February 2024 arXiv papers — page 110

Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl, Ali Vakilian

In strategic classification, agents modify their features, at a cost, to ideally obtain a positive classification from the learner's classifier. The typical response of the learner is to carefully modify their classifier to be robust to such strategic behavior. When reasoning about agent manipulations, most papers that study strategic classification rely on

Tamás Geszti

A nonlinear extension of Schr\"odinger's wave equation is proposed that ensures non-signaling by keeping linear the evolution of \textit{coordinate-diagonal} elements of the density matrix. The equation contains a negative kinetic energy term that turns spreading of wave packets into its opposite: collapsing, as some effective mass $M$ grows beyond a univers

Maurice Diesendruck, Jianzhe Lin, Shima Imani, Gayathri Mahalingam

When LLMs perform zero-shot inference, they typically use a prompt with a task specification, and generate a completion. However, there is no work to explore the possibility of the reverse - going from completion to task specification. In this paper, we employ both directions to perform cycle-supervised learning entirely in-context. Our goal is to create a f

Andrea Coletta, Kshama Dwarakanath, Penghang Liu, Svitlana Vyetrenko

Modeling subrational agents, such as humans or economic households, is inherently challenging due to the difficulty in calibrating reinforcement learning models or collecting data that involves human subjects. Existing work highlights the ability of Large Language Models (LLMs) to address complex reasoning tasks and mimic human communication, while simulatio

Marco Edoardo Palma, Alex Wolf, Pasquale Salza, Harald C. Gall

On-the-fly syntax highlighting is the task of rapidly associating visual secondary notation values with each character of a language derivation. Research in this domain is driven by the prevalence of online software development tools, which frequently display source code on screen and heavily rely on syntax highlighting mechanisms. In this context, three con

Aaron Roth, Mirah Shi

We study the problem of making predictions so that downstream agents who best respond to them will be guaranteed diminishing swap regret, no matter what their utility functions are. It has been known since Foster and Vohra (1997) that agents who best-respond to calibrated forecasts have no swap regret. Unfortunately, the best known algorithms for guaranteein

Joris Kattemölle

We develop the theory of the edge coloring of infinite lattice graphs, proving a necessary and sufficient condition for a proper edge coloring of a patch of a lattice graph to induce a proper edge coloring of the entire lattice graph by translation. This condition forms the cornerstone of a method that finds nearly minimal or minimal edge colorings of infini

Kordag Mehmet Kilic, Jin Sima, Jehoshua Bruck

Neural networks successfully capture the computational power of the human brain for many tasks. Similarly inspired by the brain architecture, Nearest Neighbor (NN) representations is a novel approach of computation. We establish a firmer correspondence between NN representations and neural networks. Although it was known how to represent a single neuron usin

Yuhang Lu, Touradj Ebrahimi

Over the past years, image generation and manipulation have achieved remarkable progress due to the rapid development of generative AI based on deep learning. Recent studies have devoted significant efforts to address the problem of face image manipulation caused by deepfake techniques. However, the problem of detecting purely synthesized face images has bee

Marina Manso Jimeno, Keerthi Sravan Ravi, Maggie Fung, John Thomas Vaughan,

Quality assessment, including inspecting the images for artifacts, is a critical step during MRI data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning model to detect rigid motion in T1-weighted brain images. We leveraged a 2D CNN for three-class classification and tested it on publ

Kordag Mehmet Kilic, Jin Sima, Jehoshua Bruck

The Nearest Neighbor (NN) Representation is an emerging computational model that is inspired by the brain. We study the complexity of representing a neuron (threshold function) using the NN representations. It is known that two anchors (the points to which NN is computed) are sufficient for a NN representation of a threshold function, however, the resolution

Shivam Bajaj, Pranoy Das, Yevgeniy Vorobeychik, Vijay Gupta

Many learning algorithms are known to converge to an equilibrium for specific classes of games if the same learning algorithm is adopted by all agents. However, when the agents are self-interested, a natural question is whether agents have a strong incentive to adopt an alternative learning algorithm that yields them greater individual utility. We capture su

Ioannis Caragiannis, Rob LeGrand, Evangelos Markakis, Emmanouil Pountourakis

Multi-winner approval-based voting has received considerable attention recently. A voting rule in this setting takes as input ballots in which each agent approves a subset of the available alternatives and outputs a committee of alternatives of given size $k$. We consider the scenario when a coalition of agents can act strategically and alter their ballots s

Andrés S. Villares, Wladimir E. Banda-Barragán, Clara Rojas

Stellar-driven galactic winds regulate the mass and energy content of star-forming galaxies. Emission- and absorption-line spectroscopy shows that these outflows are multiphase and comprised of dense gas clouds embedded in much hotter winds. Explaining the presence of cold gas in such environments is a challenging endeavour that requires numerical modelling.

