February 2024 arXiv papers — page 58

John Morris, Stefan Tatschner, Michael P. Heinl, Patrizia Heinl

With the increasing sophistication and sheer number of cyberattacks, more and more companies come to the conclusion that they have to strengthen their cybersecurity posture. At the same time, well-educated Information technology (IT) security personnel are scarce. Cybersecurity as a service (CSaaS) is one possible solution to tackle this problem by outsourci

Shirsh Lata Soni, Mojtaba Akhavan-Tafti, Gabriel Ho Hin Suen, Justin Kasper

Magnetic switchbacks are distinct magnetic structures characterized by their abrupt reversal in the radial component of the magnetic field within the pristine solar wind. Switchbacks are believed to lose magnetic energy with heliocentric distance. To investigate this switchbacks originating from similar solar source regions are identified during a radial ali

Pengcheng Qiu, Chaoyi Wu, Xiaoman Zhang, Weixiong Lin

The development of open-source, multilingual medical language models can benefit a wide, linguistically diverse audience from different regions. To promote this domain, we present contributions from the following: First, we construct a multilingual medical corpus, containing approximately 25.5B tokens encompassing 6 main languages, termed as MMedC, enabling

Xia Zhu, Ke Wu, Xiaohang Xie, Stephan W. Anderson

A body area network (BAN) involving wearable sensors populated around the human body can continuously monitor physiological signals, finding applications in personal healthcare and athletic evaluation. Existing near-field communication (NFC)-enabled BAN solutions, while facilitating reliable and secure interconnection among battery-free sensors, face challen

Yulia Alexandr, Miles Bakenhus, Mark Curiel, Sameer K. Deshpande

In the last quarter of a century, algebraic statistics has established itself as an expanding field which uses multilinear algebra, commutative algebra, computational algebra, geometry, and combinatorics to tackle problems in mathematical statistics. These developments have found applications in a growing number of areas, including biology, neuroscience, eco

Shuo Sun, Chandan Kumar, Kevin Shen, Elvira Shishenina

Solving electronic structure problems is considered one of the most promising applications of quantum computing. However, due to limitations imposed by the coherence time of qubits in the Noisy Intermediate Scale Quantum (NISQ) era or the capabilities of early fault-tolerant quantum devices, it is vital to design algorithms with low-depth circuits. In this w

Shichao Ma

I examine a conceptual model of a recommendation system (RS) with user inflow and churn dynamics. When inflow and churn balance out, the user distribution reaches a steady state. Changing the recommendation algorithm alters the steady state and creates a transition period. During this period, the RS behaves differently from its new steady state. In particula

Yugo Takada, Keisuke Fujii

Color codes are promising quantum error correction (QEC) codes because they have an advantage over surface codes in that all Clifford gates can be implemented transversally. However, thresholds of color codes under circuit-level noise are relatively low mainly because measurements of their high-weight stabilizer generators cause an increase in a circuit dept

Navin Kamuni, Sathishkumar Chintala, Naveen Kunchakuri, Jyothi Swaroop Arlagadda Narasimharaju

Audio fingerprinting, exemplified by pioneers like Shazam, has transformed digital audio recognition. However, existing systems struggle with accuracy in challenging conditions, limiting broad applicability. This research proposes an AI and ML integrated audio fingerprinting algorithm to enhance accuracy. Built on the Dejavu Project's foundations, the study

William Merrill, Zhaofeng Wu, Norihito Naka, Yoon Kim

Do LMs infer the semantics of text from co-occurrence patterns in their training data? Merrill et al. (2022) argue that, in theory, sentence co-occurrence probabilities predicted by an optimal LM should reflect the entailment relationship of the constituent sentences, but it is unclear whether probabilities predicted by neural LMs encode entailment in this w

Mohammad Mahdi Dehshibi, David Masip

In this study, we investigate how environmental factors, specifically the scenes and objects involved, can affect the expression of emotions through body language. To this end, we introduce a novel multi-stream deep convolutional neural network named BEE-NET. We also propose a new late fusion strategy that incorporates meta-information on places and objects

Rahul Zalkikar, Kanchan Chandra

Transformer language models have achieved state-of-the-art performance for a variety of natural language tasks but have been shown to encode unwanted biases. We evaluate the social biases encoded by transformers trained with the masked language modeling objective using proposed proxy functions within an iterative masking experiment to measure the quality of

