March 2024 arXiv papers — page 179

Mohammad Rostami, Amin Ghariyazi, Hamed Dashti, Mohammad Hossein Rohban

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a gene editing technology that has revolutionized the fields of biology and medicine. However, one of the challenges of using CRISPR is predicting the on-target efficacy and off-target sensitivity of single-guide RNAs (sgRNAs). This is because most existing methods are trained on separate

Haochen Shi, Zhiyuan Sun, Xingdi Yuan, Marc-Alexandre Côté

Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions. Recent advancements have seen a surge in employing large language models (LLMs) within a framework-centric approach to enhance performance in embodied learni

Emma Tolley

Classifying the morphologies of radio galaxies is important to understand their physical properties and evolutionary histories. A galaxy's morphology is often determined by visual inspection, but as survey size increases robust automated techniques will be needed. Deep neural networks are an attractive method for automated classification, but have many free

Xin Su, Ruisi He, Peng Zhang, Bo Ai

Reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communication is emerging as a key technology to support ultra-high data rates in future sixth-generation networks. However, the acquisition of accurate channel state information (CSI) in such systems is challenging due to the passive nature of RIS and the hybrid beamforming architecture typic

Thomas Britton, Michael Goodrich, Naomi Jarvis, Torri Jeske

The GlueX Central Drift Chamber (CDC) in Hall D at Jefferson Lab, used for detecting and tracking charged particles, is calibrated and controlled during data taking using a Gaussian process. The system dynamically adjusts the high voltage applied to the anode wires inside the chamber in response to changing environmental and experimental conditions such that

Vipin Vijayan, Braeden Bowen, Scott Grigsby, Timothy Anderson

A good evaluation framework should evaluate multimodal machine translation (MMT) models by measuring 1) their use of visual information to aid in the translation task and 2) their ability to translate complex sentences such as done for text-only machine translation. However, most current work in MMT is evaluated against the Multi30k testing sets, which do no

Manuel Fernandez, Lutz Warnke

The clique chromatic number of a graph is the smallest number of colors in a vertex coloring so that no maximal clique is monochromatic. In this paper, we determine the order of magnitude of the clique chromatic number of the random graph G_{n,p} for most edge-probabilities p in the range n^{-2/5} \ll p \ll 1. This resolves open problems and questions of Lic

Naoya Torii, Shigeru Ida, Eiichiro Kokubo, Shugo Michikoshi

Observations by the Voyager and Cassini spacecrafts have revealed various striking features of the gap structure in Saturn's ring, such as the density waves, sharp edge, and vertical wall structure. In order to explain these features in a single simulation, we perform a high-resolution (N~10^6-10^7) global full N-body simulation of gap formation by an embedd

Felix Gerick, Philip Livermore

Linear modes for which the Coriolis acceleration is almost entirely in balance with the Lorentz force are called Magneto-Coriolis (MC) modes. These MC modes are thought to exist in Earth's liquid outer core and could therefore contribute to the variations observed in Earth's magnetic field. The background state on which these waves ride is assumed here to be

Alexandre Lê, Guillaume Rance, Fabrice Rouillier, Damien Chablat

This paper aims to study a specific kind of parallel robot: Spherical Parallel Manipulators (SPM) that are capable of unlimited rolling. A focus is made on the kinematics of such mechanisms, especially taking into account uncertainties (e.g. on conception & fabrication parameters, measures) and their propagations. Such considerations are crucial if we want t

Maximilian J. Zawierucha, Till Rehmert, Jonas Keller, Tanja E. Mehlstäubler

The demand for efficient preparation methods for dual-species ion crystals is rapidly expanding across quantum technology and fundamental physics applications with trapped ions. We present a deterministic and efficient technique to produce such crystals, utilizing the segmented structure of a linear Paul trap. By precisely tailoring the trapping potentials,

Hasan Abu-Rasheed, Christian Weber, Madjid Fathi

In the era of personalized education, the provision of comprehensible explanations for learning recommendations is of a great value to enhance the learner's understanding and engagement with the recommended learning content. Large language models (LLMs) and generative AI in general have recently opened new doors for generating human-like explanations, for an

Finlay Ryburn, Kevin Künstle, Yangzhan Zhang, Yannik Kunz

We study surface acoustic waves (SAWs) in yttrium iron garnet (YIG)/zinc oxide (ZnO) heterostructures, comparing the results of a computationally lightweight analytical model with time-resolved micro-focused Brillouin light scattering data. Interdigital transducers (IDTs), with operational frequencies in the gigahertz regime, were fabricated on 50 and 100nm

