February 2024 arXiv papers — page 66

Ata Deniz Aydin, Mikaela Iacobelli

The quantization problem looks for best approximations of a probability measure on a given metric space by finitely many points, where the approximation error is measured with respect to the Wasserstein distance. On particular smooth domains, such as $\mathbb{R}^d$ or complete Riemannian manifolds, the quantization error is known to decay polynomially as the

Uri Andrews, Steffen Lempp, Alberto Marcone, Joseph S. Miller

A partial order $(P,\le)$ admits a jump operator if there is a map $j\colon P \to P$ that is strictly increasing and weakly monotone. Despite its name, the jump in the Weihrauch lattice fails to satisfy both of these properties: it is not degree-theoretic and there are functions $f$ such that $f\equiv_{\mathrm{W}} f'$. This raises the question: is there a ju

Xiaofen Huang, Naihuan Jing

As one of the most profound features of quantum mechanics, entanglement is a vital resource for quantum information processing. Inspired by the recent work on PT-moments and separablity [Phys. Rev. Lett. {\bf 127}, 060504 (2021)], we propose two sets of separability criteria using moments of the correlation tensor for bipartite and multipartite quantum state

Samuel L. Rudge, Christoph Kaspar, Robin L. Grether, Steffen Wolf

A novel mixed quantum-classical approach to simulating nonadiabatic dynamics of molecules at metal surfaces is presented. The method combines the numerically exact hierarchical equations of motion approach for the quantum electronic degrees of freedom with Langevin dynamics for the classical degrees of freedom, namely, low-frequency vibrational modes within

Guillaume Cecile, Hugo Lóio, Jacopo De Nardis

We study the time evolution of long quantum spin chains subjected to continuous monitoring via matrix product states (MPS) at fixed bond dimension, with the Time-Dependent Variational Principle (TDVP) algorithm. The latter gives an effective classical non-linear evolution with a conserved charge, which approximates the real quantum evolution up to an error.

Ivor van der Hoog, Fabian Klute, Irene Parada, Patrick Schnider

Imagine you are a dog behind a fence $Q$ and a hiker is passing by at constant speed along the hiking path $P$. In order to fulfil your duties as a watchdog, you desire to bark as long as possible at the human. However, your barks can only be heard in a fixed radius $\rho$ and, as a dog, you have bounded speed $s$. Can you optimize your route along the fence

Mohamed Jleli, Michael Ruzhansky, Bessem Samet, Berikbol T. Torebek

We consider a higher order in (time) semilinear evolution inequality posed on the Kor\'{a}nyi ball under an inhomogeneous Dirichlet-type boundary condition. The problem involves an inverse-square potential $\lambda/|\xi|_\mathbb{H}^2$, where $\lambda \geq -(Q-2)^2/4$ and a general weight function $V$ depending on the space variable in front of the power nonl

Q. Pears Stefano, A. G. Magnoni, D. Rodrigues, J. Tiffenberg

Optical phase determination is an important and established tool in diverse fields such as astronomy, biology, or quantum optics. There is increasing interest in using a lower number of total photons. However, different noise sources, such as electronic readout noise in the detector, and shot noise, hamper the phase estimation in regimes of very low illumina

Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi

We study the fundamental problem of designing contracts in principal-agent problems under uncertainty. Previous works mostly addressed Bayesian settings in which principal's uncertainty is modeled as a probability distribution over agent's types. In this paper, we study a setting in which the principal has no distributional information about agent's type. In

Julian Baumstark, Tobias Jahnke

High-frequency wave propagation is often modelled by nonlinear Friedrichs systems where both the differential equation and the initial data contain the inverse of a small parameter $\varepsilon$, which causes oscillations with wavelengths proportional to $\varepsilon$ in time and space. A prominent example is the Maxwell--Lorentz system, which is a well-esta

Soshun Ozaki, Hosho Katsura

We introduce two disorder-free variants of the Sachdev-Ye-Kitaev (SYK) model, demonstrate their integrability, and study their static and dynamical properties. Unlike diagrammatic techniques, the integrability of these models allows us to obtain dynamical correlation functions even when the number of Majorana fermions is finite. From the solutions, we find t

Simon D. Fink, Matthias Pfretzschner, Ignaz Rutter, Marie Diana Sieper

We consider variants of the clustered planarity problem for level-planar drawings. So far, only convex clusters have been studied in this setting. We introduce two new variants that both insist on a level-planar drawing of the input graph but relax the requirements on the shape of the clusters. In unrestricted Clustered Level Planarity (uCLP) we only require

