January 2023 arXiv papers

Thiago Cavalheiro, Alexandre Santana, João Cossich, Victor Ayala

The objective of this paper is to study the controllability of discrete-time linear control systems in solvable Lie groups. In the special case of nilpotent Lie groups, a necessary and sufficient condition for controllability is established. Furthermore, the class of discrete-time linear systems in the two-dimensional affine Lie group is constructed and a co

Carolus Vitalis, Tobias Wenzel

Microfluidic droplet screens serve as an innovative platform for high-throughput biotechnology, enabling significant advancements in discovery, product optimization, and analysis. This review sheds light on the emerging trend of interaction assays in microfluidic droplets, underscoring the unique suitability of droplets for these applications. Encompassing a

Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael R. Kosorok

Offline model selection (OMS), that is, choosing the best policy from a set of many policies given only logged data, is crucial for applying offline RL in real-world settings. One idea that has been extensively explored is to select policies based on the mean squared Bellman error (MSBE) of the associated Q-functions. However, previous work has struggled to

Huy The Nguyen, Shengwen Wang

We consider the varifold associated to the Allen--Cahn phase transition problem in $\mathbb R^{n+1}$(or $n+1$-dimensional Riemannian manifolds with bounded curvature) with integral $L^{q_0}$ bounds on the Allen--Cahn mean curvature (first variation of the Allen--Cahn energy) in this paper. It is shown here that there is an equidistribution of energy between

Ilya Trofimov, Daniil Cherniavskii, Eduard Tulchinskii, Nikita Balabin

We propose a method for learning topology-preserving data representations (dimensionality reduction). The method aims to provide topological similarity between the data manifold and its latent representation via enforcing the similarity in topological features (clusters, loops, 2D voids, etc.) and their localization. The core of the method is the minimizatio

Á. Rincón, G. Panotopoulos, I. Lopes

We investigate exotic stars composed of dark energy within the context of Einstein's General Relativity, by applying an extended Chaplygin gas equation-of-state. To account for anisotropies, we utilize a formalism based on the complexity factor to obtain numerical solutions. By applying well-established criteria, we demonstrate that the solutions are phy

Xi Wu, Shaleen Deep, Joe Benassi, Fengan Li

Many data insight questions can be viewed as searching in a large space of tables and finding important ones, where the notion of importance is defined in some adhoc user defined manner. This paper presents Holistic Cube Analysis (HoCA), a framework that augments the capabilities of relational queries for such problems. HoCA first augments the relational dat

Yilun Du, Mengjiao Yang, Bo Dai, Hanjun Dai

A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks. Recent progress in text-guided image synthesis has yielded models with an impressive ability to generate complex novel images, exhibiting combinatorial generalization across domains. Motivated by this success, we investigate whether such tools can be used to co

Lorenzo Mori, Maria Rosaria Ferrante

We propose a Small Area Estimation model based on Generalized Additive Models for Location, Scale and Shape (SAE-GAMLSS), for the estimation of household economic indicators. SAE-GAMLSS release the exponential family distributional assumption and allow each distributional parameter to depend on covariates. A bootstrap approach to estimate MSE is proposed. Th

Parfait Atchade-Adelomou, Kent Larson

In this work, we aim to confirm the impact of the Fourier series on the quantum machine learning model. We will propose models, tests, and demonstrations to achieve this objective. We designed a quantum machine learning leveraged on the Hamiltonian encoding. With a subtle change, we performed the trigonometric interpolation, binary and multiclass classifier,

Xinru Wei, Shuai Dong, Zhao Su, Lili Tang

Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard symptom-based diagnosis often unreliable and unrepeatable. We developed a novel network-based machine-learning

Melanie Subbiah, Amrita Bhattacharjee, Yilun Hua, Tharindu Kumarage

Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread. We argue that while misinformation and disinformation detection have been studied, there has been a lack of investment in the important open challenge of detecting harmful agendas in news articles; identifying harmful agendas is critical to fla

Martin Veresko, Ming-Cheng Cheng

Multi-dimensional direct numerical simulation (DNS) of the Schrödinger equation is needed for design and analysis of quantum nanostructures that offer numerous applications in biology, medicine, materials, electronic/photonic devices, etc. In large-scale nanostructures, extensive computational effort needed in DNS may become prohibitive due to the high degre

