May 2024 arXiv papers — page 68

Shuang Zhang, Feifan Liu, Haoxiang Xia

Despite persistent efforts to understand the dynamics of creativity of scientists over careers in terms of productivity, impact, and prize, little is known about the dynamics of scientists' disruptive efforts that affect individual academic careers and drive scientific advance. Drawing on millions of data over six decades and across nineteen disciplines, ass

Enrico Caprioglio, Luc Berthouze

Oscillatory complex networks in the metastable regime have been used to study the emergence of integrated and segregated activity in the brain, which are hypothesised to be fundamental for cognition. Yet, the parameters and the underlying mechanisms necessary to achieve the metastable regime are hard to identify, often relying on maximising the correlation w

José Guimarães, Dorsa S. Fartab, Michal Moravec, Marcus Schmidt

In this work, we report on the concurrent emergence of the directional Kondo behavior and incommensurate magnetic ordering in a layered material. We employ temperature- and magnetic field-dependent resistivity measurements, susceptibility measurements, and high resolution wavelength X-ray diffraction spectroscopy to study the electronic properties of AgCrSe$

Anthony Bardou, Patrick Thiran, Giovanni Ranieri

Bayesian Optimization (BO) has proven to be very successful at optimizing a static, noisy, costly-to-evaluate black-box function $f : \mathcal{S} \to \mathbb{R}$. However, optimizing a black-box which is also a function of time (i.e., a dynamic function) $f : \mathcal{S} \times \mathcal{T} \to \mathbb{R}$ remains a challenge, since a dynamic Bayesian Optimiz

Luqi Wang, Yang Xu, Shaojie Shen

State estimation is a critical foundational module in robotics applications, where robustness and performance are paramount. Although in recent years, many works have been focusing on improving one of the most widely adopted state estimation methods, visual inertial odometry (VIO), by incorporating multiple cameras, these efforts predominantly address synchr

ALICE Collaboration

The transverse momentum ($p_{\rm T}$) differential production cross section of the promptly produced charm-strange baryon $\Xi_{\rm c}^{0}$ (and its charge conjugate $\overline{\Xi_{\rm c}^{0}}$) is measured at midrapidity via its hadronic decay into ${\rm \pi^{+}}\Xi^{-}$ in p$-$Pb collisions at a centre-of-mass energy per nucleon$-$nucleon collision $\sqrt

Philipp Horak

We present recent measurements of $\tau$ physics at the Belle II experiment at SuperKEKB. Measurements include a test of $e-\mu$ lepton flavor universality in $\tau$ decays, a search for the lepton flavor violating $\tau \rightarrow \mu \mu \mu$ decay and a precision measurement of the $\tau$ lepton invariant mass. In addition, we present a measurement of th

Kun Li, Xiuwen Gong, Shirui Pan, Jia Wu

Drug response prediction (DRP) is a crucial phase in drug discovery, and the most important metric for its evaluation is the IC50 score. DRP results are heavily dependent on the quality of the generated molecules. Existing molecule generation methods typically employ classifier-based guidance, enabling sampling within the IC50 classification range. However,

Jiajie Tao, Hao Ni, Chong Liu

Since the weak convergence for stochastic processes does not account for the growth of information over time which is represented by the underlying filtration, a slightly erroneous stochastic model in weak topology may cause huge loss in multi-periods decision making problems. To address such discontinuities Aldous introduced the extended weak convergence, w

Basel Mousi, Nadir Durrani, Fahim Dalvi, Majd Hawasly

Despite their remarkable ability to capture linguistic nuances across diverse languages, questions persist regarding the degree of alignment between languages in multilingual embeddings. Drawing inspiration from research on high-dimensional representations in neural language models, we employ clustering to uncover latent concepts within multilingual models.

Rino Bandiera, Oleh Petruk

Diagnostics based on the polarization properties of the synchrotron emission can provide precious information on both the ordered structure and the random level of the magnetic field. While this issue has been already analysed in the radio, the polarization data recently obtained by the mission IXPE have shown the need to extend this analysis to the X-rays.