José-M. Acosta-Triana, David Gimeno-Gómez, Carlos-D. Martínez-Hinarejos

More than 7,000 known languages are spoken around the world. However, due to the lack of annotated resources, only a small fraction of them are currently covered by speech technologies. Albeit self-supervised speech representations, recent massive speech corpora collections, as well as the organization of challenges, have alleviated this inequality, most stu

Sanjeev Khanna, Aaron L. Putterman, Madhu Sudan

Recently, a number of variants of the notion of cut-preserving hypergraph sparsification have been studied in the literature. These variants include directed hypergraph sparsification, submodular hypergraph sparsification, general notions of approximation including spectral approximations, and more general notions like sketching that can answer cut queries u

Gergely Bunth, József Pitrik, Tamás Titkos, Dániel Virosztek

Quantum Wasserstein divergences are modified versions of quantum Wasserstein distances defined by channels, and they are conjectured to be genuine metrics on quantum state spaces by De Palma and Trevisan. We prove triangle inequality for quantum Wasserstein divergences for every quantum system described by a separable Hilbert space and any quadratic cost ope

Esraa Alhenawi, Shatha Awawdeh, Ruba Abu Khurma, Maribel García-Arenas

Software requirements prioritization plays a crucial role in software development. It can be viewed as the process of ordering requirements by determining which requirements must be done first and which can be done later. Powerful requirements prioritization techniques are of paramount importance to finish the implementation on time and within budget. Many f

Yujun Zhou, Yufei Han, Haomin Zhuang, Kehan Guo

Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications. However, concerns regarding their security, particularly the vulnerability to jailbreak attacks, persist. Drawing inspiration from adversarial training in deep learning and LLM agent learning processes, we introduce the In-Context Adversarial Game (ICAG) for defendi

Huy Hoang, Tien Mai, Pradeep Varakantham

We focus on offline imitation learning (IL), which aims to mimic an expert's behavior using demonstrations without any interaction with the environment. One of the main challenges in offline IL is the limited support of expert demonstrations, which typically cover only a small fraction of the state-action space. While it may not be feasible to obtain numerou

Adnen Abdessaied, Manuel von Hochmeister, Andreas Bulling

We present the Object Language Video Transformer (OLViT) - a novel model for video dialog operating over a multi-modal attention-based dialog state tracker. Existing video dialog models struggle with questions requiring both spatial and temporal localization within videos, long-term temporal reasoning, and accurate object tracking across multiple dialog turn

Yujie Shao, Xinrong Yao, Xingwei Qu, Chenghua Lin

Metaphor is a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. This paper introduces a large-scale high quality annotated Chinese Metaphor Corpus, which comprises around 28K sentences drawn from a diverse range of Chinese literary sources, such as poems, prose, song lyr

Kai Wang, Dongwen Tang, Boya Zeng, Yida Yin

Diffusion models have achieved remarkable success in image and video generation. In this work, we demonstrate that diffusion models can also \textit{generate high-performing neural network parameters}. Our approach is simple, utilizing an autoencoder and a diffusion model. The autoencoder extracts latent representations of a subset of the trained neural netw

Emilio Ormeño, Fernando Pinciroli

A systematic mapping protocol is a method for conducting a literature review in a rigorous and transparent way. It aims to provide an overview of the current state of research on a specific topic, identify gaps and opportunities, and guide future work. In this document, we present a systematic mapping protocol for investigating the role of the UX designer in

Frank Lukas

Let $R$ be a ring with unity and $\mathcal{X}$ a semibrick in the module category $\mathrm{Mod}\,R$, that is, a class of pairwise orthogonal finitely presented modules whose endomorphism rings are division rings. We study the full subcategory $\mathrm{Filt}(\mathcal{X})$ consisting of all modules admitting a filtration with factors in $\mathcal{X}$. We show

Faris M. AL-Oqla, Hossam Faris, Maria Habib, Pedro A. Castillo-Valdivieso

Advanced modern technology and industrial sustainability theme have contributed implementing composite materials for various industrial applications. Green composites are among the desired alternatives for the green products. However, to properly control the performance of the green composites, predicting their constituents properties are of paramount import

Naihong Hu, Xiao Xu

The classification of one-parameter small quantum groups remains a fascinating open problem. This paper uncovers a novel phenomenon: beyond the Lusztig small quantum groups-equipped with double group-like elements -there exists a plethora of exotic small quantum groups, approximately fivefold more numerous than their standard counterparts, which originate fr