Rupert L. Frank, Bernard Helffer

We are interested in the number of nodal domains of eigenfunctions of sub-Laplacians on sub-Riemannian manifolds. Specifically, we investigate the validity of Pleijel's theorem, which states that, as soon as the dimension is strictly larger than 1, the number of nodal domains of an eigenfunction corresponding to the k-th eigenvalue is strictly (and uniformly

Alberto Fernández-Hernández

Let $(X,S)$ be an isolated complete intersection singularity of dimension $n$, and let $f:(X,S)\rightarrow (\mathbb{C}^{n+1},0)$ be a germ of $\mathscr{A}$-finite mapping. In this master's degree final project, our main contribution is that we show the case $n=2$ of the general Mond conjecture, which states that $\mu_I(X,f)\geq \text{codim}_{\mathscr{A}_e}(X

Sreejata Kishor Bhattacharya

In an attempt to show that the acceptance probability of a quantum query algorithm making $q$ queries can be well-approximated almost everywhere by a classical decision tree of depth $\leq \text{poly}(q)$, Aaronson and Ambainis proposed the following conjecture: let $f: \{ \pm 1\}^n \rightarrow [0,1]$ be a degree $d$ polynomial with variance $\geq \epsilon$.

Júnior D. Toniato, Martín G. Richarte

We performed a post-Newtonian analysis of the regularized four-dimensional Einstein-Gauss-Bonnet gravitational theory (4D-EGB). The resulting metric differs from the classical parametrized post-Newtonian (PPN) formalism in that a new gravitational potential arises from the integration of the approximate field equations. We also investigated the conserved qua

J. S. Dowker

Making use of an 1847 result of Newman, a (known) closed formula for a log-sine integral is rapidly obtained in terms of Riemann Zeta and Clausen functions.

Debjit Paul, Robert West, Antoine Bosselut, Boi Faltings

Large language models (LLMs) have been shown to perform better when asked to reason step-by-step before answering a question. However, it is unclear to what degree the model's final answer is faithful to the stated reasoning steps. In this paper, we perform a causal mediation analysis on twelve LLMs to examine how intermediate reasoning steps generated by th

Jhon P. F. Charaja, Isabell Wochner, Pierre Schumacher, Winfried Ilg

The mimicking of human-like arm movement characteristics involves the consideration of three factors during control policy synthesis: (a) chosen task requirements, (b) inclusion of noise during movement execution and (c) chosen optimality principles. Previous studies showed that when considering these factors (a-c) individually, it is possible to synthesize

Peeter Saari, Ioannis M. Besieris

Hall and Abouraddy [1] have reported first experimental observation of optical de Broglie-Mackinnon wave packets, which is a seminal achievement in the study of so-called non-diffracting optical pulses. These wave packets propagate in free space without spreading with subluminal relativistic velocities, i.e., with speeds slower but close to the velocity of l

Gastón De Boni Rovella, Meryem Benammar

In this work, we investigate the problem of neural-based error correction decoding, and more specifically, the new so-called syndrome-based decoding technique introduced to tackle scalability in the training phase for larger code sizes. We improve on previous works in terms of allowing full decoding of the message rather than codewords, allowing thus the app

Anastasia Nikitina, Kristina Frizyuk

In this study, we investigate the nonlinear optical phenomena emerging from the interaction of vortex and vector beams with achiral nanoparticles or nanostructures. We reveal the conditions under which linear or nonlinear dichroism can be observed. Despite the achiral symmetry of the nanostructure, the interplay between the symmetries of the vortex beam, the

Phuc Phan, Hieu Tran, Long Phan

We propose a straightforward approach called Distillation Contrastive Decoding (DCD) to enhance the reasoning capabilities of Large Language Models (LLMs) during inference. In contrast to previous approaches that relied on smaller amateur models or analysis of hidden state differences, DCD employs Contrastive Chain-of-thought Prompting and advanced distillat

Vasudev Gohil, Satwik Patnaik, Dileep Kalathil, Jeyavijayan Rajendran

Machine learning has shown great promise in addressing several critical hardware security problems. In particular, researchers have developed novel graph neural network (GNN)-based techniques for detecting intellectual property (IP) piracy, detecting hardware Trojans (HTs), and reverse engineering circuits, to name a few. These techniques have demonstrated o

Farhad Pourkamali-Anaraki, Jamal F. Husseini, Scott E. Stapleton

This paper investigates the use of probabilistic neural networks (PNNs) to model aleatoric uncertainty, which refers to the inherent variability in the input-output relationships of a system, often characterized by unequal variance or heteroscedasticity. Unlike traditional neural networks that produce deterministic outputs, PNNs generate probability distribu

