February 2024 arXiv papers — page 103

Junseok Lee, Kai Murai, Fuminobu Takahashi, Wen Yin

We study the dynamics of axions at first-order phase transitions in non-Abelian gauge theories. When the duration of the phase transition is short compared to the timescale of the axion oscillations, the axion dynamics is similar to the trapped misalignment mechanism. On the other hand, if this is not the case, the axions are initially expelled from the insi

Marie Zinnkann, Henriette Wirth, Pavel Kroupa

Recent work suggested that the variation of the initial mass function (IMF) of stars depends on the physical conditions, notably, the metallicity and gas density. We investigated the properties of two clusters, namely the main cluster (MC) and the subcluster (SC), in the low-metallicity HII region Sh 2-209 (S209) based on recently derived IMFs. We tested thr

Matthew Fox

Building on work by Alfonseca et al. (2021), we study the conditions necessary for it to be logically possible to prove that an arbitrary artificially intelligent machine will exhibit certain behavior. To do this, we develop a formalism like -- but mathematically distinct from -- the theory of formal languages and their properties. Our formalism affords a pr

Siwei Yang, Bingchen Zhao, Cihang Xie

This paper introduces AQA-Bench, a novel benchmark to assess the sequential reasoning capabilities of large language models (LLMs) in algorithmic contexts, such as depth-first search (DFS). The key feature of our evaluation benchmark lies in its interactive evaluation protocol - for example, in DFS, the availability of each node's connected edge is contingen

Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher Choquette-Choo

Differential privacy (DP) offers a theoretical upper bound on the potential privacy leakage of analgorithm, while empirical auditing establishes a practical lower bound. Auditing techniques exist forDP training algorithms. However machine learning can also be made private at inference. We propose thefirst framework for auditing private prediction where we in

Hannah Lange, Anka Van de Walle, Atiye Abedinnia, Annabelle Bohrdt

Due to the exponential growth of the Hilbert space dimension with system size, the simulation of quantum many-body systems has remained a persistent challenge until today. Here, we review a relatively new class of variational states for the simulation of such systems, namely neural quantum states (NQS), which overcome the exponential scaling by compressing t

Kaixuan Ji, Jiafan He, Quanquan Gu

Aligning large language models (LLM) with human preference plays a key role in building modern generative models and can be achieved by reinforcement learning from human feedback (RLHF). Despite their superior performance, current RLHF approaches often require a large amount of human-labelled preference data, which is expensive to collect. In this paper, ins

Martin Gembé, Lasse Gresista, Heinz-Jürgen Schmidt, Ciarán Hickey

A promising route towards the realization of chiral spin liquids is the quantum melting of classically noncoplanar spin states via quantum fluctuations. In the classical realm, such noncoplanar orders can effectively be stabilized by interactions beyond nearest neighbors. Motivated by the recent synthesis of materials with a maple-leaf lattice geometry, we s

C. Gallart, F. Surot, S. Cassisi, E. Fernández-Alvar

The current major challenge to reconstruct the chronology of the Milky Way (MW) is the difficulty to derive precise stellar ages. CMD-fitting offers an alternative to individual age determinations to derive the star formation history (SFH). We present CMDft.Gaia and use it to analyse the CMD of the Gaia Catalogue of Nearby Stars (GCNS), which contains a cens

Harry Dong, Xinyu Yang, Zhenyu Zhang, Zhangyang Wang

Many computational factors limit broader deployment of large language models. In this paper, we focus on a memory bottleneck imposed by the key-value (KV) cache, a computational shortcut that requires storing previous KV pairs during decoding. While existing KV cache methods approach this problem by pruning or evicting large swaths of relatively less importa

Weizhen Wang, Chongxiu Yu, Zhongzhan Zhang

A reasonable confidence interval should have a confidence coefficient no less than the given nominal level and a small expected length to reliably and accurately estimate the parameter of interest, and the bootstrap interval is considered to be an efficient interval estimation technique. In this paper, we offer a first attempt at computing the coverage proba

Manuel Friedrich, Camille Labourie, Kerrek Stinson

In this note we show Ahlfors-regularity for a large class of quasiminimizers of the Griffith functional. This allows us to prove that, for a range of free discontinuity problems in linear elasticity with anisotropic, cohesive, or heterogeneous behavior, minimizers have an essentially closed jump set and are thus strong minimizers. Our notion of quasiminimali

Lars J. Corbijn van Willenswaard, Stef Smeets, Nicolas Renaud, Matthias Schlottbom