Laurens Walleghem, Shashaank Khanna, Rutvij Bhavsar

In a recent paper [Carcassi, Oldofredi and Aidala, Found Phys 54, 14 (2024)] it is claimed that the whole Harrigan--Spekkens framework of ontological models is inconsistent with quantum theory. They show this by showing that all pure quantum states in $\psi$-ontic models must be orthogonal. In this note, we identify some crucial mistakes in their argument to

Aleksander B. G. Christiansen

Given a dynamic graph $G$ with $n$ vertices and $m$ edges subject to insertion an deletions of edges, we show how to maintain a $(1+\varepsilon)\Delta$-edge-colouring of $G$ without the use of randomisation. More specifically, we show a deterministic dynamic algorithm with an amortised update time of $2^{\tilde{O}_{\log \varepsilon^{-1}}(\sqrt{\log n})}$ usi

Neelam Kandhil, Rashi Lunia

We introduce some generalizations of the Euler-Kronecker constant of a number field and study their arithmetic nature.

Jesujoba O. Alabi, Marius Mosbach, Matan Eyal, Dietrich Klakow

We analyze the operation of transformer language adapters, which are small modules trained on top of a frozen language model to adapt its predictions to new target languages. We show that adapted predictions mostly evolve in the source language the model was trained on, while the target language becomes pronounced only in the very last layers of the model. M

Nilesh Vyas, Paulo Mendes

Quantum security over long distances with untrusted relays is largely unfounded and is still an open question for active research. Nevertheless, quantum networks based on trusted relays are being built across the globe. However, standard QKD network architecture implores a complete trust requirement on QKD relays, which is too demanding and limits the use ca

Nidhika Chauhan, Navneet Kaur, Kamaljit Singh Saini, Sahil Verma

As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techni

Roy H. Goodman

Here we provide a complete English translation of Walter Gr\"obli's 1877 Ph.D. Thesis, together with some notes on the process. The work considers the dynamics of point vortices in a two-dimensional inviscid incompressible fluid and derives a number of exact solutions in the cases of three, four and $2n$ vortices with certain restriction on the vortices' cir

Zhubing Jia, William Huie, Lintao Li, Won Kyu Calvin Sun

We present an architecture for encoding two qubits within the optical "clock" transition and nuclear spin-1/2 degree of freedom of neutral ytterbium-171 atoms. Inspired by recent high-fidelity control of all pairs of states within this four-dimensional ququart space, we present a toolbox for intra-ququart (single atom) one- and two-qubit gates, inter-ququart

Gilberto A. de la Pena Munoz, Alfredo A. Correa, Shan Yang, Olivier Delaire

In the prevalent picture of ultrafast structural phase transitions, the atomic motion occurs in a slowly varying potential energy surface determined adiabatically by the fast electrons. However, this ignores non-conservative forces caused by electron-lattice collisions, which can significantly influence atomic motion. Most ultrafast techniques only probe the

Zhuangzhuang Cui, Franco Minucci, Rizqi Hersyandika, Rodney Martinez Alonso

Reconfigurable intelligent surface (RIS) used as infrastructure in wireless networks has been a trend, thanks to its low cost and high flexibility. Working in many ways including reflective mirrors and phase-shifted surfaces, RIS is able to enhance the coverage in communications and provide more degrees of freedom for sensing. However, the key issue lies in

Maximilian Hahn, Bernhard Egger

Statistical Shape Models of faces and various body parts are heavily used in medical image analysis, computer vision and visualization. Whilst the field is well explored with many existing tools, all of them aim at experts, which limits their applicability. We demonstrate the first tool that enables the convenient exploration of statistical shape models in t

Ivan Rep, David Dukić, Jan Šnajder

While BERT produces high-quality sentence embeddings, its pre-training computational cost is a significant drawback. In contrast, ELECTRA provides a cost-effective pre-training objective and downstream task performance improvements, but worse sentence embeddings. The community tacitly stopped utilizing ELECTRA's sentence embeddings for semantic textual simil

Sejoong Kim

We present a comprehensive investigation of the magnetic ordering in biphenylene network (BPN) layers, employing density functional theory (DFT) calculations under external perturbations, including uniaxial strains and hole doping. We compute fully relaxed structures, energy bands, and magnetic states by performing DFT calculations augmented with extended Hu