Nathalie Aubrun, Nicolás Bitar

We study dynamical and computational properties of the set of bi-infinite self-avoiding walks on Cayley graphs, as well as ways to compute, approximate and bound their connective constant. To do this, we introduce the skeleton $X_{G,S}$ of a finitely generated group $G$ relative to a generating set $S$, which is a one-dimensional subshift made of configurati

Bradley Emi, Max Spero

We present Pangram Text, a transformer-based neural network trained to distinguish text written by large language models from text written by humans. Pangram Text outperforms zero-shot methods such as DetectGPT as well as leading commercial AI detection tools with over 38 times lower error rates on a comprehensive benchmark comprised of 10 text domains (stud

Johannes Beierlein, Oleg A. Egorov, Philipp Gagel, Tristan H. Harder

One-dimensional models with topological non-trivial band structures are a simple and effective way to study novel and exciting concepts in topological photonics. In this work we are studying the propagation of light-matter quasi-particles, so called exciton-polaritons, in waveguide arrays. Specifically, we are investigating topological states at the interfac

Igor I. Smolyaninov

Geometry and gravity induced effective photon mass is known to arise in many cases, such as various optical waveguides, Kaluza-Klein theories, and many other optical and general relativity situations. Here we study the appearance of effective photon mass in the Newtonian limit due to the presence of gravity gradient in a locally inertial reference frame. The

Norman M. Cao, Di Qi

This work introduces the "potential vorticity bucket brigade," a mechanism for explaining the resilience of vortex structures in magnetically confined fusion plasmas and geophysical flows. Drawing parallels with zonal jet formation, we show how inhomogeneous patterns of mixing can reinforce, rather than destroy non-zonal flow structure. We accomplish this th

Alberto Fernández-Hernández

This final degree project is devoted to study the topological classification of complex plane curves. These are subsets of $\mathbb{C}^2$ that can be described by an equation $f(x,y)=0$. Loosely speaking, curves are said to be equivalent in a topological sense whenever they are ambient homeomorphic, i.e., there exists an orientation-preserving homeomorphism

Mohsen Khalili, Tim Vogel, Yicheng Wang, Samira Mansourzadeh

Emitters based on photoconductive materials excited by ultrafast lasers are well established and popular devices for THz generation. However, so far, these emitters, both photoconductive antennas and large area emitters, were mostly explored using driving lasers with moderate average powers (either fiber lasers with up to hundreds of milliwatts or Ti:Sapphir

Dian Lei, Yao He, Jianyou Zeng

With the widespread application of artificial intelligence(AI), the explainable AI (XAI) field has undergone a notable resurgence. In this background, the importance of user experience in XAI has become increasingly prominent. Simultaneously, the user interface (UI) serves as a crucial link between XAI and users. However, despite the existence of UI design p

Júlia Baligács, Yann Disser, Andreas Emil Feldmann, Anna Zych-Pawlewicz

In the Tricolored Euclidean Traveling Salesperson problem, we are given~$k=3$ sets of points in the plane and are looking for disjoint tours, each covering one of the sets. Arora (1998) famously gave a PTAS based on ``patching'' for the case $k=1$ and, recently, Dross et al.~(2023) generalized this result to~$k=2$. Our contribution is a $(5/3+\epsilon)$-appr

Christopher Hojny, Shiqiang Zhang, Juan S. Campos, Ruth Misener

Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when we can trust them. We develop a computationally effective approach towards providing robust certificates for message-passing neural networks (MPNNs) using a Rectified Linear Unit (ReLU) activation function. Because our work builds on mixed-integer optimization, it encodes

Antoine Chaffin, Ewa Kijak, Vincent Claveau

Training image captioning models using teacher forcing results in very generic samples, whereas more distinctive captions can be very useful in retrieval applications or to produce alternative texts describing images for accessibility. Reinforcement Learning (RL) allows to use cross-modal retrieval similarity score between the generated caption and the input

A. S. Kravchenko

We consider the space $M(X)$ of separable measures on the Borel $\sigma$-algebra ${\cal B}(X)$ of a metric space $X$. The space $M(X)$ is furnished with the Kantorovich-Rubinshte\u{i}n metric known also as the ``Hutchinson distance''. We prove that $M(X)$ is complete if and only if $X$ is complete. We consider applications of this theorem in the theory of se

