November 2024 arXiv papers — page 83

Ke Liang Xiao, Noah Marshall, Atish Agarwala, Elliot Paquette

In recent years, signSGD has garnered interest as both a practical optimizer as well as a simple model to understand adaptive optimizers like Adam. Though there is a general consensus that signSGD acts to precondition optimization and reshapes noise, quantitatively understanding these effects in theoretically solvable settings remains difficult. We present a

Hari Bhandari, Zhenhua Ning, Po-Hao Chang, Peter E. Siegfried

The unique connectivity of kagome lattices gives rise to topological properties, such as flat bands and Dirac cones. When combined with ferromagnetism and a chemical potential near the 2D Dirac points, this structure offers the potential to realize the highly sought-after topological Chern magnetotransport. Recently, there was considerable excitement surroun

Jun Hashimoto, Yuhiko Aoyama

A planetary growth rate, a.k.a., the mass accretion rate, is a fundamental parameter in planet formation, as it determines a planet's final mass. Planetary mass accretion rates have been estimated using hydrogen lines, based on the models originally developed for accreting stars, known as the accretion flow model. Recently, Aoyama et al. (2018) introduced th

Kai Li, Xiang Gao, Di-Fu Guo, Dong-Yang Gao

This paper presents the first analysis of the contact binary TYC 3801-1529-1. We observed four sets of multiple bands complete light curves and one set of radial velocity curve of the primary component. Based on a simultaneous investigation of our observed and TESS light curves and the radial velocity curve, we found that TYC 3801-1529-1 is an extremely low-

Venkateswaran Kasirajan, Torey Battelle, Bob Wold

Simulating quantum systems using classical computing equipment has been a significant research focus. This work demonstrates that circuits as large and complex as the random circuit sampling (RCS) circuits published as a part of Google's pioneering work [4-7] claiming quantum supremacy can be effectively simulated with high fidelity on classical systems comm

Hugo Gimbert, Corto Mascle, Patrick Totzke

The population control problem is a parameterised problem where a controller sends messages to a whole population of identical finite-state agents, aiming to eventually move them all into a target state. The decision problem asks whether this can be achieved for arbitrarily large finite populations. We focus on the randomised version of this problem, where e

Kejun Chen, Truc Nguyen, Abhijeet Sahu, Malik Hassanaly

Smart inverters are instrumental in the integration of distributed energy resources into the electric grid. Such inverters rely on communication layers for continuous control and monitoring, potentially exposing them to cyber-physical attacks such as false data injection attacks (FDIAs). We propose to construct a defense strategy against a priori unknown FDI

Mladen Bestvina, George Domat, Kasra Rafi

We provide a complete classification of when the homeomorphism group of a stable surface, $Σ$, has the automatic continuity property: Any homomorphism from Homeo$(Σ)$ to a separable group is necessarily continuous. This result descends to a classification of when the mapping class group of $Σ$ has the automatic continuity property. Towards this classificatio

Shuiying Liao, P. Y. Mok, Li Li

This paper reports on the development of a Consistency Regularized model for Bayesian Personalized Ranking (CR-BPR), addressing to the drawbacks in existing complementary clothing recommendation methods, namely limited consistency and biased learning caused by diverse feature scale of multi-modal data. Compared to other product types, fashion preferences are

Serena Dipierro, Edoardo Proietti Lippi, Caterina Sportelli, Enrico Valdinoci

This paper deals with the fractional Sobolev spaces $W^{s, p}(Ω)$, with $s\in (0, 1]$ and $p\in[1,+\infty]$. Here, we use the interpolation results in [4] to provide suitable conditions on the exponents $s$ and $p$ so that the spaces $W^{s, p}(Ω)$ realize a continuous embedding when either $Ω=\mathbb R^N$ or $Ω$ is any open and bounded domain with Lipschitz

Yongyan Wen, Siyuan Li, Rongchang Zuo, Lei Yuan

Deep reinforcement learning (DRL) has achieved remarkable success in various research domains. However, its reliance on neural networks results in a lack of transparency, which limits its practical applications. To achieve explainability, decision trees have emerged as a popular and promising alternative to neural networks. Nonetheless, due to their limited

Emiliano Torti

Let R be a local Artin ring with residue field k of positive characteristic. We prove that every finite flat group scheme over R whose special fiber belongs to a certain explicit family of non-commutative k-group schemes is killed by its order. This is achieved via a classification result which rely on the explicit study of the infinitesimal deformation theo

