November 2024 arXiv papers — page 162

Murat Gunaydin

Minimal unitary representation of $SO(d,2)$ and its deformations describe all the conformally massless fields in $d$ dimensional Minkowskian spacetimes. In critical dimensions these spacetimes admit extensions with twistorial coordinates plus a dilatonic coordinate to causal spacetimes coordinatized by Jordan algebras $J_3^{A}$ of degree three over the four

Matthias Hübler, Denis M. Basko, Wolfgang Belzig

We formulate the problem of near-field radiative heat transfer as an effective quantum scattering theory for excitations of the matter. Built from the same ingredients as the semiclassical fluctuational electrodynamics, the standard tool to handle this problem, our construction makes manifest its relation to the Landauer-B\"uttiker scattering framework, whic

Xiechao Hu, Chengxi Hu, Tieyan Guo, Zhi Yao

The Si based TiO2 thin films were prepared via the combination both of Sol-Gel and Spin-Coating method. The films were sintered at 850 degrees Celsius for half an hour, and the resulting films were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) for their phase composition and microstructure. It was found that the films contai

Lillian I. Payne Torres, Anna O. Schouten, LeeAnn M. Sager-Smith, David A. Mazziotti

Exciton condensation, the Bose-Einstein-like condensation of quasibosonic particle-hole pairs, has been the subject of much theoretical and experimental interest and holds promise for ultra-energy-efficient technologies. Recent advances in bilayer systems, such as transition metal dichalcogenide heterostructures, have brought us closer to the experimental re

Utkarsh Anand, Diya Parekh, Thakur Pranav G. Singh, Hrishikesh S. Yadav

The use of parallel manipulators in aerospace engineering has gained significant attention due to their ability to provide improved stability and precision. This paper presents the design, control, and analysis of 'STEWIE', which is a three-degree-of-freedom (DoF) parallel manipulator robot developed by members of the thrustMIT rocketry team, as a payload st

Sergey K. Tolpygo, Evan B. Golden, Christopher L. Ayala, Lieze Schindler

Adiabatic quantum-flux-parametron (AQFP) logic is a proven energy-efficient superconductor technology for various applications. To address the scalability challenges, we investigated AQFP shift registers with the AQFP footprint area reduced by 25% with respect to prior work and with more than 2x denser overall designs obtained by eliminating the previously u

Davide Rusca, Nicolas Gisin

This chapter highlights the transformation of secure communications through the incorporation of quantum mechanics. Over the past four decades, this groundbreaking theory has quietly revolutionized private communication. The chapter provides a concise historical overview of this field's inception, tracking the development of its pioneering protocol, BB84. It

Luca Griguolo, Luigi Guerrini, Alessandro Testa

We study $\frac{1}{4}$-BPS Wilson loops in four-dimensional SU$(N$) ${\mathcal{N}}=2$ super-Yang-Mills theories with conformal matter in an arbitrary representation $\mathcal{R}$. These operators are formed of two meridians on the two-sphere separated by an arbitrary opening angle. We conjecture that these observables are encoded in a modification of Pestun'

Kiminad A. Mamo, Ismail Zahed

We introduce a string-based parametrization for nucleon quark and gluon generalized parton distributions (GPDs) that is valid for all skewness. Our approach leverages conformal moments, representing them as the sum of spin-j nucleon A-form factor and skewness-dependent spin-j nucleon D-form factor, derived from t-channel string exchange in AdS spaces consist

Liese Bekkers, Frank Neven, Stijn Vansummeren, Yisu Remy Wang

Acyclic join queries can be evaluated instance-optimally using Yannakakis' algorithm, which avoids needlessly large intermediate results through semi-join passes. Recent work proposes to address the significant hidden constant factors arising from a naive implementation of Yannakakis by decomposing the hash join operator into two suboperators, called Lookup

Nicola F. Zaugg, Leonardo Perotti, Lech A. Grzelak

It is a market practice to express market-implied volatilities in some parametric form. The most popular parametrizations are based on or inspired by an underlying stochastic model, like the Heston model (SVI method) or the SABR model (SABR parametrization). Their popularity is often driven by a closed-form representation enabling efficient calibration. Howe

Avia Raviv-Moshe, Siwei Zhong

We study point impurities in non-relativistic quantum field theories, with a focus on scale-invariant fixed points. We establish the framework of conformal defects in Schr\"{o}dinger field theories and their correspondence to many-body states in a harmonic trap. We discuss a multi-critical quantization of the one-body wave function induced by an impurity, wi