Lola Etievant, Mitchell H. Gail

The case-cohort design allows analysis of multiple endpoints and only requires covariates to be measured for cases and non-cases in a random subcohort from the cohort. Stratification of subcohort sampling and weight calibration increase efficiency of estimates of log-relative hazards and covariate-specific pure risk, but they may require specifically adapted

Shanfeng Hu

The volume of image repositories continues to grow. Despite the availability of content-based addressing, we still lack a lightweight tool that allows us to discover images of distinct characteristics from a large collection. In this paper, we propose a fast and training-free algorithm for novel image discovery. The key of our algorithm is formulating a coll

Fodil Fadli, Yassine Himeur, Mariam Elnour, Abbes Amira

Anomaly detection in sport facilities has gained significant attention due to its potential to promote energy saving and optimizing operational efficiency. In this research article, we investigate the role of machine learning, particularly deep learning, in anomaly detection for sport facilities. We explore the challenges and perspectives of utilizing deep l

Ofer Gabber, Bogdan Zavyalov

First, we prove an algebraization result for rig-smooth algebras over a general noetherian ring; this positively answers the question raised in [Sta24, Tag 0GAX]. Then we prove a general partial algebraization result in non-archimedean geometry. The result says that we can always algebraize a geometrically reduced affinoid rigid-analytic space in "one direct

Matteo Boschini, Davide Gerosa, Om Sharan Salafia, Massimo Dotti

The compact object in quasar 3C 186 is one of the most promising recoiling black-hole candidates, exhibiting both an astrometric displacement between the quasar and the host galaxy as well as a spectroscopic shift between broad and narrow lines. 3C 186 also presents a radio jet which, when projected onto the plane of the sky, appears to be perpendicular to t

Pouya Mahdi Gholami, Henry Hoffmann

Both energy-aware, batteryless intermittent systems and signal-aware adaptive sampling algorithms (ASA) aim to maximize sensor data accuracy under energy constraints in edge devices. Intuitively, combining both into a signal- & energy-aware solution would yield even better accuracy. Unfortunately, ASAs and intermittent systems rely on conflicting energy avai

Roshan Maharana, Debankur Das, Pinaki Chaudhuri, Kabir Ramola

We present a universal characterization of stress correlations in athermal systems, across crystalline to amorphous packings. Via numerical analysis of static configurations of particles interacting through harmonic as well as Lennard-Jones potentials, for a variety of preparation protocols and ranges of microscopic disorder, we show that the properties of t

Sophia M. Walls, Ian J. Ford

We use continuous, stochastic quantum trajectories within a framework of quantum state diffusion (QSD) to describe alternating measurements of two non-commuting observables. Projective measurement of an observable completely destroys memory of the outcome of a previous measurement of the conjugate observable. In contrast, measurement under QSD is not project

Onur Agirseven, M. A. Ollis

Label the vertices of the complete graph $K_v$ with the integers $\{0, 1, \ldots, v-1\}$ and define the {\em length} $\ell$ of the edge between distinct vertices labeled $x$ and $y$ by $\ell(x,y) = \min( |y-x|, v - |y-x| )$. A {\em realization} of a multiset $L$ of size $v-1$ is a Hamiltonian path through $K_v$ whose edge labels are $L$. The {\em Buratti-Hor

Marcus Stålhammar

Weyl semimetals are predicted to host signature magneto-optical properties sourced by their peculiar Landau level structure, including the chiral level. Analytical studies are often leaving out the Hall component of the conductivity due to its complicated nature, and even though the chiral anomaly requires Weyl nodes to come in charge-conjugate pairs, toy-mo

Nathan A. Kaib, Sean N. Raymond

Reconstructions of the paleoclimate indicate that ancient climatic fluctuations on Earth are often correlated with variations in its orbital elements. However, the chaos inherent in the solar system's orbital evolution prevents numerical simulations from confidently predicting Earth's past orbital evolution beyond 50-100 Myrs. Gravitational interactions amon

Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison

Identifying how much a model ${\widehat{p}}_{\theta}(Y|X)$ knows about the stochastic real-world process $p(Y|X)$ it was trained on is important to ensure it avoids producing incorrect or "hallucinated" answers or taking unsafe actions. But this is difficult for generative models because probabilistic predictions do not distinguish between per-response noise