Loran Ts. Adzhemyan, Daniil A. Evdokimov, Mikhail V. Kompaniets

Parametric integration with hyperlogarithms so far has been successfully used in problems of high energy physics (HEP) and critical statics. In this work, for the first time, it is applied to a problem of critical dynamics, namely, a stochastic model of developed turbulence in high-dimensional spaces, which has a propagator that is non-standard with respect

Mathieu Even, Luca Ganassali, Jakob Maier, Laurent Massoulié

The Procrustes-Wasserstein problem consists in matching two high-dimensional point clouds in an unsupervised setting, and has many applications in natural language processing and computer vision. We consider a planted model with two datasets $X,Y$ that consist of $n$ datapoints in $\mathbb{R}^d$, where $Y$ is a noisy version of $X$, up to an orthogonal trans

Riccardo Aldrigo, Roberto Menichetti, Raffaello Potestio

The analysis of complex systems such as neural networks is made particularly difficult by the overwhelming number of their interacting components. In the absence of prior knowledge, identifying a small but informative subset of network nodes on which the analysis should focus is a rather challenging task. In this work, we address this problem in the context

Xinran Nicole Han, Todd Zickler, Ko Nishino

Models for inferring monocular shape of surfaces with diffuse reflection -- shape from shading -- ought to produce distributions of outputs, because there are fundamental mathematical ambiguities of both continuous (e.g., bas-relief) and discrete (e.g., convex/concave) types that are also experienced by humans. Yet, the outputs of current models are limited

Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer

Recent advances in multimodal foundation models have set new standards in few-shot anomaly detection. This paper explores whether high-quality visual features alone are sufficient to rival existing state-of-the-art vision-language models. We affirm this by adapting DINOv2 for one-shot and few-shot anomaly detection, with a focus on industrial applications. W

Fernando E. Casado

The global increase in the elderly population necessitates innovative long-term care solutions to improve the quality of life for vulnerable individuals while reducing caregiver burdens. Assistive robots, leveraging advancements in Machine Learning, offer promising personalised support. However, their integration into daily life raises significant privacy co

Guillaume Couairon, Christian Lessig, Anastase Charantonis, Claire Monteleoni

One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in the neural network architecture. A popular prior is locality, where the atmospheric data is processed with local neural interactions, like 3D convolutions or 3D local attention windows as in Pangu-Weather. On the other hand

Ranjit Singh, Alexander E. Teretenkov

We investigate entangled Schr\"odinger cat-like states (SCLSs) in degenerate and non-degenerate spontaneous parametric down-conversion (SPDC) with a fully quantized, depleted pump. Our fully quantum treatment, visualized via Wigner functions, reveals non-Gaussian features and interference patterns absent in semiclassical models. For degenerate SPDC, we demon

ALICE Collaboration

This Letter presents the first measurement of the impact-parameter dependent angular anisotropy in the decay of coherently photoproduced $\rho^0$ mesons. The $\rho^0$ mesons are reconstructed through their decay into pion pairs. The measured anisotropy corresponds to the amplitude of the $\cos(2\phi)$ modulation, where $\phi$ is the angle between the two vec

Abuzar B. M. Adam, Xiaoyu Wan, Mohammed Saleh Ali Muthanna

In this work, we address the issue of quality of experience (QoE) in unmanned aerial vehicle (UAV) aided multiuser rate-splitting multiple access (RSMA) networks under secrecy constraints. The problem is formulated as maximization of sum mean opinion scores (MOSs) of the users. The problem is decomposed into two subproblems, beamforming and rate allocation a

Youssef Jeyar, Minggang Luo, Brahim Guizal, H. B. Chan

We study the Casimir-Lifshitz force (CLF) between a gold plate and a graphene-covered dielectric grating. Using a scattering matrix (S-matrix) approach derived from the Fourier Modal Method (FMM), we find a significant enhancement in the CLF as compared to a mere dielectric slab coated with graphene, over a wide range of temperatures. Additionally, we demons

Musaddiq Al Ali, Masatoshi Shimoda

In this research, multi-physics topology optimization is employed to achieve the detailed design of a lightweight porous linear actuation mechanism that harnesses energy through laser activation. A multiscale topology optimization methodology is introduced for micro- and macroscale design, considering energy dissipation via heat convection and radiation. Thi

Yuya Yoshikawa, Masanari Kimura, Ryotaro Shimizu, Yuki Saito

Techniques that explain the predictions of black-box machine learning models are crucial to make the models transparent, thereby increasing trust in AI systems. The input features to the models often have a nested structure that consists of high- and low-level features, and each high-level feature is decomposed into multiple low-level features. For such inpu