Simiao Ren, Saad Lahrichi, Yang Deng, Willie J. Padilla

Deep active learning (DAL) methods have shown significant improvements in sample efficiency compared to simple random sampling. While these studies are valuable, they nearly always assume that optimal DAL hyperparameter (HP) settings are known in advance, or optimize the HPs through repeating DAL several times with different HP settings. Here, we argue that

Duncan Dauvergne

The parabolic Airy line ensemble $\mathfrak A$ is a central limit object in the KPZ universality class and related areas. On any compact set $K = \{1, \dots, k\} \times [a, a + t]$, the law of the recentered ensemble $\mathfrak A - \mathfrak A(a)$ has a density $X_K$ with respect to the law of $k$ independent Brownian motions. We show that $$ X_K(f) = \exp \

Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales

Large language models have achieved impressive performance on various natural language processing tasks. However, so far they have been evaluated primarily on benchmarks where all information in the input context is relevant for solving the task. In this work, we investigate the distractibility of large language models, i.e., how the model problem-solving ac

Zhenghao Zeng, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi

When estimating causal effects, it is important to assess external validity, i.e., determine how useful a given study is to inform a practical question for a specific target population. One challenge is that the covariate distribution in the population underlying a study may be different from that in the target population. If some covariates are effect modif

Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay

Retrieval-Augmented Language Modeling (RALM) methods, which condition a language model (LM) on relevant documents from a grounding corpus during generation, were shown to significantly improve language modeling performance. In addition, they can mitigate the problem of factually inaccurate text generation and provide natural source attribution mechanism. Exi

C. A. Sackett, J. A. Stickney

We present a design for an atom chip trap that uses the time-orbiting potential technique. The design offers several advantages compared to other chip-trap methods. It uses a simple crossed-wire pattern on the chip, along with a rotating bias field. The trap is naturally close to spherically symmetric, and it can be modified to be exactly symmetric in quadra

Karl Karu, Eva Roos Nerut, Xueran Tao, Sergei A. Kislenko

Progress in electrochemical applications of ionic liquids builds on an understanding of electrical double-layer. This computational study focuses on structure-determined quantities -- maximum packing density, potentials and capacitances -- evaluated using a one-electrode electrical double-layer model. Interfaces of 40 studied ions are grouped into four disti

Neil G. MacLaren, Lingqi Meng, Melissa Collier, Naoki Masuda

The social brain hypothesis states that the relative size of the neocortex is larger for species with higher social complexity as a result of evolution. Various lines of empirical evidence have supported the social brain hypothesis, including evidence from the structure of social networks. Social complexity may itself positively impact cooperation among indi

Blaise Delmotte

We investigate the individual and collective dynamics of torque-driven particles, called microrollers, near fluid-fluid interfaces. We find that the viscosity ratio across the interface controls the speed and direction of the particles, their relative motion, the growth of a fingering instability, and the self-assembled motile structures that emerge from it.

Pietro Dona, Pietropaolo Frisoni

We introduce a strategy to compute EPRL spin foam amplitudes with many internal faces numerically. We work with sl2cfoam-next, the state-of-the-art framework to numerically evaluate spin foam transition amplitudes. We find that uniform sampling Monte Carlo is exceptionally effective in approximating the sum over internal quantum numbers of a spin foam amplit

Ching-Yao Chuang, Varun Jampani, Yuanzhen Li, Antonio Torralba

Machine learning models have been shown to inherit biases from their training datasets. This can be particularly problematic for vision-language foundation models trained on uncurated datasets scraped from the internet. The biases can be amplified and propagated to downstream applications like zero-shot classifiers and text-to-image generative models. In thi

Rhys Howard, Lars Kunze

Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. The creation of models to describe these relationships is typically accomplished through the application of causal discovery techniques. However, as it stands observational causal discovery techniques struggle to adequately cope with conditions such as causal

Thi Kieu Khanh Ho, Ali Karami, Narges Armanfard

With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challeng

Ilse Plaisier, Sjoerd Bouma, Anna Nelles

In-ice radio detectors are a promising tool for the discovery of EeV neutrinos. For astrophysics, the implications of such a discovery will rely on the reconstruction of the neutrino arrival direction. This paper describes a first complete neutrino arrival direction reconstruction for detectors employing deep antennas such as RNO-G or planning to employ them