Pascal M. Keller, Nithyanandan Thyagarajan, Ajay Kumar, Nissim Kanekar

Quasars at redshifts $z>6$ are an excellent probe of the formation and evolution of supermassive black holes in the early Universe. The population of radio-luminous quasars is of particular interest, as such quasars could potentially be used to study the neutral intergalactic medium during cosmic reionisation via H$\,$I 21$\,$cm absorption studies. However,

Hannah Richstein, Nitya Kallivayalil, Joshua D. Simon, Christopher T. Garling

We present deep Hubble Space Telescope (HST) photometry of ten targets from Treasury Program GO-14734, including six confirmed ultra-faint dwarf galaxies (UFDs), three UFD candidates, and one likely globular cluster. Six of these targets are satellites of, or have interacted with, the Large Magellanic Cloud (LMC). We determine their structural parameters usi

Anthony Hegg, Ruoshi Jiang, Jie Wang, Jinning Hou

Low-temperature thermal fluctuations offer an essential window in characterizing the true nature of a quantum state of matter, a quintessential example being Fermi liquid theory. Here, we examine the leading thermal fluctuation of the superfluid density across numerous families ranging from relatively conventional to highly unconventional superconductors (Mg

Jakub Szpila, Romeel Davé, Douglas Rennehan, Weiguang Cui

We examine the nature, origin, and fate of early ($z\geq 2$) massive ($M_\star>10^{10}M_\odot$) quenched galaxies (EQGs) in a new $(100h^{-1}{\rm Mpc}^3)$ run of the Simba-C galaxy formation model. We define ``quenched'' to be $>4\sigma$ below an iterative polynomial fit to the star-forming sequence (SFS), and find that Simba-C produces EQGs as early as $z\s

Dongha Kim, Donghyeong Kim, Sanghyeok Park, N. Asger Mortensen

Surface plasmon polaritons (SPPs) carry transverse optical spin within the evanescent field, which has enabled the demonstration of various chiral light-matter interactions in classical and quantum systems. To achieve high spin selectivity in the interactions, the elliptical polarization of the evanescent field should be made circular, but the engineering pr

Martin Millon, Kevin Michalewicz, Frédéric Dux, Frédéric Courbin

We present STARRED, a Point Spread Function (PSF) reconstruction, two-channel deconvolution, and light curve extraction method designed for high-precision photometric measurements in imaging time series. An improved resolution of the data is targeted rather than an infinite one, thereby minimizing deconvolution artifacts. In addition, STARRED performs a join

Federico Faedo, Alessio Fontanarossa, Dario Martelli

We construct new families of supersymmetric AdS$_2\times\mathbb{M}_4$ solutions of $D=6$ gauged supergravity and AdS$_3\times\mathbb{M}_4$ solutions of $D=7$ gauged supergravity, where $\mathbb{M}_4$ are four-dimensional toric orbifolds with four fixed points. These are presented in a unified fashion, that highlights their common underlying geometry. The $D=

Anish Bhardwaj, Victor Porée, Han Yan, Nicolas Gauthier

The recently discovered dipole-octupole pyrochlore magnet Ce$_2$Hf$_2$O$_7$ is a promising three-dimensional quantum spin liquid candidate which shows no signs of ordering at low temperature. The low energy effective pseudospin-1/2 description in a magnetic field is characterized by the XYZ Hamiltonian and a Zeeman term where the dipolar local $z$-component

Joheen Chakraborty, Riccardo Arcodia, Erin Kara, Giovanni Miniutti

Quasi-Periodic Eruptions (QPEs) are luminous X-ray outbursts recurring on hour timescales, observed from the nuclei of a growing handful of nearby low-mass galaxies. Their physical origin is still debated, and usually modeled as (a) accretion disk instabilities or (b) interaction of a supermassive black hole (SMBH) with a lower mass companion in an extreme m

P. Singkanipa, D. A. Lidar

Variational quantum algorithms (VQAs) hold much promise but face the challenge of exponentially small gradients. Unmitigated, this barren plateau (BP) phenomenon leads to an exponential training overhead for VQAs. Perhaps the most pernicious are noise-induced barren plateaus (NIBPs), a type of unavoidable BP arising from open system effects, which have so fa

Takuya Okuda, Aswin Parayil Mana, Hiroki Sukeno

Previous work [SciPost Phys. 14, 129 (2023)] has demonstrated that quantum simulation of abelian lattice gauge theories (Wegner models including the toric code in a limit) in general dimensions can be achieved by local adaptive measurements on symmetry-protected topological (SPT) states with higher-form generalized global symmetries. The entanglement structu

