February 2024 arXiv papers — page 128

Nicolas Fabre, Ulysse Chabaud

The celebrated Hong--Ou--Mandel effect illustrates the richness of two-photon interferometry. In this work, we demonstrate that this extends to the realm of time-frequency interferometry. Taking advantage of the mathematical analogy which can be drawn between the frequency and quadrature degrees of freedom of light when there is a single photon in each auxil

Komla Domelevo, Stefanie Petermichl, Sergei Treil, Alexander Volberg

We show that the famous matrix $A_2$ conjecture is false: the norm of the Hilbert Transform in the space $L^2(W)$ with matrix weight $W$ is estimated below by $C[W]_{{A}_2}^{3/2}$.

Mattia Miotto, Lorenzo Di Rienzo, Leonardo Bo', Giancarlo Ruocco

Shape complementarity of molecular surfaces at the interfaces is a well-known characteristic of protein-protein binding regions, and it is critical in influencing the stability of the complex. Measuring such complementarity is at the basis of methods for both the prediction of possible interactions and for the design/optimization of speficic ones. However, o

Hsuan-Fu Wang, Yi-Jen Shih, Heng-Jui Chang, Layne Berry

The recently proposed visually grounded speech model SpeechCLIP is an innovative framework that bridges speech and text through images via CLIP without relying on text transcription. On this basis, this paper introduces two extensions to SpeechCLIP. First, we apply the Continuous Integrate-and-Fire (CIF) module to replace a fixed number of CLS tokens in the

Noureddine Rochdi, Atta ur Rahman, Rachid Ahl Laamara, Mohamed Bennai

The Quantum Rabi model serves as a pivotal theoretical framework for elucidating the nuanced interplay between light and matter. Utilizing circuit quantum electrodynamics on a chip, we address the challenge of achieving deep strong coupling in Quantum Cavity Electrodynamics (cQED). Despite progress in superconducting circuits and trapped ions, experimental r

Harry Langford, Ilia Shumailov, Yiren Zhao, Robert Mullins

While previous research backdoored neural networks by changing their parameters, recent work uncovered a more insidious threat: backdoors embedded within the definition of the network's architecture. This involves injecting common architectural components, such as activation functions and pooling layers, to subtly introduce a backdoor behavior that persists

Michiel de Bondt

A camel can carry one banana at a time on its back. It is on a diet and therefore can only have one banana at a time in its stomach. As soon as it has eaten a banana it walks a mile and then it needs a new banana (in order to be able to continue its itinerary). Let there be a stock of N bananas at the border of the desert. How far can the camel penetrate int

Nikolay Filonov, Michael Levitin, Iosif Polterovich, David A. Sher

We prove explicit uniform two-sided bounds for the phase functions of Bessel functions and of their derivatives. As a consequence, we obtain new enclosures for the zeros of Bessel functions and their derivatives in terms of inverse values of some elementary functions. These bounds are valid, with a few exceptions, for all zeros and all Bessel functions with

Chengxi Zeng, Tilo Burghardt, Alberto M Gambaruto

In this paper, the training dynamics of PINNs with a feature mapping layer via the limiting Conjugate Kernel and Neural Tangent Kernel is investigated, shedding light on the convergence of PINNs; Although the commonly used Fourier-based feature mapping has achieved great success, we show its inadequacy in some physics scenarios. Via these two scopes, we prop

Rui Ye, Wenhao Wang, Jingyi Chai, Dihan Li

Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting reality is that high-quality public data will be exhausted in a few years. In this paper, we offer a potential next step for contemporary LLMs: collaborative an

Yi Ren, Biwei Jiang, Yuxi Wang, Ming Yang

The star formation histories (SFHs) of galaxies provide valuable insights into galaxy evolution and stellar physics. Understanding the SFHs enables the study of chemical enrichment of galaxies, star formation triggered by interactions, and the behavior of various stellar populations. This work investigates the SFHs of ten dwarf galaxies in the Local Group (L