State-of-the-art computational methods combined with common idealized structural models provide an incomplete understanding of experiments on real nanostructures, since manufacturing introduces unavoidable deviations from the design. We propose to close this knowledge gap by using the real structure of a manufactured crystal as input in computations to obtai

Robert Collins

The Sentient House project is an investigation into approaches that the artistdesigner can take to better involve the public in developing a critical perspective on pervasive technology in the home and the surrounding environment. Using Interaction Design approaches including workshops, surveys, rapidprototyping and critical thinking, this thesis suggests a

Domenic Rosati, Robie Gonzales, Jinkun Chen, Xuemin Yu

Evaluations of model editing currently only use the `next few token' completions after a prompt. As a result, the impact of these methods on longer natural language generation is largely unknown. We introduce long-form evaluation of model editing (LEME) a novel evaluation protocol that measures the efficacy and impact of model editing in long-form generative

Sergio Davis

Non-equilibrium systems in steady states are commonly described by generalized statistical mechanical theories such as non-extensive statistics and superstatistics. Superstatistics assumes that the inverse temperature $\beta = 1/(k_B T)$ follows some pre-established statistical distribution, however, it has been previously proved (Physica A 505, 864-870 [201

Mehdi Fatemi, Sindhu Gowda

We address causal reasoning in multivariate time series data generated by stochastic processes. Existing approaches are largely restricted to static settings, ignoring the continuity and emission of variations across time. In contrast, we propose a learning paradigm that directly establishes causation between events in the course of time. We present two key

Adithya Raman, Bekir Turkkan, Tevfik Kosar

Over the recent years, research and development in adaptive bitrate (ABR) algorithms for live video streaming have been successful in improving users' quality of experience (QoE) by reducing latency to near real-time levels while delivering higher bitrate videos with minimal rebuffering time. However, the QoE models used by these ABR algorithms do not take i

Botao Yu, Frazier N. Baker, Ziqi Chen, Xia Ning

Chemistry plays a crucial role in many domains, such as drug discovery and material science. While large language models (LLMs) such as GPT-4 exhibit remarkable capabilities on natural language processing tasks, existing research indicates that their performance on chemistry tasks is discouragingly low. In this paper, however, we demonstrate that our develop

Yihao Fang, Stephen W. Thomas, Xiaodan Zhu

With the widespread adoption of large language models (LLMs) in numerous applications, the challenge of factuality and the propensity for hallucinations has emerged as a significant concern. To address this issue, particularly in retrieval-augmented in-context learning, we introduce the hierarchical graph of thoughts (HGOT), a structured, multi-layered graph

Roy A. Lacey

Anisotropy scaling functions derived from comprehensive measurements of transverse momentum- and centrality-dependent anisotropy coefficients \(v_2(p_T,\text{cent})\) and \(v_3(p_T,\text{cent})\) in Pb+Pb collisions at 5.02 and 2.76 TeV, Xe+Xe collisions at 5.44 TeV and Au+Au collisions at 0.2 TeV, offer new insights into the `ultra-central flow puzzle.' The

Harrison Delecki, Marcell Vazquez-Chanlatte, Esen Yel, Kyle Wray

Model-based planners for partially observable problems must accommodate both model uncertainty during planning and goal uncertainty during objective inference. However, model-based planners may be brittle under these types of uncertainty because they rely on an exact model and tend to commit to a single optimal behavior. Inspired by results in the model-free

Allen M. Wang, Oswin So, Charles Dawson, Darren T. Garnier

The tokamak offers a promising path to fusion energy, but plasma disruptions pose a major economic risk, motivating considerable advances in disruption avoidance. This work develops a reinforcement learning approach to this problem by training a policy to safely ramp-down the plasma current while avoiding limits on a number of quantities correlated with disr

M. Garcia-Bosque, G. Díez-Señorans, C. Sánchez-Azqueta, S. Celma

During the last years, Physically Unclonable Functions (PUFs) have become a very important research area in the field of hardware security due to their capability of generating volatile secret keys as well as providing a low-cost authentication. In this paper, an introduction to Physically Unclonable Functions is given, including their definition, properties

Pietro Pampili, Vitaly Z. Zubialevich, Peter J. Parbrook

In this study we report on a novel two-step epitaxial growth technique that enables a significant improvement of the crystal quality of nitrogen-polar GaN. The starting material is grown on 4{\deg} vicinal sapphire substrates by metal organic vapour phase epitaxy, with an initial high-temperature sapphire nitridation to control polarity. The material is then

Ruqing Xu

A principal designs an algorithm that generates a publicly observable prediction of a binary state. She must decide whether to act directly based on the prediction or to delegate the decision to an agent with private information but potential misalignment. We study the optimal design of the prediction algorithm and the delegation rule in such environments. T

Sucharita Biswas, Angsuman Das

In this paper, we construct a family of quasi-strongly regular Cayley graphs $\Gamma_H(G)$ which is defined on a finite group $G$ with respect to a subgroup $H$ of $G$. We also compute its full automorphism group and characterize various transitivity properties of it.