Gaurav Maheshwari, Aurélien Bellet, Pascal Denis, Mikaela Keller

In this paper, we introduce a data augmentation approach specifically tailored to enhance intersectional fairness in classification tasks. Our method capitalizes on the hierarchical structure inherent to intersectionality, by viewing groups as intersections of their parent categories. This perspective allows us to augment data for smaller groups by learning

Xingguang Jiang, Xiaofeng Bian, Chenggang Guo

Depth estimation based on stereo matching is a classic but popular computer vision problem, which has a wide range of real-world applications. Current stereo matching methods generally adopt the deep Siamese neural network architecture, and have achieved impressing performance by constructing feature matching cost volumes and using 3D convolutions for cost a

Marco Rando, Luca Demetrio, Lorenzo Rosasco, Fabio Roli

Machine learning malware detectors are vulnerable to adversarial EXEmples, i.e., carefully-crafted Windows programs tailored to evade detection. Unlike other adversarial problems, attacks in this context must be functionality-preserving, a constraint that is challenging to address. As a consequence, heuristic algorithms are typically used, which inject new c

Bikram Keshari Behera, Surendra Kumar Gour, Shyamal Biswas

We have analytically determined the refractive index for the mechanical refraction of a relativistic particle for its all possible speeds. We have critically analysed the importance of Descartes' metaphysical theory and extended it in this regard. We have considered the conservation of the tangential component of the relativistic momentum and the relativisti

Alexandru Chirvasitu

We characterize those (continuously-normed) Banach bundles $\mathcal{E}\to X$ with compact Hausdorff base whose spaces $\Gamma(\mathcal{E})$ of global continuous sections are topologically finitely-generated over the function algebra $C(X)$, answering a question of I. Gogi\'c's and extending analogous work for metrizable $X$. Conditions equivalent to topolog

Songze Li, Ruoxi Cheng, Xiaojun Jia

The widespread usage of large-scale multimodal models like CLIP has heightened concerns about the leakage of PII. Existing methods for identity inference in CLIP models require querying the model with full PII, including textual descriptions of the person and corresponding images (e.g., the name and the face photo of the person). However, applying images may

Yuanpeng He, Lijian Li

Open-world long-tailed semi-supervised learning (OLSSL) has increasingly attracted attention. However, existing OLSSL algorithms generally assume that the distributions between known and novel categories are nearly identical. Against this backdrop, we construct a more \emph{Realistic Open-world Long-tailed Semi-supervised Learning} (\textbf{ROLSSL}) setting

Jeong-Jung Kim, Doo-Yeol Koh, Chang-Hyun Kim

This paper presents a novel manipulation strategy that uses keypoint correspondences extracted from visuo-tactile sensor images to facilitate precise object manipulation. Our approach uses the visuo-tactile feedback to guide the robot's actions for accurate object grasping and placement, eliminating the need for post-grasp adjustments and extensive training.

Xhek Turkeshi, Pasquale Calabrese, Andrea De Luca

The essence of the Mpemba effect is that non-equilibrium systems may relax faster the further they are from their equilibrium configuration. In the quantum realm, this phenomenon arises in the dynamics of closed systems, where it is witnessed by fundamental features such as symmetry and entanglement. Here, we study the quantum Mpemba effect in charge-preserv

Andrés Gúrpide, Noel Castro Segura

A major prediction of most super-Eddington accretion theories is the presence of anisotropic emission from supercritical disks, but the degree of anisotropy and its dependency with energy remain poorly constrained observationally. A key breakthrough allowing to test such predictions was the discovery of high-excitation photoionized nebulae around Ultralumino

ALICE Collaboration

A study of strange hadron production associated with hard scattering processes and with the underlying event is conducted to investigate the origin of the enhanced production of strange hadrons in small collision systems characterised by large charged-particle multiplicities. For this purpose, the production of the single-strange meson ${\rm K^0_S}$ and the

Estrid Buhl Naver, Okan Yetik, Noémie Ott, Matteo Busi

Neutron imaging has gained significant importance as a material characterisation technique and is particularly useful to visualise hydrogenous materials in objects opaque to other radiations. Particular fields of application include investigations of hydrogen in metals as well as metal corrosion, thanks to the fact that neutrons can penetrate metals better t

Even Moa Myklebust, Arnoldo Frigessi, Fredrik Schjesvold, Jasmine Foo

Predicting cancer dynamics under treatment is challenging due to high inter-patient heterogeneity, lack of predictive biomarkers, and sparse and noisy longitudinal data. Mathematical models can summarize cancer dynamics by a few interpretable parameters per patient. Machine learning methods can then be trained to predict the model parameters from baseline co

