February 2024 arXiv papers — page 157

Ruofan Wu, Guanhua Fang, Qiying Pan, Mingyang Zhang

Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations. This paper investigates the structural vulnerabilities in graph neural models where sensitive topological information can be inferred through edge reco

Youngsuk Kim, Junghwan Lim, Hyuk-Jae Lee, Chae Eun Rhee

Although deep learning-based personalized recommendation systems provide qualified recommendations, they strain data center resources. The main bottleneck is the embedding layer, which is highly memory-intensive due to its sparse, irregular access patterns to embeddings. Recent near-memory processing (NMP) and processing-in-memory (PIM) architectures have ad

Zolnamar Dorjsembe, Hsing-Kuo Pao, Furen Xiao

This study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks, aimed at enhancing the segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the diffusi

Alexander Mathiasen, Hatem Helal, Paul Balanca, Adam Krzywaniak

Density Functional Theory (DFT) accurately predicts the quantum chemical properties of molecules, but scales as $O(N_{\text{electrons}}^3)$. Sch\"utt et al. (2019) successfully approximate DFT 1000x faster with Neural Networks (NN). Arguably, the biggest problem one faces when scaling to larger molecules is the cost of DFT labels. For example, it took years

Mateusz Gabor, Tomasz Piotrowski, Renato L. G. Cavalcante

Deep equilibrium (DEQ) models are widely recognized as a memory efficient alternative to standard neural networks, achieving state-of-the-art performance in language modeling and computer vision tasks. These models solve a fixed point equation instead of explicitly computing the output, which sets them apart from standard neural networks. However, existing D

Erion Çano, Dario Lamaj

The scarcity of available text corpora for low-resource languages like Albanian is a serious hurdle for research in natural language processing tasks. This paper introduces AlbNews, a collection of 600 topically labeled news headlines and 2600 unlabeled ones in Albanian. The data can be freely used for conducting topic modeling research. We report the initia

Daniyar Shamkanov

We present a justification logic corresponding to the modal logic of transitive closure $\mathsf{K}^+$ and establish a normal realization theorem relating these two systems. The result is obtained by means of a sequent calculus allowing non-well-founded proofs.

Jingwei Li, Ruixuan Wang, Lutong Cai, Qing Li

The existing silicon-carbide-on-insulator photonic platform utilizes a thin layer of silicon dioxide under silicon carbide to provide optical confinement and mode isolation. Here, we replace the underneath silicon dioxide layer with a 1-$\mu$m-thick aluminum nitride and demonstrate a 4H-silicon-carbide-on-aluminum-nitride integrated photonics platform for th

Christian Biello, Aparna Sankar, Marius Wiesemann, Giulia Zanderighi

We consider Higgs production through bottom-quark annihilation at hadron colliders and we calculate next-to-next-to-leading-order (NNLO) corrections in QCD perturbation theory matched to parton showers (NNLO+PS). To this end, we have adapted the MINNLO$_{\text{PS}}$ method to account for the extra scale dependence induced by an overall Yukawa coupling that i

Zuhan Geng, Fangting Chen, Yichun Gao, Lining Yang

We report the realization of epitaxial indium thin films on PbTe nanowires. The film is continuous and forms an atomically sharp interface with PbTe. Tunneling devices reveal a hard superconducting gap. The gap size, 1.08 to 1.18 meV, is twice as large as bulk indium's ($\sim$ 0.5 meV), due to the presence of PbTe. A similar enhancement is also observed in t

Armand Mihai Cismaru

In this paper, we introduce DeepTraderX (DTX), a simple Deep Learning-based trader, and present results that demonstrate its performance in a multi-threaded market simulation. In a total of about 500 simulated market days, DTX has learned solely by watching the prices that other strategies produce. By doing this, it has successfully created a mapping from ma

Jaleh Delfani, Constantin Orasan, Hadeel Saadany, Ozlem Temizoz

This study explores the use of Google Translate (GT) for translating mental healthcare (MHealth) information and evaluates its accuracy, comprehensibility, and implications for multilingual healthcare communication through analysing GT output in the MHealth domain from English to Persian, Arabic, Turkish, Romanian, and Spanish. Two datasets comprising MHealt

Liang Ding, Rui Tuo, Lu Zhou

Gaussian process (GP) regression provides a flexible, nonparametric framework for probabilistic modeling, yet remains computationally demanding in large-scale applications. For one-dimensional data, state space (SS) models achieve linear-time inference by reformulating GPs as stochastic differential equations (SDEs). However, SS approaches are confined to gr