Roberta Angius, Stefano Giaccari, Roberto Volpato

We consider the topological defect lines commuting with the spectral flow and the $\mathcal{N}=(4,4)$ superconformal symmetry in two dimensional non-linear sigma models on K3. By studying their fusion with boundary states, we derive a number of general results for the category of such defects. We argue that while for certain K3 models infinitely many simple

Sean Heston, Shunsaku Horiuchi, Satoshi Shirai

We constrain the neutrino-dark matter cross section using properties of the dark matter density profiles of Milky Way dwarf spheroidal galaxies. The constraint arises from core-collapse supernova neutrinos scattering on dark matter as a form of energy injection, allowing the transformation of the dark matter density profile from a cusped profile to a flatter

Xuejian Shen, Mark Vogelsberger, Josh Borrow, Yongao Hu

We investigate galaxy sizes at redshift $z\gtrsim 6$ with the cosmological radiation-magneto-hydrodynamic simulation suite THESAN(-HR). These simulations simultaneously capture the reionization of the large-scale intergalactic medium and resolved galaxy properties. The intrinsic size ($r^{\ast}_{1/2}$) of simulated galaxies increases moderately with stellar

Ameen Ismail, Seung J. Lee, Bingrong Yu

We propose a new mechanism to produce axion dark matter from inflationary fluctuations. Quantum fluctuations during inflation are strengthened by a coupling of the axion kinetic term to the inflaton, which we parametrize as an effective curvature $\kappa$ in the axion equation of motion. A nonvanishing curvature breaks the scale invariance of the axion power

Aneesh V. Manohar, Julie Pagès, Jasper Roosmale Nepveu

Field redefinitions are commonly used to reduce the number of operators in the Lagrangian by removing redundant operators and transforming to a minimal operator basis. We give a general argument that such field redefinitions, while leaving the $S$-matrix invariant and consequently finite, lead not only to infinite Green's functions, but also to infinite fiel

Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova

Most text-to-3D generators build upon off-the-shelf text-to-image models trained on billions of images. They use variants of Score Distillation Sampling (SDS), which is slow, somewhat unstable, and prone to artifacts. A mitigation is to fine-tune the 2D generator to be multi-view aware, which can help distillation or can be combined with reconstruction netwo

Guilherme S. Y. Giardini, Carlo R. da Cunha

This work demonstrates the application of a birth-death Markov process, inspired by radioactive decay, to capture the dynamics of innovation processes. Leveraging the Bass diffusion model, we derive a Gompertz-like function explaining the long-term innovation trends. The validity of our model is confirmed using citation data, Google trends, and a recurrent n

Linxi Zhao, Yihe Deng, Weitong Zhang, Quanquan Gu

The advancement of Large Vision-Language Models (LVLMs) has increasingly highlighted the critical issue of their tendency to hallucinate non-existing objects in the images. To address this issue, previous works focused on using specially curated datasets or powerful LLMs to rectify the outputs of LVLMs. However, these approaches require either costly trainin

Xingang Guo, Fangxu Yu, Huan Zhang, Lianhui Qin

Jailbreaks on large language models (LLMs) have recently received increasing attention. For a comprehensive assessment of LLM safety, it is essential to consider jailbreaks with diverse attributes, such as contextual coherence and sentiment/stylistic variations, and hence it is beneficial to study controllable jailbreaking, i.e. how to enforce control on LLM

Ali Behrouz, Farnoosh Hashemi

Graph Neural Networks (GNNs) have shown promising potential in graph representation learning. The majority of GNNs define a local message-passing mechanism, propagating information over the graph by stacking multiple layers. These methods, however, are known to suffer from two major limitations: over-squashing and poor capturing of long-range dependencies. R

Fei Deng, Qifei Wang, Wei Wei, Matthias Grundmann

Reward finetuning has emerged as a promising approach to aligning foundation models with downstream objectives. Remarkable success has been achieved in the language domain by using reinforcement learning (RL) to maximize rewards that reflect human preference. However, in the vision domain, existing RL-based reward finetuning methods are limited by their inst

Aravind Devarakonda, Alan Chen, Shiang Fang, David Graf

Electronic properties of crystals can be manipulated using spatially periodic modulations. Long-wavelength, incommensurate modulations are of particular interest, exemplified recently by moir\'e patterned van der Waals (vdW) heterostructures. Bulk vdW superlattices hosting interfaces between clean 2D layers represent scalable bulk analogs of vdW heterostruct