Pablo Perrella, Sebastián Velazquez

Given a foliation $\mathcal{F}$ on $X$ and an embedding $X\subseteq Y$, is there a foliation on $Y$ extending $\mathcal{F}$? Using formal methods, we show that this question has an affirmative answer whenever the embedding is sufficiently positive with respect to $(X,\mathcal{F})$ and the singularities of $\mathcal{F}$ belong to a certain class. These tools

Andreas Knauf, Manuel Quaschner

We consider systems of n particles that move with constant velocity between collisions. Their total momentum but not necessarily their kinetic energy is preserved at collisions. As there are no further constraints, these systems are nondeterministic. In particular we examine trajectories with infinitely many collisions.

Lorenzo Cima, Benedetta Tessa, Stefano Cresci, Amaury Trujillo

In today's online environments, users encounter harm and abuse on a daily basis. Therefore, content moderation is crucial to ensure their safety and well-being. However, the effectiveness of many moderation interventions is still uncertain. Here, we apply a causal inference approach to shed light on the effectiveness of The Great Ban, a massive social media

Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Ramchalam Kinattinkara Ramakrishnan

Continuously adapting pre-trained models to local data on resource constrained edge devices is the $\emph{last mile}$ for model deployment. However, as models increase in size and depth, backpropagation requires a large amount of memory, which becomes prohibitive for edge devices. In addition, most existing low power neural processing engines (e.g., NPUs, DS

Kun Fang, Hamza Fawzi, Omar Fawzi

The asymptotic equipartition property (AEP) states that in the limit of a large number of independent and identically distributed (i.i.d.) random experiments, the output sequence is virtually certain to come from the typical set, each member of which is almost equally likely. This property is a form of the law of large numbers and lies at the heart of inform

Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle

Neural networks are traditionally trained under the assumption that data come from a stationary distribution. However, settings which violate this assumption are becoming more popular; examples include supervised learning under distributional shifts, reinforcement learning, continual learning and non-stationary contextual bandits. In this work we introduce a

Serge N. Gavrilov, Anton M. Krivtsov, Ekaterina V. Shishkina

The Schr\"odinger equation is not frequently used in the framework of the classical mechanics, though historically this equation was derived as a simplified equation, which is equivalent to the classical Germain-Lagrange dynamic plate equation. The question concerning the exact meaning of this equivalence is still discussed in modern literature. In this note

Ekaterina Artemova, Jason Lucas, Saranya Venkatraman, Jooyoung Lee

The rapid proliferation of large language models (LLMs) has increased the volume of machine-generated texts (MGTs) and blurred text authorship in various domains. However, most existing MGT benchmarks include single-author texts (human-written and machine-generated). This conventional design fails to capture more practical multi-author scenarios, where the u

Mansi Sakarvadia

Answering multi-hop reasoning questions requires retrieving and synthesizing information from diverse sources. Language models (LMs) struggle to perform such reasoning consistently. We propose an approach to pinpoint and rectify multi-hop reasoning failures through targeted memory injections on LM attention heads. First, we analyze the per-layer activations

Ivano Ciardelli

We explore an inquisitive modal logic designed to reason about neighborhood models. This logic is based on an inquisitive strict conditional operator, which quantifies over neighborhoods, and which can be applied to both statements and questions. In terms of this operator we also define two unary modalities that function respectively as a universal and exist

Tariq Berrada, Pietro Astolfi, Melissa Hall, Marton Havasi

Latent diffusion models (LDMs) power state-of-the-art high-resolution generative image models. LDMs learn the data distribution in the latent space of an autoencoder (AE) and produce images by mapping the generated latents into RGB image space using the AE decoder. While this approach allows for efficient model training and sampling, it induces a disconnect

Christopher Battarbee, Christoph Striecks, Ludovic Perret, Sebastian Ramacher

Authenticated Key Exchange (AKE) between any two entities is one of the most important security protocols available for securing our digital networks and infrastructures. In PQCrypto 2023, Bruckner, Ramacher and Striecks proposed a novel hybrid AKE (HAKE) protocol, dubbed Muckle+, that is particularly useful in large quantum-safe networks consisting of a lar