Adem Alparslan

This study examines conversational business analytics, an approach that utilizes AI to address the technical competency gaps that hinder end users from effectively using traditional self-service analytics. By facilitating natural language interactions, conversational business analytics aims to empower end users to independently retrieve data and generate ins

Jesse Friedbaum, Sudarshan Adiga, Ravi Tandon

We propose a new and intuitive metric for aleatoric uncertainty quantification (UQ), the prevalence of class collisions defined as the same input being observed in different classes. We use the rate of class collisions to define the collision matrix, a novel and uniquely fine-grained measure of uncertainty. For a classification problem involving $K$ classes,

Xiaomin Ouyang, Jason Wu, Tomoyoshi Kimura, Yihan Lin

Multimodal sensing systems are increasingly prevalent in various real-world applications. Most existing multimodal learning approaches heavily rely on training with a large amount of synchronized, complete multimodal data. However, such a setting is impractical in real-world IoT sensing applications where data is typically collected by distributed nodes with

Jingyao Wang, Junyi Lee, Hudson Loughlin, Morgan Hedges

Alkali-metal-noble-gas comagnetometers are precision probes well-suited for tests of fundamental physics and inertial rotation sensing, combining high sensitivity of the spin-exchange-relaxation free (SERF) magnetometers with inherent suppression of magnetic field noise. Past versions of the device utilizing continuous-wave optical pumping are sensitive to a

Maria Axenovich, Dingyuan Liu

A subset $M$ of vertices in a graph $G$ is a mutual-visibility set if for any two vertices $u,v\in{M}$ there exists a shortest $u$-$v$ path in $G$ that contains no elements of $M$ as internal vertices. Let $\chi_{\mu}(G)$ be the least number of colors needed to color the vertices of $G$, so that each color class is a mutual-visibility set. Let $n\in\mathbb{N

Jorge Galindo, Enrique Jordá, Alberto Rodríguez-Arenas

Let G be a locally compact group and let $\phi$ be a positive definite function on G with $\phi(e)=1$. This function defines a multiplication operator $M_\phi$ on the Fourier algebra $A(G)$ of $G$. The aim of this paper is to classify the ergodic properties of the operators $M_\phi$, focusing on several key factors, including the subgroup $H_\phi=\{x\in G\co

Madhurima Khirbat, Yongli Ren, Pablo Castells, Mark Sanderson

With the rise of Large Language Models (LLMs) such as ChatGPT, researchers have been working on how to utilize the LLMs for better recommendations. However, although LLMs exhibit black-box and probabilistic characteristics (meaning their internal working is not visible), the evaluation framework used for assessing these LLM-based recommender systems (RS) are

Jupiter Ding, Roohi Dalal, Tomomi Sunayama, Michael A. Strauss

The "miscentering effect," i.e., the offset between a galaxy cluster's optically-defined center and the center of its gravitational potential, is a significant systematic effect on brightest cluster galaxy (BCG) studies and cluster lensing analyses. We perform a cross-match between the optical cluster catalog from the Hyper Suprime-Cam (HSC) Survey S19A Data

Monitirtha Dey

Correlated observations are ubiquitous phenomena in a plethora of scientific avenues. Tackling this dependence among test statistics has been one of the pertinent problems in simultaneous inference. However, very little literature exists that elucidates the effect of correlation on different testing procedures under general distributional assumptions. In thi

Tiberiu Musat

In this paper, I introduce the retrieval problem, a simple yet common reasoning task that can be solved only by transformers with a minimum number of layers, which grows logarithmically with the input size. I empirically show that large language models can solve the task under different prompting formulations without any fine-tuning. To understand how transf

Youyuan Zhang, Zehua Liu, Zenan Li, Zhaoyu Li

In this paper, we consider the conditional generation problem by guiding off-the-shelf unconditional diffusion models with differentiable loss functions in a plug-and-play fashion. While previous research has primarily focused on balancing the unconditional diffusion model and the guided loss through a tuned weight hyperparameter, we propose a novel framewor

Jillian Bellovary, Yuantong Luo, Thomas Quinn, Ferah Munshi

A consequence of a non-zero occupation fraction of massive black holes (MBHs) in dwarf galaxies is that these MBHs can become residents of larger galaxy halos via hierarchical merging and tidal stripping. Depending on the parameters of their orbits and original hosts, some of these MBHs will merge with the central supermassive black hole in the larger galaxy

Olimpio Hiroshi Miyagaki, Carlos Alberto Reyes Peña, Rodrigo da Silva Rodrigues

For a generalization of the Gellerstedt operator with mixed-type Dirichlet boundary conditions to a suitable Tricomi domain, we prove the existence and uniqueness of weak solutions of the linear problem and for a generalization of this problem. The classical method introduced by Didenko, which study the energy integral argument, will be used to prove estimat