Nigel Hitchin

We consider the twistor theory approach to Kronheimer's ALE metrics on resolutions of the quotient of C^2 by a finite subgroup of SU(2). The circle action on the 4-manifold induces a C^* action on a compactification of the twistor space and we identify the orbit of a generic twistor line as a nodal rational curve in a particular cohomology class of a project

Mustafa Halilsoy, Chia-Li Hsieh, Mert Mangut

The accretion disks around black holes consist of infalling matter boosted almost to the speed of light making collisions with opposite counterpart. This is the rough picture occurring near black holes or other strongly gravitating centers that produce observed phenomena such as astrophysical jets. A toy model that can be considered imitating such a process

Hao Fang, Yixiang Qiu, Hongyao Yu, Wenbo Yu

Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional performance across numerous applications. However, Model Inversion (MI) attacks, which disclose private information about the training dataset by abusing access to the trained models, have emerged as a formidable privacy threat. Given a trained network, these attacks enabl

Janka Kőmíves, Krisztina Éva Gabányi, Sándor Frey, Emma Kun

Recent studies suggest that high-energy neutrinos can be produced in the jets of blazars, radio-loud active galactic nuclei (AGN) with jets pointing close to the line of sight. Due to the relatively poor angular resolution of current neutrino detectors, several sources can be regarded as the possible counterpart of a given neutrino event. Therefore, follow-u

Yihan Wang, Yifan Zhu, Xiao-Shan Gao

Availability attacks can prevent the unauthorized use of private data and commercial datasets by generating imperceptible noise and making unlearnable examples before release. Ideally, the obtained unlearnability prevents algorithms from training usable models. When supervised learning (SL) algorithms have failed, a malicious data collector possibly resorts

Stefan Schoepf, Jack Foster, Alexandra Brintrup

Data entry constitutes a fundamental component of the machine learning pipeline, yet it frequently results in the introduction of labelling errors. When a model has been trained on a dataset containing such errors its performance is reduced. This leads to the challenge of efficiently unlearning the influence of the erroneous data to improve the model perform

Ningyuan Tang, Minghao Fu, Ke Zhu, Jianxin Wu

In finetuning a large pretrained model to downstream tasks, parameter-efficient fine-tuning (PEFT) methods can effectively finetune pretrained models with few trainable parameters, but suffer from high GPU memory consumption and slow training speed. Because learnable parameters from these methods are entangled with the pretrained model, gradients related to

Oscar Sage David O'Hara, Fred Dulwich, Eloy de Lera Acedo, Jiten Dhandha

The Cosmic Dawn was marked by the formation of the first stars, and preceded the Epoch of Reionization (EoR), when the Universe underwent a fundamental transformation caused by the radiation from these first stars and galaxies. Interferometric 21-cm experiments aim to probe redshifted neutral hydrogen signals from these periods, constraining the conditions o

Jochen Bartels, Carlos Contreras, Gian Paolo Vacca

We study multifield extensions of Reggeon Field Theory (also equivalent to Directed Percolation model) at criticality in the perturbative epsilon-expansion below the upper critical dimension Dc=4 at one loop, for the special case when all fields have the same scale (anomalous) dimensions. Analyzing all the fixed points of the renormalization group flow for N

G. Aymerich, M. Douspis, G. W. Pratt, L. Salvati

We provide a new scaling relation between $Y_{\text{SZ}}$, the integrated Sunyaev-Zeldovich signal and $M_{500}^{Y_{\text{X}}}$, the cluster mass derived from X-ray observations, using a sample of clusters from the Planck Early Sunyaev-Zeldovich (ESZ) catalogue observed in X-rays by Chandra, and compare it to the results of the Planck collaboration obtained

Idan Achituve, Idit Diamant, Arnon Netzer, Gal Chechik

As machine learning becomes more prominent there is a growing demand to perform several inference tasks in parallel. Running a dedicated model for each task is computationally expensive and therefore there is a great interest in multi-task learning (MTL). MTL aims at learning a single model that solves several tasks efficiently. Optimizing MTL models is ofte

Alex Havrilla, Maia Iyer

During both pretraining and fine-tuning, Large Language Models (\textbf{LLMs}) are trained on trillions of tokens of text of widely varying quality. Both phases of training typically involve heuristically filtering out ``low-quality'' or \textit{noisy} training samples, yet little is known quantitatively about how the type or intensity of noise affects downs