Kohei Higashi

We consider an integro-differential equation model for traffic flow which is an extension of the Burgers equation model. To discuss the model, we first examine general settings for integrable integro-differential equations and find that they are obtained through a simple residue formula from integrable eqations in a complex domain. As demonstration of the ef

Neelam Kandhil, Rashi Lunia, Jyothsnaa Sivaraman

For a number field $K$, the Euler-Kronecker constant $\gamma_K$ associated to $K$ is an arithmetic invariant the size and nature of which is linked to some of the deepest questions in number theory. This theme was given impetus by Ihara who obtained bounds, both unconditional as well as under GRH for Dedekind zeta functions. In this note, we study the analog

Yan Pang, Baicheng Chen, Yang Zhang, Tianhao Wang

With the rapid advancement in video generation, people can conveniently use video generation models to create videos tailored to their specific desires. As a result, there are also growing concerns about the potential misuse of video generation for spreading illegal content and misinformation. In this work, we introduce VGMShield: a set of straightforward bu

Xiang Li, Yunshi Lan, Chao Yang

Recently, numerous new benchmarks have been established to evaluate the performance of large language models (LLMs) via either computing a holistic score or employing another LLM as a judge. However, these approaches suffer from data leakage due to the open access of the benchmark and inflexible evaluation process. To address this issue, we introduce $\textb

David J. Fernández-Bretón, Eliseo Sarmiento Rosales, Germán Vera

Inspired by Owings's problem, we investigate whether, for a given an Abelian group $G$ and cardinal numbers $\kappa,\theta$, every colouring $c:G\longrightarrow\theta$ yields a subset $X\subseteq G$ with $|X|=\kappa$ such that $X+X$ is monochromatic. (Owings's problem asks this for $G=\mathbb Z$, $\theta=2$ and $\kappa=\aleph_0$; this is known to be false fo

Yimeng Liu, Misha Sra

Choreography creation is a multimodal endeavor, demanding cognitive abilities to develop creative ideas and technical expertise to convert choreographic ideas into physical dance movements. Previous endeavors have sought to reduce the complexities in the choreography creation process in both dimensions. Among them, non-AI-based systems have focused on reinfo

Claudia Cuttano, Antonio Tavera, Fabio Cermelli, Giuseppe Averta

Many practical applications require training of semantic segmentation models on unlabelled datasets and their execution on low-resource hardware. Distillation from a trained source model may represent a solution for the first but does not account for the different distribution of the training data. Unsupervised domain adaptation (UDA) techniques claim to sol

Mikhail Karpukhin, Robert Kusner, Peter McGrath, Daniel Stern

In 1970, Lawson solved the topological realization problem for minimal surfaces in the sphere, showing that any closed orientable surface can be minimally embedded in $\mathbb{S}^3$. The analogous problem for surfaces with boundary was posed by Fraser and Li in 2014, and it has attracted much attention in recent years, stimulating the development of many new

J. P. Linares Fernandez, N. Michel, M. Płoszajczak

The Gamow shell model provides the open quantum system formulation of nuclear shell model. In the coupled-channel representation, Gamow shell model provides the unified theory of nuclear structure and reactions which is well suited for the study of resonances and clusterization. In this work, we apply this approach to study the continuum-coupling correlation

Rubén Martín-Rodríguez, Alexandre L. Ratschat, Laura Marchal-Crespo, Yasemin Vardar

Haptic rendering of weight plays an essential role in naturalistic object interaction in virtual environments. While kinesthetic devices have traditionally been used for this aim by applying forces on the limbs, tactile interfaces acting on the skin have recently offered potential solutions to enhance or substitute kinesthetic ones. Here, we aim to provide a

Nicki Mullins, Mauricio Hippert, Jorge Noronha, Lorenzo Gavassino

When two nuclei collide close to the speed of light, a fluid state known as the quark-gluon plasma is formed. Attempts to understand the dynamics of this fluid have generated significant research into dissipative relativistic fluid dynamics. The fluctuation-dissipation theorem implies that any dissipative dynamical system will also experience thermal fluctua

Ferenc Iglói, Csaba Király

We use entanglement witnesses related to the entanglement negativity of the state to detect entanglement in the $XY$ chain in the postquench states in the thermodynamic limit after a quench when the parameters of the Hamiltonian are changed suddenly. The entanglement negativity is related to correlations, which in the postquench stationary state are describe