Brian B. Moser, Federico Raue, Tobias C. Nauen, Stanislav Frolov

Dataset distillation has gained significant interest in recent years, yet existing approaches typically distill from the entire dataset, potentially including non-beneficial samples. We introduce a novel "Prune First, Distill After" framework that systematically prunes datasets via loss-based sampling prior to distillation. By leveraging pruning before class

Roman Golovko

In this short note we provide the examples of pairs of closed, connected Legendrian non-isotopic Legendrian submanifolds $(\Lambda_{-}, \Lambda_{+})$ of the $(4n+1)$-dimensional contact vector space, $n>1$, such that there exist Lagrangian concordances from $\Lambda_-$ to $\Lambda_+$ and from $\Lambda_+$ to $\Lambda_-$. This contradicts anti-symmetry of the

Xuancheng Shao, Igor E. Shparlinski, Laurence P. Wijaya

Recently there has been a large number of works on bilinear sums with Kloosterman sums and on sums of Kloosterman sums twisted by arithmetic functions. Motivated by these, we consider several related new questions about sums of Kloosterman sums parametrised by square-free and smooth integers.

Pritom Sarma, Einat Aharonov, Renaud Toussaint, Stanislav Parez

Natural faults often contain a fluid-saturated, granular fault-gouge layer, whose failure and sliding processes play a central role in earthquake dynamics. Using a two-dimensional discrete element model coupled with fluid dynamics, we simulate a fluid-saturated granular layer, where fluid pressure is incrementally raised. At a critical fluid pressure level,

J. Tumelty, C. Beaume, A. M. Rucklidge

Fluids subject to both thermal and compositional variations can undergo doubly diffusive convection when these properties both affect the fluid density and diffuse at different rates. In natural doubly diffusive convection, the gradients of temperature and salinity are aligned with each other and orthogonal to gravity. The resulting buoyancy-driven flows are

Rozane Bezerra de Siqueira, Jose Ricardo Bezerra Nogueira, Carlos Feitosa Luna

The aim of this study is to estimate the implications for the reference rate and for the distribution of the tax burden among households from the consumption tax reform as outlined in the version of the Projeto de Lei Complementar (PLP) 68 approved by the Brazilian Chamber of Deputies in July 2024.

Julien Brillon, Siva Nadarajah

The performance of the nonlinearly stable flux reconstruction (NSFR) schemes for resolving subsonic viscous turbulent free-shear flows is investigated. The schemes are extensively verified for the direct numerical simulation (DNS) of the Taylor-Green Vortex (TGV) problem. Several under-resolved simulations of the TGV problem are conducted to assess the perfo

Mihail N. Popescu, Bogdan Adrian Nicola, William E. Uspal, Alvaro Domínguez

Patches of catalyst imprinted on supporting walls induce motion of the fluid around them once they are supplied with the chemical species (``fuel'') that are converted by the catalytic chemical reaction. While the functioning of such chemically active micropumps is conceptually well understood, an in-depth characterization of the induced hydrodynamic flow, a

Donggeun Tak, Z. Lucas Uhm, Gregory S. H. Paek, Myungshin Im

Gamma-ray bursts (GRBs) are the most energetic explosions in the universe, and their afterglow emission provides an opportunity to probe the physics of relativistic shock waves in an extreme environment. Several key pieces for completing the picture of the GRB afterglow physics are still missing, including jet properties, emission mechanism, and particle acc

Minggang Luo, Youssef Jeyar, Brahim Guizal, Mauro Antezza

We study the normal and lateral components of the Casimir-Lifshitz (CL) force between a nanoparticle and 1D graphene grating deposited on a fused silica slab. For this purpose, the scattering matrix approach together with the Fourier modal method augmented with local basis functions are used. We find that, by covering a fused silica slab by a graphene gratin

Xuan He, Danny H. K. Tsang, Yize Chen

Growing concerns over climate change call for improved techniques for estimating and quantifying the greenhouse gas emissions associated with electricity generation and transmission. Among the emission metrics designated for power grids, locational marginal emission (LME) can provide system operators and electricity market participants with valuable informat

Jai Doshi, Asa Cooper Stickland

Large language model unlearning aims to remove harmful information that LLMs have learnt to prevent their use for malicious purposes. LLMU and RMU have been proposed as two methods for LLM unlearning, achieving impressive results on unlearning benchmarks. We study in detail the impact of unlearning on LLM performance metrics using the WMDP dataset as well as