Enis Belgacem, Francesco Iacovelli, Michele Maggiore, Michele Mancarella

At third-generation (3G) gravitational-wave detector networks, compact binaries coalescences produce a ``confusion noise'' due to unresolved sources and to the error in the reconstruction of resolved sources, that can degrade the sensitivity to cosmological backgrounds. We show how to characterize from first-principles this astrophysical confusion noise by r

Enis Belgacem, Francesco Iacovelli, Michele Maggiore, Michele Mancarella

We provide a detailed derivation of the spectral density of the stochastic background generated by the superposition of coalescing compact binaries. We show how the expression often used in the literature emerges from an average over the extrinsic parameters of the binaries (times of arrival, polarization angles, arrival directions and orbit inclinations) an

Joshua Moore, Aly Sabri Abdalla, Charles Ueltschey, Vuk Marojevic

The Open Radio Access Network (O-RAN) architecture is reshaping the telecommunications landscape by enhancing network flexibility, openness, and intelligence. This paper establishes the requirements, evaluates the design tradeoffs, and introduces a scalable architecture and prototype of an open-source O-RAN experimentation platform within the Aerial Experime

Dibyendu Adak, Duc P. Truong, Radoslav Vuchkov, Saibal De

In this paper, we present a new space-time Petrov-Galerkin-like method. This method utilizes a mixed formulation of Tensor Train (TT) and Quantized Tensor Train (QTT), designed for the spectral element discretization (Q1-SEM) of the time-dependent convection-diffusion-reaction (CDR) equation. We reformulate the assembly process of the spectral element discre

Aniket Deroy, Subhankar Maity

Language Identification (LI) is crucial for various natural language processing tasks, serving as a foundational step in applications such as sentiment analysis, machine translation, and information retrieval. In multilingual societies like India, particularly among the youth engaging on social media, text often exhibits code-mixing, blending local languages

Nikos Piperigkos, Alexandros Gkillas, Christos Anagnostopoulos, Aris S. Lalos

This paper proposes a novel localization framework based on collaborative training or federated learning paradigm, for highly accurate localization of autonomous vehicles. More specifically, we build on the standard approach of KalmanNet, a recurrent neural network aiming to estimate the underlying system uncertainty of traditional Extended Kalman Filtering,

Simone Albanesi

We introduce a novel approach to describe real-valued $m=0$ modes from inspiral to merger and ringdown in effective-one-body models, including both oscillatory and null memory contributions. A crucial aspect of the modelization of the oscillatory part is the complexification of the real modes via a Hilbert transform. This procedure allows for an accurate des

Marcello Porta, Harman Preet Singh

We consider the transport properties of non-interacting, gapless one-dimensional quantum systems and of the edge modes of two-dimensional topological insulators, in the presence of time-dependent perturbations. We prove the validity of Kubo formula, in the zero temperature and infinite volume limit, for a class of perturbations that are weak and slowly varyi

Young Jo Choi, Min Kyoon Yoo, Yu Rang Park

Deep learning models generally display catastrophic forgetting when learning new data continuously. Many incremental learning approaches address this problem by reusing data from previous tasks while learning new tasks. However, the direct access to past data generates privacy and security concerns. To address these issues, we present a novel method that pre

Muhammad Talha Rahim, Saif Al-Kuwari, Asad Ali

Quantum optimal control (QOC) schemes can be employed to enhance the sensitivity of quantum metrology (QM) protocols undergoing Markovian noise, which can limit their precision to a standard quantum limit (SQL)-like scaling. In this paper, we propose a QOC scheme for QM that leverages entanglement and optimized coupling interactions with an ancillary system

Linda Hoyer

For every partition $\lambda$ of a positive integer $n$, let $S^{\lambda}$ be the corresponding Specht module of the symmetric group $\mathfrak{S}_n$, and let $\det(\lambda)\in \mathbb Z$ denote the Gram determinant of the canonical bilinear form with respect to the standard basis of $S^{\lambda}$. Writing $\det(\lambda)=m \cdot 2^{a_{\lambda}^{(2)}}$ for in

Dinithi Jayasuriya, Nastaran Darabi, Maeesha Binte Hashem, Amit Ranjan Trivedi

We introduce a precision polarization scheme for DNN inference that utilizes only very low and very high precision levels, assigning low precision to the majority of network weights and activations while reserving high precision paths for targeted error compensation. This separation allows for distinct optimization of each precision level, thereby reducing m