Sovanmonynuth Heng, Myeongseong Go, Youngsun Han

Current advancements in technology have focused the attention of the quantum computing community toward exploring the potential of near-term devices whose computing power surpasses that of classical computers in practical applications. An unresolved central question revolves around whether the inherent noise in these devices can be overcome or whether any po

Yifan Zhu, Lingjuan Miao, Haitao Wu, Zhiqiang Zhou

Visual relocalization is crucial for autonomous visual localization and navigation of mobile robotics. Due to the improvement of CNN-based object detection algorithm, the robustness of visual relocalization is greatly enhanced especially in viewpoints where classical methods fail. However, ellipsoids (quadrics) generated by axis-aligned object detection may

Marina Pasquet, François Boulogne, Frédéric Restagno, Emmanuelle Rio

We consider the lifetime of rectangular vertical soap films and we explore the influence of relative humidity and both dimensions on the stability of large soap films, reaching heights of up to 1.2 m. Using an automated rupture detection system, we achieve a robust statistical measurement of their lifetimes and we also measure the film thinning dynamics. We

O. Hryhorchak, G. Panochko, V. Pastukhov

The properties of the one-dimensional $SU(3)$ population-imbalanced fermions are discussed. The system is assumed to be in the two-body resonance where all two-body scattering lengths diverge, and the only interaction between fermions that is taken into account is the short-range three-body one. In particular, we consider the situation when only one `flavor'

Nefeli Gkouti, Prodromos Malakasiotis, Stavros Toumpis, Ion Androutsopoulos

NLP research has explored different neural model architectures and sizes, datasets, training objectives, and transfer learning techniques. However, the choice of optimizer during training has not been explored as extensively. Typically, some variant of Stochastic Gradient Descent (SGD) is employed, selected among numerous variants, using unclear criteria, of

Christian Ferko, Sergei M. Kuzenko, Kurt Lechner, Dmitri P. Sorokin

In this paper we initiate the study of six-dimensional non-linear chiral two-form gauge theories as deformations of free chiral two-form gauge theories driven by stress-tensor $T\overline T$-like flows. To lay the background for this study, we elaborate on the relationship between different Lagrangian formulations of duality-invariant p-form theories and cor

Muhammad AbuGhanem

Characterizing quantum dynamics is a cornerstone pursuit across quantum physics, quantum information science, and quantum computation. The precision of quantum gates in manipulating input basis states and their intricate superpositions is paramount. In this study, we conduct a thorough analysis of the SQSCZ gate, a universal two-qubit entangling gate, using

Sérgio M. Rebelo, J. J. Merelo, João Bicker, Penousal Machado

Computational Design approaches facilitate the generation of typographic design, but evaluating these designs remains a challenging task. In this paper, we propose a set of heuristic metrics for typographic design evaluation, focusing on their legibility, which assesses the text visibility, aesthetics, which evaluates the visual quality of the design, and se

Etienne Barthel

Over the last half-century, linear viscoelastic models for crack growth in soft solids have flourished but their predictions have rarely been compared to experiments. In fact, most available models are either very approximate or cast in forms which are not quite suitable for the analysis of actual data. Here, we propose a linear viscoelastic approach which c

Paola Boito, Yuli Eidelman, Luca Gemignani

We provide a new approach to obtain solutions of certain evolution equations set in a Banach space and equipped with nonlocal boundary conditions. From this approach we derive a family of numerical schemes for the approximation of the solutions. We show by numerical tests that these schemes are numerically robust and computationally efficient.

Jiacheng Wang, Hongyang Du, Dusit Niyato, Jiawen Kang

The advancement of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The widespread of these applications relies on the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE, GAI faces challenges in res

Mine Gokce Dogan, Jaimin Shah, Martina Cardone, Christina Fragouli

Millimeter-wave (mmWave) spectrum is expected to support data-intensive applications that require ultra-reliable low-latency communications (URLLC). However, mmWave links are highly sensitive to blockage, which may lead to disruptions in the communication. Traditional techniques that build resilience against such blockages (among which are interleaving and f