Zhiyuan Zhang, Krzysztof Grykiel, Zhaocheng Liu, Stefanos Papanicolopulos

Point masses connected by springs, or mass-spring systems, are widely used in computer animation to approximate the behavior of deformable objects. One of the restrictions imposed by these models is that points that are not topologically constrained (linked by a spring) are unable to interact with each other explicitly. Such interactions would introduce a ne

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, A. G. Abac

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change

Gen Luo, Yiyi Zhou, Yuxin Zhang, Xiawu Zheng

Despite remarkable progress, existing multimodal large language models (MLLMs) are still inferior in granular visual recognition. Contrary to previous works, we study this problem from the perspective of image resolution, and reveal that a combination of low- and high-resolution visual features can effectively mitigate this shortcoming. Based on this observa

Ankur Singh

The thesis investigates the utilization of memristive and memcapacitive crossbar arrays in low-power machine learning accelerators, offering a comprehensive co-design framework for deep neural networks (DNN). The model, implemented through a hybrid Python and PyTorch approach, accounts for various non-idealities, achieving exceptional training accuracies of

Subhaditya Bhattacharya, Sanjoy Biswas, Abhik Sarkar

We study the Higgs couplings involved in the $Zh$ associated production mode at the Large Hadron Collider (LHC) in presence of Higgs-gauge boson coupling modifiers via $\kappa$ framework, and dimension 6 Standard Model Effective Theory (SMEFT) operators. The analysis is performed mainly in context of the HL-LHC (with $\sqrt{s}=$14 TeV and luminosity 3000 $fb

R. Cesaroni, L. Moscadelli, A. Caratti o Garatti, J. Eisloeffel

We report new Very Large Array high-resolution observations of the radio jet from the outbursting high-mass star S255IR~NIRS3. The images at 6, 10, and 22.2 GHz confirm the existence of a new lobe emerging to the SW and expanding at a mean speed of ~285 km/s, about half as fast as the NE lobe. The new data allow us to reproduce both the morphology and the co

David A. Bader, Fuhuan Li, Zhihui Du, Palina Pauliuchenka

Listing and counting triangles in graphs is a key algorithmic kernel for network analyses, including community detection, clustering coefficients, k-trusses, and triangle centrality. In this paper, we propose the novel concept of a cover-edge set that can be used to find triangles more efficiently. Leveraging the breadth-first search (BFS) method, we can qui

Himanshu Prabhat, Raktim Bhattacharya

This paper proposes a novel convex optimization framework for designing robust Kalman filters that guarantee a user-specified steady-state error while maximizing process and sensor noise. The proposed framework simultaneously determines the Kalman gain and the robustness margin in terms of the process and sensor noise. This is the first paper to present such

Ehsan Nowroozi, Nada Jadalla, Samaneh Ghelichkhani, Alireza Jolfaei

Malicious URLs provide adversarial opportunities across various industries, including transportation, healthcare, energy, and banking which could be detrimental to business operations. Consequently, the detection of these URLs is of crucial importance; however, current Machine Learning (ML) models are susceptible to backdoor attacks. These attacks involve ma

Sourabh Magare, Anupreeta More, Sunil Choudary

By the end of the next decade, we hope to have detected strongly lensed gravitational waves by galaxies or clusters. Although there exist optimal methods for identifying lensed signal, it is shown that machine learning (ML) algorithms can give comparable performance but are orders of magnitude faster than non-ML methods. We present the SLICK pipeline which c

Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu

The efficacy of large language models (LLMs) in understanding and generating natural language has aroused a wide interest in developing prompt-based methods to harness the power of black-box LLMs. Existing methodologies usually prioritize a global optimization for finding the global optimum, which however will perform poorly in certain tasks. This thus motiv

Aseel Omar, Alejandro Cohen

An integrate-and-fire time-encoding machine (IF-TEM) is an effective asynchronous sampler that translates amplitude information into non-uniform time sequences. In this work, we propose a novel Adaptive IF-TEM (AIF-TEM) approach. This design dynamically adjusts the TEM's sensitivity to changes in the input signal's amplitude and frequency in real-time. We pr

Jianjian Cao, Peng Ye, Shengze Li, Chong Yu

Vision-Language Transformers (VLTs) have shown great success recently, but are meanwhile accompanied by heavy computation costs, where a major reason can be attributed to the large number of visual and language tokens. Existing token pruning research for compressing VLTs mainly follows a single-modality-based scheme yet ignores the critical role of aligning

Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo

In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection. This survey explores the transformative impact of LLMs on DA, particularly addressing the unique challenges and opportunitie

D. Vaccaro, H. Akamatsu, L. Gottardi, M. de Wit

At SRON we have been developing X-ray TES micro-calorimeters as backup technology for the X-ray Integral Field Unit (X-IFU) of the Athena mission, demonstrating excellent resolving powers both under DC and AC bias. We also developed a frequency-domain multiplexing (FDM) readout technology, where each TES is coupled to a superconducting band-pass LC resonator

Brian Doolittle, Felix Leditzky, Eric Chitambar

Quantum resources, such as entanglement or quantum communication, offer significant communication advantages in information processing. We develop an operational framework for realizing these communication advantages in resource-constrained quantum networks. The framework computes linear bounds on the input/output probabilities of classical networks with lim

Dominik Weber, Annika Morgenstern, Daniel Beer, Dietrich R. T. Zahn

We present the development of multifunctional blue-emission organic light-emitting diodes (OLEDs) using TADF-exciplex materials. These OLEDs exhibit sensitivity to external stimuli and achieve a maximum external quantum efficiency (EQE) of 11.6 % through partly liquid processing. This technique allows for large-scale production on arbitrary geometries. The p

Haigang Hu, Xiao-Chuang Wang, Yu Ye

Any gentle algebra $A$ with one maximal path corresponds to a unique quasi-diagram $\alpha$. We introduce the regularity for $\alpha$, and show that $A$ has finite global dimension if and only if $\alpha$ is regular. We characterize regular quasi-diagrams which remain regular under the dihedral group action. We prove that the set of maximal chord diagrams is

Robert Tjarko Lange, Yingtao Tian, Yujin Tang

Evolutionary optimization algorithms are often derived from loose biological analogies and struggle to leverage information obtained during the sequential course of optimization. An alternative promising approach is to leverage data and directly discover powerful optimization principles via meta-optimization. In this work, we follow such a paradigm and intro

Jerónimo Fotinós, María Paula Marks, Lucas Barberis, Luciano Vellón

In previous theoretical research, we inferred that cancer stem cells (CSCs), the cells that presumably drive tumor growth and resistance to conventional cancer treatments, are not uniformly distributed in the bulk of a tumorsphere. To confirm this theoretical prediction, we cultivated tumorspheres enriched in CSCs, and performed immunofluorecent detection of

Ehsan Nowroozi, Imran Haider, Rahim Taheri, Mauro Conti

Federated Learning (FL) is a machine learning (ML) approach that enables multiple decentralized devices or edge servers to collaboratively train a shared model without exchanging raw data. During the training and sharing of model updates between clients and servers, data and models are susceptible to different data-poisoning attacks. In this study, our motiv

Alejandro Andrés Hnilo

Louis Sica derived Bell's inequalities from the hypothesis that the time series of outcomes observed in one station does not change if the setting in the other (distant) station is changed. This derivation is based on arithmetical properties only. It does not involve the controversial definitions of Locality and Realism, it does not require the definition of

Xue Song, Jiequan Cui, Hanwang Zhang, Jingjing Chen

We study text-based image editing (TBIE) of a single image by counterfactual inference because it is an elegant formulation to precisely address the requirement: the edited image should retain the fidelity of the original one. Through the lens of the formulation, we find that the crux of TBIE is that existing techniques hardly achieve a good trade-off betwee

Holger Gies, Kevin K. K. Tam

We test the physical viability of a recent proposal for an asymptotically safe modification of quantum electrodynamics (QED), whose ultraviolet physics is dominated by a non-perturbative Pauli spin-field coupling. We focus in particular on its compatibility with the absence of dynamical generation of fermion mass in QED. Studying the renormalization group fl

Lennie Wells, Kumar Thurimella, Sergio Bacallado

Recent developments in regularized Canonical Correlation Analysis (CCA) promise powerful methods for high-dimensional, multiview data analysis. However, justifying the structural assumptions behind many popular approaches remains a challenge, and features of realistic biological datasets pose practical difficulties that are seldom discussed. We propose a nov