Angela A. Albanese, José Bonet, Werner J. Ricker

An investigation is made of the generalized Ces\`aro operators $C_t$, for $t\in [0,1]$, when they act on the space $H(\mathbb{D})$ of holomorphic functions on the open unit disc $\mathbb{D}$, on the Banach space $H^\infty$ of bounded analytic functions and on the weighted Banach spaces $H_v^\infty$ and $H_v^0$ with their sup-norms. Of particular interest are

Alice S. Booth, Milou Temmink, Ewine F. van Dishoeck, Lucy Evans

The Atacama Large Millimeter/submillimeter Array (ALMA) can probe the molecular content of planet-forming disks with unprecedented sensitivity. These observations allow us to build up an inventory of the volatiles available for forming planets and comets. Herbig Ae transition disks are fruitful targets due to the thermal sublimation of complex organic molecu

Alice S. Booth, Margot Leemker, Ewine F. van Dishoeck, Lucy Evans

Observations of disks with the Atacama Large Millimeter/submillimeter Array (ALMA) allow us to map the chemical makeup of nearby protoplanetary disks with unprecedented spatial resolution and sensitivity. The typical outer Class II disk observed with ALMA is one with an elevated C/O ratio and a lack of oxygen-bearing complex organic molecules, but there are

Ilaria Viglino

For $ f\in\mathbb{Z}[X] $ an irreducible polynomial of degree $ n $, the Cilleruelo's conjecture states that$$\log(\mbox{lcm}(f(1),\dots,f(M)))\sim(n-1)M\log M$$as $ M\rightarrow+\infty $, where $ \mbox{lcm}(f(1),\dots,f(M)) $ is the least common multiple of $f(1),\dots,f(M)$. It's well-known for $ n=1 $ as a consequence of Dirichlet's Theorem for primes in

A. Belyaev, R. Benbrik, M. Boukidi, M. Chakraborti

The recent results reported by the CMS collaboration, indicating "bumps" in the $\gamma\gamma$ and $\tau\tau$ channels at $m_\phi\approx 95$ GeV, provide interesting hints for new physics. We find that the lightest Higgs state of the general 2HDM (2HDM Type-III) can perfectly and simultaneously accommodate the two excesses alongside with the LEP long-standin

Dmitry Protasov, Alexander Tolmachev, Vsevolod Voronov

We consider the problem of partitioning a two-dimensional flat torus $T^2$ into $m$ sets in order to minimize the maximal diameter of a part. For $m \leqslant 25$ we give numerical estimates for the maximal diameter $d_m(T^2)$ at which the partition exists. Several approaches are proposed to obtain such estimates. In particular, we use the search for mesh pa

Michael Albanese, Giuseppe Barbaro, Mehdi Lejmi

We generalize K\"ahler-Ricci solitons to the almost-K\"ahler setting as the zeros of Inoue's moment map \cite{MR4017922}, and show that their existence is an obstruction to the existence of first-Chern-Einstein almost-K\"ahler metrics on compact symplectic Fano manifolds. We prove deformation results of such metrics in the $4$-dimensional case. Moreover, we

Sanath K. Devalapurkar

Ben-Zvi--Sakellaridis--Venkatesh described a conjectural extension of the geometric Satake equivalence to spherical varieties, whose spectral decomposition is described by Hamiltonian varieties. The goal of this article is to study their conjecture, especially in the case of spherical varieties of relative rank 1, using tools from homotopy theory. Our discus

Andrea Schioppa

The study of modern machine learning models often necessitates storing vast quantities of gradients or Hessian vector products (HVPs). Traditional sketching methods struggle to scale under these memory constraints. We present a novel framework for scalable gradient and HVP sketching, tailored for modern hardware. We provide theoretical guarantees and demonst

Thomas Blomme, Erwan Brugallé, Cristhian Garay

We study real bitangents of real algebraic plane curves from two perspectives. We first show that there exists a signed count of such bitangents that only depends on the real topological type of the curve. From this follows that a generic real algebraic curve of even degree $d$ has at least $\frac{d(d-2)}{2}$ real bitangents. Next we explain how to locate (r

Rui-Jing Wang

For any positive integer $n$, let $\sigma (n)$ be the sum of all positive divisors of $n.$ In this paper, it is proved that for every integer $ 1\leq k\leq 29,\ (k,30)=1, $ we have $$\sum_{n\leq K}\sigma(30n)>\sum_{n\leq K}\sigma(30n+k)$$ for all $K\in \mathbb{N},$ which gives a positive answer to a problem posed by Pongsriiam recently.