David Tolpin

We present an algorithmic solution to the problem of incremental belief updating in the context of Monte Carlo inference in Bayesian statistical models represented by probabilistic programs. Given a model and a sample-approximated posterior, our solution constructs a set of weighted observations to condition the model such that inference would result in the

Stefano Lia, Geertrui Van de Voorde

This paper studies {\em strong blocking sets} in the $N$-dimensional finite projective space $\mathrm{PG}(N,q)$. We first show that certain unions of blocking sets cannot form strong blocking sets, which leads to a new lower bound on the size of a strong blocking set in $\mathrm{PG}(N,q)$. Our second main result shows that, for $q>\frac{2}{ln(2)}(N+1)$, ther

Junjie Chu, Prashant Singh, Salman Toor

In the past few decades, the rapid development of information and internet technologies has spawned massive amounts of data and information. The information explosion drives many enterprises or individuals to seek to rent cloud computing infrastructure to put their applications in the cloud. However, the agreements reached between cloud computing providers a

Andrew K. Schulz, Adam S. Gouge, Christine L. Madliger

Conservation science is an interdisciplinary field that primarily draws on knowledge from the natural sciences, social sciences, and humanities to inform policy, planning, and practice. Since its formalization as a discipline, conservation science has also increasingly incorporated tools from integrative biological fields, such as animal behavior, genetics,

Masoumeh Javanbakhat, Md Tasnimul Hasan, Cristoph Lippert

In recent years, machine learning has witnessed extensive adoption across various sectors, yet its application in medical image-based disease detection and diagnosis remains challenging due to distribution shifts in real-world data. In practical settings, deployed models encounter samples that differ significantly from the training dataset, especially in the

Ketan Kotwal, Tanay Deshmukh, Preeti Gopal

Large occlusions result in a significant decline in image classification accuracy. During inference, diverse types of unseen occlusions introduce out-of-distribution data to the classification model, leading to accuracy dropping as low as 50%. As occlusions encompass spatially connected regions, conventional methods involving feature reconstruction are inade

Dominika Draesslerová, Omar Ahmed, Travis Gagie, Jan Holub

For taxonomic classification, we are asked to index the genomes in a phylogenetic tree such that later, given a DNA read, we can quickly choose a small subtree likely to contain the genome from which that read was drawn. Although popular classifiers such as Kraken use $k$-mers, recent research indicates that using maximal exact matches (MEMs) can lead to bet

Denis V. Mikryukov, Ivan I. Shevchenko

We study how close passages of interstellar objects of planetary and substellar masses may affect the immediate and long-term dynamics of the Solar system. We consider two nominal approach orbits, namely, the orbits of actual interstellar objects 1I/'Oumuamua and 2I/Borisov, assuming them to be typical or representative for interstellar swarms of matter. Thu

Darius Modirrousta-Galian, Jun Korenaga

Photoevaporation is thought to play an important role in the early planetary evolution. In this study, we investigate the diffusion limit of X-ray and ultraviolet induced photoevaporation in primordial atmospheres. We find that compositional fractionation resulting from mass loss is more significant than currently recognized because it is controlled by the c

Tajima Shinji, Ren Sugihara, Ryota Kitahara, Masayuki Karasuyama

The graph classification problem has been widely studied; however, achieving an interpretable model with high predictive performance remains a challenging issue. This paper proposes an interpretable classification algorithm for attributed graph data, called LAGRA (Learning Attributed GRAphlets). LAGRA learns importance weights for small attributed subgraphs,

Masoud Shokrnezhad, Tarik Taleb

Anticipation for 6G's arrival comes with growing concerns about increased energy consumption in computing and networking. The expected surge in connected devices and resource-demanding applications presents unprecedented challenges for energy resources. While sustainable resource allocation strategies have been discussed in the past, these efforts have prima

José Luis Alonso, Carlos Bouthelier-Madre, Jesús Clemente-Gallardo, David Martínez-Crespo

In this series of papers we aim to provide a mathematically comprehensive framework to the Hamiltonian pictures of quantum field theory in curved spacetimes. Our final goal is to study the kinematics and the dynamics of the theory from the point of differential geometry in infinite dimensions. In this second part we use the tools of Gaussian analysis in infi