D. Vaccaro, M. de Wit, J. van der Kuur, L. Gottardi

SRON (Netherlands Institute for Space Research) is developing the Focal Plane Assembly (FPA) for Athena X-IFU, whose Demonstration Model (DM) will use for the first time a time domain multiplexing (TDM)-based readout system for the on-board transition-edge sensors (TES). We report on the characterization activities on a TDM setup provided by NASA Goddard Spa

Qianhao Wang, Zhepei Wang, Mingyang Wang, Jialin Ji

Convex polytopes have compact representations and exhibit convexity, which makes them suitable for abstracting obstacle-free spaces from various environments. Existing generation methods struggle with balancing high-quality output and efficiency. Moreover, another crucial requirement for convex polytopes to accurately contain certain seed point sets, such as

Stefan Forste, Hans Jockers, Joshua Kames-King, Alexandros Kanargias

In this work we study families of $\mathbb{Z}_2$ orbifolds of toroidal conformal field theories based on both factorizable and non-factorizable target space tori. For these classes of theories, we analyze their moduli spaces, and compute their partition functions. Building on previous work, we express the calculated partition functions in terms of suitable S

Dong Yao

Sentence semantic matching is a research hotspot in natural language processing, which is considerably significant in various key scenarios, such as community question answering, searching, chatbot, and recommendation. Since most of the advanced models directly model the semantic relevance among words between two sentences while neglecting the \textit{keywor

Shibei Zhu, Tran Nguyen Le, Samuel Kaski, Ville Kyrki

Real-time collaboration with humans poses challenges due to the different behavior patterns of humans resulting from diverse physical constraints. Existing works typically focus on learning safety constraints for collaboration, or how to divide and distribute the subtasks between the participating agents to carry out the main task. In contrast, we propose to

Daniel Limon, Antonio Ferramosca, Ignacio Alvarado, Teodoro Alamo

The main objective of tracking control is to steer the tracking error, that is the difference between the reference and the output, to zero while the plant's operation limits are satisfied. This requires that some assumptions on the evolution of the future values of the reference must be taken into account. Typically a simple evolution of the reference is co

Jiang Xu, Gang Sun, Jingyu Xu, Pujie Su

The advent of the post-human era has blurred the boundary between the body and artefacts. Further, external materials and information are more deeply integrated into the body, making emerging technology a key driving force for shaping post-human existence and promoting bodily evolution. Based on this, this study analyses the transformation process of three t

Xiaoyi Zhu, Yuxiang Tian, Lingxiao Huang, Zengfeng Huang

The $(k, z)$-Clustering problem in Euclidean space $\mathbb{R}^d$ has been extensively studied. Given the scale of data involved, compression methods for the Euclidean $(k, z)$-Clustering problem, such as data compression and dimension reduction, have received significant attention in the literature. However, the space complexity of the clustering problem, s

Michael Mommert, Robin Barta, Christian Bauer, Marie-Christine Volk

We apply physics-informed neural networks to three-dimensional Rayleigh-B\'enard convection in a cubic cell with a Rayleigh number of Ra = 10^6 and a Prandtl number of Pr = 0.7 to assimilate the velocity vector field from given temperature fields and vice versa. With the respective ground truth data provided by a direct numerical simulation, we are able to e

Junwen He, Yifan Wang, Lijun Wang, Huchuan Lu

Multimodal Large Language Model (MLLMs) leverages Large Language Models as a cognitive framework for diverse visual-language tasks. Recent efforts have been made to equip MLLMs with visual perceiving and grounding capabilities. However, there still remains a gap in providing fine-grained pixel-level perceptions and extending interactions beyond text-specific

Andreas Bluhm, Matthias C. Caro, Aadil Oufkir

Locality is a fundamental feature of many physical time evolutions. Assumptions on locality and related structural properties also underlie recently proposed procedures for learning an unknown Hamiltonian from access to the induced time evolution. However, no protocols to rigorously test whether an unknown Hamiltonian is local were known. We investigate Hami

Yuan Gao, Anton Rodomanov, Sebastian U. Stich

The stochastic proximal gradient method is a powerful generalization of the widely used stochastic gradient descent (SGD) method and has found numerous applications in Machine Learning. However, it is notoriously known that this method fails to converge in non-convex settings where the stochastic noise is significant (i.e. when only small or bounded batch si

Sungho Ko, Hyunjin Cho, Hyungjoo Chae, Jinyoung Yeo

Recent studies have investigated utilizing Knowledge Graphs (KGs) to enhance Quesetion Answering (QA) performance of Large Language Models (LLMs), yet structured KG verbalization remains challengin. Existing methods, such as triple-form or free-form textual conversion of triple-form facts, encounter several issues. These include reduced evidence density due