Rui Jiao, Wenbing Huang, Yu Liu, Deli Zhao

Crystals are the foundation of numerous scientific and industrial applications. While various learning-based approaches have been proposed for crystal generation, existing methods seldom consider the space group constraint which is crucial in describing the geometry of crystals and closely relevant to many desirable properties. However, considering space gro

Emanuele Zangrando, Piero Deidda, Simone Brugiapaglia, Nicola Guglielmi

We present a unified theoretical framework connecting the first property of Deep Neural Collapse (DNC1) to the emergence of implicit low-rank bias in nonlinear networks trained with $L^2$ weight decay regularization. Our main contributions are threefold. First, we derive a quantitative relation between the Total Cluster Variation (TCV) of intermediate embedd

Ossi Räisä, Joonas Jälkö, Antti Honkela

We study how the batch size affects the total gradient variance in differentially private stochastic gradient descent (DP-SGD), seeking a theoretical explanation for the usefulness of large batch sizes. As DP-SGD is the basis of modern DP deep learning, its properties have been widely studied, and recent works have empirically found large batch sizes to be b

Anton Backhaus, Thorsten Luettel, Hans-Joachim Wuensche

Intelligent vehicles of the future must be capable of understanding and navigating safely through their surroundings. Camera-based vehicle systems can use keypoints as well as objects as low- and high-level landmarks for GNSS-independent SLAM and visual odometry. To this end we propose YOLOPoint, a convolutional neural network model that simultaneously detec

Liang-Hsuan Tseng, En-Pei Hu, Cheng-Han Chiang, Yuan Tseng

Unsupervised automatic speech recognition (ASR) aims to learn the mapping between the speech signal and its corresponding textual transcription without the supervision of paired speech-text data. A word/phoneme in the speech signal is represented by a segment of speech signal with variable length and unknown boundary, and this segmental structure makes learn

Boaz Moav, Ryan Gabrys, Eitan Yaakobi

The increasing demand for data storage has prompted the exploration of new techniques, with molecular data storage being a promising alternative. In this work, we develop coding schemes for a new storage paradigm that can be represented as a collection of two-dimensional arrays. Motivated by error patterns observed in recent prototype architectures, our stud

R. Namba, K. Imamura, R. Ishioka, K. Ishihara

The layered honeycomb magnet $\alpha$-RuCl$_3$ is the most promising candidate for a Kitaev quantum spin liquid (KQSL) that can host charge-neutral Majorana fermions. Recent studies have shown significant sample dependence of thermal transport properties, which are a key probe of Majorana quasiparticles in the KQSL state, highlighting the importance of prepa

Songnan Yang, Xiaohui Zhang, Shiliang Zhang, Xuehui Ma

Various animals exhibit accurate navigation using environment cues. The Earth's magnetic field has been proved a reliable information source in long-distance fauna migration. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the

Ossi Räisä, Antti Honkela

Recent studies have highlighted the benefits of generating multiple synthetic datasets for supervised learning, from increased accuracy to more effective model selection and uncertainty estimation. These benefits have clear empirical support, but the theoretical understanding of them is currently very light. We seek to increase the theoretical understanding

Rong-Gen Cai, Jing-Rui Zhang, Yun-Long Zhang

The pulsar timings are sensitive to both the nanohertz gravitational-wave background and the oscillation of ultralight dark matter. The Hellings-Downs angular correlation curve provides a criterion to search for stochastic gravitational-wave backgrounds at nanohertz via pulsar timing arrays. We study the angular correlation of the timing residuals induced by

Julien Pierre Edmond Ghali, Kosuke Shima, Koichi Moriyama, Atsuko Mutoh

In the rapidly changing world of smart technology, searching for documents has become more challenging due to the rise of advanced language models. These models sometimes face difficulties, like providing inaccurate information, commonly known as "hallucination." This research focuses on addressing this issue through Retrieval-Augmented Generation (RAG), a t

Evan Randles

Resolving an open question of J. A. Carillo and G. Toscani, M. Stawiska recently proved that certain metric spaces of probability measures equipped with Fourier-based metrics are complete. In this note, we extend such Fourier-based metrics to the class of complex Borel measures and consider the question of completeness in this broader setting.