Subhadip Dey, Hee Oh

Let $G$ be a connected semisimple real algebraic group. We prove that limit cones vary continuously under deformations of Anosov subgroups of $G$ under a certain convexity assumption, which turns out to be necessary. We apply this result to the notion of sharpness for the action of a discrete subgroup on a non-Riemannian homogeneous space. Finally, we show t

Zhenning Liu, Andrew M. Childs, Daniel Gottesman

Quantum symmetrization is the task of transforming a non-strictly increasing list of $n$ integers into an equal superposition of all permutations of the list (or more generally, performing this operation coherently on a superposition of such lists). This task plays a key role in initial state preparation for first-quantized simulations. Motivated by an appli

Behzad Azmi, Marvin Fritz, Sérgio S. Rodrigues

In this work, it is proven the semiglobal exponential stabilization to time-dependent trajectories of the nonisothermal Cahn-Hilliard equations. In the model, the input controls are given by explicit feedback operators that involve appropriate oblique projections. The actuators are given by a finite number of indicator functions. The results also hold for th

Marcio S. Gomes-Filho, Eugene M. Terentjev

Understanding and computing the entropic forces exerted by polymer chains under confinement is important for many reasons, from research to applications. However, extracting properties related to the free energy, such as the force (or pressure) on confining walls, does not readily emerge from conventional polymer dynamics simulations due to the entropic cont

Nina van Tiel, Robin Zbinden, Emanuele Dalsasso, Benjamin Kellenberger

Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of spatial data (environmental rasters, satellite images) as model predictors, allowing the model to consider the spatial cont

Maurício Corrêa, Fernando Lourenço, Diogo Machado

We prove a Baum--Bott type residual formula for one-dimensional holomorphic foliations, and logarithmic along free divisors. More precisely, this provides a Baum--Bott theorem for a foliated triple $(X, \mathcal{F}, D)$, where $\mathcal{F}$ is a foliation by curves and $D$ is a free divisor on a complex manifold $X$. From the local point of view, we show tha

Wenyan Wang, Lele Liu, Yi Wang

The spread of a graph is the difference between the largest and smallest eigenvalue of its adjacency matrix. In this paper, we investigate spread problems for graphs with excluded clique-minors. We show that for sufficiently large $n$, the $n$-vertex $K_r$-minor free graph with maximum spread is the join of a clique and an independent set, with $r-2$ and $n-

Themistoklis Haris

Despite their power, Transformers face challenges with long sequences due to the quadratic complexity of self-attention. To address this limitation, methods like $k$-Nearest-Neighbor ($k$NN) attention have been introduced [Roy, Saffar, Vaswani, Grangier, 2021] enabling each token to attend to only its $k$ closest tokens. While $k$NN attention has shown empir

Nicolas Faroß

We generalize categories of spatial partitions in the sense of C\'ebron-Weber by introducing new base partitions. This allows us to construct additional examples of free orthogonal quantum groups but yields the same class of spatial partition quantum groups as before. Further, we use these new base partitions to show that the class of spatial partition quant

Fabio Pavirani, Jonas Van Gompel, Seyed Soroush Karimi Madahi, Bert Claessens

The growing reliance on renewable energy sources, particularly solar and wind, has introduced challenges due to their uncontrollable production. This complicates maintaining the electrical grid balance, prompting some transmission system operators in Western Europe to implement imbalance tariffs that penalize unsustainable power deviations. These tariffs cre

Dong An, Andrew M. Childs, Lin Lin, Lexing Ying

Eigenvalue transformations, which include solving time-dependent differential equations as a special case, have a wide range of applications in scientific and engineering computation. While quantum algorithms for singular value transformations are well studied, eigenvalue transformations are distinct, especially for non-normal matrices. We propose an efficie

Reiner Brüning, Levente Rózsa, Roberto Lo Conte, André Kubetzka

The generation of topologically non-trivial magnetic configurations has been a pivotal topic in both basic and applied nanomagnetism research. Localized non-coplanar magnetic defects such as skyrmions or merons were found to interact strongly with currents, making them interesting candidates for future spintronics applications. Here, we study a low-symmetry

Bharat Chandra Yalavarthi, Nalini Ratha

In mission-critical domains such as law enforcement and medical diagnosis, the ability to explain and interpret the outputs of deep learning models is crucial for ensuring user trust and supporting informed decision-making. Despite advancements in explainability, existing methods often fall short in providing explanations that mirror the depth and clarity of