Ahmad Hassanpour, Yasamin Kowsari, Hatef Otroshi Shahreza, Bian Yang

This paper explores the application of large language models (LLMs), like ChatGPT, for biometric tasks. We specifically examine the capabilities of ChatGPT in performing biometric-related tasks, with an emphasis on face recognition, gender detection, and age estimation. Since biometrics are considered as sensitive information, ChatGPT avoids answering direct

Christophe Charlier, Jonatan Lenells

We consider the balayage of a measure $\mu$ defined on a domain $\Omega$ onto its boundary $\partial \Omega$. Assuming that $\Omega$ has a corner of opening $\pi \alpha$ at a point $z_0 \in \partial \Omega$ for some $0 < \alpha \leq 2$ and that $d\mu(z) \asymp |z-z_{0}|^{2b-2}d^{2}z$ as $z\to z_0$ for some $b > 0$, we obtain the precise rate of vanishing of

Kangjie Zheng, Siyu Long, Tianyu Lu, Junwei Yang

Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for application

Burhan Wafai, Sarbani Ghose, Chinmoy Kundu, Ankit Dubey

In this paper, we provide expressions for the secrecy outage probability (SOP) for suboptimal and optimal opportunistic scheduling schemes in a reconfigurable intelligent surface (RIS) aided {single antenna} system with multiple eavesdroppers in approximate closed form. A suboptimal scheduling (SS) scheme is analyzed, which is used when the channel state inf

Zheng Li, Xiang Chen, Xiaojun Wan

Tabular data, as a crucial form of data representation, exists in diverse formats on the Web. When confronted with complex and irregular tables, manual modification becomes a laborious task. This paper investigates the performance of Large Language Models (LLMs) in the context of table editing tasks. Existing research mainly focuses on regular-shaped tables,

Yunuo Xiong, Hongwei Xiong

Chang and Bertsch [Phys. Rev. A 76, 021603(R) (2007)] proposed a simple formula for the ground state energy of a unitary Fermi gas in a harmonic trap, based on their Green's function Monte Carlo simulations of up to 22 fermions, combined with general assumptions about the universal thermodynamic behavior of the unitary Fermi gas. In this work, we perform the

Nawapon Nakharutai, Sébastien Destercke, Matthias C. M. Troffaes

One way to make decisions under uncertainty is to select an optimal option from a possible range of options, by maximizing the expected utilities derived from a probability model. However, under severe uncertainty, identifying precise probabilities is hard. For this reason, imprecise probability models uncertainty through convex sets of probabilities, and co

Zhitao He, Pengfei Cao, Chenhao Wang, Zhuoran Jin

With the development of deep learning, natural language processing technology has effectively improved the efficiency of various aspects of the traditional judicial industry. However, most current efforts focus on tasks within individual judicial stages, making it difficult to handle complex tasks that span multiple stages. As the autonomous agents powered b

Haitao Lin, Odin Zhang, Huifeng Zhao, Dejun Jiang

Therapeutic peptides have proven to have great pharmaceutical value and potential in recent decades. However, methods of AI-assisted peptide drug discovery are not fully explored. To fill the gap, we propose a target-aware peptide design method called \textsc{PPFlow}, based on conditional flow matching on torus manifolds, to model the internal geometries of

N. A. Rather, Naseer Ahmad Wani, Ishfaq Dar

Using a variety of matrix techniques, the problem of locating the left eigenvalues of the quaternion companion matrices are investigated in this paper. In a recent paper, Dar et al. [6], proved that the zeros of a quaternionic polynomial and the left eigenvalues of corresponding companion matrix are same. In view of this, we use various newly developed matri

Benedikt Fesl, Benedikt Böck, Florian Strasser, Michael Baur

Diffusion models (DMs) as generative priors have recently shown great potential for denoising tasks but lack theoretical understanding with respect to their mean square error (MSE) optimality. This paper proposes a novel denoising strategy inspired by the structure of the MSE-optimal conditional mean estimator (CME). The resulting DM-based denoiser can be co

Mingyu Yu, Maria Hilse, Qihua Zhang, Yongchen Liu

Nanolayered post-transition metal monochalcogenides (PTMMCs) stand out as promising advanced two-dimensional (2D) materials. Beyond inheriting the general advantages associated with traditional 2D materials, they exhibit unique properties, including a wide bandgap range covering the ultraviolet to the mid-infrared spectral ranges, thickness-dependent bandgap

