October 2023 arXiv papers — page 35

Shengwen Gan

Fix integers $1<k<n$. For $V\in G(k,n)$, let $P_V: \mathbb{R}^n\rightarrow V$ be the orthogonal projection. For $V\in G(k,n)$, define the map \[ \pi_V: A(1,n)\rightarrow A(1,V)\bigsqcup V. \] \[ \ell\mapsto P_V(\ell). \] For any $0<a<\text{dim}(A(1,n))$, we find the optimal number $s(a)$ such that the following is true. For any Borel set $\boldsymbol{A} \sub

Hanwool Bae, Cheol-Hyun Cho, Dongwook Choa, Wonbo Jeong

The variation operator in singularity theory maps relative homology cycles to compact cycles in the Milnor fiber using the monodromy. We construct its symplectic analogue for an isolated singularity. We define the monodromy Lagrangian Floer cohomology, which provides categorifications of the standard theorems on the variation operator and the Seifert form. T

Jean Alexandre, Nick E. Mavromatos, Vasiliki A. Mitsou, Emanuela Musumeci

High-Electric-Charge compact Objects (HECOs) appear in several theoretical particle physics models beyond the Standard Model, and are actively searched for in current colliders, such as the Large Hadron Collider at CERN. In such searches, mass bounds of these objects have been placed, using Drell-Yan and photon-fusion processes at tree level so far. However,

Yifei Peng, Zijie Zha, Yu Jin, Zhexu Luo

Making neural visual generative models controllable by logical reasoning systems is promising for improving faithfulness, transparency, and generalizability. We propose the Abductive visual Generation (AbdGen) approach to build such logic-integrated models. A vector-quantized symbol grounding mechanism and the corresponding disentanglement training method ar

Surojit Saha, Michael J. Williams, Laurence Datrier, Fergus Hayes

The discovery of the optical counterpart, along with the gravitational waves from GW170817, of the first binary neutron star merger, opened up a new era for multi-messenger astrophysics. Combining the GW data with the optical counterpart, also known as AT2017gfo, classified as a kilonova, has revealed the nature of compact binary merging systems by extractin

Yong Li, David Sauzin, Shanzhong Sun

This note discusses the location of the singularities of the Hadamard inverse of an endlessly continuable function, in the case when the original function has only one singular singularity which is either a single pole or a simple singularity.

Tanel Alumäe, Jiaming Kong, Daniil Robnikov

This paper describes Tallinn University of Technology (TalTech) systems developed for the ASRU MADASR 2023 Challenge. The challenge focuses on automatic speech recognition of dialect-rich Indian languages with limited training audio and text data. TalTech participated in two tracks of the challenge: Track 1 that allowed using only the provided training data

Pietro Capuozzo, John Estes, Brandon Robinson, Benjamin Suzzoni

In this note, we study $1/4$- and $1/2$-BPS co-dimension two superconformal defects in the $6d$ $\mathcal{N}=(2,0)$ $A_{N-1}$ SCFT at large $N$ using their holographic descriptions as solutions of $11d$ supergravity. In this regime, we are able to compute the defect contribution to the sphere entanglement entropy and the change in the stress-energy tensor on

Tony J. Puthenpurakal

Let $(A,\mathfrak{m})$ be an excellent equi-charateristic Gorenstein isolated singularity of dimension $d \geq 2$. Assume the residue field of $A$ is perfect. Let $I$ be any $\mathfrak{m}$-primary ideal. Let $G_I(A) = \bigoplus_{n \geq 0}I^n/I^{n+1}$ be the associated graded ring of $A$ with respect to $I$ and let $\mathcal{R}_I(A) = \bigoplus_{n \in \mathbb

Qiang Li, Vince D. Calhoun, Adithya Ram Ballem, Shujian Yu

The human brain has a complex, intricate functional architecture. While many studies primarily emphasize pairwise interactions, delving into high-order associations is crucial for a comprehensive understanding of how functional brain networks intricately interact beyond simple pairwise connections. Analyzing high-order statistics allows us to explore the nua

Zhidong Yang, Yifeng Sun, Lie-Wen Chen

The $\phi$ meson and $\Omega$ baryon provide unique probes of the properties of the quark-gluon plasma (QGP) at hadronization in relativistic heavy-ion collisions. Using the quark recombination model with the quark phase-space information parameterized in a viscous blastwave, we perform Bayesian inference of the shear and bulk viscosities of the QGP at hadro