Moritz Hehl

We study the Ollivier-Ricci curvature and its modification introduced by Lin, Lu, and Yau on graphs. We provide a complete characterization of all graphs with Lin-Lu-Yau curvature at least one. We then explore the relationship between the Lin-Lu-Yau curvature and the Ollivier-Ricci curvature with vanishing idleness on regular graphs. An exact formula for the

Richard Kurle, Alexej Klushyn, Ralf Herbrich

We introduce a new method for learning Bayesian neural networks, treating them as a stack of multivariate Bayesian linear regression models. The main idea is to infer the layerwise posterior exactly if we know the target outputs of each layer. We define these pseudo-targets as the layer outputs from the forward pass, updated by the backpropagated gradients o

Emil Vladu

In this paper, we consider nonsymmetric solutions to certain Lyapunov and Riccati equations and inequalities with coefficient matrices corresponding to cone-preserving dynamical systems. Most results presented here appear to be novel even in the special case of positive systems. First, we provide a simple eigenvalue criterion for a Sylvester equation to admi

Waseem Akram, Takuya Mieno

The string indexing problem is a fundamental computational problem with numerous applications, including information retrieval and bioinformatics. It aims to efficiently solve the pattern matching problem: given a text T of length n for preprocessing and a pattern P of length m as a query, the goal is to report all occurrences of P as substrings of T. Navarr

Xiang Li, Gagan Agrawal, Rajiv Ramnath, Ruoming Jin

Graph-level representations (and clustering/classification based on these representations) are required in a variety of applications. Examples include identifying malicious network traffic, prediction of protein properties, and many others. Often, data has to stay in isolated local systems (i.e., cannot be centrally shared for analysis) due to a variety of c

Wiliam S. Hipólito-Ricaldi, Rodrigo von Marttens, Felipe de Melo-Santos, Davi C. Rodrigues

We investigate the observational implications of a gravitational model wherein the gravitational constant $G$ and the cosmological constant $\Lambda$ exhibit scale-dependent behavior at the perturbative level, while preserving the General Relativity (GR) field equations at the background. This model is motivated by the potential influence of large-scale (inf

Mingjian Wen, Wei-Fan Huang, Jin Dai, Santosh Adhikari

Machine learning interatomic potentials (MLIPs) have substantially advanced atomistic simulations in materials science and chemistry by balancing accuracy and computational efficiency. While leading MLIPs rely on representing atomic environments using spherical tensors, Cartesian representations offer potential advantages in simplicity and efficiency. Here,

Tianqi Gao, Mohammad Alsulimane, Sergey Burdin, Gabriele DAmen

The feasibility study of a new technique for thermal neutron detection using a Timepix3 camera (TPX3Cam) with custom-made optical add-ons operated in event-mode data acquisition is presented. The camera has a spatial resolution of ~ 16 um and a temporal resolution of 1.56 ns. Thermal neutrons react with 6 Lithium to produce a pair of 2.73 MeV tritium and 2.0

Ali Seif, Mina Zarei

This study explores the dynamics of two-layer multiplex networks, focusing on how frequency distributions among mirror nodes influence phase transitions and synchronization across layers. We present a Regular frequency assignment model for duplex networks, where the layers are fully connected and share identical sets of natural frequencies. By adjusting the

Humberto Figueroa, Mitchell Sinclair, Chan Joshi

This work analytically compares the growth of Backward Stimulated Raman Scattering (B-SRS) induced by temporal laser pulses on the order of a few tens of laser cycles or a couple of picoseconds for nominally 10 um wavelength IR pulse with Gaussian versus the constant intensity profile assumed in the original theory, both delivering an equivalent energy. By e

Alejandro Villena, Lorenzo J. Tardon, Isabel Barbancho, Ana M. Barbancho

We dealt with the problem of artifacts in eeg signals in relation to the usage of lengthy trials. Specifically, we considered eye artifacts found in eeg signals,their influence in the analysis of the data and alternatives to diminish their impact on later studies of brain activity on lengthy tasks. We proposed a scheme of partial rejection on independent sig

Quilee Simeon, Anshul Kashyap, Konrad P Kording, Edward S Boyden

There is renewed interest in modeling and understanding the nervous system of the nematode $\textit{Caenorhabditis elegans}$ ($\textit{C. elegans}$), as this small model system provides a path to bridge the gap between nervous system structure (connectivity) and function (physiology). However, existing physiology datasets, whether involving passive recording