Luca Ballotta, Rajat Talak

Safe operation of multi-robot systems is critical, especially in communication-degraded environments such as underwater for seabed mapping, underground caves for navigation, and in extraterrestrial missions for assembly and construction. We address safety of networked autonomous systems where the information exchanged between robots incurs communication dela

Ali Azizpour, Advait Balaji, Todd J. Treangen, Santiago Segarra

Repetitive DNA (repeats) poses significant challenges for accurate and efficient genome assembly and sequence alignment. This is particularly true for metagenomic data, where genome dynamics such as horizontal gene transfer, gene duplication, and gene loss/gain complicate accurate genome assembly from metagenomic communities. Detecting repeats is a crucial f

Tristan Benoist, Laurent Bruneau, Vojkan Jakšić, Annalisa Panati

We provide a justification, via the thermodynamic limit, of the modular formula for entropy production in two-times measurement proposed in [Benoist, Bruneau, Jak\v{s}i\'c, Panati and Pillet: Lett. Math. Phys. 114:32, (2023)]. We consider the cases of open quantum systems in which all thermal reservoirs are either (discrete) quantum spin systems or free Ferm

Andris Huang, Yash Melkani, Paolo Calafiura, Alina Lazar

Particle tracking is crucial for almost all physics analysis programs at the Large Hadron Collider. Deep learning models are pervasively used in particle tracking related tasks. However, the current practice is to design and train one deep learning model for one task with supervised learning techniques. The trained models work well for tasks they are trained

Shengpeng Ji, Ziyue Jiang, Hanting Wang, Jialong Zuo

Zero-shot text-to-speech (TTS) has gained significant attention due to its powerful voice cloning capabilities, requiring only a few seconds of unseen speaker voice prompts. However, all previous work has been developed for cloud-based systems. Taking autoregressive models as an example, although these approaches achieve high-fidelity voice cloning, they fal

Rodrigo Landa Andraca, Mahdi Zareei

Function-as-a-Service (FaaS) allows to directly submit function code to a cloud provider without the burden of managing infrastructure resources. Each cloud provider establishes execution time limits to their FaaS offerings, which impose the risk of spending computation time without achieving partial results. In this work, a framework that enables limitless

Smik Patel, Artur F. Izmaylov

Exactly solvable Hamiltonians are useful in the study of quantum many-body systems using quantum computers. In the variational quantum eigensolver, a decomposition of the target Hamiltonian into exactly solvable fragments can be used for evaluation of the energies via repeated quantum measurements. In this work, we apply more general classes of exactly solva

Jan Böhnke, Beatrice Andres, Larissa Boie, Angela Richter

Strongly differing static dipole moments of the trans and cis isomers of photochromic azobenzene allow for optical switching the work function of azobenzene-functionalized self-assembled monolayers (SAMs). We apply these properties in a fundamental experiment to manipulate the area size of the switched SAM. Azobenzene molecules were excited by ultraviolet la

Nikolai Leonenko, Yu Sun, Emanuele Taufer

The Varentropy is a measure of the variability of the information content of random vector and it is invariant under affine transformations. We introduce the statistical estimate of varentropy of random vector based on the nearest neighbor graphs (distances). The asymptotic unbiasedness and L2-consistency of the estimates are established.

Ignacio Hounie, Javier Porras-Valenzuela, Alejandro Ribeiro

Several applications in time series forecasting require predicting multiple steps ahead. Despite the vast amount of literature in the topic, both classical and recent deep learning based approaches have mostly focused on minimising performance averaged over the predicted window. We observe that this can lead to disparate distributions of errors across foreca

Jiancheng Yang, Rui Shi, Liang Jin, Xiaoyang Huang

Rib fractures are a common and potentially severe injury that can be challenging and labor-intensive to detect in CT scans. While there have been efforts to address this field, the lack of large-scale annotated datasets and evaluation benchmarks has hindered the development and validation of deep learning algorithms. To address this issue, the RibFrac Challe