Sabino Di Trani

We use some moment graph techniques, recently introduced by Lanini and P\"utz, to provide a combinatorial description of the smooth locus in flat linear degenerations of flag varieties, generalizing a result proved by Cerulli Irelli, Feigin and Reineke for the Feigin degeneration. Moreover, we propose a different combinatorial criterion linking the smoothnes

Mahir Habib, Muhammad Ashad Kabir, Lihong Zheng, Shawn McGrath

Data-driven advances have resulted in significant improvements in dairy production. However, the meat industry has lagged behind in adopting data-driven approaches, underscoring the crucial need for data standardisation to facilitate seamless data transmission to maximise productivity, save costs, and increase market access. To address this gap, we propose a

James Belk, Bradley Forrest

We develop a theory of quasisymmetries for finitely ramified fractals, with applications to finitely ramified Julia sets. We prove that certain finitely ramified fractals admit a naturally defined class of "undistorted metrics" that are all quasi-equivalent. As a result, piecewise-defined homeomorphisms of such a fractal that locally preserve the cell struct

Francesco Picciariello, Ilektra Karakosta-Amarantidou, Edoardo Rossi, Marco Avesani

Intermodal quantum key distribution enables the full interoperability of fiber networks and free-space channels, which are both necessary elements for the development of a global quantum network. We present a field trial of an intermodal quantum key distribution system in a simple 3-node heterogeneous quantum network - comprised of two polarization-based tra

Antony M. Overstall, Jacinta Holloway-Brown, James M. McGree

Bayesian optimal design is a well-established approach to planning experiments. A distribution for the responses, i.e. a statistical model, is assumed which is dependent on unknown parameters. A utility function is then specified giving gain in information in estimating the true values of the parameters, using the Bayesian posterior distribution. A Bayesian

Suryansh Upadhyay, Rupshali Roy, Swaroop Ghosh

Quantum computing (QC) holds the promise of revolutionizing problem-solving by exploiting quantum phenomena like superposition and entanglement. It offers exponential speed-ups across various domains, from machine learning and security to drug discovery and optimization. In parallel, quantum encryption and key distribution have garnered substantial interest,

Elena Blundo, Federico Tuzi, Salvatore Cianci, Marzia Cuccu

Moir\'{e} excitons (MXs) are electron-hole pairs localised by the periodic (moir\'{e}) potential forming in two-dimensional heterostructures (HSs). MXs can be exploited, $e.g.$, for creating nanoscale-ordered quantum emitters and achieving or probing strongly correlated electronic phases at relatively high temperatures. Here, we studied the exciton propertie

Franco Ronchetti, Facundo Manuel Quiroga, César Estrebou, Laura Lanzarini

Automatic sign language recognition (SLR) is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of

Kira Maag, Asja Fischer

State-of-the-art deep neural networks have been shown to be extremely powerful in a variety of perceptual tasks like semantic segmentation. However, these networks are vulnerable to adversarial perturbations of the input which are imperceptible for humans but lead to incorrect predictions. Treating image segmentation as a sum of pixel-wise classifications, a

Gabriel Romon, Victor-Emmanuel Brunel

We are interested in measures of central tendency for a population $μ$ on a network, which is modeled by a metric tree. The location parameters that we study are generalized Fréchet means defined as minimizers of the objective function $α\mapsto \mathbb E[\ell(d(α,X)) - \ell(d(o,X))]$, where $\ell$ is a convex, strictly increasing loss, $X\sim μ$ and $o$ is

Lucy D'Agostino McGowan, Sarah C. Lotspeich, Staci A. Hepler

Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it

Shen Yuan, Hongteng Xu

As one of the most popular neural network modules, Transformer plays a central role in many fundamental deep learning models, e.g., the ViT in computer vision and the BERT and GPT in natural language processing. The effectiveness of the Transformer is often attributed to its multi-head attention (MHA) mechanism. In this study, we discuss the limitations of M

Benedek Kovács

We consider the following question by Balister, Gy\H{o}ri and Schelp: given $2^{n-1}$ nonzero vectors in $\mathbb{F}_2^n$ with zero sum, is it always possible to partition the elements of $\mathbb{F}_2^n$ into pairs such that the difference between the two elements of the $i$-th pair is equal to the $i$-th given vector for every $i$? An analogous question in

Joseph Goodier, Neill D. F. Campbell

Out-of-Distribution detection between dataset pairs has been extensively explored with generative models. We show that likelihood-based Out-of-Distribution detection can be extended to diffusion models by leveraging the fact that they, like other likelihood-based generative models, are dramatically affected by the input sample complexity. Currently, all Out-