Jack Dongarra, John Gunnels, Harun Bayraktar, Azzam Haidar

The evolution of floating-point computation has been shaped by algorithmic advancements, architectural innovations, and the increasing computational demands of modern technologies, such as artificial intelligence (AI) and high-performance computing (HPC). This paper examines the historical progression of floating-point computation in scientific applications

Fangyu Wu, Yuhao Chen

In the real world, objects reveal internal textures when sliced or cut, yet this behavior is not well-studied in 3D generation tasks today. For example, slicing a virtual 3D watermelon should reveal flesh and seeds. Given that no available dataset captures an object's full internal structure and collecting data from all slices is impractical, generative meth

Aditya Sridhar

Music genre classification is a critical component of music recommendation systems, generation algorithms, and cultural analytics. In this work, we present an innovative model for classifying music genres using attention-based temporal signature modeling. By processing spectrogram sequences through Convolutional Neural Networks (CNNs) and multi-head attentio

A. Jain, D. T. Adroja, S. Rayaprol, A. D. Hillier

Entanglement of spin and orbital degrees of freedom, via relativistic spin-orbit coupling, in 4$d$ transition metal oxides can give rise to a variety of novel quantum phases. A previous study of mixed 3$d$-4$d$ quasi-1D spin-chain oxide Sr$_3$NiRhO$_6$ using the magnetization measurements by Mohapatra et al. [Phys. Rev. B 75, 214422 (2007)] revealed a partia

Yier Ma, Takeyuki Tamura

In computational metabolic design, it is often necessary to modify the original constraint-based metabolic networks to lead to growth-coupled production, where cell growth forces target metabolite production. However, in genome-scale models, finding strategies to simultaneously delete and add genes to induce growth-coupled production is challenging. This is

Max Beveridge, Zach Goldstein, Hee Cheol Chung

Many data sets cannot be accurately described by standard probability distributions due to the excess number of zero values present. For example, zero-inflation is prevalent in microbiome data and single-cell RNA sequencing data, which serve as our real data examples. Several models have been proposed to address zero-inflated datasets including the zero-infl

Diego Cifuentes, Santanu S. Dey, Jingye Xu

For mixed integer programs (MIPs) with block structures and coupling constraints, on dualizing the coupling constraints the resulting Lagrangian relaxation becomes decomposable into blocks which allows for the use of parallel computing. However, the resulting Lagrangian dual can have non-zero duality gap due to the inherent non-convexity of MIPs. In this pap

David Gonzalez, Matthew Harrison-Trainor

We demonstrate that any $\Pi_\alpha$ sentence of the infinitary logic $L_{\omega_1 \omega}$ extending the theory of linear orderings has a model with a $\Pi_{\alpha+4}$ Scott sentence and hence of Scott rank at most $\alpha+3$. In other words, the gap between the complexity of the theory and the complexity of the simplest model is always bounded by $4$. This

Yixin Chen, Yue Fu, Zeya Chen, Jenny Radesky

In the attention economy, online platforms are incentivized to design products that maximize user engagement, even when such practices conflict with users' best interests. We conducted a structured content analysis of all Very Large Online Platforms (VLOPs) to identify the designs these influential apps and sites use to capture attention and extend engagemen

Bryan Cain

Here the definitions of nearest neighbor, robustness, concordance, and correlation, all of which feature in (Temple 2023) (henceforth abbreviated (T23)), are adjusted to make them completely mathematical while preserving their significance. A characterization is given of the possible limits of a function of distance matrices as the data matrices from which t

Raheleh Jafari, Francesco Strazzanti, Santiago Zarzuela Armengou

We describe the canonical module of a simplicial affine semigroup ring $\mathbb{K}[S]$ and its trace ideal. As a consequence, we characterize when $\mathbb{K}[S]$ is nearly Gorenstein in terms of arithmetic properties of the semigroup $S$. Then, we find some bounds for the Cohen-Macaulay type of $\mathbb{K}[S]$ when it is nearly Gorenstein. In particular, if

H. Mete Soner, Valentin Tissot-Daguette, Jianfeng Zhang

We consider the optimal control of occupied processes which record all positions of the state process. Dynamic programming yields nonlinear equations on the space of positive measures. We develop the viscosity theory for this infinite dimensional parabolic $occupied$ PDE by proving a comparison result between sub and supersolutions, and thus provide a charac

Cristian González-Riquelme, José Madrid

Let $K_n=(V,E)$ be the complete graph with $n\geq 3$ vertices (here $V$ and $E$ denote the set of vertices and edges of $K_n$ respectively). We find the optimal value ${\bf{C}}_{n,p}$ such that the inequality $$\|f-m_f\|_p\le {\bf C}_{n,p}{\rm Var}_{p}f$$ holds for every $f:V\to \mathbb{R},$ where ${\rm Var}_p$ stands for the $p$-variation, and $m_f$ stands

Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu

Fragment-based drug discovery, in which molecular fragments are assembled into new molecules with desirable biochemical properties, has achieved great success. However, many fragment-based molecule generation methods show limited exploration beyond the existing fragments in the database as they only reassemble or slightly modify the given ones. To tackle thi

Matteo Rosani, Gianluca Cena, Dave Cavalcanti, Valerio Frascolla

Multi-Link Operation (MLO) in Wi-Fi 7 is expected to tangibly boost throughput while lowering transmission latency at the same time. This is very relevant in industrial scenarios and makes MLO suitable, e.g., to support seamless device mobility. Benefits depend on the ability of multi-link devices to select at run-time the best link, among the available ones

Gadi Fibich, Yonatan Warman

We derive explicit expressions for the expected adoption and infection level in the Bass and SI models, respectively, on sparse Erd\H{o}s-R\'enyi networks and on $d$-regular networks. These expressions are soloutions of first-order ordinary differential equations, which are fairly easy to analyze. To prove that these expressions are exact, we show that the e

Igor Fedorov, Kate Plawiak, Lemeng Wu, Tarek Elgamal

This paper presents Llama Guard 3-1B-INT4, a compact and efficient Llama Guard model, which has been open-sourced to the community during Meta Connect 2024. We demonstrate that Llama Guard 3-1B-INT4 can be deployed on resource-constrained devices, achieving a throughput of at least 30 tokens per second and a time-to-first-token of 2.5 seconds or less on a co

Constantin Simovski, Mohammad Sajjad Mirmoosa, Sergei Tretyakov

In this work, we theoretically study temporal interfaces between media with strong spatial dispersion and dielectrics. In particular, we consider a temporal discontinuity that transforms a wire medium sample, a metamaterial with resonant spatial dispersion, into a uniaxial dielectric. We show that this transition results in a transformation of the deeply sub

Navya Yarrabelly, Vinay Damodaran, Feng-Guang Su

Word embeddings have been shown to produce remarkable results in tackling a vast majority of NLP related tasks. Unfortunately, word embeddings also capture the stereotypical biases that are prevalent in society, affecting the predictive performance of the embeddings when used in downstream tasks. While various techniques have been proposed \cite{bolukbasi201

Arundhati S. Shanbhag, Brian B. Moser, Tobias C. Nauen, Stanislav Frolov

Diffusion models, celebrated for their generative capabilities, have recently demonstrated surprising effectiveness in image classification tasks by using Bayes' theorem. Yet, current diffusion classifiers must evaluate every label candidate for each input, creating high computational costs that impede their use in large-scale applications. To address this l

Brian B. Moser, Stanislav Frolov, Tobias C. Nauen, Federico Raue

Large-scale, pre-trained Text-to-Image (T2I) diffusion models have gained significant popularity in image generation tasks and have shown unexpected potential in image Super-Resolution (SR). However, most existing T2I diffusion models are trained with a resolution limit of 512x512, making scaling beyond this resolution an unresolved but necessary challenge f

Nicole Meng, Caleb Manicke, David Chen, Yingjie Lao

Adversarial examples represent a serious issue for the application of machine learning models in many sensitive domains. For generating adversarial examples, decision based black-box attacks are one of the most practical techniques as they only require query access to the model. One of the most recently proposed state-of-the-art decision based black-box atta

Zuzanna Buchnajzer, Kacper Dobek, Stanisław Hapke, Daniel Jankowski

Deep learning architectures based on convolutional neural networks tend to rely on continuous, smooth features. While this characteristics provides significant robustness and proves useful in many real-world tasks, it is strikingly incompatible with the physical characteristic of the world, which, at the scale in which humans operate, comprises crisp objects

Cauã Ferreira Barros, Bruna Borges Azevedo, Valdemar Vicente Graciano Neto, Mohamad Kassab

The exponential growth of text-based data in domains such as healthcare, education, and social sciences has outpaced the capacity of traditional qualitative analysis methods, which are time-intensive and prone to subjectivity. Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools capable of automating and enhan

Kristóf Bérczi, Vasilis Livanos, José Soto, Victor Verdugo

The Matroid Secretary Problem (MSP) is one of the most prominent settings for online resource allocation and optimal stopping. A decision-maker is presented with a ground set of elements $E$ revealed sequentially and in random order. Upon arrival, an irrevocable decision is made in a take-it-or-leave-it fashion, subject to a feasibility constraint on the set