Kai Yang, Jan Ackermann, Zhenyu He, Guhao Feng

As transformer-based language models are trained on increasingly large datasets and with vast numbers of parameters, finding more efficient alternatives to the standard Transformer has become very valuable. While many efficient Transformers and Transformer alternatives have been proposed, none provide theoretical guarantees that they are a suitable replaceme

Asmita Roy, Xianyang Zhang

In genome-wide epigenetic studies, exposures (e.g., Single Nucleotide Polymorphisms) affect outcomes (e.g., gene expression) through intermediate variables such as DNA methylation. Mediation analysis offers a way to study these intermediate variables and identify the presence or absence of causal mediation effects. Testing for mediation effects lead to a com

Huaqian Wu, Clara Brémond-Martin, Kévin Bouaou, Cédric Clouchoux

Tumor segmentation stands as a pivotal task in cancer diagnosis. Given the immense dimensions of whole slide images (WSI) in histology, deep learning approaches for WSI classification mainly operate at patch-wise or superpixel-wise level. However, these solutions often struggle to capture global WSI information and cannot directly generate the binary mask. D

Daria Kubyshkina

Atmospheric mass loss is a fundamental phenomenon shaping the structure and evolution of planetary atmospheres. It can engage processes ranging from global interactions with the host star and large-scale hydrodynamic outflows to essentially microphysical kinetic effects. The relevance of these processes is expected to change between planets of different prop

Paul Daoudi, Bogdan Robu, Christophe Prieur, Ludovic Dos Santos

This paper addresses the problem of integrating local guide policies into a Reinforcement Learning agent. For this, we show how to adapt existing algorithms to this setting before introducing a novel algorithm based on a noisy policy-switching procedure. This approach builds on a proper Approximate Policy Evaluation (APE) scheme to provide a perturbation tha

Shanchuan Lin, Anran Wang, Xiao Yang

We propose a diffusion distillation method that achieves new state-of-the-art in one-step/few-step 1024px text-to-image generation based on SDXL. Our method combines progressive and adversarial distillation to achieve a balance between quality and mode coverage. In this paper, we discuss the theoretical analysis, discriminator design, model formulation, and

Raaja Ganapathy Subramanian, Barry Moest, Bart Paarhuis

This paper presents a partial state-feedback reduced-order switching predictive model designed to support the next-generation lithography roadmap. The proposed approach addresses the trade-off between increasing the number of measurements to improve overlay accuracy and the resulting challenges, including higher measurement noise, reduced throughput and over

Yun-Shiuan Chuang, Jerry Zhu, Timothy T. Rogers

Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground truth outcome. We consider how people can learn which opinions to trust in such scenarios by

Federico Bianchi, James Zou

The risks derived from large language models (LLMs) generating deceptive and damaging content have been the subject of considerable research, but even safe generations can lead to problematic downstream impacts. In our study, we shift the focus to how even safe text coming from LLMs can be easily turned into potentially dangerous content through Bait-and-Swi

S. Lucarini, E. Martínez-Pañeda

We present a wrapper that allows Abaqus user material subroutines (UMATs) to be used as an External Material library in the software COMSOL Multiphysics. The wrapper, written in C language, transforms COMSOL's external material subroutine inputs and outputs into Fortran-coded Abaqus UMAT inputs and outputs, by means of a consistent variable transformation. T

Md. Asraful Haque, Shuai Li

Artificial intelligence has been around for a while, but suddenly it has received more attention than ever before. Thanks to innovations from companies like Google, Microsoft, Meta, and other major brands in technology. OpenAI, though, has triggered the button with its ground-breaking invention ChatGPT. ChatGPT is a Large Language Model (LLM) based on Transf

Nadir Ijaz, Mansoor Ur Rehman

This study investigates the realization of R-symmetric Higgs inflation within the framework of no-scale-like supergravity, aiming to elucidate the formation of primordial black holes and observable gravitational waves within a class of GUT models. We explore the possibility of an ultra-slow-roll phase in a hybrid inflation framework, where the GUT Higgs fiel

Justin Dallant

We show that for large enough $n$, the number of non-isomorphic pseudoline arrangements of order $n$ is greater than $2^{c\cdot n^2}$ for some constant $c > 0.2604$, improving the previous best bound of $c>0.2083$ by Dumitrescu and Mandal (2020). Arrangements of pseudolines (and in particular arrangements of lines) are important objects appearing in many for