Yusu Hong, Junhong Lin

The Adaptive Momentum Estimation (Adam) algorithm is highly effective in training various deep learning tasks. Despite this, there's limited theoretical understanding for Adam, especially when focusing on its vanilla form in non-convex smooth scenarios with potential unbounded gradients and affine variance noise. In this paper, we study vanilla Adam under th

Yiming Xu, Hao Cheng, Monika Sester

In autonomous driving tasks, trajectory prediction in complex traffic environments requires adherence to real-world context conditions and behavior multimodalities. Existing methods predominantly rely on prior assumptions or generative models trained on curated data to learn road agents' stochastic behavior bounded by scene constraints. However, they often f

Idriss Mazari-Fouquer, Yannick Privat, Emmanuel Trélat

Given a well-posed linear evolution system settled on a domain $\Omega$ of $\mathbb{R}^d$, an observation subset $\omega\subset\Omega$ and a time horizon $T$, the observability constant is defined as the largest possible nonnegative constant such that the observability inequality holds for the pair $(\omega,T)$. In this article we investigate the large-time

Li Guo, George Andriopoulos, Zifan Zhao, Shuyang Ling

Label smoothing loss is a widely adopted technique to mitigate overfitting in deep neural networks. This paper studies label smoothing from the perspective of Neural Collapse (NC), a powerful empirical and theoretical framework which characterizes model behavior during the terminal phase of training. We first show empirically that models trained with label s

Matthew Deakin, Xu Deng

A low-cost reconfiguration stage connected at the output of balanced three-phase, multi-terminal ac/dc/ac converters can increase the feasible set of power injections substantially, increasing converter utilization and therefore achieving a lower system cost. However, the approach has yet to be explored for phase unbalance mitigation in power distribution ne

Tao Yuan, Xuefei Ning, Dong Zhou, Zhijie Yang

State-of-the-art large language models (LLMs) are now claiming remarkable supported context lengths of 256k or even more. In contrast, the average context lengths of mainstream benchmarks are insufficient (5k-21k), and they suffer from potential knowledge leakage and inaccurate metrics, resulting in biased evaluation. This paper introduces LV-Eval, a challen

Ofer Aharony, Ohad Mamroud, Shimon Nowik, Meir Weissman

A few years ago it was shown that the superconformal index of the $\mathcal{N}=4$ supersymmetric $SU(N)$ Yang-Mills theory in the large $N$ limit matches with the entropy of $1/16$-supersymmetric black holes in type IIB string theory on $AdS_5\times S^5$. In some cases, an even more detailed match between the two sides is possible. When the two angular momen

Bhoomika Maheshwari, Ashok Kumar Jain

The longer-lived excited nuclear states, referred as nuclear isomers, exist due to the hindered decays owing to their peculiar nucleonic structural surroundings. Some of these conditions, being exceptionally rare and limited to achieve, elevate certain isomers to the status of extreme and unusual isomers among their kin. For example, the $E5$ coupling of sin

Bruno Loff, Mateusz Skomra

We devise a policy-iteration algorithm for deterministic two-player discounted and mean-payoff games, that runs in polynomial time with high probability, on any input where each payoff is chosen independently from a sufficiently random distribution. This includes the case where an arbitrary set of payoffs has been perturbed by a Gaussian, showing for the fir

Alberto Debernardi

We show that the Hankel transform of a general monotone function converges uniformly if and only if the limit function is bounded. To this end, we rely on an Abel-Olivier test for real-valued functions. Analogous results for cosine series are derived as well. We also show that our statements do not hold without the general monotonicity assumption in the case

Netta Ollikka, Amro Abbas, Andrea Perin, Markku Kilpeläinen

Deep learning is closing the gap with human vision on several object recognition benchmarks. Here we investigate this gap for challenging images where objects are seen in unusual poses. We find that humans excel at recognizing objects in such poses. In contrast, state-of-the-art deep networks for vision (EfficientNet, SWAG, ViT, SWIN, BEiT, ConvNext) and sta

José Joaquín Bernal, Diana H. Bueno-Carreño, Juan Jacobo Simón

In this note, we apply some techniques developed in [1]-[3] to give a particular construction of bivariate Abelian Codes from cyclic codes, multiplying their dimension and preserving their apparent distance. We show that, in the case of cyclic codes whose maximum BCH bound equals its minimum distance the obtained abelian code verifies the same property; that

Maxime Toquebiau, Nicolas Bredeche, Faïz Benamar, Jae-Yun Jun

Multi-agent deep reinforcement learning (MADRL) problems often encounter the challenge of sparse rewards. This challenge becomes even more pronounced when coordination among agents is necessary. As performance depends not only on one agent's behavior but rather on the joint behavior of multiple agents, finding an adequate solution becomes significantly harde

G. Auzinger, H. Bakhshiansohi, A. Dabrowski, A. G. Delannoy

The high-luminosity upgrade of the LHC brings unprecedented requirements for real-time and precision bunch-by-bunch online luminosity measurement and beam-induced background monitoring. A key component of the CMS Beam Radiation, Instrumentation and Luminosity system is a stand-alone luminometer, the Fast Beam Condition Monitor (FBCM), which is fully independ