Ben Pinhasov, Raz Lapid, Rony Ohayon, Moshe Sipper

We introduce a novel methodology for identifying adversarial attacks on deepfake detectors using eXplainable Artificial Intelligence (XAI). In an era characterized by digital advancement, deepfakes have emerged as a potent tool, creating a demand for efficient detection systems. However, these systems are frequently targeted by adversarial attacks that inhib

Kazuho Nomura, Yuichi Ikeda

Language endangerment is a phenomenon in which approximately 40% of languages spoken worldwide are predicted to disappear within the next few decades, resulting in the loss of cultures associated with these languages. To take effective measures against language endangerment, it is essential to quantitatively understand its characteristics because it is a phe

Hadi Nemati, Pedro Sánchez-Martín, Ana Baringo, Álvaro Ortega

This paper proposes a novel single-level robust mathematical approach to model the RES-only Virtual Power Plant (RVPP) bidding problem in the simultaneous Day Ahead Market (DAM) and Secondary Reserve Market (SRM). The worst-case profit of RVPP due to uncertainties related to electricity prices, Non-dispatchable Renewable Energy Sources (ND-RES) production, a

Przemysław Pogorzelec, Bartłomiej Dybiec

L\'evy noise is a paradigmatic noise used to describe out-of-equilibrium systems. Typically, properties of L\'evy noise driven systems are very different from their Gaussian white noise driven counterparts. In particular, under action of L\'evy noise, stationary states in single-well, super-harmonic, potentials are no longer unimodal. Typically, they are bim

Bin Zhang, Yuxiao Ye, Guoqing Du, Xiaoru Hu

Large Language Models (LLMs) have emerged as a powerful tool in advancing the Text-to-SQL task, significantly outperforming traditional methods.Nevertheless, as a nascent research field, there is still no consensus on the optimal prompt templates and design frameworks. Additionally, existing benchmarks inadequately explore the performance of LLMs across the

Yufei Zhao, Dingji Wang, Bihuan Chen, Ziqian Chen

Backdoor attacks have been one of the emerging security threats to deep neural networks (DNNs), leading to serious consequences. One of the mainstream backdoor defenses is model reconstruction-based. Such defenses adopt model unlearning or pruning to eliminate backdoors. However, little attention has been paid to survive from such defenses. To bridge the gap

Dan J. Hill, David J. B. Lloyd

Isolated patches of spatially oscillating pattern have been found to emerge near a pattern-forming instability in a wide variety of experiments and mathematical models. However, there is currently no mathematical theory to explain this emergence or characterise the structure of these patches. We provide a method for formally deriving radial amplitude equatio

Wei Zhi, Ruike Cao, Jiannan Tang, Mingxin Wang

TRopIcal DEep-sea Neutrino Telescope (TRIDENT) plans to incorporate silicon photomultipliers (SiPMs) with superior time resolution in addition to photomultiplier tubes (PMTs) into its detection units, namely hybrid Digital Optical Modules (hDOMs), to improve its angular resolution. However, the time resolution significantly degrades for large-area SiPMs due

Mahdi Taheri, Masoud Daneshtalab, Jaan Raik, Maksim Jenihhin

Systolic array has emerged as a prominent architecture for Deep Neural Network (DNN) hardware accelerators, providing high-throughput and low-latency performance essential for deploying DNNs across diverse applications. However, when used in safety-critical applications, reliability assessment is mandatory to guarantee the correct behavior of DNN accelerator

Harry Mayne, Guy Parsons, Adam Mahdi

The use of unsupervised learning to identify patient subgroups has emerged as a potentially promising direction to improve the efficiency of Intensive Care Units (ICUs). By identifying subgroups of patients with similar levels of medical resource need, ICUs could be restructured into a collection of smaller subunits, each catering to a specific group. Howeve

Hagyeong Lee, Minkyu Kim, Jun-Hyuk Kim, Seungeon Kim

Recent advances in text-guided image compression have shown great potential to enhance the perceptual quality of reconstructed images. These methods, however, tend to have significantly degraded pixel-wise fidelity, limiting their practicality. To fill this gap, we develop a new text-guided image compression algorithm that achieves both high perceptual and p

Rahul Munshi, Jia Ling, Sergey Ryabichko, Eric F Wieschaus

Deciphering how genes interpret information from the concentration of transcription factors (TFs) within the cell nucleus remains a fundamental question in gene regulation. Recent advancements have unveiled the heterogeneous distribution of TF molecules in the nucleus, posing challenges to the precise decoding of concentration signals. To explore this phenom