Avinash Chetry, Arup Nandy

Electromagnetic forming and perforations (EMFP) are complex and innovative high strain rate processes that involve electromagnetic-mechanical interactions for simultaneous metal forming and perforations. Instead of spending costly resources on repetitive experimental work, a properly designed numerical model can be effectively used for detailed analysis and

Sidhanth Mohanty, Prasad Raghavendra, David X. Wu

We study the problem of $\textit{robust community recovery}$: efficiently recovering communities in sparse stochastic block models in the presence of adversarial corruptions. In the absence of adversarial corruptions, there are efficient algorithms when the $\textit{signal-to-noise ratio}$ exceeds the $\textit{Kesten--Stigum (KS) threshold}$, widely believed

Saumya Talera, Parth Bansal, Shabnam Khan, Shahbaz Khan

Genome assembly is a prominent problem studied in bioinformatics, which computes the source string using a set of its overlapping substrings. Classically, genome assembly uses assembly graphs built using this set of substrings to compute the source string efficiently, having a tradeoff between scalability and avoiding information loss. The scalable de Bruijn

André Hottung, Mridul Mahajan, Kevin Tierney

Reinforcement learning-based methods for constructing solutions to combinatorial optimization problems are rapidly approaching the performance of human-designed algorithms. To further narrow the gap, learning-based approaches must efficiently explore the solution space during the search process. Recent approaches artificially increase exploration by enforcin

Prakamya Mishra, Zonghai Yao, Parth Vashisht, Feiyun Ouyang

Large Language Models (LLMs) such as GPT & Llama have demonstrated significant achievements in summarization tasks but struggle with factual inaccuracies, a critical issue in clinical NLP applications where errors could lead to serious consequences. To counter the high costs and limited availability of expert-annotated data for factual alignment, this study

Loddo Fabio, Dario Piga, Michelucci Umberto, El Ghazouali Safouane

Satellites equipped with optical sensors capture high-resolution imagery, providing valuable insights into various environmental phenomena. In recent years, there has been a surge of research focused on addressing some challenges in remote sensing, ranging from water detection in diverse landscapes to the segmentation of mountainous and terrains. Ongoing inv

Ryandito Diandaru, Lucky Susanto, Zilu Tang, Ayu Purwarianti

Large Language Models (LLMs) demonstrate strong machine translation capabilities on languages they are trained on. However, the impact of factors beyond training data size on translation performance remains a topic of debate, especially concerning languages not directly encountered during training. Our study delves into Llama2's translation capabilities. By

Stefan Jonas, Kevin Winter, Bernhard Brodbeck, Angela Meyer

Wind energy plays a critical role in the transition towards renewable energy sources. However, the uncertainty and variability of wind can impede its full potential and the necessary growth of wind power capacity. To mitigate these challenges, wind power forecasting methods are employed for applications in power management, energy trading, or maintenance sch

Jianzhuang Zhao, Francesco Tassi, Yanlong Huang, Elena De Momi

Humans' ability to smoothly switch between locomotion and manipulation is a remarkable feature of sensorimotor coordination. Leaning and replication of such human-like strategies can lead to the development of more sophisticated robots capable of performing complex whole-body tasks in real-world environments. To this end, this paper proposes a combined learn

Przemyslaw Biecek, Wojciech Samek

Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incompatible goals. We separate various threads tangled within the area of XAI into two complementary cultures of human/value-oriented explanations (BLUE XAI) and model/validation-orien

Samer J. El-Abd, Crystal L. Brogan, Todd R. Hunter, Kin Long Kelvin Lee

Much of the information gleaned from observations of star-forming regions comes from the analysis of their molecular emission spectra, particularly in the radio regime. The time-consuming nature of fitting synthetic spectra to observations interactively for such line-rich sources, however, often results in such analysis being limited to data extracted from a

Nisha Grewal, Joe Zuntz, Tilman Tröster

Using higher-order statistics to capture cosmological information from weak lensing surveys often requires a transformation of observed shear to a measurement of the convergence signal. This inverse problem is complicated by noise and boundary effects, and various reconstruction methods have been developed to implement the process. Here we evaluate the reten

Mostafa Esmaeilzadeh, Melika Amirzadeh

Current hydrological modeling methods combine data-driven Machine Learning (ML) algorithms and traditional physics-based models to address their respective limitations incorrect parameter estimates from rigid physics-based models and the neglect of physical process constraints by ML algorithms. Despite the accuracy of ML in outcome prediction, the integratio