Akshara Viswanathan, Else Starkenburg, Helmer H. Koppelman, Amina Helmi

The Milky Way halo is one of the few galactic haloes that provides a unique insight into galaxy formation by resolved stellar populations. Here, we present a catalogue of $\sim$47 million halo stars selected independent of parallax and line-of-sight velocities, using a combination of Gaia DR3 proper motion and photometry by means of their reduced proper moti

Simon Geisler, Yujia Li, Daniel Mankowitz, Ali Taylan Cemgil

Transformers were originally proposed as a sequence-to-sequence model for text but have become vital for a wide range of modalities, including images, audio, video, and undirected graphs. However, transformers for directed graphs are a surprisingly underexplored topic, despite their applicability to ubiquitous domains, including source code and logic circuit

Kshitij Goel, Nathan Michael, Wennie Tabib

This letter presents a continuous probabilistic modeling methodology for spatial point cloud data using finite Gaussian Mixture Models (GMMs) where the number of components are adapted based on the scene complexity. Few hierarchical and adaptive methods have been proposed to address the challenge of balancing model fidelity with size. Instead, state-of-the-a

Warren Li, Maxime Van de Moortel

We study the interior of black holes in the presence of charged scalar hair of small amplitude $ε$ on the event horizon and show their terminal boundary is a crushing Kasner-like singularity. These spacetimes are spatially homogeneous and they differ significantly from the hairy black holes with uncharged matter previously studied in [M. Van de Moortel, Viol

Nathan Gaby, Xiaojing Ye, Haomin Zhou

We develop a novel computational framework to approximate solution operators of evolution partial differential equations (PDEs). By employing a general nonlinear reduced-order model, such as a deep neural network, to approximate the solution of a given PDE, we realize that the evolution of the model parameter is a control problem in the parameter space. Base

Héctor Ochoa, Rafael M. Fernandes

Due to its incommensurate nature, moiré superlattices host not only acoustic phonons but also another type of soft collective modes called phasons. Here, we investigate the impact of electron-phason scattering on the transport properties of moiré systems. We show that the resistivity can scale linearly with temperature down to temperatures much lower than th

Marius Kadek, Baokai Wang, Marc Joosten, Wei-Chi Chiu

Two-dimensional (2D) materials exhibit a wide range of remarkable phenomena, many of which owe their existence to the relativistic spin-orbit coupling (SOC) effects. To understand and predict properties of materials containing heavy elements, such as the transition-metal dichalcogenides (TMDs), relativistic effects must be taken into account in first-princip

Sebastiano Don, Valentino Magnani

We prove an integral formula for the spherical measure of hypersurfaces in equiregular sub-Riemannian manifolds. Among various technical tools, we establish a general criterion for the uniform convergence of parametrized sub-Riemannian distances, and local uniform asymptotics for the diameter of small metric balls.

Chenjing Bu

In this series of papers, we propose a theory of enumerative invariants counting self-dual objects in self-dual categories. Ordinary enumerative invariants in abelian categories can be seen as invariants for the structure group $\mathrm{GL} (n)$, and our theory is an extension of this to structure groups $\mathrm{O} (n)$ and $\mathrm{Sp} (2n)$. Examples of o

Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad

Hierarchical Clustering is a popular unsupervised machine learning method with decades of history and numerous applications. We initiate the study of differentially private approximation algorithms for hierarchical clustering under the rigorous framework introduced by (Dasgupta, 2016). We show strong lower bounds for the problem: that any $ε$-DP algorithm mu

Federico Lelli, Zhi-Yu Zhang, Thomas G. Bisbas, Lingrui Lin

We study the dynamics of cold molecular gas in two main-sequence galaxies at cosmic noon (zC-488879 at $z\simeq1.47$ and zC-400569 at $z\simeq2.24$) using new high-resolution ALMA observations of multiple $^{12}$CO transitions. For zC-400569 we also re-analyze high-quality H$α$ data from the SINS/zC-SINF survey. We find that (1) Both galaxies have regularly

Mehedi H. Raju, Lee Friedman, Troy M. Bouman, Oleg V. Komogortsev

The Fourier theorem states that any time-series can be decomposed into a set of sinusoidal frequencies, each with its own phase and amplitude. The literature suggests that some frequencies are important to reproduce key qualities of eye-movements ("signal") and some of frequencies are not important ("noise"). To investigate what is signal and

Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai

Supernova (SN) plays an important role in galaxy formation and evolution. In high-resolution galaxy simulations using massively parallel computing, short integration timesteps for SNe are serious bottlenecks. This is an urgent issue that needs to be resolved for future higher-resolution galaxy simulations. One possible solution would be to use the Hamiltonia

Nastaran Okati, Stratis Tsirtsis, Manuel Gomez Rodriguez

Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes. In this context, it has been recently shown that, if a classifier is calibrated, one can identify the smallest set of candidates which contains, in expectation, a desired number of qualified candidates using a threshold decision rule. This lends

Yunxuan Zhu, Jiawei Yang, Jaime Abad-Arredondo, Antonio I. Fernández-Domínguez

The manipulation of coupled quantum excitations is of fundamental importance in realizing novel photonic and optoelectronic devices. We use electroluminescence to probe plasmon-exciton coupling in hybrid structures consisting of a nanoscale plasmonic tunnel junction and few-layer two-dimensional transition-metal dichalcogenide transferred onto the junction.

Tommaso Micallo, Carl Lehmann, Jan Carl Budich

Non-Hermitian (NH) Hamiltonians have been shown to exhibit unique signatures, including the NH skin effect and an exponential spectral sensitivity with respect to boundary conditions. Here, we investigate as to what extent these remarkable phenomena, recently predicted and observed in a broad range of settings, may also occur in closed correlated fermionic s

T. L. Killestein, M. Mould, D. Steeghs, J. Casares

Low-mass X-ray binaries have long been theorised as potential sources of continuous gravitational-wave radiation, yet there is no observational evidence from recent LIGO/Virgo observing runs. Even for the theoretically 'loudest' source, Sco X-1, the upper limit on gravitational-wave strain has been pushed ever lower. Such searches require precise mea

Alberto Castellano, Álvaro Herráez, Luis E. Ibáñez

Based on Quantum Gravity arguments, it has been suggested that all kinetic terms of light particles below the UV cut-off could arise in the IR via quantum (loop) corrections. These loop corrections involve infinite towers of states becoming light (e.g. Kaluza-Klein or string towers). We study implications of this Emergence Proposal for fundamental scales in

Giuseppe De Tomasi, Ivan M. Khaymovich

The power-law random banded matrix (PLRBM) is a paradigmatic ensemble to study the Anderson localization transition (AT). In $d$-dimension the PLRBM are random matrices with algebraic decaying off-diagonal elements $H_{\vec{n}\vec{m}}\sim 1/|\vec{n}-\vec{m}|^α$, having AT at $α=d$. In this work, we investigate the fate of the PLRBM to non-Hermiticity. We con

Pau Amaro Seoane

In dense stellar clusters like galactic nuclei and globular clusters stellar densities are so high that stars might physically collide with each other. In galactic nuclei the energy and power output can be close, and even exceed, to those from supernovae events. We address the event rate and the electromagnetic characteristics of collisions of main sequence

Aditya Parikh, Juri Smirnov, Weishuang Linda Xu, Bei Zhou

In this work, we present UV completions of the recently proposed number-changing Co-SIMP freeze-out mechanism. In contrast to the standard cannibalistic-type dark matter picture that occurs entirely in the dark sector, the $3\to 2$ process setting the relic abundance in this case requires one Standard Model particle in the initial and final states. This prev

Chandan Setty, Shouvik Sur, Lei Chen, Fang Xie

Lattice symmetries are central to the characterization of electronic topology. Recently, it was shown that Green's function eigenvectors form a representation of the space group. This formulation has allowed the identification of gapless topological states even when quasiparticles are absent. Here we demonstrate the profundity of the framework in the ext

David Aaron Nicholson, Vincent Emanuele

In spite of intense research efforts, deep neural networks remain vulnerable to adversarial examples: an input that forces the network to confidently produce incorrect outputs. Adversarial examples are typically generated by an attack algorithm that optimizes a perturbation added to a benign input. Many such algorithms have been developed. If it were possibl

Simon Frieder, Luca Pinchetti, Alexis Chevalier, Ryan-Rhys Griffiths

We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology. In contrast to formal mathematics, where large databases of formal proofs are available (e.g., the Lean Mathematical Libra