Siva Viknesh, Ali Tohidi, Fatemeh Afghah, Rob Stoll

Wildfires propagate through intricate interactions between wind, fuel, and terrain, resulting in complex behaviors that pose challenges for accurate predictions. This study investigates the interaction between wind velocity topology and wildfire spread dynamics, aiming to enhance our understanding of wildfire spread patterns. We revisited the non-dimensional

Wojciech Łapacz, Daniel Marczak, Filip Szatkowski, Tomasz Trzciński

Continual learning (CL) has emerged as a critical area in machine learning, enabling neural networks to learn from evolving data distributions while mitigating catastrophic forgetting. However, recent research has identified the stability gap -- a phenomenon where models initially lose performance on previously learned tasks before partially recovering durin

Davide Buoso, Luke Robinson, Giuseppe Averta, Philip Torr

This study explores the potential of off-the-shelf Vision-Language Models (VLMs) for high-level robot planning in the context of autonomous navigation. Indeed, while most of existing learning-based approaches for path planning require extensive task-specific training/fine-tuning, we demonstrate how such training can be avoided for most practical cases. To do

Yuanpei Chen, Chen Wang, Yaodong Yang, C. Karen Liu

Manipulating objects to achieve desired goal states is a basic but important skill for dexterous manipulation. Human hand motions demonstrate proficient manipulation capability, providing valuable data for training robots with multi-finger hands. Despite this potential, substantial challenges arise due to the embodiment gap between human and robot hands. In

Yuan Bi, Lucie Huang, Ricarda Clarenbach, Reza Ghotbi

Anomaly detection in medical imaging plays a crucial role in identifying pathological regions across various imaging modalities, such as brain MRI, liver CT, and carotid ultrasound (US). However, training fully supervised segmentation models is often hindered by the scarcity of expert annotations and the complexity of diverse anatomical structures. To addres

Steffen van Bergerem, Nicole Schweikardt

In the logical framework introduced by Grohe and Tur\'an (TOCS 2004) for Boolean classification problems, the instances to classify are tuples from a logical structure, and Boolean classifiers are described by parametric models based on logical formulas. This is a specific scenario for supervised passive learning, where classifiers should be learned based on

Marie Analiz April Limpoco, Christel Faes, Niel Hens

Upholding data privacy especially in medical research has become tantamount to facing difficulties in accessing individual-level patient data. Estimating mixed effects binary logistic regression models involving data from multiple data providers like hospitals thus becomes more challenging. Federated learning has emerged as an option to preserve the privacy

Qing-Hua Zhu

The efforts to probe the horizon-scale structure of black holes, such as Event Horizon Telescope and GRAVITY interferometer, might provide valuable insights into the strong-field regime of Einstein's theory of gravity. In the near field region of a black hole, the observational signatures of moving hotspots might potentially reveal the mechanism causing the

Sabir Ahammed, Molla Basir Ahamed

In this paper, we first establish an improved Bohr inequality for the class of operator-valued holomorphic functions $f$ on a simply connected domain $\Omega$ in $\mathbb{C}$. Next, we establish a generalization of refined version of the Bohr inequality and the Bohr-Rogosinski inequality with the help of the sequence $\varphi=\{\varphi_n(r) \}^{\infty}_{n=0}

Ziji Shi, Jialin Li, Yang You

Recent advances in Generative Artificial Intelligence have fueled numerous applications, particularly those involving Generative Adversarial Networks (GANs), which are essential for synthesizing realistic photos and videos. However, efficiently training GANs remains a critical challenge due to their computationally intensive and numerically unstable nature.

Charles Zhang, Benji Peng, Xintian Sun, Qian Niu

Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces. This review visits foundational concepts such as the distributional hypothesis and contextual similarity, tracing the evolution from sparse representations like one-hot encoding to dense

Oleg O. Khamisov, Stepan P. Vasilev

In this paper virtual synchronous generation (VSG) approach is investigated in application to low- and zero-inertia grids operated by grid-forming (GFM) inverters. The key idea here is to introduce dynamic inertia and damping constants in order to keep power gird stable during different types of faults, islanding or large power balance oscillations. In order

Korsarun Nirunwiroj, Dmitri Tseluiko, Karima Khusnutdinova

Internal waves in a two-layer fluid with rotation are considered within the framework of Helfrich's f-plane extension of the Miyata-Maltseva-Choi-Camassa (MMCC) model. Within the scope of this model, we develop an asymptotic procedure which allows us to obtain a description of a large class of uni-directional waves leading to the Ostrovsky equation and allow