Chufan Shi, Deng Cai, Yujiu Yang

In the rapidly evolving field of text generation, the demand for more precise control mechanisms has become increasingly apparent. To address this need, we present a novel methodology, LIFI, which offers a lightweight approach with fine-grained control for controlled text generation. Unlike previous studies that train pre-trained language models to follow di

Jae Young Suh, Minsoo Kwak, Soo Yong Kim, Hyoungseo Cho

In this paper, we are going to share a draft of the development of a conversational agent created to disseminate information about historical sites located in the Seoul. The primary objective of the agent is to increase awareness among visitors who are not familiar with Seoul, about the presence and precise locations of valuable cultural heritage sites. It a

Richard Tjörnhammar

In this work, we present an approach for evaluating segmentation strategies and solving the biological problem of creating robust interpretable maps of biological data by employing wards agglomerative hierarchical clustering applied to coexpression coordinates to deduce a faithful representation of the input. We adopt and quantify two analyte-centric metrics

Colin John Cotter, Dan Crisan, Maneesh Kumar Singh

In this study, we explore data assimilation for the Stochastic Camassa-Holm equation through the application of the particle filtering framework. Specifically, our approach integrates adaptive tempering, jittering, and nudging techniques to construct an advanced particle filtering system. All filtering processes are executed utilizing ensemble parallelism. W

Kaushik Bal, Stuti Das

We will prove several existence and regularity results for the mixed local-nonlocal parabolic equation of the form \begin{eqnarray} \begin{split} u_t-\Delta u+(-\Delta)^s u&=\frac{f(x,t)}{u^{\gamma(x,t)}} \text { in } \Omega_T:=\Omega \times(0, T), \\ u&=0 \text { in }(\mathbb{R}^n \backslash \Omega) \times(0, T), \\ u(x, 0)&=u_0(x) \text { in } \Omega ; \en

Chufan Shi, Haoran Yang, Deng Cai, Zhisong Zhang

Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers. Prior research on decoding methods, primarily focusing on task-specific models, may not extend to the current era of general-purpose large language models (LLMs). Moreover, the recent influx of decoding strategies has further comp

Edison Alberto Fernández-Culma

We study the concept of extended derivations of algebras which expands diverse definitions of generalized derivations given in the literature. We concentrate on the family of the anti-commutative algebras and classify such spaces of derivations by considering a suitable notion of equivalence when we restrict attention to such algebras. Afterwards, we investi

Ioannis Ziogas, Hessa Alfalahi, Ahsan H. Khandoker, Leontios J. Hadjileontiadis

Self-supervised learning (SSL) for automated speech recognition in terms of its emotional content, can be heavily degraded by the presence noise, affecting the efficiency of modeling the intricate temporal and spectral informative structures of speech. Recently, SSL on large speech datasets, as well as new audio-specific SSL proxy tasks, such as, temporal an

Jonathan Evertz, Merlin Chlosta, Lea Schönherr, Thorsten Eisenhofer

Large language model (LLM)-based agents combine LLMs with external tools to automate tasks such as scheduling meetings, managing documents, or booking travel. While these integrations unlock powerful capabilities, they also create new and more severe attack surfaces. In particular, prompt injection attacks become far more dangerous in the agentic setting: ma

Sanjeev Kumar

Local translational and scaling symmetries in space-time is exploited for modelling ductile damage in metals and alloys over wide ranges of strain rate and temperature. The invariant energy density corresponding to the ductile deformation is constructed through the gauge invariant curvature tensor by imposing the Weyl like condition. The energetics of the pl

Hsin Lin, Yi-Kang Su, Hong-Qi Chen, La-Fei Ko

Despite advances in cellular network technology, base station (BS) load balancing remains a persistent problem. Although centralized resource allocation methods can address the load balancing problem, it still remains an NP-hard problem. In this research, we study how federated deep Q learning can be used to inform each user equipment (UE) of the each BS's l

María Teresa García-Ordás, Héctor Alaiz-Moretón, José-Luis Casteleiro-Roca, Esteban Jove