ATLAS Collaboration

A search for long-lived particles decaying into hadrons is presented. The analysis uses 139 fb$^{-1}$ of $pp$ collision data collected at $\sqrt{s} = 13$ TeV by the ATLAS detector at the LHC using events that contain multiple energetic jets and a displaced vertex. The search employs dedicated reconstruction techniques that significantly increase the sensitiv

Rafail V. Abramov

In recent works, we proposed a hypothesis, according to which turbulence in gases is created by the mean field effect of an intermolecular potential. We discovered that, in a numerically simulated inertial flow, turbulent solutions indeed spontaneously emerge from a laminar initial condition, as observed in nature and experiments. To study the origin of turb

Brandon B. May, N. Joseph Tatro, Dylan Walker, Piyush Kumar

We propose a novel algorithm, Salient Conditional Diffusion (Sancdifi), a state-of-the-art defense against backdoor attacks. Sancdifi uses a denoising diffusion probabilistic model (DDPM) to degrade an image with noise and then recover said image using the learned reverse diffusion. Critically, we compute saliency map-based masks to condition our diffusion,

Matthias Werner, Artur García-Sáez, Marta P. Estarellas

In the context of adiabatic quantum computation (AQC), it has been argued that first-order quantum phase transitions (QPTs) due to localisation phenomena cause AQC to fail by exponentially decreasing the minimal spectral gap of the Hamiltonian along the annealing path as a function of the qubit number. The vanishing of the spectral gap is often linked to the

F. Eppel, M. Kadler, E. Ros, F. Roesch

In the past years, evidence has started piling up that some high-energy cosmic neutrinos can be associated with blazars in flaring states. On February 26, 2022, a new blazar-neutrino coincidence has been reported: the track-like neutrino event IC220225A detected by IceCube is spatially coincident with the flat-spectrum radio quasar PKS 0215+015. Like previou

Esraa Ali Elkhateeb

In this work, we reconstruct the cosmological unified dark fluid model proposed previously by Elkhateeb \cite{Elkhateeb:2017oqy} in the framework of $f(R)$ gravity. Utilizing the equivalence between the scalar-tensor theory and the $f(R)$ gravity theory, the scalar field for the dark fluid is obtained, whence the $f(R)$ function is extracted and its viabilit

Jonathan N. Lee, Alekh Agarwal, Christoph Dann, Tong Zhang

POMDPs capture a broad class of decision making problems, but hardness results suggest that learning is intractable even in simple settings due to the inherent partial observability. However, in many realistic problems, more information is either revealed or can be computed during some point of the learning process. Motivated by diverse applications ranging

Isaac Reid, Krzysztof Choromanski, Valerii Likhosherstov, Adrian Weller

We present Simplex Random Features (SimRFs), a new random feature (RF) mechanism for unbiased approximation of the softmax and Gaussian kernels by geometrical correlation of random projection vectors. We prove that SimRFs provide the smallest possible mean square error (MSE) on unbiased estimates of these kernels among the class of weight-independent geometr

John Ellis, Malcolm Fairbairn, Gert Hütsi, Martti Raidal

NANOGrav and other Pulsar Timing Arrays (PTAs) have discovered a common-spectrum process in the nHz range that may be due to gravitational waves (GWs): if so, they are likely to have been generated by black hole (BH) binaries with total masses $> 10^9 M_{\odot}$. Using the Extended Press-Schechter formalism to model the galactic halo mass function and a simp

Mara Gutzeit, Tim Drevelow, Moritz A. Goerzen, Soumyajyoti Haldar

We study the emergence of spontaneous skyrmion lattices in an Fe monolayer in fcc and hcp stacking on the Ir(111) surface using density functional theory (DFT). For fcc-Fe/Ir(111) we find the well-known square nanoskyrmion lattice. However, for hcp-Fe/Ir(111) the hexagonal skyrmion lattice previously proposed based on experiments is energetically unfavorable

Aleksandar Nikolov, Haohua Tang

We investigate unbiased high-dimensional mean estimators in differential privacy. We consider differentially private mechanisms whose expected output equals the mean of the input dataset, for every dataset drawn from a fixed bounded $d$-dimensional domain $K$. A classical approach to private mean estimation is to compute the true mean and add unbiased, but p

Ayush Singh, Harikrishnan Pisharody Gopalkrishnan, Mahesh Raveendranatha Panicker