Guri Zabërgja, Arlind Kadra, Christian M. M. Frey, Josif Grabocka

Tabular data represent one of the most prevalent data formats in applied machine learning, largely because they accommodate a broad spectrum of real-world problems. Existing literature has studied many of the shortcomings of neural architectures on tabular data and has repeatedly confirmed the scalability and robustness of gradient-boosted decision trees acr

Johannes A. Schubert, Akshay K. Jagadish, Marcel Binz, Eric Schulz

We study the in-context learning dynamics of large language models (LLMs) using three instrumental learning tasks adapted from cognitive psychology. We find that LLMs update their beliefs in an asymmetric manner and learn more from better-than-expected outcomes than from worse-than-expected ones. Furthermore, we show that this effect reverses when learning a

Vivek Kumar Jha

The innermost regions of Active Galactic Nuclei (AGN) are critical for understanding galaxy evolution and the dynamics of matter near a Supermassive Black Hole (SMBH). Yet, due to smaller angular projections, it is very difficult to resolve these regions. This thesis explores indirect methods to understand these objects. We use the reverberation mapping tech

Alia Abbara, Lisa Pagani, Celia García-Pareja, Anne-Florence Bitbol

In nature, most microbial populations have complex spatial structures that can affect their evolution. Evolutionary graph theory predicts that some spatial structures modelled by placing individuals on the nodes of a graph affect the probability that a mutant will fix. Evolution experiments are beginning to explicitly address the impact of graph structures o

César Bravo, Alexander Kozachinskiy, Cristóbal Rojas

We revisit the classical result of Morris et al.~(AAAI'19) that message-passing graphs neural networks (MPNNs) are equal in their distinguishing power to the Weisfeiler--Leman (WL) isomorphism test. Morris et al.~show their simulation result with ReLU activation function and $O(n)$-dimensional feature vectors, where $n$ is the number of nodes of the graph. B

José Joaquín Bernal, Diana H. Bueno-Carreño, Juan Jacobo Simón

In this paper we study the family of cyclic codes such that its minimum distance reaches the maximum of its BCH bounds. We also show a way to construct cyclic codes with that property by means of computations of some divisors of a polynomial of the form X^n-1. We apply our results to the study of those BCH codes C, with designed distance delta, that have min

Ajeet Kumar, Subhamoy Maitra, Somjit Roy

In dimension $d$, Mutually Unbiased Bases (MUBs) are a collection of orthonormal bases over $\mathbb{C}^d$ such that for any two vectors $v_1, v_2$ belonging to different bases, the scalar product $|\braket{v_1|v_2}| = \frac{1}{\sqrt{d}}$. The upper bound on the number of such bases is $d+1$. Constructions to achieve this bound are known when $d$ is some pow

Sweta Singh, K. Giridhar

The efficiency of the broadcast network is impacted by the different types of services that may be transmitted over it. Global services serve users across the entire network, while local services cater to specific regions, and hyper-local services have even narrower coverage. Multimedia Broadcast over a Single-Frequency Network (MBSFN) is typically used for

Patrick Altmeyer, Andrew M. Demetriou, Antony Bartlett, Cynthia C. S. Liem

Developments in the field of Artificial Intelligence (AI), and particularly large language models (LLMs), have created a 'perfect storm' for observing 'sparks' of Artificial General Intelligence (AGI) that are spurious. Like simpler models, LLMs distill meaningful representations in their latent embeddings that have been shown to correlate with external vari

Hiroki Sayama, Chrystopher L. Nehaniv

The year of 2024 marks the 25th anniversary of the publication of evoloops, an evolutionary variant of Chris Langton's self-reproducing loops which proved constructively that Darwinian evolution of self-reproducing organisms by variation and natural selection is possible within deterministic cellular automata. Over the last few decades, this line of Artifici

Eloy Peña-Asensio, Josep M. Trigo. Rodríguez, Jordi Sort, Jordi Ibáñez-Insa

This study analyzes the mechanical and elemental properties of lunar meteorites DHOFAR 1084, JAH 838, NWA 11444, and HED meteorite NWA 6013. Utilizing microscale rock mechanics experiments, i.e., nanoindentation testing, this research reveals significant heterogeneity in both mechanical and elemental attributes across the minerals of the samples. Olivines, p