Burak Çakmak, Yue M. Lu, Manfred Opper

Motivated by the recent application of approximate message passing (AMP) to the analysis of convex optimizations in multi-class classifications [Loureiro, et. al., 2021], we present a convergence analysis of AMP dynamics with non-separable multivariate nonlinearities. As an application, we present a complete (and independent) analysis of the motivated convex

Jaehyeok Jin, David R. Reichman, Jeppe C. Dyre, Ulf R. Pedersen

Understanding the structure and dynamics of liquids is pivotal for the study of larger spatiotemporal processes, especially in glass-forming materials at low temperatures. Density scaling, observed in many molecular systems through experiments, offers an efficient means for exploring a vast range of time scales along a one-dimensional phase diagram. However,

Jacob Russin, Ellie Pavlick, Michael J. Frank

Human learning embodies a striking duality: sometimes, we appear capable of following logical, compositional rules and benefit from structured curricula (e.g., in formal education), while other times, we rely on an incremental approach or trial-and-error, learning better from curricula that are randomly interleaved. Influential psychological theories explain

Ben Elias, Hankyung Ko, Nicolas Libedinsky, Leonardo Patimo

The notion of a reduced expression for a double coset in a Coxeter group was introduced by Williamson, and recent work of Elias and Ko has made this theory more accessible and combinatorial. One result of Elias-Ko is that any coset admits a reduced expression which factors through a reduced expression for a related coset called its core. In this paper we def

Elise Han, Chengpiao Huang, Kaizheng Wang

We investigate model assessment and selection in a changing environment, by synthesizing datasets from both the current time period and historical epochs. To tackle unknown and potentially arbitrary temporal distribution shift, we develop an adaptive rolling window approach to estimate the generalization error of a given model. This strategy also facilitates

Matthieu Vilain, Rémi Giraud, Hugo Germain, Guillaume Bourmaud

Semi-dense detector-free approaches (SDF), such as LoFTR, are currently among the most popular image matching methods. While SDF methods are trained to establish correspondences between two images, their performances are almost exclusively evaluated using relative pose estimation metrics. Thus, the link between their ability to establish correspondences and

Yuqing Liu, Yu Wang, Lichao Sun, Philip S. Yu

The development of large vision-language models (LVLMs) offers the potential to address challenges faced by traditional multimodal recommendations thanks to their proficient understanding of static images and textual dynamics. However, the application of LVLMs in this field is still limited due to the following complexities: First, LVLMs lack user preference

Thibault Bonnemain, Benjamin Doyon

Generalised hydrodynamics (GHD) is a recent and powerful framework to study many-body integrable systems, quantum or classical, out of equilibrium. It has been applied to several models, from the delta Bose gas to the XXZ spin chain, the KdV soliton gas and many more. Yet it has only been applied to (1+1)-dimensional systems and generalisation to higher dime

CMS Collaboration, ATLAS Collaboration

A combination of fifteen top quark mass measurements performed by the ATLAS and CMS experiments at the LHC is presented. The data sets used correspond to an integrated luminosity of up to 5 and 20$^{-1}$ of proton-proton collisions at center-of-mass energies of 7 and 8 TeV, respectively. The combination includes measurements in top quark pair events that exp

Valentin De Bortoli, Michael Hutchinson, Peter Wirnsberger, Arnaud Doucet

Denoising Score Matching estimates the score of a noised version of a target distribution by minimizing a regression loss and is widely used to train the popular class of Denoising Diffusion Models. A well known limitation of Denoising Score Matching, however, is that it yields poor estimates of the score at low noise levels. This issue is particularly unfav

Irina Saparina, Mirella Lapata

Text-to-SQL semantic parsing has made significant progress in recent years, with various models demonstrating impressive performance on the challenging Spider benchmark. However, it has also been shown that these models often struggle to generalize even when faced with small perturbations of previously (accurately) parsed expressions. This is mainly due to t

Marcelo Laca, Sergey Neshveyev, Makoto Yamashita

Given a C$^*$-algebra $A$ with an almost periodic time evolution $\sigma$, we define a new C$^*$-algebra $A_c$, which we call the crystal of $(A,\sigma)$, that represents the zero temperature limit of $(A, \sigma)$. We prove that there is a one-to-one correspondence between the ground states of $(A,\sigma)$ and the states on $A_c$, justifying the name. In or

Tim Dullweber, Roman Belousov, Camilla Autorino, Nicoletta Petridou

Living materials adapt their shape to signals from the environment, yet the impact of shape changes on signal processing and associated feedback dynamics remain unclear. We find that droplets with signal-responsive interfacial tensions exhibit shape bistability, excitable dynamics, and oscillations. The underlying critical points reveal novel mechanisms for