Martin Willame, Hasan Can Yildirim, Laurent Storrer, François Horlin

This study investigates the problem of angle-based localization of multiple targets using a multistatic OFDM radar. Although the maximum likelihood (ML) approach can be employed to merge data from different radar pairs, this method requires a high complexity multi-dimensional search process. The multiple signal classification (MUSIC) algorithm simplifies the

Ivor van der Hoog, Thijs van der Horst, Tim Ophelders

Given a trajectory $T$ and a distance $\Delta$, we wish to find a set $C$ of curves of complexity at most $\ell$, such that we can cover $T$ with subcurves that each are within Fr\'echet distance $\Delta$ to at least one curve in $C$. We call $C$ an $(\ell,\Delta)$-clustering and aim to find an $(\ell,\Delta)$-clustering of minimum cardinality. This problem

Xiaohan Xu, Ming Li, Chongyang Tao, Tao Shen

In the era of Large Language Models (LLMs), Knowledge Distillation (KD) emerges as a pivotal methodology for transferring advanced capabilities from leading proprietary LLMs, such as GPT-4, to their open-source counterparts like LLaMA and Mistral. Additionally, as open-source LLMs flourish, KD plays a crucial role in both compressing these models, and facili

Arianna Favale, Maria Giovanna Dainotti, Adrià Gómez-Valent, Marina Migliaccio

Current data on baryon acoustic oscillations and Supernovae of Type Ia cover up to $z\sim 2.5$. These observations play a very important role in the determination of cosmological parameters and have been widely used to constrain the $\Lambda$CDM and models beyond it. To extend the investigation to higher $z$, Gamma-Ray Bursts (GRBs) stand out as one of the m

Qian Wang, Zemin Liu, Zhen Zhang, Bingsheng He

Class imbalance in graph-structured data, where minor classes are significantly underrepresented, poses a critical challenge for Graph Neural Networks (GNNs). To address this challenge, existing studies generally generate new minority nodes and edges connecting new nodes to the original graph to make classes balanced. However, they do not solve the problem t

Brielen Madureira, Patrick Kahardipraja, David Schlangen

Incremental models that process sentences one token at a time will sometimes encounter points where more than one interpretation is possible. Causal models are forced to output one interpretation and continue, whereas models that can revise may edit their previous output as the ambiguity is resolved. In this work, we look at how restart-incremental Transform

Yuechun Jiao, Weilun Jiang, Yu Zhang, Jingxu Bai

Time crystals, as temporal analogs of space crystals, manifest as stable and periodic behavior that breaks time translation symmetry. In an open quantum system, many-body interaction subjected to dissipation allows one to develop the time crystalline order in an unprecedented way, as refer to dissipative time crystals. Here we report the observation of multi

Pierre-François Loos, Yann Damour, Abdallah Ammar, Michel Caffarel

Recently, a new distributed implementation of the full configuration interaction (FCI) method has been reported [Gao et al. J. Chem Theory Comput. 2024, 20, 1185]. Thanks to a hybrid parallelization scheme, the authors were able to compute the exact energy of propane (\ce{C3H8}) in the minimal basis STO-3G. This formidable task involves handling an active sp

Dennis Laurijssen, Walter Daems, Jan Steckel

Airborne 3D imaging using ultrasound is a promising sensing modality for robotic applications in harsh environments. Over the last decade, several high-performance systems have been proposed in the literature. Most of these sensors use a reduced aperture microphone array, leading to artifacts in the resulting acoustic images. This paper presents a novel in-a

Yizhi LI, Ge Zhang, Xingwei Qu, Jiali Li

The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-following. Yet, their effectiveness often diminishes in low-resource languages like Chinese, exacerbated by biased evaluations from data leakage, casting doubt on their true generaliz

Alexandru Crăciun, Debarghya Ghoshdastidar

A vast literature on convergence guarantees for gradient descent and derived methods exists at the moment. However, a simple practical situation remains unexplored: when a fixed step size is used, can we expect gradient descent to converge starting from any initialization? We provide fundamental impossibility results showing that convergence becomes impossib

Fernando Cortés Kühnast, Justin Dallant, Stefan Felsner, Manfred Scheucher

Arrangements of pseudolines are classic objects in discrete and computational geometry. They have been studied with increasing intensity since their introduction almost 100 years ago. The study of the number $B_n$ of non-isomorphic simple arrangements of $n$ pseudolines goes back to Goodman and Pollack, Knuth, and others. It is known that $B_n$ is in the ord