Marta Zagorowska, Christopher König, Hanlin Yu, Efe C. Balta

Optimization-based controller tuning is challenging because it requires formulating optimization problems explicitly as functions of controller parameters. Safe learning algorithms overcome the challenge by creating surrogate models from measured data. To ensure safety, such data-driven algorithms often rely on exhaustive grid search, which is computationall

Lynda Boukela, Gongxuan Zhang, Meziane Yacoub, Samia Bouzefrane

Intrusion detection is a traditional practice of security experts, however, there are several issues which still need to be tackled. Therefore, in this paper, after highlighting these issues, we present an architecture for a hybrid Intrusion Detection System (IDS) for an adaptive and incremental detection of both known and unknown attacks. The IDS is compose

Milutin Obradovic, Nikola Tuneski

In this paper we study functions $ \omega(z) = c_1z+c_2z^2+c_3z^3+\cdots$ analytic in the open unit disk ${\mathbb D}$ and such that $|\omega'(z)|\le1$ for all $z\in{\mathbb D}$. For these functions we give estimates (sometimes sharp) for the following moduli: $|c_3-c_1c_2|$, $|c_1c_3-c_2^2|$, and $|c_4-c_2^2|$.

Franco Ronchetti, Facundo Manuel Quiroga, César Estrebou, Laura Lanzarini

Automatic sign language recognition is a research area that encompasses human-computer interaction, computer vision and machine learning. Robust automatic recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language to the hearing population. Sign languages diffe

Rishav Hada, Agrima Seth, Harshita Diddee, Kalika Bali

Language serves as a powerful tool for the manifestation of societal belief systems. In doing so, it also perpetuates the prevalent biases in our society. Gender bias is one of the most pervasive biases in our society and is seen in online and offline discourses. With LLMs increasingly gaining human-like fluency in text generation, gaining a nuanced understa

Franco Ronchetti, Facundo Manuel Quiroga, César Estrebou, Laura Lanzarini

Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign l

Suryansh Upadhyay, Swaroop Ghosh

Quantum computing (QC) holds tremendous promise in revolutionizing problem-solving across various domains. It has been suggested in literature that 50+ qubits are sufficient to achieve quantum advantage (i.e., to surpass supercomputers in solving certain class of optimization problems).The hardware size of existing Noisy Intermediate-Scale Quantum (NISQ) com

Runnan Liu, Liang Liu, Yin Xu, Dazhi He

The knowledge of channel covariance matrices is crucial to the design of intelligent reflecting surface (IRS) assisted communication. However, channel covariance matrices may change suddenly in practice. This letter focuses on the detection of the above change in IRS-assisted communication. Specifically, we consider the uplink communication system consisting

Léo Bigorgne, Anibal Velozo Ruiz, Renato Velozo Ruiz

In this paper, we study small data solutions to the Vlasov-Poisson system with the simplest external potential, for which unstable trapping holds for the associated Hamiltonian flow. First, we provide a new proof of global existence for small data solutions to the Vlasov-Poisson system with the trapping potential $\frac{-|x|^2}{2}$ in dimension two. We explo

Max Mendel

Economic engineering is a new field wherein economic systems are modelled in the same manner as traditional mechanical and electrical engineering systems. In this paper, we use Newton's theory of motion as the basis for the theory of demand; thereby establishing a theoretical foundation for economic engineering. We follow Newton's original development, as se

Davide Nuzzi, Leonardo Banchi, Ruggero Vaia, Enrico Compagno

The Toffoli gate is the essential ingredient for reversible computing, an energy efficient classical computational paradigm that evades the energy dissipation resulting from Landauer's principle. In this paper we analyze different setups to realize a magnetic implementation of the Toffoli gate using three interacting classical spins, each one embodying one o

Facundo Manuel Quiroga, Franco Ronchetti, Laura Lanzarini, Cesar Eestrebou

Human action recognition from skeletal data is an important and active area of research in which the state of the art has not yet achieved near-perfect accuracy on many well-known datasets. In this paper, we introduce the Distribution of Action Movements Descriptor, a novel action descriptor based on the distribution of the directions of the motions of the j

Sayan Bhattacharya, Martín Costa, Silvio Lattanzi, Nikos Parotsidis

We present a $O(1)$-approximate fully dynamic algorithm for the $k$-median and $k$-means problems on metric spaces with amortized update time $\tilde O(k)$ and worst-case query time $\tilde O(k^2)$. We complement our theoretical analysis with the first in-depth experimental study for the dynamic $k$-median problem on general metrics, focusing on comparing ou

You-Ming Chang, Chen Yeh, Wei-Chen Chiu, Ning Yu

Deep generative models can create remarkably photorealistic fake images while raising concerns about misinformation and copyright infringement, known as deepfake threats. Deepfake detection technique is developed to distinguish between real and fake images, where the existing methods typically learn classifiers in the image domain or various feature domains.