Armine Bagyan, Donald Richards

For positive integers $d$ and $p$ such that $d \ge p$, let $\mathbb{R}^{d \times p}$ denote the set of $d \times p$ real matrices, $I_p$ be the identity matrix of order $p$, and $V_{d,p} = \{x \in \mathbb{R}^{d \times p} \mid x'x = I_p\}$ be the Stiefel manifold in $\mathbb{R}^{d \times p}$. Complete asymptotic expansions as $d \to \infty$ are obtained for t

Jenny Zhang, Steve Heim, Se Hwan Jeon, Sangbae Kim

We present a minimal phase oscillator model for learning quadrupedal locomotion. Each of the four oscillators is coupled only to itself and its corresponding leg through local feedback of the ground reaction force, which can be interpreted as an observer feedback gain. We interpret the oscillator itself as a latent contact state-estimator. Through a systemat

Guillermo Angeris, Theo Diamandis, Ciamac Moallemi

In this paper we show that, using only mild assumptions, previously proposed multidimensional blockchain fee markets are essentially optimal, even against worst-case adversaries. In particular, we show that the average welfare gap between the following two scenarios is at most $O(1/\sqrt{T})$, where $T$ is the length of the time horizon considered. In the fi

Alessandro Lehmann, Wendy Lowen

We propose a solution to the "curvature problem" from arXiv:1505.03698 and arXiv:0905.3845 for infinitesimal deformations. Let $k$ be a field, $A$ a dg algebra over $k$ and $A_n = A[t]/(t^{n+1})$ a cdg algebra over $R_n = k[t]/(t^{n+1})$, $n \geq 0$, with reduction $A_n/tA_n = A$. We define the $n$-derived category $D^n(A_n)$ as the quotient of the homotopy

Hugh Garsden, Philip Bull, Mike Wilensky, Zuhra Abdurashidova

Radio interferometers targeting the 21cm brightness temperature fluctuations at high redshift are subject to systematic effects that operate over a range of different timescales. These can be isolated by designing appropriate Fourier filters that operate in fringe-rate (FR) space, the Fourier pair of local sidereal time (LST). Applications of FR filtering in

David Haag, Devender Kumar, Sebastian Gruber, Dominik Hofer

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individua

Michael Dorkenwald, Nimrod Barazani, Cees G. M. Snoek, Yuki M. Asano

Vision-Language Models (VLMs), such as Flamingo and GPT-4V, have shown immense potential by integrating large language models with vision systems. Nevertheless, these models face challenges in the fundamental computer vision task of object localisation, due to their training on multimodal data containing mostly captions without explicit spatial grounding. Wh

Avinash Kumar Chaurasia, Matin Fallahi, Thorsten Strufe, Philipp Terhörst

Biometric systems based on brain activity have been proposed as an alternative to passwords or to complement current authentication techniques. By leveraging the unique brainwave patterns of individuals, these systems offer the possibility of creating authentication solutions that are resistant to theft, hands-free, accessible, and potentially even revocable

Daeun Lee, Jaehong Yoon, Sung Ju Hwang

Continual Test Time Adaptation (CTTA) is required to adapt efficiently to continuous unseen domains while retaining previously learned knowledge. However, despite the progress of CTTA, it is still challenging to deploy the model with improved forgetting-adaptation trade-offs and efficiency. In addition, current CTTA scenarios assume only the disjoint situati

Samantha Aziz, Oleg Komogortsev

The recent emergence of ubiquitous, multi-platform eye tracking has raised user privacy concerns over re-identification across platforms, where a person is re-identified across multiple eye tracking-enabled platforms using personally identifying information that is implicitly expressed through their eye movement. We present an empirical investigation quantif

Ta-Ying Cheng, Matheus Gadelha, Thibault Groueix, Matthew Fisher

Current controls over diffusion models (e.g., through text or ControlNet) for image generation fall short in recognizing abstract, continuous attributes like illumination direction or non-rigid shape change. In this paper, we present an approach for allowing users of text-to-image models to have fine-grained control of several attributes in an image. We do t

Wuxinlin Cheng, Chenhui Deng, Ali Aghdaei, Zhiru Zhang

Modern graph neural networks (GNNs) can be sensitive to changes in the input graph structure and node features, potentially resulting in unpredictable behavior and degraded performance. In this work, we introduce a spectral framework known as SAGMAN for examining the stability of GNNs. This framework assesses the distance distortions that arise from the nonl

Daniel Paulin, Peter A. Whalley

A method for analyzing non-asymptotic guarantees of numerical discretizations of ergodic SDEs in Wasserstein-2 distance is presented by Sanz-Serna and Zygalakis in ``Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations". They analyze the UBU integrator which is strong order two and only