Chufan Shi, Cheng Yang, Xinyu Zhu, Jiahao Wang

Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency. In MoE, each token in the input sequence activates a different subset of experts determined by a routing mechanism. However, the unchosen experts in MoE models do not contribute to the output, potentially leading to underutiliza

Zuoyong Li, Qinghua Lin, Haoyi Fan, Tiesong Zhao

Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification. Specifically, we design a video augmentation module called the Super Augmentation Block (SAB). SAB adds Gaussi

Jaime González-González, Silvia García-Méndez, Francisco de Arriba-Pérez, Francisco J. González-Castaño

Concerns about the effect of greenhouse gases have motivated the development of certification protocols to quantify the industrial carbon footprint (CF). These protocols are manual, work-intensive, and expensive. All of the above have led to a shift towards automatic data-driven approaches to estimate the CF, including Machine Learning (ML) solutions. Unfort

Xuanle Zhao, Yue Sun, Ziyi Wang, Bo Xu

Spatiotemporal prediction is important in solving natural problems and processing video frames, especially in weather forecasting and human action recognition. Recent advances attempt to incorporate prior physical knowledge into the deep learning framework to estimate the unknown governing partial differential equations (PDEs) in complex dynamics, which have

Gaëtan Laziou, Rémi Lemoy, Marion Le Texier

Urban scaling laws summarize how urban attributes evolve with city size. Recent criticism questions notably the aggregate view of this approach, which leads to neglecting the internal structure of cities. This is all the more relevant for housing prices due to their important variations across space. Based on a dataset compiling millions of real estate trans

Baotong Lu, Kaisong Huang, Chieh-Jan Mike Liang, Tianzheng Wang

Memory disaggregation can potentially allow memory-optimized range indexes such as B+-trees to scale beyond one machine while attaining high hardware utilization and low cost. Designing scalable indexes on disaggregated memory, however, is challenging due to rudimentary caching, unprincipled offloading and excessive inconsistency among servers. This paper pr

Luca Delussu, Tilman Birnstiel, Anna Miotello, Paola Pinilla

Large mm surveys of star forming regions enable the study of entire populations of planet-forming disks and reveal correlations between their observable properties. Population studies of disks have shown that the correlation between disk size and millimeter flux could be explained either through disks with strong substructure, or alternatively by the effects

Rafik Belhadef, Henri-Alex Esbelin

In this paper, we study the complexity of p-adic continued fractions of a rational number, which is the p-adic analogue of the theorem of Lame. We calculate the length of Browkin expansion, and the length of Schneider expansion. Also, some numerical examples have been given.

Andrea Spinelli, Francesca Maggioni, Tânia Rodrigues Pereira Ramos, Ana Paula Barbosa-Póvoa

In this paper we present a multi-stage stochastic optimization model to solve an inventory routing problem for recyclable waste collection. The objective is the maximization of the total expected profit of the waste collection company. The decisions are related to the selection of the bins to be visited and the corresponding routing plan in a predefined time

Selçuk Topal

Given the complex nature of galaxies' interstellar medium (ISM), multi-wavelength data are required to probe the interplay among gas, dust, and stellar populations. Spiral galaxies are ideal laboratories for such a goal as they are rich in gas and dust. Using carbon monoxide (CO) along with GALEX far-ultraviolet (FUV) and Spitzer near-infrared (NIR) data we

Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir, M. Saquib Sarfraz

In this work, we tackle the problem of domain generalization for object detection, specifically focusing on the scenario where only a single source domain is available. We propose an effective approach that involves two key steps: diversifying the source domain and aligning detections based on class prediction confidence and localization. Firstly, we demonst

Sujai Hiremath, Jacqueline R. M. A. Maasch, Mengxiao Gao, Promit Ghosal

Learning the unique directed acyclic graph corresponding to an unknown causal model is a challenging task. Methods based on functional causal models can identify a unique graph, but either suffer from the curse of dimensionality or impose strong parametric assumptions. To address these challenges, we propose a novel hybrid approach for global causal discover

Maksim Stebliy, Zhimba Namsaraev, Michail Bazrov, Mikhail Letushev

Ferrimagnets have the potential to play a key role in spintronics due to their high stability, low energy consumption, and rapid magnetic state switching. These characteristics are typically observed in ferrimagnetic materials near magnetic or angular compensation states. Near the magnetic compensation point, an external field can disrupt the collinearity be