Jialv Zou, Xinggang Wang, Jiahao Guo, Wenyu Liu

As the size of circuit designs continues to grow rapidly, artificial intelligence technologies are being extensively used in Electronic Design Automation (EDA) to assist with circuit design. Placement and routing are the most time-consuming parts of the physical design process, and how to quickly evaluate the placement has become a hot research topic. Prior

Hing Lie, Kachina Studer, Zhen Zhao, Ben Thomson

Virtual Reality (VR) can support effective and scalable training of psychomotor skills in manufacturing. However, many industry training modules offer experiences that are close-ended and do not allow for human error. We aim to address this gap in VR training tools for psychomotor skills training by exploring an open-ended approach to the system design. We d

Kaushik Dey, Satheesh K. Perepu, Abir Das

Intent-based management will play a critical role in achieving customers' expectations in the next-generation mobile networks. Traditional methods cannot perform efficient resource management since they tend to handle each expectation independently. Existing approaches, e.g., based on multi-agent reinforcement learning (MARL) allocate resources in an efficie

Yang Tan, Mingchen Li, Pan Tan, Ziyi Zhou

Large protein language models are adept at capturing the underlying evolutionary information in primary structures, offering significant practical value for protein engineering. Compared to natural language models, protein amino acid sequences have a smaller data volume and a limited combinatorial space. Choosing an appropriate vocabulary size to optimize th

Mahir Habib, Muhammad Ashad Kabir, Lihong Zheng

Livestock producers often need help in standardising (i.e., converting and validating) their livestock event data. This article introduces a novel solution, LEI2JSON (Livestock Event Information To JSON). The tool is an add-on for Google Sheets, adhering to the livestock event information (LEI) schema. The core objective of LEI2JSON is to provide livestock p

Anas Belfathi, Nicolas Hernandez, Laura Monceaux

We propose a comprehensive study of one-stage elicitation techniques for querying a large pre-trained generative transformer (GPT-3.5-turbo) in the rhetorical role prediction task of legal cases. This task is known as requiring textual context to be addressed. Our study explores strategies such as zero-few shots, task specification with definitions and clari

Christos Gennetidis, Patrice Chantrenne, Thomas Wood

Silicon Emitting Centers (SEC) constitute promising candidates for quantum telecommunication technologies. Their operation depends on the fabrication of light emitting defect centers such as the triinterstitial Si complex, the W-Center. In this paper the formation of Si tri-interstitial clusters after Ga ion beam bombardment on pure silicon substrates and a

Guilherme P. Temporão, Pedro Ripper, Thiago B. Guerreiro, Gustavo C. do Amaral

We provide a tool for measuring the Stokes parameters and the degree of polarization of single photons by employing second order interference, namely the Hong-Ou-Mandel (HOM) interferometer. It is shown that the technique is able to distinguish a partially polarized photon where the polarization state is coupled to an internal degree of freedom, such as time

Ritam Raha, Rajarshi Roy, Nathanael Fijalkow, Daniel Neider

In runtime verification, manually formalizing a specification for monitoring system executions is a tedious and error-prone process. To address this issue, we consider the problem of automatically synthesizing formal specifications from system executions. To demonstrate our approach, we consider the popular specification language Metric Temporal Logic (MTL),

I. H. Whittam, M. Prescott, C. L. Hale, M . J. Jarvis

In this paper we combine the Early Science radio continuum data from the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) Survey, with optical and near-infrared data and release the cross-matched catalogues. The radio data used in this work covers $0.86$ deg$^2$ of the COSMOS field, reaches a thermal noise of $1.7$ $\mu$Jy/beam and contai

Adrián Castelló, Julian Bellavita, Grace Dinh, Yuka Ikarashi

The optimization of the matrix multiplication (or GEMM) has been a need during the last decades. This operation is considered the flagship of current linear algebra libraries such as BLIS, OpenBLAS, or Intel OneAPI because of its widespread use in a large variety of scientific applications. The GEMM is usually implemented following the GotoBLAS philosophy, w