Adil Jueid, Shinya Kanemura

We suggest a simplified model that simultaneously addresses the dark-matter problem and give rise to top quark flavor changing neutral current (FCNC) interactions at the one-loop order. The model consists of two extra $SU(2)_L$ gauge singlets: a colored mediator of spin zero ($S$) and a right-handed fermion ($\chi$) both are odd under an ad-hoc $Z_2$ symmetr

Dingyuan Liu

Given $s,t\in\mathbb{N}$, a complete bipartite poset $\mathcal{K}_{s,t}$ is a poset whose Hasse diagram consists of $s$ pairwise incomparable vertices in the upper layer and $t$ pairwise incomparable vertices in the lower layer, such that every vertex in the upper layer is larger than all vertices in the lower layer. A family $\mathcal{F}\subseteq2^{[n]}$ is

David Marco, Isael Herrera, Sophie Brasselet, Miguel A. Alonso

We introduce and produce experimentally optical beams exhibiting periodic skyrmionic polarization lattices at each transverse plane of propagation. These textures are meron lattices formed by tiles mapping hemispheres of the Poincar\'e sphere. All presented fields are combinations of a small number of plane waves. Firstly, we propose square lattices with a S

Paolo Testolina, Michele Polese, Tommaso Melodia

The upper midband, spanning 7 to 24 GHz, strikes a good balance between large bandwidths and favorable propagation environments for future 6th Generation (6G) networks. Wireless networks in the upper midband, however, will need to share the spectrum and safely coexist with a variety of incumbents, ranging from radiolocation to fixed satellite services, as we

Gautham Anil, Vishnu Vinod, Apurva Narayan

Quantum Machine Learning (QML) has emerged as a promising field of research, aiming to leverage the capabilities of quantum computing to enhance existing machine learning methodologies. Recent studies have revealed that, like their classical counterparts, QML models based on Parametrized Quantum Circuits (PQCs) are also vulnerable to adversarial attacks. Mor

Diogo Belloni, Joanna Mikołajewska, Matthias R. Schreiber

To shed light on the origin of magnetic symbiotic stars, we investigated the system FN Sgr in detail. We searched for a reasonable formation pathway to explain its stellar and binary parameters including the magnetic field of the accreting white dwarf. We used the MESA code to carry out pre-CE and post-CE binary evolution and determined the outcome of CE evo

Hiroyuki Kido

Inspired by empirical work in neuroscience for Bayesian approaches to brain function, we give a unified probabilistic account of various types of symbolic reasoning from data. We characterise them in terms of formal logic using the classical consequence relation, an empirical consequence relation, maximal consistent sets, maximal possible sets and maximum li

Tunhou Zhang, Feng Yan, Hai Li, Yiran Chen

The utilization of residual learning has become widespread in deep and scalable neural nets. However, the fundamental principles that contribute to the success of residual learning remain elusive, thus hindering effective training of plain nets with depth scalability. In this paper, we peek behind the curtains of residual learning by uncovering the "dissipat

Aishwarya P S, Pranav Ajit Nair, Yashas Samaga, Toby Boyd

The autoregressive nature of conventional large language models (LLMs) inherently limits inference speed, as tokens are generated sequentially. While speculative and parallel decoding techniques attempt to mitigate this, they face limitations: either relying on less accurate smaller models for generation or failing to fully leverage the base LLM's representa

Stephanie Chan, Peter Koymans, Carlo Pagano, Efthymios Sofos

For a general family of non-negative functions matching upper and lower bounds are established for their average over the values of any equidistributed sequence.

Shaily, Akanksha Singh, J. K. Singh, Saddam Hussain

We study a cosmological model in Rastall's theory of gravity in the framework of the flat FLRW metric. We formulate the value of the Hubble parameter, which contains two model parameters, $ \alpha $ and $ j $. Employing the Markov Chain Monte Carlo (MCMC) sampling technique, we determine the values of these model parameters along with their uncertainties. Mo

Chih-Yu Lai, Dung Tran, Kazuhito Koishida

Learned image compression has gained widespread popularity for their efficiency in achieving ultra-low bit-rates. Yet, images containing substantial textual content, particularly screen-content images (SCI), often suffers from text distortion at such compressed levels. To address this, we propose to minimize a novel text logit loss designed to quantify the d

Chiara Animali, Vincent Vennin

We study how large fluctuations are spatially correlated in the presence of quantum diffusion during inflation. This is done by computing real-space correlation functions in the stochastic-$\delta N$ formalism. We first derive an exact description of physical distances as measured by a local observer at the end of inflation, improving on previous works. Our