Carter Lyons, Raghu G. Raj, Margaret Cheney

Algorithm unfolding or unrolling is the technique of constructing a deep neural network (DNN) from an iterative algorithm. Unrolled DNNs often provide better interpretability and superior empirical performance over standard DNNs in signal estimation tasks. An important theoretical question, which has only recently received attention, is the development of ge

Gregory Matousek, Anselm Vossen

Dihadron semi-inclusive deep inelastic scattering (SIDIS) of 10.6 GeV longitudinally polarized electrons off the proton has been measured using the CLAS12 detector at Jefferson Lab. Two separate channels, $\pi^+\pi^0$ and $\pi^-\pi^0$, were analyzed, requiring the reconstruction of diphoton pairs. In this analysis, we addressed the problem of false neutral p

Johann Haselberger, Bernhard Schick, Steffen Müller

Advancements in technology are steering attention toward creating comfortable and acceptable driving characteristics in autonomous vehicles. Ensuring a safe and comfortable ride experience is vital for the widespread adoption of autonomous vehicles, as mismatches in driving styles between humans and autonomous systems can impact passenger confidence. Current

Rahul Bordoloi, Clémence Réda, Orell Trautmann, Saptarshi Bej

Functional linear discriminant analysis (FLDA) is a powerful tool that extends LDA-mediated multiclass classification and dimension reduction to univariate time-series functions. However, in the age of large multivariate and incomplete data, statistical dependencies between features must be estimated in a computationally tractable way, while also dealing wit

C. Aguiar, I. Camps

Why is the question in the title pertinent? Toxic gases, which are detrimental to both human health and the environment, have been released in greater quantities as a result of industrial development. These gases necessitate capture, immobilization, and measurement. Consequently, the present study investigates the interactions between boron-nitride nanobelt

J. Storm, I. B. C. M. Rocha, F. P. van der Meer

Simulating the mechanical response of advanced materials can be done more accurately using concurrent multiscale models than with single-scale simulations. However, the computational costs stand in the way of the practical application of this approach. The costs originate from microscale Finite Element (FE) models that must be solved at every macroscopic int

Minhao Yao, Zhonghua Liu

Omics biomarkers play a pivotal role in personalized medicine by providing molecular-level insights into the etiology of diseases, guiding precise diagnostics, and facilitating targeted therapeutic interventions. Recent advancements in omics technologies have resulted in an increasing abundance of multimodal omics data, providing unprecedented opportunities

Lingyuan Ye

This is the first of a series of papers on stack representation of finitely presented Heyting pretoposes. In this paper, we provide the first step by constructing a (2, 1)-site, which can be thought of as the site of finite Kripke frames, such that the (2,1)-category of finitely presented Heyting pretoposes contravariantly embeds into the (2,1)- topos of sta

Yifei Zhang, Bo Pan, Chen Ling, Yuntong Hu

The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences. Traditional distillation methods, which transfer the capabilities of LLMs to smaller models, often fail to determine whether the knowledge has been sufficiently transferred, potentially resultin

Francesco Esposito, Mario Marietti

In this work, we investigate a novel approach to the Combinatorial Invariance Conjecture of Kazhdan--Lusztig polynomials for the symmetric group. Using the new concept of flipclasses, we introduce some combinatorial invariants of intervals in the symmetric group whose analysis leads us to a recipe to compute the coefficients of $q^h$ of the Kazhdan--Lusztig

Görkem Berkay Koç, Berk Çiloğlu, Metin Ozturk, Halim Yanikomeroglu

This study investigates the integration of a high altitude platform station (HAPS), a non-terrestrial network (NTN) node, into the cell-switching paradigm for energy saving. By doing so, the sustainability and ubiquitous connectivity targets can be achieved. Besides, a delay-aware approach is also adopted, where the delay profiles of users are respected in s

Ayesha Jamal, Muhammad Kamran, Tahir Malik, Fahim ul Haq

In Discrete Variable Quantum Key Distribution (DV-QKD), homodyne detection method is frequently employed for its simplicity in use, effectiveness in terms of error correction, and suitability with contemporary optical communication systems. Being a coherent detection method, it relies on a local oscillator whose frequency is matched to that of the transmitte

Andreas Säuberli, Franz Holzknecht, Patrick Haller, Silvana Deilen

Text simplification refers to the process of increasing the comprehensibility of texts. Automatic text simplification models are most commonly evaluated by experts or crowdworkers instead of the primary target groups of simplified texts, such as persons with intellectual disabilities. We conducted an evaluation study of text comprehensibility including parti