Ruizhe Wang, Hong Ren, Cunhua Pan, Gui Zhou

Classical linear statistical models, like the first-order auto-regressive (AR) model, are commonly used as channel model in high-mobility scenarios. However, compared to sub-6G, the effect of Doppler frequency shifts is more significant at millimeter wave (mmWave) frequencies, and the effectiveness of the statistical channel model in high-mobility mmWave sce

Timofei Shashkov

Let $\mathbf{B}(t)=(B_1(t), B_2(t))$, $t\geq 0$ be a two-dimensional Brownian motion with independent components and define the $\mathbf{\gamma}$-reflected process $$\mathbf{X}(t)=(X_1(t),X_2(t))=\left(B_1(t)-c_1t-\gamma_1\inf_{s_1\in[0,t]}(B_1(s_1)-c_1s_1),B_2(t)-c_2t-\gamma_2\inf_{s_2\in[0,t]}(B_2(s_2)-c_2s_2)\right),$$ with given finite constants $c_1,c_2

Zhen-Chen Guo, Xin Liang

In this paper, we propose an RADI-type method for large-scale stochastic continuous-time algebraic Riccati equations with sparse and low-rank matrices. This new variant of RADI-type methods is developed by integrating the core concept of the original RADI method with the implicit appearance of the left semi-tensor product in stochastic continuous-time algebr

Yoonjoo Lee, Hyeonsu B. Kang, Matt Latzke, Juho Kim

With the rapid growth of scholarly archives, researchers subscribe to "paper alert" systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers. However, researchers sometimes struggle to make sense of nuanced connections between recommended papers and their own research context, as

Kazuki Kawamura, Jun Rekimoto

Since humans can listen to audio and watch videos at faster speeds than actually observed, we often listen to or watch these pieces of content at higher playback speeds to increase the time efficiency of content comprehension. To further utilize this capability, systems that automatically adjust the playback speed according to the user's condition and the ty

Omri Shmueli

Quantum computers are expected to revolutionize our ability to process information. The advancement from classical to quantum computing is a product of our advancement from classical to quantum physics -- the more our understanding of the universe grows, so does our ability to use it for computation. A natural question that arises is, what will physics allow

Mahdi Taheri, Natalia Cherezova, Samira Nazari, Ahsan Rafiq

In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators.

Sergio Giardino

The real Hilbert space formalism developed within the quaternionic quantum mechanics ($\mathbb H$QM) is fully applied to the simple model of the autonomous particle. This framework permits novel insights within the usual description of the complex autonomous particle, particulaly concening the energy of a non-stationary motion. Through the appraisal of the p

Giannis Vassiliou, Fanouris Alevizakis, Nikolaos Papadakis, Haridimos Kondylakis

The explosion in the size and the complexity of the available Knowledge Graphs on the web has led to the need for efficient and effective methods for their understanding and exploration. Semantic summaries have recently emerged as methods to quickly explore and understand the contents of various sources. However in most cases they are static not incorporatin

Matthias Lanzinger, Stefano Sferrazza, Przemysław A. Wałęga, Georg Gottlob

One of the main challenges in the area of Neuro-Symbolic AI is to perform logical reasoning in the presence of both neural and symbolic data. This requires combining heterogeneous data sources such as knowledge graphs, neural model predictions, structured databases, crowd-sourced data, and many more. To allow for such reasoning, we generalise the standard ru

Miaomiao Li, Jiaqi Zhu, Yang Wang, Yi Yang

Weakly supervised text classification (WSTC), also called zero-shot or dataless text classification, has attracted increasing attention due to its applicability in classifying a mass of texts within the dynamic and open Web environment, since it requires only a limited set of seed words (label names) for each category instead of labeled data. With the help o

Silke Adam, Mykola Makhortykh, Michaela Maier, Viktor Aigenseer

This article evaluates the quality of data collection in individual-level desktop information tracking used in the social sciences and shows that the existing approaches face sampling issues, validity issues due to the lack of content-level data and their disregard of the variety of devices and long-tail consumption patterns as well as transparency and priva

Osman Alperen Koraş, Jörg Schlötterer, Christin Seifert

We present a detailed replication study of the BASS framework, an abstractive summarization system based on the notion of Unified Semantic Graphs. Our investigation includes challenges in replicating key components and an ablation study to systematically isolate error sources rooted in replicating novel components. Our findings reveal discrepancies in perfor