Alessio Vegliante, Saleta Fernandez, Ricardo Ortiz, Manuel Vilas-Varela

Open-shell polycyclic aromatic hydrocarbons (PAHs) represent promising building blocks for carbon-based functional magnetic materials. Their magnetic properties stem from the presence of unpaired electrons localized in radical states of $\pi$ character. Consequently, these materials are inclined to exhibit spin delocalization, form extended collective states

Xuemei Gu, Mario Krenn

The exponential growth in scientific publications poses a severe challenge for human researchers. It forces attention to more narrow sub-fields, which makes it challenging to discover new impactful research ideas and collaborations outside one's own field. While there are ways to predict a scientific paper's future citation counts, they need the research to

Andrea Guidolin, Antonio Lerario, Isaac Ren, Martina Scolamiero

Let $Y\subseteq \mathbb{R}^n$ be a closed definable subset and $X\subseteq \mathbb{R}^n$ be a smooth manifold. We construct a version of Morse theory for the restriction to $X$ of the Euclidean distance function from $Y$. This is done using the notion of critical points of Lipschitz functions and applying the theory of continuous selections. In this theory,

Nedjma Ousidhoum, Shamsuddeen Hassan Muhammad, Mohamed Abdalla, Idris Abdulmumin

Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily focused on semantic similarity, often within the English language context, we instead investigate the broader phenomenon of semantic relatedness. In this paper, we present \textit

Aviad Rubinstein, Junyao Zhao

We study repeated first-price auctions and general repeated Bayesian games between two players, where one player, the learner, employs a no-regret learning algorithm, and the other player, the optimizer, knowing the learner's algorithm, strategizes to maximize its own utility. For a commonly used class of no-regret learning algorithms called mean-based algor

Micheal Kahangirwe, Steffen A. Bass, Elena Bratkovskaya, Johannes Jahan

We present a novel construction of the QCD equation of state (EoS) at finite baryon density. Our work combines a recently proposed resummation scheme for lattice QCD results with the universal critical behavior at the QCD critical point. This allows us to obtain a family of equations of state in the range $0 \leq \mu_B \leq 700$ MeV and 25 MeV $\leq T \leq 8

Husne Ara Rubaiyeat, Hasan Mahmud, Ahsan Habib, Md. Kamrul Hasan

Sign language discourse is an essential mode of daily communication for the deaf and hard-of-hearing people. However, research on Bangla Sign Language (BdSL) faces notable limitations, primarily due to the lack of datasets. Recognizing wordlevel signs in BdSL (WL-BdSL) presents a multitude of challenges, including the need for well-annotated datasets, captur

Daniel Greve, Giorgio Lovato, Tobias Frohoff-Hülsmann, Uwe Thiele

Employing a two-species Cahn-Hilliard model with nonreciprocal interactions we show that the interplay of nonreciprocity and conservation laws results in the robust coexistence of uniform stationary and oscillatory phases as well as of uniform and crystalline phases. For nonequilibrium models with a spurious gradient dynamics structure such coexistencies bet

Christopher J. Larsen

The seminal paper of Francfort and Marigo [FM] introduced a variational formulation for Griffith fracture that has resulted in substantial theoretical and practical progress in modeling and simulating fracture. In particular, it led to the phase-field approximation proposed in [BFM], which has been widely implemented. However, the formulation in [FM] is know

Zefeng Li, Robert J. J. Grand, Emily Wisnioski, J. Trevor Mendel

We study the cosmological evolution of the two-point correlation functions of galactic gas-phase metal distributions using the 28 simulated galaxies from the Auriga Project. Using mock observations of the $z = 0$ snapshots to mimic our past work, we show that the correlation functions of the simulated mock observations are well matched to the correlation fun

Xiaoshuai Song, Zhengyang Wang, Keqing He, Guanting Dong

Knowledge editing (KE) aims to efficiently and precisely modify the behavior of large language models (LLMs) to update specific knowledge without negatively influencing other knowledge. Current research primarily focuses on white-box LLMs editing, overlooking an important scenario: black-box LLMs editing, where LLMs are accessed through interfaces and only t

Alessandro Druetto, Andrea Grosso, Jully Jeunet, Fabio Salassa

We consider the problem of scheduling non preemptively a set of jobs on parallel identical machines with prior setup operations on a single shared server, where the objective is to minimise the makespan. We develop an arc-flow formulation to the problem with two multigraphs, one for the machines and one for the server, with a same set of nodes representing p