Alexander Modell

In this paper, we derive entrywise error bounds for low-rank approximations of kernel matrices obtained using the truncated eigen-decomposition (or singular value decomposition). While this approximation is well-known to be optimal with respect to the spectral and Frobenius norm error, little is known about the statistical behaviour of individual entries. Ou

Bubai Manna

In a connected simple graph G = (V,E), each vertex of V is colored by a color from the set of colors C={c1, c2,..., c_{\alpha}}$. We take a subset S of V, such that for every vertex v in V\S, at least one vertex of the same color is present in its set of nearest neighbors in S. We refer to such a S as a consistent subset. The Minimum Consistent Subset (MCS)

Tim Gyger, Reinhard Furrer, Fabio Sigrist

Gaussian processes are flexible probabilistic regression models which are widely used in statistics and machine learning. However, a drawback is their limited scalability to large data sets. To alleviate this, full-scale approximations (FSAs) combine predictive process methods and covariance tapering, thus approximating both global and local structures. We s

ALICE Collaboration

This work aims to differentiate strangeness produced from hard processes (jet-like) and softer processes (underlying event) by measuring the angular correlation between a high-momentum trigger hadron (h) acting as a jet-proxy and a produced strange hadron ($\phi(1020)$ meson). Measuring h$-\phi$ correlations at midrapidity in p$-$Pb collisions at $\sqrt{s_{\

Johan S Daniel, Anand Pal

The advancement of large language models has significantly improved natural language processing. However, challenges such as jailbreaks (prompt injections that cause an LLM to follow instructions contrary to its intended use), hallucinations (generating incorrect or misleading information), and comprehension errors remain prevalent. In this report, we presen

Kesavaraj V, Anuprabha M, Anil Kumar Vuppala

Identifying user-defined keywords is crucial for personalizing interactions with smart devices. Previous approaches of user-defined keyword spotting (UDKWS) have relied on short-term spectral features such as mel frequency cepstral coefficients (MFCC) to detect the spoken keyword. However, these features may face challenges in accurately identifying closely

Yanrui Du, Sendong Zhao, Danyang Zhao, Ming Ma

Large Language Models (LLMs) are increasingly deployed in various applications. As their usage grows, concerns regarding their safety are rising, especially in maintaining harmless responses when faced with malicious instructions. Many defense strategies have been developed to enhance the safety of LLMs. However, our research finds that existing defense stra

Mohammed Hassanin, Nour Moustafa

The recent progression of Large Language Models (LLMs) has witnessed great success in the fields of data-centric applications. LLMs trained on massive textual datasets showed ability to encode not only context but also ability to provide powerful comprehension to downstream tasks. Interestingly, Generative Pre-trained Transformers utilised this ability to br

Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo

Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents RefChecker, a framework that introduces claim-triplets to represent claims in LLM responses, aiming to detect fine-grained hallucinations. In RefChecker, an extractor generates claim-triplets from a response, which are then evalu

X Zhang, C Marsden, M Moscheni, E Maartensson

The scrape-off layer parallel heat flux decay lengths measured at ST40, a high field, low aspect ratio spherical tokamak, have been observed to bifurcate into two groups. The wide group matches closely with the scale of ion poloidal Larmour radius and follows existing H-mode scalings, while the narrow group falls up to 10 times below scalings, on the scale o

Jialin Hong, Baohui Hou, Liying Sun

In this manuscript, we propose efficient stochastic semi-explicit symplectic schemes tailored for nonseparable stochastic Hamiltonian systems (SHSs). These semi-explicit symplectic schemes are constructed by introducing augmented Hamiltonians and using symmetric projection. In the case of the artificial restraint in augmented Hamiltonians being zero, the pro

Alexander A. Marchuk

To address questions about the physical nature and origin of spiral arms in galaxies, it is necessary to measure their dynamical properties, such as the angular speed, $\Omega_p$, or the corotation radius. Observations suggest that galaxies may contain several independent spiral patterns simultaneously. It was shown that so-called non-linear resonance coupli

Anda Skeja, Sofia C. Olhede

Multiplex graphs, characterised by their layered structure, exhibit informative interdependencies within layers that are crucial for understanding complex network dynamics. Quantifying the interaction and shared information among these layers is challenging due to the non-Euclidean structure of graphs. Our paper introduces a comprehensive theory of multivari