Lorenzo Liso, Erik Sandström, Vladimir Yugay, Luc Van Gool

Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM), yet face challenges such as error accumulation during camera tracking resulting in distorted maps. In response, we introduce Loopy-SLAM that globally optimizes poses and the dense 3D model. We use frame-to-model tracking using a data-driven point-based subma

Yongchao Zhou, Uri Alon, Xinyun Chen, Xuezhi Wang

Length generalization, defined as the ability to extrapolate from shorter training sequences to longer test ones, is a significant challenge for language models. This issue persists even with large-scale Transformers handling relatively straightforward tasks. In this paper, we test the Transformer's ability of length generalization using the task of addition

Miranda Christ, Sam Gunn

We construct pseudorandom error-correcting codes (or simply pseudorandom codes), which are error-correcting codes with the property that any polynomial number of codewords are pseudorandom to any computationally-bounded adversary. Efficient decoding of corrupted codewords is possible with the help of a decoding key. We build pseudorandom codes that are robus

Yi Fung, Ruining Zhao, Jae Doo, Chenkai Sun

Pretrained large language models have revolutionized many applications but still face challenges related to cultural bias and a lack of cultural commonsense knowledge crucial for guiding cross-culture communication and interactions. Recognizing the shortcomings of existing methods in capturing the diverse and rich cultures across the world, this paper introd

Ze Ma, Daquan Zhou, Chun-Hsiao Yeh, Xue-She Wang

Creating content with specified identities (ID) has attracted significant interest in the field of generative models. In the field of text-to-image generation (T2I), subject-driven creation has achieved great progress with the identity controlled via reference images. However, its extension to video generation is not well explored. In this work, we propose a

Sina Borzooei, Leonardo Scabini, Gisele Miranda, Saba Daneshgar

Microbial communities play a key role in biological wastewater treatment processes. Activated sludge settling characteristics, for example, are affected by microbial community composition, varying by changes in operating conditions and influent characteristics of wastewater treatment plants (WWTPs). Timely assessment and prediction of changes in microbial co

P. Bangale, B. Bhattacharyya, F. Camilo, C. J. Clark

We have searched for radio pulsations towards 49 Fermi Large Area Telescope (LAT) 1FGL Catalog $\gamma$-ray sources using the Green Bank Telescope at 350 MHz. We detected 18 millisecond pulsars (MSPs) in blind searches of the data; 10 of these were discoveries unique to our survey. Sixteen are binaries, with eight having short orbital periods $P_B < 1$ day.

A. Schlatter, R. E. Kastner

The question of where the inertial properties of matter come from has been open for a long time. Isaac Newton considered inertia an intrinsic property of matter. Ernst Mach held a different view whereby the inertia of a body comes from its interaction with the rest of the universe. This idea is known today as Mach's principle. We discuss Mach's principle bas

Rixin Yu, Erdzan Hodzic

This study investigates the application of machine learning, specifically Fourier Neural Operator (FNO) and Convolutional Neural Network (CNN), to learn time-advancement operators for parametric partial differential equations (PDEs). Our focus is on extending existing operator learning methods to handle additional inputs representing PDE parameters. The goal

Alvin Y. Sukmadji, Frank R. Kschischang

Using a generating function approach, a computationally tractable expression is derived to predict the frame error rate arising at the output of the binary symmetric channel when a number of outer Reed--Solomon codes are concatenated with a number of inner Bose--Ray-Chaudhuri--Hocquenghem codes, thereby obviating the need for time-consuming Monte Carlo simul

F. Pozo Nuñez, B. Czerny, S. Panda, A. Kovacevic

The Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory is poised to observe thousands of quasars using the Deep Drilling Fields (DDF) across six broadband filters over a decade. Understanding quasar accretion disc (AD) time delays is pivotal for probing the physics of these distant objects. Pozo Nu\~nez et al. (2023) has recently demonst

Matthieu Meeus, Igor Shilov, Manuel Faysse, Yves-Alexandre de Montjoye

Questions of fair use of copyright-protected content to train Large Language Models (LLMs) are being actively debated. Document-level inference has been proposed as a new task: inferring from black-box access to the trained model whether a piece of content has been seen during training. SOTA methods however rely on naturally occurring memorization of (part o

Zohreh Davoudi, William Detmold, Zhenghao Fu, Anthony V. Grebe

Neutrinoless double-beta ($0\nu\beta\beta$) decay is a heretofore unobserved process which, if observed, would imply that neutrinos are Majorana particles. Interpretations of the stringent experimental constraints on $0\nu\beta\beta$-decay half-lives require calculations of nuclear matrix elements. This work presents the first lattice quantum-chromodynamics