Min-Huan Chu, Jun-Hui Lai, Wei Wang, Jialu Zhang

We study the mass and scattering cross section of $SU(2)$ glueballs as dark matter candidates using lattice simulations. We employ both naive and improved $SU(2)$ gauge actions in $3+1$ dimensions with several $\beta$ values, and adopt both the traditional Monte Carlo method and the flow-based model based on machine learning techniques to generate lattice co

Luis Sanz-Lorenzo, Rafael Bravo de la Parra

The main aim of the work is to present a general class of two time scales discrete-time epidemic models. In the proposed framework the disease dynamics is considered to act on a slower time scale than a second different process that could represent movements between spatial locations, changes of individual activities or behaviours, or others. To include a su

Shruti Singh, Rishabh Gupta

Computation of document similarity is a critical task in various NLP domains that has applications in deduplication, matching, and recommendation. Traditional approaches for document similarity computation include learning representations of documents and employing a similarity or a distance function over the embeddings. However, pairwise similarities and di

Violaine Piengeon, Chris Guiver

Incremental stability properties are considered for certain systems of forced, nonlinear differential equations with a particular positivity structure. An incremental stability estimate is derived for pairs of input/state/output trajectories of the Lur'e systems under consideration, from which a number of consequences are obtained, including the incremental

Xiaojun Mao, Hengfang Wang, Zhonglei Wang, Shu Yang

Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey sampling, with entries following different canonical exponential distributions and subject to heterogeneous missingness. To tackle this challenging

Erdong Chen, Mengzhong Ma, Zixin Nie

In this paper, we analyze traders' behavior within both centralized exchanges (CEXs) and decentralized exchanges (DEXs), focusing on the volatility of Bitcoin prices and the trading activity of investors engaged in perpetual future contracts. We categorize the architecture of perpetual future exchanges into three distinct models, each exhibiting unique patte

Dan Zhao, Yanbing Zhou, Mengwu Huo, Yu Wang

Recently, a signature of high-temperature superconductivity above the liquid nitrogen temperature (77 K) was reported for $La_{3}Ni_{2}O_{7-\delta}$ under pressure. This finding immediately stimulated intense interest in the possible mechanism of high-$T_{c}$ superconductivity in double-layer nickelates. Notably, the pressure-dependent phase diagram inferred

Qinliang Lin, Cheng Luo, Zenghao Niu, Xilin He

Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems. To address this problem, many transferability enhancement approaches (e.g., input transformation and model augmentation) have been proposed. However, they show poor performances in attacking systems having different model genera from the s

Francois Schulz

Let $A$ and $B$ be complex unital Banach algebras, and let $\varphi, \psi: A \to B$ be surjective mappings. If $A$ is semisimple with an essential socle and $\varphi$ and $\psi$ preserves the invertibility of linear pencils in both directions, that is, for any $x, y \in A$ and $\lambda \in \mathbb{C}$, $\lambda x+y$ is invertible in $A$ if and only if $\lamb

Joris Labarbe, Alexandre Vieira, Didier Clamond

We consider the problem of recovering the surface wave profile from noisy bottom pressure measurements with (\textit{a priori} unknown) arbitrary pressure at the surface. Without noise, the direct approach developed in \cite{clamond2023steady} provides an effective way to recover the sea surface. However, the assumption of analyticity for the measurement ren

Mihir Kulkarni, Kostas Alexis

This work contributes a novel deep navigation policy that enables collision-free flight of aerial robots based on a modular approach exploiting deep collision encoding and reinforcement learning. The proposed solution builds upon a deep collision encoder that is trained on both simulated and real depth images using supervised learning such that it compresses

Busra Senderin, Nurcan Tuncbag, Elif Surer

Protein-protein interaction networks (PPIN) enable the study of cellular processes in organisms. Visualizing PPINs in extended reality (XR), including virtual reality (VR) and mixed reality (MR), is crucial for exploring subnetworks, evaluating protein positions, and collaboratively analyzing and discussing on networks with the help of recent technological a

Christian Cintrano, Francisco Chicano, Enrique Alba

In this work, we solve the problem of finding the best locations to place stations for depositing/collecting shared bicycles. To do this, we model the problem as the p-median problem, that is a major existing localization problem in optimization. The p-median problem seeks to place a set of facilities (bicycle stations) in a way that minimizes the distance b

Gustavo Madeira, Sebastien Charnoz, Nicolas Rambaux, Philippe Robutel

Target of NASA's DART mission, the system of Didymos and Dimorphos will once again be visited by a space mission -- ESA's Hera mission, scheduled to be launch in 2024. Hera will arrive in the system approximately 4 years after the DART impact, a long period compared to Dimorphos' orbital period (about 12 hours). It is therefore imperative to understand the d