Ivo Pezlar

We introduce a simple natural deduction system for reasoning with judgments of the form "there exists a proof of $\varphi$" to explore the notion of judgmental existence following Martin-L\"{o}f's methodology of distinguishing between judgments and propositions. In this system, the existential judgment can be internalized into a modal notion of propositional

Lv Tang, HaoKe Xiao, Peng-Tao Jiang, Hao Zhang

Foundational models have significantly advanced in natural language processing (NLP) and computer vision (CV), with the Transformer architecture becoming a standard backbone. However, the Transformer's quadratic complexity poses challenges for handling longer sequences and higher resolution images. To address this challenge, State Space Models (SSMs) like Ma

Zhao Liu, Bruno Mera, Manato Fujimoto, Tomoki Ozawa

Quantum geometry is a fundamental concept to characterize the local properties of quantum states. It is recently demonstrated that saturating certain quantum geometric bounds allows a topological Chern band to share many essential features with the lowest Landau level, facilitating fractionalized phases in moir\'e flat bands. In this work, we systematically

Andrea Ponte, Dmitrijs Trizna, Luca Demetrio, Battista Biggio

As a result of decades of research, Windows malware detection is approached through a plethora of techniques. However, there is an ongoing mismatch between academia -- which pursues an optimal performances in terms of detection rate and low false alarms -- and the requirements of real-world scenarios. In particular, academia focuses on combining static and d

Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley, Otmar Hilliges

Advances in latent diffusion models (LDMs) have revolutionized high-resolution image generation, but the design space of the autoencoder that is central to these systems remains underexplored. In this paper, we introduce LiteVAE, a new autoencoder design for LDMs, which leverages the 2D discrete wavelet transform to enhance scalability and computational effi

Davood Razzaghi, Ali MotazediFard, Marzieh Akbari, Seyed Ahmad Madani

In this paper, we experimentally report the saturated absorption spectroscopy (SAS) and frequency locking (FL) of a narrow-band DBR laser with 0.5MHz linewidth on the LD2-transition of Rb atoms.

Alexei G. Myasnikov, Mahmood Sohrabi

In this paper we describe all groups that are first-order (elementarily) equivalent to the classical matrix groups such as $GL_n(F), SL_n(F)$ and $T_n(F)$ over a field $F$ provided $n \geq 3$.

Ruiyuan Gao, Kai Chen, Zhihao Li, Lanqing Hong

Controllable generative models for images and videos have seen significant success, yet 3D scene generation, especially in unbounded scenarios like autonomous driving, remains underdeveloped. Existing methods lack flexible controllability and often rely on dense view data collection in controlled environments, limiting their generalizability across common da

Linghao Feng, Dongcheng Zhao, Sicheng Shen, Yiting Dong

This paper presents a novel approach leveraging Spiking Neural Networks (SNNs) to construct a Variational Quantized Autoencoder (VQ-VAE) with a temporal codebook inspired by hippocampal time cells. This design captures and utilizes temporal dependencies, significantly enhancing the generative capabilities of SNNs. Neuroscientific research has identified hipp

Hadi Vafaii, Dekel Galor, Jacob L. Yates

Variational autoencoders (VAEs) employ Bayesian inference to interpret sensory inputs, mirroring processes that occur in primate vision across both ventral (Higgins et al., 2021) and dorsal (Vafaii et al., 2023) pathways. Despite their success, traditional VAEs rely on continuous latent variables, which deviates sharply from the discrete nature of biological

Joris Depoortere, Johan Driesen, Johan Suykens, Hussain Syed Kazmi

Deep learning models have gained increasing prominence in recent years in the field of solar pho-tovoltaic (PV) forecasting. One drawback of these models is that they require a lot of high-quality data to perform well. This is often infeasible in practice, due to poor measurement infrastructure in legacy systems and the rapid build-up of new solar systems ac

Peter R. Lawson, Tanya V. Kizovski, Michael M. Tice, Benton C. Clark

Planetary rovers can use onboard data analysis to adapt their measurement plan on the fly, improving the science value of data collected between commands from Earth. This paper describes the implementation of an adaptive sampling algorithm used by PIXL, the X-ray fluorescence spectrometer of the Mars 2020 Perseverance rover. PIXL is deployed using the rover