Vladimír Havlík

Can a machine understand the meanings of natural language? Recent developments in the generative large language models (LLMs) of artificial intelligence have led to the belief that traditional philosophical assumptions about machine understanding of language need to be revised. This article critically evaluates the prevailing tendency to regard machine langu

Kevin A. Urquía-Calderón

Left-right symmetric models (LRSM) were proposed to reconcile the apparent parity violation in weak interactions with our intrinsic notion of fundamental parity symmetry. It was quickly realized that LRSM offers a viable framework for explaining neutrino masses by incorporating right-handed neutrinos, $N$, into its spectrum. In this study, we investigate for

Lars Lorch, Andreas Krause, Bernhard Schölkopf

We develop a novel approach towards causal inference. Rather than structural equations over a causal graph, we learn stochastic differential equations (SDEs) whose stationary densities model a system's behavior under interventions. These stationary diffusion models do not require the formalism of causal graphs, let alone the common assumption of acyclicity.

Facundo Manuel Quiroga, Jordina Torrents-Barrena, Laura Cristina Lanzarini, Domenec Puig-Valls

Invariances in neural networks are useful and necessary for many tasks. However, the representation of the invariance of most neural network models has not been characterized. We propose measures to quantify the invariance of neural networks in terms of their internal representation. The measures are efficient and interpretable, and can be applied to any neu

Erik Scheurer, Jenny Schmalfuss, Alexander Lis, Andrés Bruhn

Adversarial patches undermine the reliability of optical flow predictions when placed in arbitrary scene locations. Therefore, they pose a realistic threat to real-world motion detection and its downstream applications. Potential remedies are defense strategies that detect and remove adversarial patches, but their influence on the underlying motion predictio

Lucas Friedrich, Jonas Maziero

Variational Quantum Algorithms (VQAs) employ parameterized quantum circuits optimized using classical methods to minimize a cost function. While VQAs have found broad applications, certain challenges persist. Notably, a significant computational burden arises during parameter optimization. The prevailing ``parameter shift rule'' mandates a double evaluation

Hanwen Zhang, Haijian Sun, Tianyi He, Weiming Xiang

This paper investigates robust beamforming for system-centric energy efficiency (EE) optimization in the vehicular integrated sensing and communication (ISAC) system, where the mobility of vehicles poses significant challenges to channel estimation. To obtain the optimal beamforming under channel uncertainty, we first formulate an optimization problem for ma

Henrique Vitório

We develop appropriate notions of Maslov index and spectral flow for electromagnetic geodesics within a fixed energy level and prove a Morse Index type theorem in this context. This is then applied to the problem of electromagnetic geodesics, all of whose energy are the same, bifurcating from a given one.

L. Peri, M. Benito, C. J. B. Ford, M. F. Gonzalez-Zalba

Modelling the electrical response of multi-level quantum systems at finite frequency has been typically performed in the context of two incomplete paradigms: (i) input-output theory, which is valid at any frequency but neglects dynamic losses, and (ii) semiclassical theory, which captures well dynamic dissipation effects but is only accurate at low frequenci

TongKeun Chang, Bataa Lkhagvasuren, MinSuk Yang

In this paper, we study the global well-posedness of the incompressible Hall-MHD system for small initial data $( u_0, b_0) \in \dot B^{\frac3p -1}_{p,5} (\mathbb{R}) \times \Big( \dot B^{\frac3p -1}_{p,5} (\mathbb{R}) \cap \dot B^{\frac3p}_{p,1}(\mathbb{R}) \Big)$ for $1 < p < 5$. We get the result under the weaker regularity and integrability conditions of

Linlin Lei, Lingjuan He, Qinghua Liao, Wenxing Liu

A phoxonic crystal waveguide with the glide symmetry is designed, in which both electromagnetic and elastic waves can propagate along the glide plane at the same time. Due to the band-sticking effect, super-cell bands of the waveguide degenerate in pairs at the boundary of the Brillouin zone, causing the appearance of gapless guided-modes in the bandgaps. Th

Elizabeth Moreno-Hilario, Luis A. Martinez-Medina, Hui Li, Stefano O. Souza

Dwarf galaxies are known to exhibit an unusual richness in numbers of globular clusters (GCs), property quantified by the specific frequency ($S_N$), which is high for dwarf and giant elliptical galaxies, but with a minimum for intermediate-mass galaxies. In this work we study the role that GC evolution has in setting this trend, for which we use ${\it N}$-b