Alexandros Gkillas, Aris Lalos

Anomaly and missing data constitute a thorny problem in industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy is achieved with the utilization of large neural networks, increasing their storage and computational cost. Moreover, the data collected in edge

Nikunj Sangwan, Eric Jutzi, Christian Olsen, Sarah Vogel

Holes in planar germanium (Ge) heterostructures show promise for quantum applications, particularly in superconducting and spin qubits, due to strong spin-orbit interaction, low effective mass, and absence of valley degeneracies. However, charge traps cause issues such as gate hysteresis and charge noise. This study examines the effect of surface treatments

Yingjie Jing, Tian-Xiang Mao, Jie Wang, Chao Liu

Binary stars are prevalent yet challenging to detect. We present a novel approach using convolutional neural networks (CNNs) to identify binary stars from low-resolution spectra obtained by the LAMOST survey. The CNN is trained on a dataset that distinguishes binaries from single main sequence stars based on their positions on the Hertzsprung-Russell diagram

Julien Colin, Lore Goetschalckx, Thomas Fel, Victor Boutin

Interpretability research often adopts a neuron-centric lens, treating individual neurons as the fundamental units of explanation. However, neuron-level explanations can be undermined by superposition, where single units respond to mixtures of unrelated patterns. Dictionary learning methods, such as sparse autoencoders and non-negative matrix factorization,

Keiichiro Hijikata, Motonori Oka, Kensuke Okada

This study presents a scalable Bayesian estimation algorithm for sparse estimation in exploratory item factor analysis based on a classical Bayesian estimation method, namely Bayesian joint modal estimation (BJME). BJME estimates the model parameters and factor scores that maximize the complete-data joint posterior density. The algorithm's scalability is ach

Yukun Cao, Zengyi Gao, Zhiyang Li, Xike Xie

GraphRAG integrates (knowledge) graphs with large language models (LLMs) to improve reasoning accuracy and contextual relevance. Despite its promising applications and strong relevance to multiple research communities, such as databases and natural language processing, GraphRAG currently lacks modular workflow analysis, systematic solution frameworks, and in

Mathieu Colin, Tatsuya Watanabe

This paper is devoted to the study of the nonlinear Schr\"odinger-Poisson system with a doping profile. We are interested in the strong instability of standing waves associated with ground state solutions in the $L^2$-supercritical case. The presence of a doping profile causes several difficulties, especially in examining geometric shapes of fibering maps al

Abd Essamade Saufi, Hannah Bellenbaum, Martin Read, Nicolas Niasse

We present the "First Light Advanced Ignition Model" (FLAIM), a reduced model for the implosion, adiabatic compression, volume ignition and thermonuclear burn of a spherical DT fuel capsule utilising a high-Z metal pusher. FLAIM is characterised by a highly modular structure, which makes it an appropriate tool for optimisations, sensitivity analyses and para

Chenrui Tie, Yue Chen, Ruihai Wu, Boxuan Dong

Imitation learning, e.g., diffusion policy, has been proven effective in various robotic manipulation tasks. However, extensive demonstrations are required for policy robustness and generalization. To reduce the demonstration reliance, we leverage spatial symmetry and propose ET-SEED, an efficient trajectory-level SE(3) equivariant diffusion model for genera

Harbir Antil, Sergey Dolgov, Akwum Onwunta

We develop both first and second order numerical optimization methods to solve non-smooth optimization problems featuring a shared sparsity penalty, constrained by differential equations with uncertainty. To alleviate the curse of dimensionality we use tensor product approximations. To handle the non-smoothness of the objective function we employ a smoothed

Dilara Yapışkan, Cristiana J. Silva, Delfim F. M. Torres

The Zika virus, known for its potential to induce neurological conditions such as microcephaly when transmitted vertically from infected mothers to infants, has sparked widespread concerns globally. Motivated by this, we propose an optimal control problem for the prevention of vertical Zika transmission. The novelty of this study lies in its consideration of

Diyang Liu, Yichao Li, Denis Tramonte, Furen Deng

Filaments stand as pivotal structures within the cosmic web. However, direct detection of the cold gas content of the filaments remains challenging due to its inherent low brightness temperature. With the TNG hydrodynamical simulations, we demonstrate the effectiveness of isolating faint filament HI signal from the FAST HI intensity mapping (IM) survey throu