Navodita Sharma, Vishnu Vinod, Abhradeep Thakurta, Alekh Agarwal

The offline reinforcement learning (RL) problem aims to learn an optimal policy from historical data collected by one or more behavioural policies (experts) by interacting with an environment. However, the individual experts may be privacy-sensitive in that the learnt policy may retain information about their precise choices. In some domains like personalize

David T. Frazier, Ryan Kelly, Christopher Drovandi, David J. Warne

Neural posterior estimation (NPE) and neural likelihood estimation (NLE) are machine learning approaches that provide accurate posterior, and likelihood, approximations in complex modeling scenarios, and in situations where conducting amortized inference is a necessity. While such methods have shown significant promise across a range of diverse scientific ap

David Flater

Many organizations describe their processes as consensus-driven, but there is no consensus on the definition of consensus. Qualitative definitions of consensus prioritize social phenomena like "unity" that are not necessarily measurable. Quantitative definitions of consensus derive from numbers of votes and can be realized in software. When unity and coopera

P. Claudin, S. Courrech du Pont, C. Narteau

We review the main processes that drive the morphodynamics of dunes, i.e. their growth in height, migration and elongation, and emphasise the contribution of experiments to the understanding of these mechanisms. The main control parameters are the sediment flux $Q$ and the saturation length $L_{\rm sat}$ associated with the spatial relaxation of the flux tow

Yu Liu, Boris Slautin, Jason Bemis, Roger Proksch

Dynamic spectroscopies in Scanning Probe Microscopy (SPM) are critical for probing material properties, such as force interactions, mechanical properties, polarization switching, and electrochemical reactions and ionic dynamics. However, the practical implementation of these measurements is constrained by the need to balance imaging time and data quality. Si

Weixian Waylon Li, Yftah Ziser, Yifei Xie, Shay B. Cohen

Traditional Learning-To-Rank (LETOR) approaches, including pairwise methods like RankNet and LambdaMART, often fall short by solely focusing on pairwise comparisons, leading to sub-optimal global rankings. Conversely, deep learning based listwise methods, while aiming to optimise entire lists, require complex tuning and yield only marginal improvements over

Marcos V. de S. Silva, G. Alencar, R. N. Costa Filho, R. M. P. Neves

In this work, we investigate the existence of wormholes within the framework of Loop Quantum Cosmology, using isotropic dark matter as the source. We analyze three distinct density profiles and solve the modified gravity field equations alongside the stress-energy tensor conservation, applying appropriate boundary conditions to obtain traversable wormhole so

Yi-Lin Song, Yu Zhang, Vadim Baru, Feng-Kun Guo

In a recent paper, Phys. Rev. Lett. {\bf 126}, 132001 (2021), the LHCb data on the di-$J/\psi$ production in proton-proton collisions were analysed in a coupled-channel framework based on double-vector-charmonium channels. This investigation identified a robust pole near the $J/\psi J/\psi$ threshold, tagged $X(6200)$, suggesting it as a new state. The prese

Donghoon Kim, Jon Andre Ottesen, Ashwin Kumar, Brandon C. Ho

Identifying amyloid-beta positive patients is crucial for determining eligibility for Alzheimer's disease (AD) clinical trials and new disease-modifying treatments, but currently requires PET or CSF sampling. Previous MRI-based deep learning models for predicting amyloid positivity, using only T1w sequences, have shown moderate performance. We trained deep l

Joachim Schaeffer, Jinwook Rhyu, Robin Droop, Rolf Findeisen

Linear regression is often deemed inherently interpretable; however, challenges arise for high-dimensional data. We focus on further understanding how linear regression approximates nonlinear responses from high-dimensional functional data, motivated by predicting cycle life for lithium-ion batteries. We develop a linearization method to derive feature coeff

Yingxuan Ren, Fengtao Ren, Bo Yang

Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be m

Yoad Ordan, Dror Liran, Kirk W. Baldwin, Loren Pfeiffer

An extreme yet reconfigurable nonlinear response to a single photon by a photonic system is crucial for realizing a universal two-photon gate, an elementary building block for photonic quantum computing. Yet such a response, characterized by the photon blockade effect, has only been achieved in atomic systems or solid states ones that are difficult to scale

Satvik Dixit, Laurie M. Heller, Chris Donahue

We demonstrate that vision language models (VLMs) are capable of recognizing the content in audio recordings when given corresponding spectrogram images. Specifically, we instruct VLMs to perform audio classification tasks in a few-shot setting by prompting them to classify a spectrogram image given example spectrogram images of each class. By carefully desi