Charlotte Muth, Benedikt Geiger, Daniel Gil Gaviria, Laurent Schmalen

We evaluate the influence of multi-snapshot sensing and varying signal-to-noise ratio (SNR) on the overall performance of neural network (NN)-based joint communication and sensing (JCAS) systems. To enhance the training behavior, we decouple the loss functions from the respective SNR values and the number of sensing snapshots, using bounds of the sensing per

Mingyue Zhang, Jialong Li, Nianyu Li, Eunsuk Kang

In the realm of autonomous vehicles, dynamic user preferences are critical yet challenging to accommodate. Existing methods often misrepresent these preferences, either by overlooking their dynamism or overburdening users as humans often find it challenging to express their objectives mathematically. The previously introduced framework, which interprets dyna

Khalil Gholami, Asef Nazari, Dhananjay Thiruvady, Valeh Moghaddam

High penetration of renewable generation in the electricity grid presents power system operators with challenges including voltage instability mainly due to fluctuating power generation. To cope with intermittent generation, community batteries introduce an elegant solution for storing excess generation of renewable resources and reverting to the grid in pea

Yali Jia, Zhaohua Tian, Qi Liu, Zihan Mo

High emission rate, high collection efficiency, and immunity to the defects are the requirements of implementing on-chip single photon sources. Here, we theoretically demonstrate that both cascade enhancement and high collection efficiency of emitted photons from single emitter can be achieved simultaneously in topological photonic crystal containing a reson

Carsten Schütt, Christoph Thaele, Nicola Turchi, Elisabeth M. Werner

The purpose of this paper is to introduce the new concept of weighted floating functions associated with log concave or $s$-concave functions. This leads to new notions of weighted functional affine surface areas. Their relation to more traditional versions of functional affine surface areas as well as to the classical affine surface areas for convex bodies

C. Dalfó, M. A. Fiol, E. Steffen

We introduce the concept of a $k$-token signed graph and study some of its combinatorial and algebraic properties. We prove that two switching isomorphic signed graphs have switching isomorphic token graphs. Moreover, we show that the Laplacian spectrum of a balanced signed graph is contained in the Laplacian spectra of its $k$-token signed graph. Besides, w

Yu-Sheng Chang, Michael Fuchs, Guan-Ru Yu

We propose the class of galled tree-child networks which is obtained as intersection of the classes of galled networks and tree-child networks. For the latter two classes, (asymptotic) counting results and stochastic results have been proved with very different methods. We show that a counting result for the class of galled tree-child networks follows with s

Yihang She, Clement Atzberger, Andrew Blake, Srinivasan Keshav

Advances in machine learning have boosted the use of Earth observation data for climate change research. Yet, the interpretability of machine-learned representations remains a challenge, particularly in understanding forests' biophysical reactions to climate change. Traditional methods in remote sensing that invert radiative transfer models (RTMs) to retriev

Matthias Troyer, Emily Violi Benjamin, Ani Gevorkian

Quantum computing promises to help humanity solve problems that would otherwise be intractable on classical computers. Unlike today's machines, quantum computers use a novel computing process that leverages the foundational quantum mechanical laws of nature. This unlocks unparalleled compute power for certain applications and promises to help solve some of o

Tobias Christian Nauen, Sebastian Palacio, Andreas Dengel

The quadratic complexity of the attention mechanism represents one of the biggest hurdles for processing long sequences using Transformers. Current methods, relying on sparse representations or stateful recurrence, sacrifice token-to-token interactions, which ultimately leads to compromises in performance. This paper introduces TaylorShift, a novel reformula

Sho Shimotsumagari, Shumpei Takezaki, Daichi Haraguchi, Seiichi Uchida

The purpose of this paper is to enable the conversion between machine-printed character images (i.e., font images) and handwritten character images through machine learning. For this purpose, we propose a novel unpaired image-to-image domain conversion method, CycleDM, which incorporates the concept of CycleGAN into the diffusion model. Specifically, CycleDM

Yue Li, Koen V Hindriks, Florian Kunneman

In this paper, we study how well human speech can automatically be filtered when this overlaps with the voice and fan noise of a social robot, Pepper. We ultimately aim for an HRI scenario where the microphone can remain open when the robot is speaking, enabling a more natural turn-taking scheme where the human can interrupt the robot. To respond appropriate