Luca Ciotti

It has been proposed that the flat rotation curves observed at large radii in disk galaxies can be interpreted as an effect of General Relativity (GR) instead of the presence of dark matter (DM) halos in Newtonian gravity. In Ciotti (2022) the problem is rigorously explored in the special setting of the weak-field, low-velocity gravitomagnetic limit of GR. T

Hui Huang, Hicham Kouhkouh

We establish a uniform-in-time estimate for the mean-field convergence of the Consensus-Based Optimization (CBO) algorithm by rescaling the consensus point in the dynamics with a small parameter $\kappa \in (0,1)$. This uniform-in-time estimate is essential, as CBO convergence relies on a sufficiently large time horizon and is crucial for ensuring stable, re

Long Pan

In this paper, we describe the wall-crossing of the two parameter K-moduli space of pairs (P 2,aQ + bL), where Q is a plane quintic curve and L is a line.

Jean Goubault-Larrecq

We relate the so-called powercone models of mixed non-deterministic and probabilistic choice proposed by Tix, Keimel, Plotkin, Mislove, Ouaknine, Worrell, Morgan, and McIver, to our own models of previsions. Under suitable topological assumptions, we show that they are isomorphic. We rely on Keimel's cone-theoretic variants of the classical Hahn-Banach separ

Christopher Lutsko, Balint Toth

Consider the motion of a charged, point particle moving in the complement of a Poisson distribution of hard sphere scatterers in two dimensions under the effect of a fixed magnetic field. Building on, and extending a coupling method established by the authors, we show that this 'magnetic Lorentz gas' satisfies an invariance principle in an intermediate scali

Nurdaulet N. Tobakhanov, Berikbol T. Torebek

In this paper, we investigate the critical behavior of solutions to the semilinear biharmonic heat equation with forcing term $f(x),$ under six homogeneous boundary conditions. This paper is the first since the seminal work by Bandle, Levine, and Zhang [J. Math. Anal. Appl. 251 (2000) 624-648], to focus on the study of critical exponents in exterior problems

Ashutosh Srivastava, Tarun Ram Menta, Abhinav Java, Avadhoot Jadhav

Modern Text-to-Image (T2I) Diffusion models have revolutionized image editing by enabling the generation of high-quality photorealistic images. While the de facto method for performing edits with T2I models is through text instructions, this approach non-trivial due to the complex many-to-many mapping between natural language and images. In this work, we add

Beatriz Ribeiro Lopes

Top quark production in association with a photon offers a unique test ground for the standard model predictions, as it is sensitive to the top-photon coupling. These processes are rare when compared to standard top pair production, however the large amounts of data delivered by the LHC open the window to precise measurements. This talk covered the recent in

Harel Kol-Namer, Moshe Goldstein

Neural networks has recently attracted much interest as useful representations of quantum many body ground states, which might help address the infamous sign problem. Most attention was directed at their representability properties, while possible limitations on finding the desired optimal state have not been suitably explored. By leveraging well-established

Giacomo Franceschetto, Marcin Płodzień, Maciej Lewenstein, Antonio Acín

Quantum measurements affect the state of the observed systems via back-action. While projective measurements extract maximal classical information, they drastically alter the system's configuration. In contrast, indirect measurements balance information extraction with the degree of disturbance. Considering the prevalent use of projective measurements in qua

Jiawei Yao, Qi Qian, Juhua Hu

Multiple clustering aims to discover various latent structures of data from different aspects. Deep multiple clustering methods have achieved remarkable performance by exploiting complex patterns and relationships in data. However, existing works struggle to flexibly adapt to diverse user-specific needs in data grouping, which may require manual understandin

Yang He, Chengchuan An, Yuheng Jia, Jiachao Liu

Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption. In this study, we propose an efficient and robust low-rank model for precise spatiotemporal traffic speed state estimation (TSE) using lowpenetration vehicle trajectory data. Leveraging traffic wave priors

Nico Lombardi, Christian Richter, Eugenia Saorín Gómez

Giannopoulos, Hartzoulaki and Paouris asked in \cite{GHP} whether the best ratio between volume and surface area of convex bodies sharing a given orthogonal projection onto a fixed hyperplane is attained in the limit by a cylinder over the given projection. The answer to the question is known to be negative. In this paper, we prove a characterization of the