Hao Peng, Xiaozhi Wang, Chunyang Li, Kaisheng Zeng

Knowledge editing aims at updating knowledge of large language models (LLMs) to prevent them from becoming outdated. Existing work edits LLMs at the level of factual knowledge triplets. However, natural knowledge updates in the real world come from the occurrences of new events rather than direct changes in factual triplets. In this paper, we propose a new t

Nicola Guglielmi, Arturo De Marinis, Anton Savostianov, Francesco Tudisco

We propose a novel methodology to solve a key eigenvalue optimization problem which arises in the contractivity analysis of neural ODEs. When looking at contractivity properties of a one layer weight-tied neural ODE $\dot{u}(t)=\sigma(Au(t)+b)$ (with $u,b \in {\mathbb R}^n$, $A$ is a given $n \times n$ matrix, $\sigma : {\mathbb R} \to {\mathbb R}$ denotes a

Yuxiang Xu, Minghui Du, Peng Xu, Bo Liang

Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges like high parameter space dimension, superposition of large number of signals etc., gravitational wave detections

Philipp Schmitz, Manuel Schaller, Matthias Voigt, Karl Worthmann

Recently, data-enabled predictive control (DeePC) schemes based on Willems' fundamental lemma have attracted considerable attention. At the core are computations using Hankel-like matrices and their connection to the concept of persistency of excitation. We propose an iterative solver for the underlying data-driven optimal control problems resulting from lin

Dongyang Fan, Bettina Messmer, Martin Jaggi

In this study, we systematically evaluate the impact of common design choices in Mixture of Experts (MoEs) on validation performance, uncovering distinct influences at token and sequence levels. We also present empirical evidence showing comparable performance between a learned router and a frozen, randomly initialized router, suggesting that learned routing

Jiaqi Xu, Cuiling Lan, Wenxuan Xie, Xuejin Chen

Video-Language Models (VLMs), powered by the advancements in Large Language Models (LLMs), are charting new frontiers in video understanding. A pivotal challenge is the development of an efficient method to encapsulate video content into a set of representative tokens to align with LLMs. In this work, we introduce Slot-VLM, a novel framework designed to gene

Zihang Xiang, Tianhao Wang, Chenglong Wang, Di Wang

We study the application of differential privacy in hyper-parameter tuning, a crucial process in machine learning involving selecting the best hyper-parameter from several candidates. Unlike many private learning algorithms, including the prevalent DP-SGD, the privacy implications of tuning remain insufficiently understood or often totally ignored. Recent wo

Vincent Moreau

The starting point of algebraic language theory is that regular languages of finite words are exactly those recognized by finite monoids. This finiteness condition gives rise to a topological space whose points, called profinite words, encode the limiting behavior of words with respect to finite monoids. In this work, we move from words and monoids to trees

Mike Cruchten

Automata operating on pairs of words were introduced as an alternative way of capturing acceptance of regular $\omega$-languages. Families of DFAs and lasso automata operating on such pairs followed, giving rise to minimisation algorithms, a Myhill-Nerode theorem and language learning algorithms. Yet Kleene theorems for such a well-established class are stil

Shahaboddin Shaabani

For a tempered distribution $g$, and $0 < p, q, r < \infty$ with $\frac{1}{q} = \frac{1}{p} + \frac{1}{r}$, we show that the operator norm of a Fourier paraproduct $\Pi_g$, of the form \[ \Pi_{g}(f) := \sum_{j \in \mathbb{Z}} (\varphi_{2^{-j}} * f) \cdot \Delta_jg, \] from $H^p(\mathbb{R}^n)$ to $\dot{H}^q(\mathbb{R}^n)$ is comparable to $\|g\|_{\dot{H}^r(\m

Gregor Dolinar, Dijana Ilišević, Bojan Kuzma, Janko Marovt

Let $H_{n}^{+}(\mathbb{R})$ be the cone of all positive semidefinite $n\times n$ real matrices. We describe the form of all surjective maps on $H_{n}^{+}(\mathbb{R}) $, $n\geq 3$, that preserve the minus partial order in both directions.