Izabela Babiarz, Roman Pasechnik, Wolfgang Schäfer, Antoni Szczurek

We continue our work on the light-front formulation of quarkonium $\gamma^* \gamma$ transition form factors, extending the formalism to $J^{PC} = 2^{++}$ tensor meson states. We present an analysis of $\gamma^* \gamma \to \chi_{c2}$ transition amplitude and the pertinent helicity form factors. Our relativistic formalism is based on the light-front quark-anti

Sriram Sundaram, C. P. Burgess, D. H. J. O'Dell

In this paper we show how the quantum mechanics of the inverted harmonic oscillator can be mapped to the quantum mechanics of a particle in a super-critical inverse square potential. We demonstrate this by relating both of these systems to the Berry-Keating system with hamiltonian $H=(xp+px)/2$. It has long been appreciated that the quantum mechanics of the

Valeriy A. Buryachenko

A static peridynamic (proposed by Silling, see J. Mech. Phys. Solids 2000; 48:175--209) composite materials (CMs) of the random and periodic structures are considered. In the framework of the second background of micromechanics (also called computational analytical micromechanics, CAM), one proved that local micromechanics (LM) and peridynamic micromechanics

Pratim Guha Niyogi, Ping-Shou Zhong

We address the challenge of estimation in the context of constant linear effect models with dense functional responses. In this framework, the conditional expectation of the response curve is represented by a linear combination of functional covariates with constant regression parameters. In this paper, we present an alternative solution by employing the qua

Willy Stark, Hans-Joachim Grafe, Martin Tajmar

Interstellar travel requires propulsion systems beyond present possibilities and scientists search for new technologies and breakthrough concepts in physics. Frederick E. Alzofon came up with an alternative idea on the origin of the gravitational field. He claimed that the gravitational force arises from the interaction of subatomic particles and that the gr

Alexander Leitsch, Anela Lolic

An inductive proof can be represented as a proof schema, i.e. as a parameterized sequence of proofs defined in a primitive recursive way. A corresponding cut-elimination method, called schematic CERES, can be used to analyze these proofs, and to extract their (schematic) Herbrand sequents, even though Herbrand's theorem in general does not hold for proofs wi

Qing Lyu, Kumar Shridhar, Chaitanya Malaviya, Li Zhang

Accurately gauging the confidence level of Large Language Models' (LLMs) predictions is pivotal for their reliable application. However, LLMs are often uncalibrated inherently and elude conventional calibration techniques due to their proprietary nature and massive scale. In this work, we explore the potential of deriving confidence from the distribution of

Gergely Neu, Nneka Okolo

We study the performance of stochastic first-order methods for finding saddle points of convex-concave functions. A notorious challenge faced by such methods is that the gradients can grow arbitrarily large during optimization, which may result in instability and divergence. In this paper, we propose a simple and effective regularization technique that stabi

Minoru Hirose

In this paper, we give a formula that connects two variants of multiple zeta values; multitangent functions and symmetric multiple zeta values. As an application of this formula, we give two results. First, we prove Bouillot's conjecture on the structures of the algebra of multitangent functions. Second, we prove an analogue of the linear part of Kawashima's

Yuchen Liang, Peizhong Ju, Yingbin Liang, Ness Shroff

Accelerated diffusion models hold the potential to significantly enhance the efficiency of standard diffusion processes. Theoretically, these models have been shown to achieve faster convergence rates than the standard $\mathcal O(1/\epsilon^2)$ rate of vanilla diffusion models, where $\epsilon$ denotes the target accuracy. However, current theoretical studi

Jordan L. Hobbs, Calum J. Gibb, Ewan Cruickshank, Rebecca Walker

We present an approach for rapid conformational analysis of semi-flexible liquid crystals. We use a simple graphical user interface (GUI) tool that leverages rules-based methods for efficient generation of bend-angle distributions, offering a significant improvement over traditional single-conformer analysis. Our methods demonstrated proficiency in approxima

David Prieto, Joan Quirant, Pramod Shukla

In this paper, we perform a systematic and analytical exploration of de Sitter conditions in type IIA compactifications with (non-)geometric fluxes along with the standard NS-NS and RR $p$-form fluxes. Exploiting the fact that the F-term scalar potential can be written as a bilinear form, we start by studying the most generic case. We find four conditions th