Brandon Yeung, Tianyi Chu, Oliver T. Schmidt

Energy transfer across scales is fundamental in fluid dynamics, linking large-scale flow motions to small-scale turbulent structures in engineering and natural environments. Triadic interactions among three wave components form complex networks across scales, challenging understanding and model reduction. We introduce Triadic Orthogonal Decomposition (TOD),

Mehrzad Shahinmoghadam, Ali Motamedi

Accurate mapping of the built asset information to established data classification systems and taxonomies is crucial for effective asset management, whether for compliance at project handover or ad-hoc data integration scenarios. Due to the complex nature of built asset data, which predominantly comprises technical text elements, this process remains largely

Erica Ann Metheney, Lauren Yehle

This paper explores and assesses in what ways generative AI can assist in translating survey instruments. Writing effective survey questions is a challenging and complex task, made even more difficult for surveys that will be translated and deployed in multiple linguistic and cultural settings. Translation errors can be detrimental, with known errors renderi

Mindaugas Bloznelis, Dominykas Marma

We present two models of sparse dynamic networks that display transitivity - the tendency for vertices sharing a common neighbour to be neighbours of one another. Our first network is a continuous time Markov chain $G=\{G_t=(V,E_t), t\ge 0\}$ whose states are graphs with the common vertex set $V=\{1,\dots, n\}$. The transitions are defined as follows. Given

Hanshang Jin, Owen Moulding, James C. Fettinger, Yingzheng Gao

We report the successful growth of CeCuSi single crystals using a metallic flux method and the physical properties using structural, magnetic, electrical transport, optical, and heat capacity measurements. CeCuSi crystallizes in a hexagonal-bar shape, and single crystal x-ray diffraction confirms the ZrBeSi-type structure (space group $P6_{3}/mmc$). CeCuSi o

Shubhen Biswas

The anomalous velocity deviation in the osculating planetary flyby attracts enough attention as a problem of General Relativity. In connection of rotating weak field massive source the Lense Thirring metric is diagonalized to find the equation of motion from action invariance Hamilton principle in pure relativistic theory. The computation for near Earth flyb

Thomas Bailie, Yun Sing Koh, Karthik Mukkavilli

Graphs model latent variable relationships in many real-world systems, and Message Passing Neural Networks (MPNNs) are widely used to learn such structures for downstream tasks. While edge-based MPNNs effectively capture local interactions, their expressive power is theoretically bounded, limiting the discovery of higher-order relationships. We introduce the

Xiaohan Dan, Eitan Geva, Victor S. Batista

Quantum computing offers promising new avenues for tackling the long-standing challenge of simulating the quantum dynamics of complex chemical systems, particularly open quantum systems coupled to external baths. However, simulating such non-unitary dynamics on quantum computers is challenging since quantum circuits are specifically designed to carry out uni

J. Sedaghat, G. H. Bordbar, S. M. Zebarjad

This study investigates the structural properties of strange quark stars (SQS) using a Quantum Chromodynamics (QCD) perturbative model combined with the latest Particle Data Group dataset. Given the energy scale present in compact stars, QCD perturbation theory alone may not fully explain their structure. To account for non-perturbative contributions, we inc

Jiarong Kang, Xiaobin Xiong

Accurate ground reaction force (GRF) estimation can significantly improve the adaptability of legged robots in various real-world applications. For instance, with estimated GRF and contact kinematics, the locomotion control and planning assist the robot in overcoming uncertain terrains. The canonical momentum-based methods, formulated as nonlinear observers,

Yifan Zhao, Shahed-E- Zumrat, Szu-An Tsao, Mona Jarrahi

Terahertz waves hold immense potential across diverse fields, including healthcare monitoring, biomedical imaging, precision navigation, high-speed communication, security screening, industrial quality control, and space exploration. However, the widespread adoption of terahertz technology has been hindered by the bulky, complex, and costly nature of existin

Alexander Lawall

Browser fingerprinting is a growing technique for identifying and tracking users online without traditional methods like cookies. This paper gives an overview by examining the various fingerprinting techniques and analyzes the entropy and uniqueness of the collected data. The analysis highlights that browser fingerprinting poses a complex challenge from both

M. Arda Aydın, Efe Mert Çırpar, Elvin Abdinli, Gozde Unal

Recent advances in foundational Vision Language Models (VLMs) have reshaped the evaluation paradigm in computer vision tasks. These foundational models, especially CLIP, have accelerated research in open-vocabulary computer vision tasks, including Open-Vocabulary Semantic Segmentation (OVSS). Although the initial results are promising, the dense prediction c