Ravneet Bedi, Tony Gherghetta, Christophe Grojean, Guilherme Guedes

Small instantons which increase the axion mass due to an appropriate modification of QCD at a UV scale $\Lambda_{\rm SI}$, can also enhance the effect of CP-violating operators to shift the axion potential minimum by an amount, $\theta_{\rm ind}$, proportional to the flavorful couplings in the SMEFT. Since physical observables must be flavor basis independen

Yashas Samaga B L, Varun Yerram, Chong You, Srinadh Bhojanapalli

Autoregressive decoding with generative Large Language Models (LLMs) on accelerators (GPUs/TPUs) is often memory-bound where most of the time is spent on transferring model parameters from high bandwidth memory (HBM) to cache. On the other hand, recent works show that LLMs can maintain quality with significant sparsity/redundancy in the feedforward (FFN) lay

Alexey Pozdnyakov

We report on two machine learning experiments in search of statistical relationships between Dirichlet coefficients and root numbers or analytic ranks of certain low-degree $L$-functions. The first experiment is to construct interpretable models based on murmurations, a recently discovered correlation between Dirichlet coefficients and root numbers. We show

Brice Rauby, Paul Xing, Jonathan Porée, Maxime Gasse

Ultrasound Localization Microscopy (ULM) is a non-invasive technique that allows for the imaging of micro-vessels in vivo, at depth and with a resolution on the order of ten microns. ULM is based on the sub-resolution localization of individual microbubbles injected in the bloodstream. Mapping the whole angioarchitecture requires the accumulation of microbub

Kyungsu Kim, Junhyun Park, Saul Langarica, Adham Mahmoud Alkhadrawi

This study demonstrates the first in-hospital adaptation of a cloud-based AI, similar to ChatGPT, into a secure model for analyzing radiology reports, prioritizing patient data privacy. By employing a unique sentence-level knowledge distillation method through contrastive learning, we achieve over 95% accuracy in detecting anomalies. The model also accuratel

T-H. Hubert Chan, Ke Wu, Elaine Shi

Blockchains have popularized automated market makers (AMMs). An AMM exchange is an application running on a blockchain which maintains a pool of crypto-assets and automatically trades assets with users governed by some pricing function that prices the assets based on their relative demand/supply. AMMs have created an important challenge commonly known as the

Sihan Chen, Sameh Abdulah, Ying Sun, Marc G. Genton

Spatial statistical modeling and prediction involve generating and manipulating an n*n symmetric positive definite covariance matrix, where n denotes the number of spatial locations. However, when n is large, processing this covariance matrix using traditional methods becomes prohibitive. Thus, coupling parallel processing with approximation can be an elegan

Alexandre Chenu, Olivier Serris, Olivier Sigaud, Nicolas Perrin-Gilbert

Demonstrations are commonly used to speed up the learning process of Deep Reinforcement Learning algorithms. To cope with the difficulty of accessing multiple demonstrations, some algorithms have been developed to learn from a single demonstration. In particular, the Divide & Conquer Imitation Learning algorithms leverage a sequential bias to learn a control

Channyung Lee, Michael A. Scarpulla, Elif Ertekin

The low symmetry of monoclinic $\beta$-Ga$_2$O$_3$ leads to elaborate intrinsic defects, such as Ga vacancies split amongst multiple lattice sites. These defects contribute to fast, anisotropic Ga diffusion, yet their complexity makes it challenging to understand dominant diffusion mechanisms. Here, we predict the 3D diffusivity tensors for Ga interstitials

Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov

Among the widely used parameter-efficient fine-tuning (PEFT) methods, LoRA and its variants have gained considerable popularity because of avoiding additional inference costs. However, there still often exists an accuracy gap between these methods and full fine-tuning (FT). In this work, we first introduce a novel weight decomposition analysis to investigate

Dipankar Das, Anirban Kundu, Miguel Levy, Anugrah M. Prasad

The magnitudes of the couplings of the scalar resonance at 125 GeV with the SM particles are found to be consistent with those of the SM Higgs boson. However, the signs are not experimentally determined in most of the cases, a prime example being that with the $Z$-boson pair. In other words, $\kappa_Z^h$, the ratio of the couplings of the actual 125 GeV reso

Frank-Olaf Schreyer, Hoang Le Truong

In this note we give a computationally easy to use method to compute a maximal extension of certain varieties. As a application we prove that a general paracanonical curve C genus 6 as a codimension three subvarieties of P^4 extend to precisely 26 families of surfaces Y in P^5.