This work addresses the performance comparison between four clustering techniques with the objective of achieving strong hybrid models in supervised learning tasks. A real dataset from a bio-climatic house named Sotavento placed on experimental wind farm and located in Xermade (Lugo) in Galicia (Spain) has been collected. Authors have chosen the thermal sola

Vladimir Vovk

This paper places conformal testing in a general framework of statistical hypothesis testing. A standard approach to testing a composite null hypothesis $H$ is to test each of its elements and to reject $H$ when each of its elements is rejected. It turns out that we can fully cover conformal testing using this approach only if we allow forgetting some of the

Omer Luxembourg, Dor Tsur, Haim Permuter

Transfer entropy (TE) is an information theoretic measure that reveals the directional flow of information between processes, providing valuable insights for a wide range of real-world applications. This work proposes Transfer Entropy Estimation via Transformers (TREET), a novel attention-based approach for estimating TE for stationary processes. The propose

Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu

To improve the ability of the large language model (LLMs) to tackle complex reasoning problems, chain-of-thoughts (CoT) methods were proposed to guide LLMs to reason step-by-step, enabling problem solving from simple to complex. State-of-the-art methods for generating such a chain involve interactive collaboration, where the learner generates candidate inter

Jianquan Xu, Asieh Ghanekarade, Li Li, Huifeng Zhu

The temperature at which supercooled liquids turn into solid-like glasses ($T_g$) can change at the free surface, affecting the properties of nanostructured glasses and their applications. However, inadequate experimental resolution to determine the $T_g$ gradient and a longstanding debate over the role of nonequilibrium effects have hindered fundamental und

Adam Alami, Raúl Pardo, Johan Linåker

Context: Free and Open Source Software (FOSS) communities' ability to stay viable and productive over time is pivotal for society as they maintain the building blocks that digital infrastructure, products, and services depend on. Sustainability may, however, be characterized from multiple aspects, and less is known how these aspects interplay and impact comm

W. Chango, R. Cerezo, M. Sanchez-Santillan, R. Azevedo

The aim of this study was to predict university students' learning performance using different sources of data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from face recording videos, interaction zones from eye tracking, and test perf

Haeran Cho, Tobias Kley, Housen Li

For data segmentation in high-dimensional linear regression settings, the regression parameters are often assumed to be sparse segment-wise, which enables many existing methods to estimate the parameters locally via $\ell_1$-regularised maximum likelihood-type estimation and then contrast them for change point detection. Contrary to this common practice, we

Faisal Etminan, Amanullah Aalimi

The developed Faddeev three-body equations are solved to search for bound-state solutions of a phi-meson $ \left(\phi\right) $ and two nucleons $ \left( \textrm{NN} \right) $ system. The newly published spin $ 3/2 $ $ \textrm{N-\ensuremath{\phi}} $ potential based on the $ \left(\textrm{2+1}\right) $-flavor lattice QCD simulations near physical point, and re

Shahbaz Syed, Khalid Al-Khatib, Martin Potthast

This paper presents TL;DR Progress, a new tool for exploring the literature on neural text summarization. It organizes 514~papers based on a comprehensive annotation scheme for text summarization approaches and enables fine-grained, faceted search. Each paper was manually annotated to capture aspects such as evaluation metrics, quality dimensions, learning p

Annie Wong, Jacob de Nobel, Thomas Bäck, Aske Plaat

Although deep reinforcement learning methods can learn effective policies for challenging problems such as Atari games and robotics tasks, algorithms are complex, and training times are often long. This study investigates how Evolution Strategies perform compared to gradient-based deep reinforcement learning methods. We use Evolution Strategies to optimize t

Yuzhuo Chen, Sebastian V. Pios, Maxim F. Gelin, Lipeng Chen

In recent years, machine learning (ML) surrogate models have emerged as an indispensable tool to accelerate simulations of physical and chemical processes. However, there is still a lack of ML models that can accurately predict molecular vibrational spectra. Here, we present a highly efficient high-dimensional neural network potentials (HD-NNP) architecture

Romulo Leoncio Cruz Simbron

In this work we explore the relationship between the Mobius inverse transformation of an arbitrary sequence and unrestricted partitions identities.