Harpreet Singh, Noella D'Souza, Keyuan Zhong, Emanuel Druga

Quantum sensors have notably advanced high-sensitivity magnetic field detection. Here, we report quantum sensors constructed from polarized spin-triplet electrons in photoexcited organic chromophores, specifically focusing on pentacene-doped para-terphenyl (${\approx}$0.1%). We demonstrate essential quantum sensing properties at room temperature: electronic

Loïc Rakotoson, Sylvain Massip, Fréjus A. A. Laleye

Information retrieval is a rapidly evolving field. However it still faces significant limitations in the scientific and industrial vast amounts of information, such as semantic divergence and vocabulary gaps in sparse retrieval, low precision and lack of interpretability in semantic search, or hallucination and outdated information in generative models. In t

Federico Miotello, Paolo Ostan, Mirco Pezzoli, Luca Comanducci

In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to model a remote attendance teleconferencing scenario. Specifically, measurements were performed in a seminar room, where a 64-microphone ULA was used as a multichannel audio acquisit

Milos Prokop, Petros Wallden, David Joseph

Finding the shortest vector in a lattice is a problem that is believed to be hard both for classical and quantum computers. Many major post-quantum secure cryptosystems base their security on the hardness of the Shortest Vector Problem (SVP). Finding the best classical, quantum or hybrid classical-quantum algorithms for SVP is necessary to select cryptosyste

Carmine De Rosa, Valter Moretti

We introduce and study a general notion of spatial localization on spacelike smooth Cauchy surfaces of quantum systems in Minkowski spacetime. The notion is constructed in terms of a coherent family of normalized POVMs, one for each said Cauchy surface. We prove that a family of POVMs of this type automatically satisfies a causality condition which generaliz

Valdemar V. Tsanov

We study properties of convex hulls of (co)adjoint orbits of compact groups, with applications to invariant theory and tensor product decompositions. The notion of partial convex hulls is introduced and applied to define two numerical invariants of a coadjoint orbit of a semisimple connected compact Lie group. It is shown that the orbits, where any one of th

Willem Gispen, Jorge R. Espinosa, Eduardo Sanz, Carlos Vega

In this work, we introduce Variational Umbrella Seeding, a novel technique for computing nucleation barriers. This new method, a refinement of the original seeding approach, is far less sensitive to the choice of order parameter for measuring the size of a nucleus. Consequently, it surpasses seeding in accuracy, and Umbrella Sampling in computational speed.

Lukas Gruber, Markus Holzleitner, Johannes Lehner, Sepp Hochreiter

Estimating the ratio of two probability densities from finitely many samples, is a central task in machine learning and statistics. In this work, we show that a large class of kernel methods for density ratio estimation suffers from error saturation, which prevents algorithms from achieving fast error convergence rates on highly regular learning problems. To

Abhisek Chakraborty, Megan H. Murray, Ilya Lipkovich, Yu Du

The American Statistical Association (ASA) statement on statistical significance and P-values \cite{wasserstein2016asa} cautioned statisticians against making scientific decisions solely on the basis of traditional P-values. The statement delineated key issues with P-values, including a lack of transparency, an inability to quantify evidence in support of th

Saebyeok Jeong, Norton Lee

We study two types of surface observables $-$ the $\mathbf{Q}$-observables and the $\mathbf{H}$-observables $-$ of the 4d $\mathcal{N}=2$ $A_1$-quiver $U(N)$ gauge theory obtained by coupling a 2d $\mathcal{N}=(2,2)$ gauged linear sigma model. We demonstrate that the transition between the two surface defects manifests as a Fourier transformation between the

Saebyeok Jeong, Norton Lee, Nikita Nekrasov

We explore the $\textit{difference Langlands correspondence}$ using the four dimensional ${\mathcal{N}}=2$ super-QCD. Surface defects and surface observables play the crucial role. As an application, we give the first construction of the full set of quantum integrals, i.e. commuting differential operators, such that the partition function of the so-called re

Chenyang Lyu, Minghao Wu, Alham Fikri Aji

Large Language Models (LLMs) have demonstrated remarkable capabilities across various applications, fundamentally reshaping the landscape of natural language processing (NLP) research. However, recent evaluation frameworks often rely on the output probabilities of LLMs for predictions, primarily due to computational constraints, diverging from real-world LLM

Can Hong, Danhong Wu, Xi-Bo Li, Feipeng Zheng

Interlayer coupling can significantly influence the physical properties of layered transition metal compounds. The superconductivity in layered Mo$_2$C systems, belonging to the emergent family of MXene, has garnered considerable attention. However, the impact of interlayer coupling on superconductivity, and the anisotropic superconducting properties in thes