Vitalii Marchenko

The concept of $\ell_{\Phi}$-decomposition, extending the concept of $\ell_{p}$-decomposition of a Banach space, is presented and basic properties of Schauder-Orlicz decompositions and $\ell_{\Phi}$-decompositions are studied. We show that Schauder-Orlicz decompositions are orthogonal in a sense of Grinblyum-James and Singer. Simple constructions of $\ell_{p

Adrian Llanos, Giovanna Campisi, Veronica Show, Jinwoong Kim

Rare-earth diantimondes exhibit coupling between structural and electronic orders which are tunable under pressure and temperature. Here we present the discovery of a new polymorph of LaSb$_2$ stabilized in thin films synthesized using molecular beam epitaxy. Using diffraction, electron microscopy, and first principles calculations we identify a YbSb$_2$-typ

Sergey Khrapak

A practical correction formula relating the self-diffusion coefficient of dense liquids from molecular dynamics simulations with periodic boundary conditions to the self-diffusion coefficient in the thermodynamic limit is discussed. This formula applies to pure dense fluids and has a very simple form $D=D_0(1-\gamma N^{-1/3})$, where $D_0$ is the self-diffus

Manabendra Giri, Arup Kumar Pal

Crystallization of the $C^*$-algebras $C(SU_{q}(n+1))$ was introduced by Giri \& Pal as a $C^*$-algebra $C(SU_{0}(n+1))$ given by a finite set of generators and relations. Here we study representations of the $C^*$-algebra $C(SU_{0}(n+1))$ and prove a factorization theorem for its irreducible representations. This leads to a complete classification of all ir

Maryam Amirizaniani, Jihan Yao, Adrian Lavergne, Elizabeth Snell Okada

As Large Language Models (LLMs) become more pervasive across various users and scenarios, identifying potential issues when using these models becomes essential. Examples of such issues include: bias, inconsistencies, and hallucination. Although auditing the LLM for these problems is often warranted, such a process is neither easy nor accessible for most. An

Yuchun Miao, Sen Zhang, Liang Ding, Rong Bao

Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models with human values, reward hacking, also termed reward overoptimization, remains a critical challenge. This issue primarily arises from reward misgeneralization, where reward models (RMs) compute reward using spurious features that are irrelevant to human pref

Yuto Nishida, Makoto Morishita, Hidetaka Kamigaito, Taro Watanabe

Generating multiple translation candidates would enable users to choose the one that satisfies their needs. Although there has been work on diversified generation, there exists room for improving the diversity mainly because the previous methods do not address the overcorrection problem -- the model underestimates a prediction that is largely different from

Gordon Chavez

We show that $$ \sum_{n\neq m}\frac{\mu(n)\mu(m)}{nm}E_{X}\left(\{nx\}\{mx\}\right)=-\frac{9}{2\pi^{2}}+O\left(\frac{1}{X}\right), $$ where $x$ is uniformly distributed in $[0,X]$ with $X\in \mathbb{N}$, $E_{X}(.)$ denotes the expected value, $\mu(.)$ denotes the M\"obius function, and $\{.\}$ denotes the fractional part function.

Julia Lena Lienert, Bertram Bitsch, Thomas Henning

The chemical evolution of protoplanetary discs is not fully understood, several factors influence the final distribution of disc material. One such factor are inward drifting and evaporating pebbles that enrich the inner disc with vapour. In particular, it is first enriched with water vapour, resulting in a low C/O ratio, before carbon-rich gas from the oute

Malika Sanhinova, Nazim Haouchine, Steve D. Pieper, William M. Wells

Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use, however, it is challenging due to substantial change in shape and appearance due to lesions. In this paper we present a novel method to aut

Ewa Malgorzata Nowik-Boltyk, Tobias Junghoefer, Erika Giangrisostomi, Ruslan Ovsyannikov

In this work, we study the cobalt radical interface obtained by depositing a monolayer of a derivative of the Blatter radical on polycrystalline cobalt. By examining the occupied and unoccupied states at the interface, using soft X-ray techniques, and combining them with ab initio calculations, we can completely determine the electronic structure at the inte

Konstantinos Tsouvalas

Let $\mathbb{K}=\mathbb{R}$ or $\mathbb{C}$. For all but finitely many $m\in \mathbb{N}$, we exhibit the first examples of non-locally rigid, Zariski dense, robust quasi-isometric embeddings of hyperbolic groups in $\mathsf{SL}_m(\mathbb{K})$ which are not limits of Anosov representations. As a consequence, we show that higher rank analogues of Sullivan's st