Gas leakage is a serious hazard in a variety of sectors, including industrial, domestic, and gas-powered vehicles. The installation of gas leak detection systems has emerged as a critical preventative measure to solve this problem. Traditional gas sensors, such as electrochemical, infrared point, and Metal Oxide Semiconductor sensors, have been widely used f

Alexander Hazeltine, Baiying Liu

In this paper, we prove the local converse theorem for split even special orthogonal groups over a non-Archimedean local field of characteristic zero. This is the only case left on local converse theorems of split classical groups and the difficulty is the existence of the outer automorphism. We apply new ideas of considering the summation of partial Bessel

Jay DeYoung, Stephanie C. Martinez, Iain J. Marshall, Byron C. Wallace

Multi-document summarization entails producing concise synopses of collections of inputs. For some applications, the synopsis should accurately synthesize inputs with respect to a key aspect, e.g., a synopsis of film reviews written about a particular movie should reflect the average critic consensus. As a more consequential example, narrative summaries that

Cheng Peng, Stanislav Uryasev

This paper proposes a new approach to estimating the distribution of a response variable conditioned on observing some factors. The proposed approach possesses desirable properties of flexibility, interpretability, tractability and extendability. The conditional quantile function is modeled by a mixture (weighted sum) of basis quantile functions, with the we

Azadeh Tabeshnezhad, A. Lee Swindlehurst, Tommy Svensson

Non-orthogonal multiple access (NOMA) has become a promising technology for next-generation wireless communications systems due to its capability to provide access for multiple users on the same resource. In this paper, we consider an uplink power-domain NOMA system aided by a reconfigurable intelligent surface (RIS) in the presence of a jammer that aims to

Oleksandr Gamayun, Arthur Hutsalyuk, Balázs Pozsgay, Mikhail B. Zvonarev

We investigate finite temperature spin transport in one spatial dimension by considering the spin-spin correlation function of the Hubbard model in the limiting case of infinitely strong repulsion. We find that in the absence of bias the transport is diffusive, and derive the spin diffusion constant. Our approach is based on asymptotic analysis of a Fredholm

Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried

We propose an efficient method to ground pretrained text-only language models to the visual domain, enabling them to process arbitrarily interleaved image-and-text data, and generate text interleaved with retrieved images. Our method leverages the abilities of language models learnt from large scale text-only pretraining, such as in-context learning and free

F. N. Minta, S. Nozawa, K. Kozarev, A. Elsaid

Metric type II radio bursts are usually early indicators of CME-driven shocks and other space weather phenomena in the solar corona. This paper presents a detailed investigation of the spectral properties of band-splitting type II radio bursts and their association with sunspot number. Using type II radio bursts in a frequency range 20 to 200MHz observed by

Zhuoran Liu, Zhengyu Zhao, Martha Larson

Perturbative availability poisons (PAPs) add small changes to images to prevent their use for model training. Current research adopts the belief that practical and effective approaches to countering PAPs do not exist. In this paper, we argue that it is time to abandon this belief. We present extensive experiments showing that 12 state-of-the-art PAP methods

Hagit Attiya, Armando Castañeda, Thomas Nowak

The famous asynchronous computability theorem (ACT) relates the existence of an asynchronous wait-free shared memory protocol for solving a task with the existence of a simplicial map from a subdivision of the simplicial complex representing the inputs to the simplicial complex representing the allowable outputs. The original theorem relies on a corresponden

C. Hall, P. C. Stancil, J. P. Terry, C. K. Ellison

It may be possible to detect biosignatures of photosynthesis in an exoplanet's atmosphere. However, such a detection would likely require a dedicated study, occupying a large amount of telescope time. It is therefore prudent, while searching for signs of life that we may recognise, to pick the best target possible. In this work, we present a new region,

Raj Dahya

We consider characterisations of unitary dilations and approximations of irreversible classical dynamical systems on a Hilbert space. In the commutative case, building on the work in [9], one can express well known approximants (e.g. Hille- and Yosida-approximants) via expectations over certain stochastic processes. Using this, our first result characterises

Elisabetta Cornacchia, Elchanan Mossel

Curriculum learning (CL) - training using samples that are generated and presented in a meaningful order - was introduced in the machine learning context around a decade ago. While CL has been extensively used and analysed empirically, there has been very little mathematical justification for its advantages. We introduce a CL model for learning the class of