Matthias Gsänger, Volker Hösel, Christoph Mohamad-Klotzbach, Johannes Müller

We investigate the connection between Potts (Curie-Weiss) models and stochastic opinion models in the view of the Boltzmann distribution and stochastic Glauber dynamics. We particularly find that the q-voter model can be considered as a natural extension of the Zealot model which is adapted by Lagrangian parameters. We also discuss weak and strong effects co

Farkhod Eshmatov, Xabier García-Martínez, Rustam Turdibaev

We introduce a novel approach that employs techniques from noncommutative Poisson geometry to comprehend the algebra of invariants of two $n\times n$ matrices. We entirely solve the open problem of computing the algebra of invariants of two $4 \times 4$ matrices. As an application, we derive the complete description of the invariant commuting variety of $4 \

Lorenzo Giusti

The irreducible complexity of natural phenomena has led Graph Neural Networks to be employed as a standard model to perform representation learning tasks on graph-structured data. While their capacity to capture local and global patterns is remarkable, the implications associated with long-range and higher-order dependencies pose considerable challenges to s

Chen Jia-Chen, Guillem Senabre, Allane Caron

With more and more advanced data analysis techniques emerging, people will expect these techniques to be applied in more complex tasks and solve problems in our daily lives. Text Summarization is one of famous applications in Natural Language Processing (NLP) field. It aims to automatically generate summary with important information based on a given context

Tamador Alkhidir, Edmond Awad, Aamena Alshamsi

Education systems are dynamically changing to accommodate technological advances, industrial and societal needs, and to enhance students' learning journeys. Curriculum specialists and educators constantly revise taught subjects across educational grades to identify gaps, introduce new learning topics, and enhance the learning outcomes. This process is usuall

Son Tien Bui, Shinya Kawano, Van Anh Ho

Robotics hand/grippers nowadays are not limited to manufacturing lines; instead, they are widely utilized in cluttered environments, such as restaurants, farms, and warehouses. In such scenarios, they need to deal with high uncertainty of the grasped objects' shapes, postures, surfaces, and material properties, which requires complex integration of sensing a

Qiya Hu, Ziyi Li

In this paper we are concerned with restricted additive Schwarz with local impedance transformation conditions for a family of Helmholtz problems in two dimensions. These problems are discretized by the finite element method with conforming nodal finite elements. We design and analyze a new adaptive coarse space for this kind of restricted additive Schwarz m

Shaila Sharmin, Hafizah Mansor, Andi Fitriah Abdul Kadir, Normaziah A. Aziz

The development of intrusion detection systems (IDS) for the in-vehicle Controller Area Network (CAN) bus is one of the main efforts being taken to secure the in-vehicle network against various cyberattacks, which have the potential to cause vehicles to malfunction and result in dangerous accidents. These CAN IDS are evaluated in disparate experimental condi

Haotian Xu, Shuai Liu, Ling Shi

In recent years, the distributed-observer-based distributed control law has shown powerful ability to arbitrarily approximate the centralized control performance. However, the traditional distributed observer requires each local observer to reconstruct the state information of the whole system, which is unrealistic for large-scale scenarios. To fill this gap

Kilian Brenner, Michael Zimmermann, Maik Butterling, Andreas Wagner

We present the design of a Faraday cup-like charged particle detector in a four quadrant configuration aimed at facilitating the alignment of low-intensity beams of exotic particles. The device is capable of assessing the current on the electrodes with a resolution of 33fA within 15ms or a maximal resolution of 1.8fA with a measurement time of 12.4s. This pe

Washim Uddin Mondal, Veni Goyal, Satish V. Ukkusuri, Goutam Das

This paper presents a conditional generative adversarial network (cGAN) that translates base station location (BSL) information of any Region-of-Interest (RoI) to location-dependent coverage probability values within a subset of that region, called the region-of-evaluation (RoE). We train our network utilizing the BSL data of India, the USA, Germany, and Bra