Laura Mascarell, Yan L'Homme, Majed El Helou

Understanding the nature of high-quality summaries is crucial to further improve the performance of multi-document summarization. We propose an approach to characterize human-written summaries using partial information decomposition, which decomposes the mutual information provided by all source documents into union, redundancy, synergy, and unique informati

Andreas Maurer

The paper proves generalization results for a class of stochastic learning algorithms. The method applies whenever the algorithm generates an absolutely continuous distribution relative to some a-priori measure and the Radon Nikodym derivative has subgaussian concentration. Applications are bounds for the Gibbs algorithm and randomizations of stable determin

Peter Súkeník, Marco Mondelli, Christoph Lampert

Deep neural networks (DNNs) exhibit a surprising structure in their final layer known as neural collapse (NC), and a growing body of works has currently investigated the propagation of neural collapse to earlier layers of DNNs -- a phenomenon called deep neural collapse (DNC). However, existing theoretical results are restricted to special cases: linear mode

Daniel Kienzle, Marco Kantonis, Robin Schön, Rainer Lienhart

Utilizing transformer architectures for semantic segmentation of high-resolution images is hindered by the attention's quadratic computational complexity in the number of tokens. A solution to this challenge involves decreasing the number of tokens through token merging, which has exhibited remarkable enhancements in inference speed, training efficiency, and

Roy Eid, Alfred Hammond, Lucas Lavoine, Thomas Bourdel

We realize textbook experiments on Bose-Einstein condensate tunnelling through thin repulsive potential barriers. In particular, we demonstrate atom tunnelling though a single optical barrier in the quantum scattering regime where the De Broglie wavelength of the atoms is larger than the barrier width. Such a beam splitter can be used for atom interferometry

David Gepner, Mee Seong Im, Mikhail Khovanov, Nitu Kitchloo

This paper proposes a connection between algebraic K-theory and foam cobordisms, where foams are stratified manifolds with singularities of a prescribed form. We consider $n$-dimensional foams equipped with a flat bundle of finitely-generated projective $R$-modules over each facet of the foam, together with gluing conditions along the subfoam of singular poi

Hannaneh Akrami, Nidhi Rathi

We study the fundamental problem of fairly dividing a set of indivisible items among agents with (general) monotone valuations. The notion of envy-freeness up to any item (EFX) is considered to be one of the most fascinating fairness concepts in this line of work. Unfortunately, despite significant efforts, existence of EFX allocations is a major open proble

Joanna Piwnik, Joanna Gonera, Piotr Kosinski

The Fermat principle is advocated to be a convenient tool to analyze the light propagation in a curved space time. It is shown that in the weak deflection regime the light ray trajectories can be systematically described by applying the Lindstedt--Poincar\'e method of solving perturbatively the nonlinear oscillation equations. The expansion in terms of inver

Malek Ghanes, Jean-Pierre Barbot

A control based power tower function at order 2 is proposed in this paper. This leads to a new sliding mode control, which allows employing backstepping technique that combines both guaranteed and finite time convergence. The proposed control is applied to a double integrator subject to perturbation $d$. Both guaranteed and finite convergence are ensured by

Sebastian Beckert, Richard Schlitz, Gregor Skobjin, Antonin Badura

Slow magnetization relaxation processes are an important time-dependent property of many magnetic materials. We show that magnetotransport measurements based on a well-established current-reversal method can be utilized to implement a simple and robust screening scheme for such relaxation processes. We demonstrate our approach considering the anomalous Hall

Narges Kafaei, Ali Motazedifard

Due to advances in cold atom interferometry, laser spectroscopy it is possible to achieve more precise accelerometers and gyroscopes which never occurs in mechanical- and optical-based sensors. Because of the inherent and independent characteristics of atomic levels which are too sensitive respect to the external changes, a self-calibrated navigation system

Ferdinand Genans, Antoine Godichon-Baggioni, François-Xavier Vialard, Olivier Wintenberger

Optimal Transport (OT) based distances are powerful tools for machine learning to compare probability measures and manipulate them using OT maps. In this field, a setting of interest is semi-discrete OT, where the source measure $\mu$ is continuous, while the target $\nu$ is discrete. Recent works have shown that the minimax rate for the OT map is $\mathcal{

Ao Wang, Hui Chen, Lihao Liu, Kai Chen

Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. Howe

Tudor Cebere, Aurélien Bellet, Nicolas Papernot

Machine learning models can be trained with formal privacy guarantees via differentially private optimizers such as DP-SGD. In this work, we focus on a threat model where the adversary has access only to the final model, with no visibility into intermediate updates. In the literature, this hidden state threat model exhibits a significant gap between the lowe