Kevin Flanagan, Dima Damen, Michael Wray

The onset of long-form egocentric datasets such as Ego4D and EPIC-Kitchens presents a new challenge for the task of Temporal Sentence Grounding (TSG). Compared to traditional benchmarks on which this task is evaluated, these datasets offer finer-grained sentences to ground in notably longer videos. In this paper, we develop an approach for learning to ground

Qingqing Ge, Zeyuan Zhao, Yiding Liu, Anfeng Cheng

Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train and prompt" has shown promising results in adapting GNNs to various tasks with less supervised data. The success of such paradigm can be attributed to the more consistent objectives of pre-training and task-oriented prompt tuning, where the pre-

Jonas R. F. Lima, Guido Burkard

The valley splitting (VS) of a silicon quantum dot plays an important role for the performance and scalability of silicon spin qubits. In this work we investigate the VS of a SiGe/Si/SiGe heterostructure as a function of the size and location of the silicon quantum dot. We use the effective mass approach to describe a realistic system, which takes into accou

Shixin Wang

We study a robust selling problem where a seller attempts to sell one item to a buyer but is uncertain about the buyer's valuation distribution. Existing literature shows that robust screening provides a stronger theoretical guarantee than robust deterministic pricing, but at the expense of implementation complexity, as it requires a menu of infinite options

Xiao Ma, Johannes Pausch, Michael E. Cates

Hyperuniformity, whereby the static structure factor (or density correlator) obeys $S(q)\sim q^{\varsigma}$ with $\varsigma> 0$, emerges at criticality in systems having multiple absorbing states, such as periodically sheared suspensions. These lie in the conserved directed percolation (C-DP) universality class, for which analytic results for $\varsigma$ are

Y. Wang, C. Colandrea, D. S. Oliveira, C. Theiler

This paper presents a quantitative validation of SOLPS-ITER simulations against the TCV-X21 reference case and provides insights into the neutral dynamics and ionization source distribution in this scenario. TCV-X21 is a well-diagnosed diverted L-mode sheath-limited plasma scenario in both toroidal field directions, designed specifically for the validation o

Zi Lin, Zihan Wang, Yongqi Tong, Yangkun Wang

Despite remarkable advances that large language models have achieved in chatbots, maintaining a non-toxic user-AI interactive environment has become increasingly critical nowadays. However, previous efforts in toxicity detection have been mostly based on benchmarks derived from social media content, leaving the unique challenges inherent to real-world user-A

Jan Cychnerski, Bartłomiej Mróz

This paper describes an architecture design process for Networked Music Performance (NMP) platform for medium-sized conducted music ensembles, based on remote rehearsals of Academic Choir of Gdansk University of Technology. The issues of real-time remote communication, in-person music performance, and NMP are described. Three iterative steps defining and ext

Francesca Corni, Fausto Ferrari

In this paper we construct the fractional powers of the sub-Laplacian in Carnot groups through an analytic continuation approach. In addition, we characterize the powers of the fractional sub-Laplacian in the Heisenberg group, and as a byproduct we compute the $k$-th order momenta with respect to the heat kernel.

Anastasia Ivanova, Pierre Ablin

In many scenarios, one uses a large training set to train a model with the goal of performing well on a smaller testing set with a different distribution. Learning a weight for each data point of the training set is an appealing solution, as it ideally allows one to automatically learn the importance of each training point for generalization on the testing s

Juliette Achddou, Nicolò Cesa-Bianchi, Pierre Laforgue

We study multitask online learning in a setting where agents can only exchange information with their neighbors on an arbitrary communication network. We introduce $\texttt{MT-CO}_2\texttt{OL}$, a decentralized algorithm for this setting whose regret depends on the interplay between the task similarities and the network structure. Our analysis shows that the

Tanul Gupta, Guido Masella, Francesco Mattiotti, Nikolay V. Prokof'ev

The disorder-induced quantum phase transition between superfluid and non-superfluid states of bosonic particles in one dimension is generally expected to be of the Berezinskii-Kosterlitz-Thouless (BKT) type. Here, we show that hard-core lattice bosons with integrable power-law hopping decaying with distance as $1/r^\alpha$ - corresponding in spin language to

Nick Oikonomeas-Koppasis, Stefania Ketzetzi, Daniela J. Kraft, Peter Schall

Active colloidal microswimmers serve as archetypical active fluid systems, and as models for biological swimmers. Here, by studying in detail their velocity traces, we find robust power-law intermittency with system-dependent exponential cut off. We model the motion by an interplay of the field gradient-dependent active force and the locally fluctuating hydr

Łukasz Czekaj, Łukasz Radzinski, Mateusz Kolimaga, Jakub Domaszewicz

Automated interpretation of signals yields many impressive applications from the area of affective computing and human activity recognition (HAR). In this paper we ask the question about possibility of cognitive activity recognition on the base of particular set of signals. We use recognition of the game played by the participant as a playground for explorat

Eteri Samsonadze

New formulae are presented for the number $P(b)$ of non-negative integer solutions of a Diophantine equation $\sum_{i=1}^{n}a_ix_i=b$ and for the number $Q(b)$ of non-negative integer solutions of the Diophantine inequality $\sum_{i=1}^{n}a_ix_i\leq b$ $(a_i>0, b\geq 0$).