Luigi Del Debbio, Manuel Naviglio, Francesco Tarantelli

This paper presents a study of the effectiveness of Neural Network (NN) techniques for deconvolution inverse problems relevant for applications in Quantum Field Theory, but also in more general contexts. We consider NN's asymptotic limits, corresponding to Gaussian Processes (GPs), where non-linearities in the parameters of the NN can be neglected. Using the

Luis Altenkort, David de la Cruz, Olaf Kaczmarek, Guy D. Moore

We analyze the color-magnetic (or "$B$") field two-point function that encodes the finite-mass correction to the heavy quark momentum diffusion coefficient. The simulations are done on fine isotropic lattices in the quenched approximation at $1.5\,T_c$, using a range of gradient flow times for noise suppression and operator renormalization. The continuum ext

Sahil Kale, Gautam Khaire, Jay Patankar, Pujashree Vidap

The boundaries of electoral constituencies for assembly and parliamentary seats are drafted using a process referred to as delimitation, which ensures fair and equal representation of all citizens. The current delimitation exercise suffers from a number of drawbacks viz. inefficiency, gerrymandering and an uneven seat-to-population ratio, owing to existing l

Chi-Fang Chen, Jordan Docter, Michelle Xu, Adam Bouland

Unitary $T$-designs play an important role in quantum information, with diverse applications in quantum algorithms, benchmarking, tomography, and communication. Until now, the most efficient construction of unitary $T$-designs for $n$-qudit systems has been via random local quantum circuits, which have been shown to converge to approximate $T$-designs in the

Ahmed N. Bakry, Almohammady S. Alsharkawy, Mohamed S. Farag, Kamal R. Raslan

Anti-Money Laundering (AML) is a crucial task in ensuring the integrity of financial systems. One keychallenge in AML is identifying high-risk groups based on their behavior. Unsupervised learning, particularly clustering, is a promising solution for this task. However, the use of hundreds of features todescribe behavior results in a highdimensional dataset

Maryam Amirizaniani, Elias Martin, Tanya Roosta, Aman Chadha

As Large Language Models (LLMs) are integrated into various sectors, ensuring their reliability and safety is crucial. This necessitates rigorous probing and auditing to maintain their effectiveness and trustworthiness in practical applications. Subjecting LLMs to varied iterations of a single query can unveil potential inconsistencies in their knowledge bas

Florian Hopfmueller, Maxime Tremblay, Philippe St-Jean, Baptiste Royer

A promising route towards fault-tolerant quantum error correction is the concatenation of a Gottesman-Kitaev-Preskill (GKP) code with a qubit code. Development of such concatenated codes requires simulation tools which realistically model noise, while being able to simulate the dynamics of many modes. However, so far, large-scale simulation tools for concate

Alexander W. Levis, Edward H. Kennedy, Luke Keele

Instrumental variables are widely used in econometrics and epidemiology for identifying and estimating causal effects when an exposure of interest is confounded by unmeasured factors. Despite this popularity, the assumptions invoked to justify the use of instruments differ substantially across the literature. Similarly, statistical approaches for estimating

Jeremy A. Roberts

Presented here is a preliminary study of a strictly linear, discontinuous-Petrov-Galerkin scheme for the discrete-ordinates method in slab geometry. By ``linear'', we mean the discretization does not depend on the solution itself as is the case in classical ``fix-up'' schemes and other nonlinear schemes that have been explored to maintain positive solutions

Bo Li, Ali Mostafavi

The objective of this study is to characterize inequality in infrastructure quality across urban areas. While a growing of body of literature has recognized the importance of characterizing infrastructure inequality in cities and provided quantified metrics to inform urban development plans, the majority of the existing approaches focus primarily on measurin

Howard Georgi

I show that if a dimensionless parameter is tuned to be close to the boundary of the positivity domain and symmetry breaking is driven by a cubic term in the Lagrangian, the scale of the physics of symmetry breaking in a quantum field theory as measured by the Higgs mass can be much greater than the dimensional scales in the classical Lagrangian. Radiative c

Carlos Oliver, Vincent Mallet, Jérôme Waldispühl

Understanding the connection between complex structural features of RNA and biological function is a fundamental challenge in evolutionary studies and in RNA design. However, building datasets of RNA 3D structures and making appropriate modeling choices remains time-consuming and lacks standardization. In this chapter, we describe the use of rnaglib, to trai

Chun-Tse Chien, Rui-Yang Ju, Kuang-Yi Chou, Enkaer Xieerke

Wrist trauma and even fractures occur frequently in daily life, particularly among children who account for a significant proportion of fracture cases. Before performing surgery, surgeons often request patients to undergo X-ray imaging first and prepare for it based on the analysis of the radiologist. With the development of neural networks, You Only Look On