Ziyuan Ding, Yixiong Liang, Shichao Kan, Qing Liu

High resolution is crucial for precise segmentation in fundus images, yet handling high-resolution inputs incurs considerable GPU memory costs, with diminishing performance gains as overhead increases. To address this issue while tackling the challenge of segmenting tiny objects, recent studies have explored local-global fusion methods. These methods preserv

Zhaoxuan Liu, Marjolein Dijkstra

Many living systems, such as birds and fish, exhibit collective behaviors like flocking and swarming. Recently, an experimental system of active colloidal particles has been developed, where the motility of each particle is adjusted based on its visual detection of surrounding particles. These particles with visual-perception-dependent motility exhibit group

Wonjun Lee, Hyukjoon Kwon, Gil Young Cho

Quantum resources like entanglement and magic are essential for characterizing the complexity of quantum states. However, when the number of copies of quantum states and the computational time are limited by numbers polynomial in the system size $n$, accurate estimation of the amount of these resources becomes difficult. This makes it impossible to distingui

Davide Bilò, Sarel Cohen, Tobias Friedrich, Hans Gawendowicz

We live in a world full of networks where our economy, our communication, and even our social life crucially depends on them. These networks typically emerge from the interaction of many entities, which is why researchers study agent-based models of network formation. While traditionally static networks with a fixed set of links were considered, a recent str

Zhenning Liu, Xiantao Li, Chunhao Wang, Jin-Peng Liu

Modeling and simulating the protein folding process overall remains a grand challenge in computational biology. We systematically investigate end-to-end quantum algorithms for simulating various protein dynamics with effects, such as mechanical forces or stochastic noises. A major focus is the read-in of system settings for simulation, for which we discuss (

K. S. Pervakov, A. V. Sadakov, O. A. Sobolevskiy, V. A. Vlasenko

Here, by applying a comprehensive approach including magnetic, transport measurements, ARPES band structure measurements, DFT calculations, and analytical theory consideration, we unveil the puzzling origin of the negative isotropic magnetoresistance in the highly anisotropic semimetals, particularly, Eu$_2$Sn$_2$As$_2$ with AFM ordering of Eu atoms. The iso

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

For a generalization of the Gellerstedt operator with Dirichlet boundary conditions in a Tricomi domain. We establish Poho\v{z}aev-type identities and prove the nonexistence of nontrivial regular solutions. Furthermore, we investigate the critical exponent phenomenon for power-type nonlinearities, characterized by the critical exponent of a weighted Sobolev

ATLAS Collaboration

This article presents a search for a heavy charged Higgs boson produced in association with a top quark and a bottom quark, and decaying into a $W$ boson and a $125$ GeV Higgs boson $h$. The search is performed in final states with one charged lepton, missing transverse momentum, and jets using proton-proton collision data at $\sqrt{s} = 13$ TeV recorded wit

Si-Yang Wang, Zhi-Qing Zhang, Zhi-Jie Sun, Jian Chai

Different from the previous treatment in a two-body framework, we introduce the dimeson distribution amplitudes (DAs) to describe the strong dynamics between the S-wave resonances $a_0(980, 1450)$ and the $K\bar K (\pi\eta)$ pair, where the Gegenbauer coefficient required is determined from the experimental data on the time-like form factors involved. The br

Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt, Robert Jenssen

State-of-the-art methods for explaining predictions from time series involve learning an instance-wise saliency mask for each time step; however, many types of time series are difficult to interpret in the time domain, due to the inherently complex nature of the data. Instead, we propose to view time series explainability as saliency maps over interpretable

Jan Střeleček, Pavel Cejnar

We analyze the geometry of the ground-state manifold in parameter-dependent many-body systems with quantum phase transitions (QPTs) and describe finite-size precursors of the singular geometry emerging at the QPT boundary in the infinite-size limit. In particular, we elucidate the role of diabolic points in the formation of first-order QPTs, showing that the

Saif M. Mohammad

Anxiety, the anticipatory unease about a potential negative outcome, is a common and beneficial human emotion. However, there is still much that is not known, such as how anxiety relates to our body and how it manifests in language. This is especially pertinent given the increasing impact of anxiety-related disorders. In this work, we introduce WorryWords, t

Rolando Gonzales Martinez

This study explores a Bayesian algorithmic approach to personalized fragrance recommendation by integrating hierarchical Relevance Vector Machines (RVM) and Jungian personality archetypes. The paper proposes a structured model that links individual scent preferences for top, middle, and base notes to personality traits derived from Jungian archetypes, such a