like GPT-4 have shown promising results in gener

Ben Jourdan, Peter Macgregor, He Sun

Given $v_1,\ldots, v_m\in\mathbb{C}^d$ with $\|v_i\|^2= \alpha$ for all $i\in[m]$ as input and suppose $\sum_{i=1}^m | \langle u, v_i \rangle |^2 = 1$ for every unit vector $u\in\mathbb{C}^d$, Weaver's discrepancy problem asks for a partition $S_1, S_2$ of $[m]$, such that $\sum_{i\in S_{j}} |\langle u, v_i \rangle|^2 \leq 1 -\theta$ for some universal const

David Fajman, Liam Urban

We show that the maximal globally hyperbolic development of near-FLRW initial data for the Einstein scalar-field Vlasov system exhibits stable Big Bang formation in the collapsing direction. The solutions exhibit stable Kretschmann scalar blow-up, causing the spacetime to become causally geodesically past incomplete, and are asymptotically velocity term domi

Haolin Zou, Arnab Auddy, Kamiar Rahnama Rad, Arian Maleki

Despite a large and significant body of recent work focused on estimating the out-of-sample risk of regularized models in the high dimensional regime, a theoretical understanding of this problem for non-differentiable penalties such as generalized LASSO and nuclear norm is missing. In this paper we resolve this challenge. We study this problem in the proport

Vasyl Stasiv, Yaroslav Zhydachevskyy, Vitalii Stadnik, Vasyl Hreb

An effect of alloying of the monoclinic beta- Ga2O3 with Al2O3 and In2O3 on the photoluminescent, thermoluminescent and persistent luminescent properties of Cr3+ ions has been comprehensively investigated. For this purpose, various series of Cr3+ and Ca2+ co-doped microcrystalline phosphors were synthesized by the solution combustion method, including pseudo

Edith Elkind, Abheek Ghosh, Paul W. Goldberg

Tullock contests model real-life scenarios that range from competition among proof-of-work blockchain miners to rent-seeking and lobbying activities. We show that continuous-time best-response dynamics in Tullock contests with convex costs converges to the unique equilibrium using Lyapunov-style arguments. We then use this result to provide an algorithm for

Muhammad Usama, Zahid Masood, Shahroz Khan, Konstantinos Kostas

In this work, we perform a systematic comparison of the effectiveness and efficiency of generative and non-generative models in constructing design spaces for novel and efficient design exploration and shape optimization. We apply these models in the case of airfoil/hydrofoil design and conduct the comparison on the resulting design spaces. A conventional Ge

Mingyang Li, Hongyu Liu, Yixuan Li, Zejun Wang

This study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and aims to explore early detection and disease progression in Alzheimer's disease (AD). We employ innovative data preprocessing strategies, including the use of the random forest algorithm to fill missing data and the handling of outliers and invalid data, thereby fully mi

Jan Goedgebeur, Davide Mattiolo, Giuseppe Mazzuoccolo, Jarne Renders

Petersen's seminal work in 1891 asserts that the edge-set of a cubic graph can be covered by distinct perfect matchings if and only if it is bridgeless. Actually, it is known that for a very large fraction of bridgeless cubic graphs, every edge belongs to at least two distinct perfect matchings. In this paper, we study the class of non-double covered cubic g

Wenzel Kersten, Nikolaus de Zordo, Oliver Diekmann, Elena S. Redchenko

In cavity quantum electrodynamics (cQED) and particularly superradiance, emitters are typically assumed to be independent, interacting only through light shared via a common mode. While such photon-mediated interactions lead to a rich spectrum of collective optical effects, direct dipole-dipole interactions within the emitter ensemble are generally viewed as

Guillaume Olikier

On a manifold or a closed subset of a Euclidean vector space, a retraction enables to move in the direction of a tangent vector while staying on the set. Retractions are a versatile tool to perform computational tasks such as optimization, interpolation, and numerical integration. This paper studies two known definitions of retraction on a closed subset of a

Muhammad Ajaz, Majid Shehzad, Muhammad Waqas, Haifa I. Alrebdi

We examined the transverse momentum spectra of various identified particles, across different multiplicity classes in proton-proton collisions at a center-of-mass energy of $\sqrt{s}$ = 7 TeV. Utilizing the Tsallis and Hagedorn models, parameters relevant to the bulk properties of nuclear matter were extracted. Both models exhibit good agreement with experim

Kathryn M. Neilson, Sarallah Hamtaei, Koosha Nassiri Nazif, Joshua M. Carr

Semiconducting transition metal dichalcogenides (TMDs) are promising for high-specific-power photovoltaics due to desirable band gaps, high absorption coefficients, and ideally dangling-bond-free surfaces. Despite their potential, the majority of TMD solar cells are fabricated in a non-scalable fashion using exfoliated materials due to the absence of high-qu

Patrick Jaillet, Chara Podimata, Zijie Zhou

Imagine you and a friend purchase identical items at a store, yet only your friend received a discount. Would your friend's discount make you feel unfairly treated by the store? And would you be less willing to purchase from that store again in the future? Based on a large-scale online survey that we ran on Prolific, it turns out that the answers to the abov

Jason Tang, Garrin McGoldrick, Marie Al-Ghossein, Ching-Wei Chen

This paper explores the usage of multimodal image-to-text models to enhance text-based item retrieval. We propose utilizing pre-trained image captioning and tagging models, such as instructBLIP and CLIP, to generate text-based product descriptions which are combined with existing text descriptions. Our work is particularly impactful for smaller eCommerce bus