Chuanhao Wei

In this paper, we revise the Bott Vanishing on projective toric varieties by giving it an alternative proof with a condition that is compatible with the condition of Kawamata-Viehweg Vanishing. This proof can also be adapted to generalize Bott Vanishing to the setting using mixed Hodge modules. Lastly, we give a counter-example towards the relative Bott Vani

Chang Liu, Liguo Zhou, Yanliang Huang, Alois Knoll

Vehicle perception systems strive to achieve comprehensive and rapid visual interpretation of their surroundings for improved safety and navigation. We introduce YOLO-BEV, an efficient framework that harnesses a unique surrounding cameras setup to generate a 2D bird's-eye view of the vehicular environment. By strategically positioning eight cameras, each at

Dániel Rácz, Mihály Petreczky, András Csertán, Bálint Daróczy

Recent advances in deep learning have given us some very promising results on the generalization ability of deep neural networks, however literature still lacks a comprehensive theory explaining why heavily over-parametrized models are able to generalize well while fitting the training data. In this paper we propose a PAC type bound on the generalization err

Lino Reggiani, Eleonora Alfinito, Federico Intini

By analogy with the fundamental quantum units of electrical conductance $G_0^e=\frac{2 e^2}{h}$ and thermal conductance $K_0^t=\frac{2 K_B^2 T}{h}$ we define a fundamental quantum unit of conductance, $G_0^m$, and diffusion of a massive gas of atomic particles, respectively given by $$ G_0^m=\frac{m^2}{h} \ , \ D_0=\frac{h}{m}$$ with $h$ the Planck constant,

Tomas Vignau Costa, Santiago Grigera, Rodolfo Borzi

In this paper, we examine the magnetoelectric response of Ising pyrochlores, focusing on both the ordered antiferromagnetic state and the frustrated ferromagnetic case known as "spin-ice". We employ a model which accounts for magnetoelastic effects by considering the interplay between oxygen distortions and superexchange magnetic interactions within pyrochlo

Aurya S. Javeed, Drew P. Kouri, Thomas M. Surowiec

Generalized variational inference (GVI) provides an optimization-theoretic framework for statistical estimation that encapsulates many traditional estimation procedures. The typical GVI problem is to compute a distribution of parameters that maximizes the expected payoff minus the divergence of the distribution from a specified prior. In this way, GVI enable

Robson Ricardo de Araujo, Francisco Cesar Polcino Milies, Raul Antonio Ferraz

In this work we show that every minimal code in a semisimple group algebra $\mathbb{F}_qG$ is essential if $G$ is a simple group. Since the alternating group $A_n$ is simple if $n=3$ or $n\geq 5$, we present some examples of minimal codes in $\mathbb{F}_qA_n$. For this purpose, if $char(\mathbb{F}_q)> n$, we present the Wedderburn-Artin decomposition of $\ma

Sam Bowyer, Thomas Heap, Laurence Aitchison

Importance sampling is a popular technique in Bayesian inference: by reweighting samples drawn from a proposal distribution we are able to obtain samples and moment estimates from a Bayesian posterior over latent variables. Recent work, however, indicates that importance sampling scales poorly -- in order to accurately approximate the true posterior, the req

Weixin Chen, Li Chen, Yongxin Ni, Yuhan Zhao

Recently, multimodal recommendations (MMR) have gained increasing attention for alleviating the data sparsity problem of traditional recommender systems by incorporating modality-based representations. Although MMR exhibits notable improvement in recommendation accuracy, we empirically validate that an increase in the quantity or variety of modalities leads

Antonio Valerio Miceli-Barone, Alex Lascarides, Craig Innes

Simulation is an invaluable tool for developing and evaluating controllers for self-driving cars. Current simulation frameworks are driven by highly-specialist domain specific languages, and so a natural language interface would greatly enhance usability. But there is often a gap, consisting of tacit assumptions the user is making, between a concise English