Paul Martin, Eric C. Rowell, Fiona Torzewska

Here a loop braid representation is a monoidal functor $\mathsf{F}$ from the loop braid category $\mathsf{L}$ to a suitable target category, and is $N$-charge-conserving if that target is the category $\mathsf{Match}^N$ of charge-conserving matrices (specifically $\mathsf{Match}^N$ is the same rank-$N$ charge-conserving monoidal subcategory of the monoidal c

Ljuben Mutafchiev, Steven Finch

Let $\mathcal{T}_n$ be the set of all mappings $T:\{1,2,\ldots,n\}\to\{1,2,\ldots,n\}$. The corresponding graph of $T$ is a union of disjoint connected unicyclic components. We assume that each $T\in\mathcal{T}_n$ is chosen uniformly at random (i.e., with probability $n^{-n}$). The cycle of $T$ contained within its largest component is callled the deepest on

F. Bradascio, H. Rueda, J. A. Barrio, J. Biteau

NectarCAM is a Cherenkov camera which is going to equip the Medium-Sized Telescopes (MST) of the northern site of the Cherenkov Telescope Array Observatory (CTAO). NectarCAM is equipped with 265 modules, each consisting of 7 photo-multiplier tubes (PMTs), a Front-End Board and a local camera trigger system used for data acquisition. This paper addresses the

Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf

Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt. While revolutionary, current state-of-the-art diffusion models may still fail in generating images that fully convey the semantics in the given text prompt. We analyze the publicly available Stable Diffusio

Snir Hordan, Tal Amir, Steven J. Gortler, Nadav Dym

Neural networks for point clouds, which respect their natural invariance to permutation and rigid motion, have enjoyed recent success in modeling geometric phenomena, from molecular dynamics to recommender systems. Yet, to date, no model with polynomial complexity is known to be complete, that is, able to distinguish between any pair of non-isomorphic point

Isaac Legred, Yoonsoo Kim, Nils Deppe, Katerina Chatziioannou

Numerical simulations of neutron star mergers represent an essential step toward interpreting the full complexity of multimessenger observations and constraining the properties of supranuclear matter. Currently, simulations are limited by an array of factors, including computational performance and input physics uncertainties, such as the neutron star equati

Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, Chandan K. Reddy

The utilization of programming language (PL) models, pre-trained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable potential in streamlining various code generation tasks such as code completion, code translation, and program synthesis. However, current approaches mainly rely on supervised fine

Ayush Singh, Pisharody Harikrishnan Gopalkrishnan, Mahesh Raveendranatha Panicker

The creation of unique control methods for a hand prosthesis is still a problem that has to be addressed. The best choice of a human-machine interface (HMI) that should be used to enable natural control is still a challenge. Surface electromyography (sEMG), the most popular option, has a variety of difficult-to-fix issues (electrode displacement, sweat, fati

Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li

Table-based reasoning has shown remarkable progress in combining deep models with discrete reasoning, which requires reasoning over both free-form natural language (NL) questions and structured tabular data. However, previous table-based reasoning solutions usually suffer from significant performance degradation on huge evidence (tables). In addition, most e

Sepehr Sharifi, Donghwan Shin, Lionel C. Briand, Nathan Aschbacher

In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential to identify the hazard boundary of ML Components (MLCs) in the MLAS under analysis. Given that such boundary captures the conditions in terms of MLC behavior and system context that can lead to hazards, it can then be used to, for example, build a safety monitor that can take any pr

D. Kinzebulatov

We prove the unique weak solvability of stochastic differential equations with time-inhomogeneous drift in essentially the largest (scaling-invariant) Morrey class, i.e.\,with integrability parameter $q>1$ close to $1$. The constructed weak solutions constitute a Feller evolution family. The proofs are based on a detailed Sobolev regularity theory of the cor

Yao Qiang, Chengyin Li, Prashant Khanduri, Dongxiao Zhu

Vision Transformer (ViT) has recently gained significant attention in solving computer vision (CV) problems due to its capability of extracting informative features and modeling long-range dependencies through the attention mechanism. Whereas recent works have explored the trustworthiness of ViT, including its robustness and explainability, the issue of fair

Elisa El Sergany, Matthieu Wyart, Tom W. J. de Geus

A dry frictional interface loaded in shear often displays stick-slip. The amplitude of this cycle depends on the probability that a microscopic event nucleates a rupture and on the rate at which microscopic events are triggered. The latter is determined by the distribution of soft spots, $P(x)$, which is the density of microscopic regions that yield if the s