Hong T. M. Chu, Meixia Lin, Kim-Chuan Toh

We study a variety of Wasserstein distributionally robust optimization (WDRO) problems where the distributions in the ambiguity set are chosen by constraining their Wasserstein discrepancies to the empirical distribution. Using the notion of weak Lipschitz property, we derive lower and upper bounds of the corresponding worst-case loss quantity and propose su

Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong

Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress in the past decades, existing practice in causal discovery (CD) heavily relies on high-quality measured variables, which are usually given by human experts. In fact, the lack of well-defined high-level variables behind

Alex K. Schenk, Rebecca Griffin, Anton Tadich, Daniel Roberts

A boron-oxide termination of the diamond (100) surface has been formed by depositing molecular boron oxide $\rm{B_2O_3}$ onto the hydrogen-terminated (100) diamond surface under ultrahigh vacuum conditions and annealing to $\rm{950^{\circ} C}$. The resulting termination was highly oriented and chemically homogeneous, although further optimisation is required

Michele Cirafici

We explore nonequilibrium features of certain operator algebras which appear in quantum gravity. The algebra of observables in a black hole background is a Type $\mathrm{II}_\infty$ von Neumann algebra. We discuss how this algebra can be coupled to the algebra of observable of an infinite reservoir within the canonical ensemble, aiming to induce nonequilibri

José Joaquín Bernal, Diana H. Bueno-Carreño, Juan Jacobo Simón

This paper is devoted to studying two main problems: 1) computing the apparent distance of an Abelian code and 2) giving a notion of Bose, Ray-Chaudhuri, Hocquenghem (BCH) multivariate code. To do this, we first strengthen the notion of an apparent distance by introducing the notion of a strong apparent distance; then, we present an algorithm to compute the

T. Tassis, F. Brito, F. L. Semião

In this work, we study quantum heat transport in a single trapped ion, driven by laser excitation and coupled to thermal reservoirs operating at different temperatures. Our focus lies in understanding how different laser coupling scenarios impact the system dynamics. As the laser intensity reaches a regime where the ion's electronic and motional degrees of f

Muhammad Hassen, Rudi Brits, Francois Schulz

For a semisimple unital Banach algebra $A$ over $\mathbb{C}$, and elements $a,b\in A,$ we show that the similarity orbits, $\mathrm{orb}(a)$ and $\mathrm{orb}(b)$, over the principal component of the invertible group of $A$ commute precisely when there is at least one nonzero complex number not belonging to the spectrum of any product $a^\prime b^\prime$ --

Emmanuel Dervieux, Florian Tilquin, Alexis Bisiaux, Wilfried Uhring

Accurate phase extraction from sinusoidal signals is a crucial task in various signal processing applications. While prior research predominantly addresses the case of asynchronous sampling with unknown signal frequency, this study focuses on the more specific situation where synchronous sampling is possible, and the signal's frequency is known. In this fram

Tobias Haubold, Petra Linke

As data science applications gain adoption across industries, the tooling landscape matures to facilitate the life cycle of such applications and provide solutions to the challenges involved to boost the productivity of the people involved. Reinforcement learning with agents in a 3D world could still face challenges: the knowledge required to use a simulatio

Zu-Cheng Chen, Alex Hall

The detection of gravitational waves (GWs) from binary black hole (BBH) coalescences by the LIGO-Virgo-KAGRA (LVK) Collaboration has raised fundamental questions about the genesis of these events. In this chapter, we explore the possibility that PBHs, proposed candidates for dark matter, may serve as the progenitors of the BBHs observed by LVK. Employing a B

J. J. Ruiz-Lorenzo

We have studied numerically the Lee-Yang singularities of the four dimensional Ising model at criticality, which is believed to be in the same universality class as the $\phi_4^4$ scalar field theory. We have focused in the numerical characterization of the logarithmic corrections to the scaling of the zeros of the partition function and its cumulative proba

Jonas Schuff, Miguel J. Carballido, Madeleine Kotzagiannidis, Juan Carlos Calvo

Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by challenges in efficiently tuning and operating these circuits. Identifying optimal operating conditions for these qubits is complex, involving

Elisabetta Candellero, Tom Garcia-Sanchez

First passage percolation with recovery is a process aimed at modeling the spread of epidemics. On a graph $G$ place a red particle at a reference vertex $o$ and colorless particles (seeds) at all other vertices. The red particle starts spreading a \emph{red first passage percolation} of rate $1$, while all seeds are dormant. As soon as a seed is reached by