Ojasvi Pal, Tarun Kanti Ghosh

We study Berry connection polarizability (BCP) induced electric polarization and third-order Hall (TOH) effect in a two-dimensional electron/hole gas (2DEG/2DHG) with Rashba-Dresselhaus (RD) spin-orbit couplings in III-V semiconductor heterostructures. The electric polarization decreases with the increase of the Fermi energy and is responsive to the electric

Nouar AlDahoul, Joseph Hong, Matteo Varvello, Yasir Zaki

Generative Artificial Intelligence (AI) is a cutting-edge technology capable of producing text, images, and various media content leveraging generative models and user prompts. Between 2022 and 2023, generative AI surged in popularity with a plethora of applications spanning from AI-powered movies to chatbots. In this paper, we delve into the potential of ge

Daniela Teodorescu, Tiffany Cheng, Alona Fyshe, Saif M. Mohammad

Research in psychopathology has shown that, at an aggregate level, the patterns of emotional change over time -- emotion dynamics -- are indicators of one's mental health. One's patterns of emotion change have traditionally been determined through self-reports of emotions; however, there are known issues with accuracy, bias, and ease of data collection. Rece

Ali İrfan Mahmutoğulları, Tias Guns

We investigate the benefit of using contextual information in data-driven demand predictions to solve the robust capacitated vehicle routing problem with time windows. Instead of estimating the demand distribution or its mean, we introduce contextual machine learning models that predict demand quantiles even when the number of historical observations for som

Hanlong Fang, Mingyi Zhang

We give a linear algebraic construction of the Lafforgue spaces associated to the Grassmannians $G(2,n)$ by blowing up certain explicitly defined monomial ideals, which sharpens and generalizes a result of Faltings. As an application, we provide a family of homogeneous varieties with high complexity and with nice compactifications, which exhibits the notion

Yetkin Pulcu, János Koltai, Andor Kormányos, Guido Burkard

The direct bandgap found in hexagonal germanium and some of its alloys with silicon allows for an optically active material within the group-IV semiconductor family with various potential technological applications. However, there remain some unanswered questions regarding several aspects of the band structiure, including the strength of the electric dipole

N. Giovenale, L. Hernandez-Martinez, A. P. Majtey, A. Valdés-Hernández

The entanglement production is key for many applications in the realm of quantum information, but so is the identification of processes that allow to create entanglement in a fast and sustained way. Most of the advances in this direction have been circumscribed to bipartite systems only, and the rate of entanglement in multipartite system has been much less

Venkatraman Renganathan, Anders Rantzer, Olle Kjellqvist

Control of network systems with uncertain local dynamics has remained an open problem for a long time. In this paper, a distributed minimax adaptive control algorithm is proposed for such networks whose local dynamics has an uncertain parameter possibly taking finite number of values. To hedge against this uncertainty, each node in the network collects the h

Tianhao Li, Ruichang Zhang, Zhixin Liu, Zhuo Zou

Turing's model has been widely used to explain how simple, uniform structures can give rise to complex, patterned structures during the development of organisms. However, it is very hard to establish rigorous theoretical results for the dynamic evolution behavior of Turing's model since it is described by nonlinear partial differential equations. We focus on

Qing Han, Weiming Shen, Yue Wang

A version of the singular Yamabe problem in smooth domains in a closed manifold yields complete conformal metrics with negative constant scalar curvatures. In this paper, we study the blow-up phenomena of Ricci curvatures of these metrics on domains whose boundary is close to a certain limit set of a lower dimension. We will characterize the blow-up set acco

Junfeng Hu, Xu Liu, Zhencheng Fan, Yuxuan Liang

Spatio-temporal graph learning is a fundamental problem in modern urban systems. Existing approaches tackle different tasks independently, tailoring their models to unique task characteristics. These methods, however, fall short of modeling intrinsic uncertainties in the spatio-temporal data. Meanwhile, their specialized designs misalign with the current res

Haobo Jiang, Mathieu Salzmann, Zheng Dang, Jin Xie

In this paper, we introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D registration task as a denoising diffusion process, which progressively refines the pose of the source point cloud to obtain a precise alignment with the model point cloud. Trainin

Giulia Peveri

Conformal field theory (CFT) plays a key role in modern theoretical physics. Through CFT we describe real physical systems at criticality and fixed points of the renormalization group flow. It is also central in the study of quantum gravity, thanks to the AdS/CFT correspondence. This thesis originates in the context of the N=4 supersymmetric Yang-Mills (SYM)