Patrick Oliver Schenk, Christoph Kern

National Statistical Organizations (NSOs) increasingly draw on Machine Learning (ML) to improve the timeliness and cost-effectiveness of their products. When introducing ML solutions, NSOs must ensure that high standards with respect to robustness, reproducibility, and accuracy are upheld as codified, e.g., in the Quality Framework for Statistical Algorithms

Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni

In this work, we investigate the interplay between memorization and learning in the context of \emph{stochastic convex optimization} (SCO). We define memorization via the information a learning algorithm reveals about its training data points. We then quantify this information using the framework of conditional mutual information (CMI) proposed by Steinke an

Siddartha Devic, Aleksandra Korolova, David Kempe, Vatsal Sharan

Rankings are ubiquitous across many applications, from search engines to hiring committees. In practice, many rankings are derived from the output of predictors. However, when predictors trained for classification tasks have intrinsic uncertainty, it is not obvious how this uncertainty should be represented in the derived rankings. Our work considers ranking

Xiuzhong Hu, Guangming Xiong, Zheng Zang, Peng Jia

Large-scale 3D scene reconstruction and novel view synthesis are vital for autonomous vehicles, especially utilizing temporally sparse LiDAR frames. However, conventional explicit representations remain a significant bottleneck towards representing the reconstructed and synthetic scenes at unlimited resolution. Although the recently developed neural radiance

P. Y. Jiang, Z. Y. Liu, S. Y. Liu, J. Bao

A new magnetohydrodynamics (MHD) code based on initial value approach, GMEC_I, has been developed for simulating various MHD physics in tokamak plasmas, as the MHD foundation of the gyrokinetic-MHD energetic particle simulation code (GMEC) family. GMEC_I solves multi-level reduced-MHD models that form a hierarchy of physics complexity, which provide convenie

Valentijn Karemaker, Akio Tamagawa, Chia-Fu Yu

We prove uniqueness of a decomposition of $1$ into indecomposable Hermitian idempotents in an order of a finite-dimensional $\mathbb{Q}$-algebra with positive involution, by generalising a result of Eichler on unique decomposition of lattices. We use this result to prove that polarised abelian varieties over any field admit a unique decomposition into indeco

E. W. Guenther, E. Goffo, D. Sebastian, A. M. S. Smith

Ultra-short period planets have orbital periods of less than one day. Since their masses and radii can be determined to a higher precision than long-period planets, they are the preferred targets to determine the density of planets which constrains their composition. The K2-106 system is particularly interesting because it contains two planets of nearly iden

Hao Chung, Tim Roughgarden, Elaine Shi

Users bid in a transaction fee mechanism (TFM) to get their transactions included and confirmed by a blockchain protocol. Roughgarden (EC'21) initiated the formal treatment of TFMs and proposed three requirements: user incentive compatibility (UIC), miner incentive compatibility (MIC), and a form of collusion-resilience called OCA-proofness. Ethereum's EIP-1

Feifan Song, Yuxuan Fan, Xin Zhang, Peiyi Wang

Large Language Models (LLMs) rely on Human Preference Alignment (HPA) to ensure the generation of safe content. Due to the heavy cost associated with fine-tuning, fine-tuning-free methods have emerged, typically modifying LLM decoding with external auxiliary methods. However, these methods do not essentially enhance the LLM itself. In this paper, we rethink

Nico Dirkes, Fabian Key, Marek Behr

The development of blood-handling medical devices, such as ventricular assist devices, requires the analysis of their biocompatibility. Among other aspects, this includes hemolysis, i.e., red blood cell damage. For this purpose, computational fluid dynamics (CFD) methods are employed to predict blood flow in prototypes. The most basic hemolysis models direct

Pablo Alonso-Jiménez, Leonardo Pepino, Roser Batlle-Roca, Pablo Zinemanas

We present PECMAE, an interpretable model for music audio classification based on prototype learning. Our model is based on a previous method, APNet, which jointly learns an autoencoder and a prototypical network. Instead, we propose to decouple both training processes. This enables us to leverage existing self-supervised autoencoders pre-trained on much lar

Robert Denkert, Ulrich Horst

We establish a probabilistic framework for analysing extended mean-field games with multi-dimensional singular controls and state-dependent jump dynamics and costs. Two key challenges arise when analysing such games: the state dynamics may not depend continuously on the control and the reward function may not be u.s.c.~Both problems can be overcome by restri