Alain Connes

We compute the full asymptotic expansion of the heat kernel Trace$(\exp(-tD^2))$ where $D$ is, assuming RH, the self-adjoint operator whose spectrum is formed of the imaginary parts of non-trivial zeros of the Riemann zeta function. The coefficients of the expansion are explicit expressions involving Bernoulli and Euler numbers. We relate the divergent terms

Xiaoxuan Wang, Rolf Stadler

We study automated intrusion detection in an IT infrastructure, specifically the problem of identifying the start of an attack, the type of attack, and the sequence of actions an attacker takes, based on continuous measurements from the infrastructure. We apply statistical learning methods, including Hidden Markov Model (HMM), Long Short-Term Memory (LSTM),

Chung-Yun Hsieh, Shin-Liang Chen

We consider a thermodynamic framework to quantify instrument incompatibility via a resource theory subject to thermodynamic constraints. We use the minimal thermalisation time needed to erase incompatibility's signature to measure incompatibility. Unexpectedly, this time value is equivalent to incompatibility advantage in a work extraction task. Hence, both

Charles Arnal, Vivien Cabannes, Vianney Perchet

The combination of lightly supervised pre-training and online fine-tuning has played a key role in recent AI developments. These new learning pipelines call for new theoretical frameworks. In this paper, we formalize core aspects of weakly supervised and active learning with a simple problem: the estimation of the mode of a distribution using partial feedbac

Alessandro Cucinotta

We study the properties of the set where a generalized function of bounded variation has infinite approximate limit, highlighting in this way the main geometric difference with functions of bounded variation. To this aim we prove a new result on strict approximation of sets of finite perimeter from the outside with open sets.

Adeel Pervez, Francesco Locatello, Efstratios Gavves

This paper presents Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations, revealing the underlying dynamics of data and enhancing interpretability and efficiency in data m

Yang Li, Yuan Shangguan, Yuhao Wang, Liangzhen Lai

Power consumption plays a crucial role in on-device streaming speech recognition, significantly influencing the user experience. This study explores how the configuration of weight parameters in speech recognition models affects their overall energy efficiency. We found that the influence of these parameters on power consumption varies depending on factors s

Xiang Yin, Bingzhao Gao, Xiao Yu

In recent years, formal methods have been extensively used in the design of autonomous systems. By employing mathematically rigorous techniques, formal methods can provide fully automated reasoning processes with provable safety guarantees for complex dynamic systems with intricate interactions between continuous dynamics and discrete logics. This paper prov

Aritra Lahiri, Sang-Jun Choi, Björn Trauzettel

The Higgs mode in superconductors corresponds to oscillations of the amplitude of the order parameter. While its detection typically entails resonant optical excitation, we present a purely transport-based setup wherein it is excited in a voltage biased Josephson junction. Demonstrating the importance of order parameter dynamics, the interplay of Higgs reson

Huiqiang Xie, Zhijin Qin, Xiaoming Tao, Zhu Han

Deep learning enabled semantic communications have shown great potential to significantly improve transmission efficiency and alleviate spectrum scarcity, by effectively exchanging the semantics behind the data. Recently, the emergence of large models, boasting billions of parameters, has unveiled remarkable human-like intelligence, offering a promising aven

V. V. Kovtyukh, S. M. Andrievsky, K. Werner, S. A. Korotin

The purpose of this work is to spectroscopically analyse the classical Cepheid V708 Car. A preliminary check of the spectrum of V708 Car showed that this is a lithium-rich supergiant. We also found that V708 Car has an unusual chemical composition in that the abundances of various elements correlate with their condensation temperatures. We tried to find an e

Haibin Wu, Ho-Lam Chung, Yi-Cheng Lin, Yuan-Kuei Wu

The sound codec's dual roles in minimizing data transmission latency and serving as tokenizers underscore its critical importance. Recent years have witnessed significant developments in codec models. The ideal sound codec should preserve content, paralinguistics, speakers, and audio information. However, the question of which codec achieves optimal sound in

Mehrdad Shokooh-Saremi, Maxime Giteau, Mitradeep Sarkar, Georgia T. Papadakis

In this paper, narrowband emitters have been designed using particle swarm optimization (PSO) in the 10-20 {\mu}m infrared range. The device structure consists of an anisotropic {\alpha}-MoO3 layer combined with the one- and two-dimensional guided-mode resonance structures. Well-defined absorption lines are present in the reflection spectrum for both TE and

Christiana Vasilaki, Kalliopi Petraki

The formation and decay of metastable bound states can deplete significantly the density of multi-TeV thermal-relic dark matter. The effect depends on the interplay of bound-state formation, ionisation, transition and decay processes. Existing calculations take into account bound-state ionisation and excitations due to the radiation of the thermal bath. Howe