ALICE Collaboration

Recent measurements of charm-baryon production in hadronic collisions have questioned the universality of charm-quark fragmentation across different collision systems. In this work the fragmentation of charm quarks into charm baryons is probed, by presenting the first measurement of the longitudinal jet momentum fraction carried by $Λ_{\rm c}^{+}$ baryons, $

Majid Majidi, Deepan Muthirayan, Masood Parvania, Pramod P. Khargonekar

This paper proposes a two-level hierarchical matching framework for Integrated Hybrid Resources (IHRs) with grid constraints. An IHR is a collection of Renewable Energy Sources (RES) and flexible customers within a certain power system zone, endowed with an agent to match. The key idea is to pick the IHR zones so that the power loss effects within the IHRs c

Andrea Canidio

I study a repeated auction in which payments are made with a blockchain token created and initially owned by the auction designer. Unlike the ``virtual money'' previously examined in mechanism design, such tokens can be saved and traded outside the mechanism. I show that the present-discounted value of expected revenues equals that of a conventional

Charles Fefferman, Bo'az Klartag

Let $1 < p < \infty$ and suppose that we are given a function $f$ defined on the leaves of a weighted tree. We would like to extend $f$ to a function $F$ defined on the entire tree, so as to minimize the weighted $W^{1,p}$-Sobolev norm of the extension. An easy situation is when $p = 2$, where the harmonic extension operator provides such a function $F$. In

Wonyoung Kim, Garud Iyengar, Assaf Zeevi

We consider the linear contextual multi-class multi-period packing problem (LMMP) where the goal is to pack items such that the total vector of consumption is below a given budget vector and the total value is as large as possible. We consider the setting where the reward and the consumption vector associated with each action is a class-dependent linear func

Andrea Canidio, Vincent Danos

We provide a game-theoretic analysis of the problem of front-running attacks. We use it to distinguish attacks from legitimate competition among honest users for having their transactions included earlier in the block. We also use it to introduce an intuitive notion of the severity of front-running attacks. We then study a simple commit-reveal protocol and d

Nate Harman, Andrew Snowden

Galois categories can be viewed as the combinatorial analog of Tannakian categories. We introduce the notion of pre-Galois category, which can be viewed as the combinatorial analog of pre-Tannakian categories. Given an oligomorphic group $G$, the category $\mathbf{S}(G)$ of finitary smooth $G$-sets is pre-Galois. Our main theorem (approximately) says that th

Nicola De Nitti, Florian Schweiger

This work is concerned with fractional Gaussian fields, i.e. Gaussian fields whose covariance operator is given by the inverse fractional Laplacian $(-Δ)^{-s}$ (where, in particular, we include the case $s >1$). We define a lattice discretization of these fields and show that their scaling limits -- with respect to the optimal Besov space topology (up to an

Harshit Joshi, Abishai Ebenezer, José Cambronero, Sumit Gulwani

Spreadsheets are a vital tool for end-user data management. Using large language models for formula authoring assistance in these environments can be difficult, as these models are expensive to train and challenging to deploy due to their size (up to billions of parameters). We present FLAME, a transformer-based model trained exclusively on Excel formulas th

Barış Alparslan, Sinan Yıldırım, Ş. İlker Birbil

We propose a novel Bayesian inference framework for distributed differentially private linear regression. We consider a distributed setting where multiple parties hold parts of the data and share certain summary statistics of their portions in privacy-preserving noise. We develop a novel generative statistical model for privately shared statistics, which exp

Mikkel Meyer Andersen, Søren Højsgaard

The capability of R to do symbolic mathematics is enhanced by the caracas package. This package uses the Python computer algebra library SymPy as a back-end but caracas is tightly integrated in the R environment. This enables the R user with symbolic mathematics within R at a high abstraction level rather than using text strings and text string manipulation

Sarah Gift, Hugo J. Woerdeman

Suppose $Q(x)$ is a real $n\times n$ regular symmetric positive semidefinite matrix polynomial. Then it can be factored as $$Q(x) = G(x)^TG(x),$$ where $G(x)$ is a real $n\times n$ matrix polynomial with degree half that of $Q(x)$ if and only if $\det(Q(x))$ is the square of a nonzero real polynomial. We provide a constructive proof of this fact, rooted in f