Osamu Komori, Yusuke Saigusa, Shinto Eguchi, Yasuhiro Kubota

Species distribution modeling plays an important role in estimating the habitat suitability of species using environmental variables. For this purpose, Maxent and the Poisson point process are popular and powerful methods extensively employed across various ecological and biological sciences. However, the computational speed becomes prohibitively slow when u

Teng Xu, Jiamin Chen, Peng Chen, Youjia Zhang

Editing objects within a scene is a critical functionality required across a broad spectrum of applications in computer vision and graphics. As 3D Gaussian Splatting (3DGS) emerges as a frontier in scene representation, the effective modification of 3D Gaussian scenes has become increasingly vital. This process entails accurately retrieve the target objects

Samuel Garrett, Steven Robertson

In 2004, de Mathan and Teuli\'e stated the $p$-adic Littlewood Conjecture ($p$-$LC$) in analogy with the classical Littlewood Conjecture. Given a field $\mathbb{K}$ and an irreducible polynomial $p(t)$ with coefficients in $\mathbb{K}$, $p$-$LC$ admits a natural analogue over function fields, abbreviated to $p(t)$-$LC$ (and to $t$-$LC$ when $p(t)=t$). In thi

Jamie Burke, Justin Engelmann, Charlene Hamid, Diana Moukaddem

The choroid is a key vascular layer of the eye, supplying oxygen to the retinal photoreceptors. Non-invasive enhanced depth imaging optical coherence tomography (EDI-OCT) has recently improved access and visualisation of the choroid, making it an exciting frontier for discovering novel vascular biomarkers in ophthalmology and wider systemic health. However,

Zihan Zheng, Houqiang Zhong, Qiang Hu, Xiaoyun Zhang

Neural Radiance Field (NeRF) excels in photo-realistically static scenes, inspiring numerous efforts to facilitate volumetric videos. However, rendering dynamic and long-sequence radiance fields remains challenging due to the significant data required to represent volumetric videos. In this paper, we propose a novel end-to-end joint optimization scheme of dy

Ravshan Ashurov, Ilyoskhuja Sulaymonov

The paper considers the Cauchy problem for the system of partial differential equations of fractional order $D_t^{\mathcal{B}} {U}(t,x) + \mathbb{A}(D) {U} (t,x)=H(t,x) $. Here $U$ and $H$ are vector-functions, the $m\times m$ matrix of differential operators $\mathbb{A}(D)$ is triangular (elements above or below the diagonal are zero). Operators located on

Mohamed Ali Khaled, Leonardo Cancellara, Salima Fekraoui, Richard Daubriac

Topological insulators (TI) are promising materials for future spintronics applications and their epitaxial integration would allow the realization of new hybrid interfaces. As the first materials studied, Bismuth Antimony alloys (Bi1-xSbx) show great potential due to their tuneable electronic band structure and efficient charge-to-spin conversion. Here, we

Nikita Gushchin, Daniil Selikhanovych, Sergei Kholkin, Evgeny Burnaev

The Schr\"odinger Bridge (SB) problem offers a powerful framework for combining optimal transport and diffusion models. A promising recent approach to solve the SB problem is the Iterative Markovian Fitting (IMF) procedure, which alternates between Markovian and reciprocal projections of continuous-time stochastic processes. However, the model built by the I

Sebastian Opper

The paper introduces a partial integration map from the first Hochschild cohomology of any cohomologically unital A-infinity category over a field of characteristic zero to its derived Picard group. We discuss useful properties such as injectivity, naturality and the relation with the Baker-Campbell-Hausdorff formula. Based on the image of the integration ma

Davide Giraudo, Emmanuel Lesigne, Dalibor Volny

The now classical convergence in distribution theorem for well normalized sums ofstationary martingale increments has been extended to multi-indexed martingaleincrements (see Voln\'{y} (2019) and references in there). In the presentarticle we make progress in the identification of the limit law.In dimension one, as soon as the stationary martingale increment

Alex Iacob, Lorenzo Sani, Bill Marino, Preslav Aleksandrov

The reliance of language model training on massive amounts of computation and vast datasets scraped from potentially low-quality, copyrighted, or sensitive data has come into question practically, legally, and ethically. Federated learning provides a plausible alternative by enabling previously untapped data to be voluntarily gathered from collaborating orga