James A. G. Jackman, Evgenya L. Shkolnik, R. O. Parke Loyd, Tyler Richey-Yowell

We present the results of a multi-wavelength Pro-Am campaign to study the behaviour of flares from the active M1.5V star binary CR Draconis. CR Dra was observed with TESS 20-s photometry, Swift near-UV (NUV) grism spectroscopy and with ground-based optical photometry and spectroscopy from a global collaboration of amateur astronomers. We detected 14 flares w

Takafumi Niida

In non-central heavy-ion collisions, a large orbital angular momentum of the colliding system is produced, which is then partially transferred to the created medium, resulting in the particle polarization on average along the initial angular momentum, known as global polarization. It was predicted almost 20 years ago and the first observation of $\Lambda$ gl

Alessandro Daniele, Tommaso Campari, Sagar Malhotra, Luciano Serafini

Deep Learning (DL) techniques have achieved remarkable successes in recent years. However, their ability to generalize and execute reasoning tasks remains a challenge. A potential solution to this issue is Neuro-Symbolic Integration (NeSy), where neural approaches are combined with symbolic reasoning. Most of these methods exploit a neural network to map per

Peng-Ming Zhang, Peter Horvathy

The concept of pseudoclassical isospin is illustrated by the non-Abelian Aharonov-Bohm effect proposed by Wu and Yang in 1975. The spatial motion is free however the isospin precesses when the enclosed magnetic flux and the incoming particle's isosopin are not parallel. The non-Abelian phase factor $\mathfrak{F}$ of Wu and Yang acts on the isospin as an S-ma

Yacin Ameur, Erik Troedsson

In this note we prove equicontinuity for the family of one-point densities with respect to a two-dimensional Coulomb gas at an inverse temperature $\beta\ge 1/2$ confined by an external potential of Hele-Shaw (or quasi-harmonic) type. As a consequence, subsequential limiting Lipschitz continuous densities are defined on the microscopic scale. There are sever

Dmitry Levko, Laxminarayan L. Raja

In recent years, interest in striation phenomena in radio-frequency (rf) discharges has risen due to the availability of new experimental data and implementation of new computational models. Depending on the conditions, different mechanisms of discharge striations are realized. These are the ionization instability, the instability due to the electron attachm

Boyu Gao, Natalie Klco

The reduction and distortion of quantum correlations in the presence of classical noise leads to varied levels of inefficiency in the availability of entanglement as a resource for quantum information processing protocols. While generically minimizing required entanglement for mixed quantum states remains challenging, a class of many-body Gaussian quantum st

Patrick Zellekens, Russell S. Deacon, Farah Basaric, Raghavendra Juluri

Phase winding effects in hollow semiconductor nanowires with superconducting shells have been proposed as a route to engineer topological superconducting states. We investigate GaAs/InAs core/shell nanowires with half-shells of epitaxial aluminium as a potential platform for such devices, where the thin InAs shell confines the electron wave function around t

I. L. Buchbinder, S. A. Fedoruk, A. P. Isaev, V. A. Krykhtin

We present a new particle model that generalize for constant curvature space an infinite spin particle in flat space. The model is described by commuting Weyl spinor additional coordinates. It proved that such a model is consistent only in external gravitational field corresponding to the constant curvature spaces. Full set of the first-class constraints in

George M. Fuller, Lukáš Gráf, Amol V. Patwardhan, Jacob Spisak

We present a mechanism for producing a cosmologically-significant relic density of one or more sterile neutrinos. This scheme invokes two steps: First, a population of "heavy" sterile neutrinos is created by scattering-induced decoherence of active neutrinos; Second, this population is transferred, via sterile neutrino self-interaction-mediated scatterings a

Antoine Marie, Pierre-François Loos

Upon ionization of an atom or a molecule, another electron (or more) can be simultaneously excited. These concurrently generated states are called "satellites" (or shake-up transitions) as they appear in ionization spectra as higher-energy peaks with weaker intensity and larger width than the main peaks associated with single-particle ionizations. Satellites

Zhengxue Wang, Zhiqiang Yan, Ming-Hsuan Yang, Jinshan Pan

Multi-modal fusion serves as a cornerstone for successful depth map super-resolution. However, commonly used fusion strategies, such as addition and concatenation, fall short of effectively bridging the modal gap. As a result, guided image filtering methods have been introduced to mitigate this issue. Nevertheless, it is observed that their filter kernels us