Hyukhun Koh, Dohyung Kim, Minwoo Lee, Kyomin Jung

In the pursuit of developing Large Language Models (LLMs) that adhere to societal standards, it is imperative to detect the toxicity in the generated text. The majority of existing toxicity metrics rely on encoder models trained on specific toxicity datasets, which are susceptible to out-of-distribution (OOD) problems and depend on the dataset's definition o

Hiroyuki Chihara

The X-ray transform on the plane or on the three-dimensional Euclidean space can be considered as the measurements of CT scanners for normal human tissue. If the human body contains metal regions such as dental implants, stents in blood vessels, metal bones, etc., the beam hardening effect for the energy level of the X-ray causes streaking artifacts in its C

Hao-Qiang Zhang, Da-Bin Lin, Kuan Liu, En-Wei Liang

Recent Imaging X-ray Polarimetry Explorer (IXPE) observations of blazars tend to support the shock model for the X-ray emission, but report a low polarization degree ($\Pi\sim 10\%$) in X-rays compared with the previous theoretical expectations in the shock model. In order to reconcile the theoretical expectations with observations, we revisit the polarizati

Dibakar Roychowdhury

We compute the spectra associated with various semiclassical string states that propagate over $\mathcal{N}=2$ Gaiotto-Maldacena backgrounds. As an interesting special case, for the Abelian T- dual solution, we discover giant magnon and single spike configurations while imposing appropriate boundary conditions. However, for Sfetsos-Thompson backgrounds, one

Guo Yu

This article offers an elaborate description of a Kalman filter code employed in the active control system. Conventional active noise management methods usually employ an adaptive filter, such as the filtered reference least mean square (FxLMS) algorithm, to adjust to changes in the primary noise and acoustic environment. Nevertheless, the slow convergence c

A. S. Agrawal

The main objective of this study is to investigate the phenomenon of the bouncing scenario of the universe. The most widely recognized cosmological framework is the standard cosmological model, sometimes referred to as the Big Bang model. This is mainly because of its inherent properties and its consistent alignment with recent observational studies. However

Yuchen Hu, Chen Chen, Chao-Han Huck Yang, Ruizhe Li

Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. However, both translation tasks typically utilize beam search decoding and top-1 hypothesis selection for inference. These techniques struggle to fully ex

Pooja Vishwakarma, Parth Shah

The higher-curvature gravity with boundary terms i.e the $f(Q)$ theories, grounded on non-metricity as a fundamental geometric quantity, exhibit remarkable efficacy in portraying late-time universe phenomena. The aim is to delineate constraints on two prevalent models within this framework, namely the Log-square-root model and the Hyperbolic tangent-power mo

Shuho Kanda

We prove the hard Lefschetz duality for locally conformally almost K\"{a}hler manifolds. This is a generalization of that for almost K\"{a}hler manifolds studied by Cirici and Wilson. We generalize the K\"{a}hler identities to prove the duality. Based on the result, we introduce the hard Lefschetz condition for locally conformally symplectic manifolds. As ex

Masanari Kimura

Test-Time Augmentation (TTA) is a very powerful heuristic that takes advantage of data augmentation during testing to produce averaged output. Despite the experimental effectiveness of TTA, there is insufficient discussion of its theoretical aspects. In this paper, we aim to give theoretical guarantees for TTA and clarify its behavior.

Jun Murakami, Roland van der Veen

The theory of bottom tangles is used to construct a quantum fundamental group. On the other hand, the skein module is considered as a quantum analogue of the $SL(2)$ representation of the fundamental group. Here we construct the skein module of a knot complement by using the bottom tangles. We first construct the universal space of quantum representations, w

David J. Moss

Microwave photonic (MWP) signal processors, which process microwave signals based on pho-tonic technologies, bring advantages intrinsic to photonics such as low loss, large processing bandwidth, and strong immunity to electromagnetic interference. Optical microcombs can offer a large number of wavelength channels and compact device footprints, which make the

Fan Zhou

In this paper we homologically construct a (functorial) BGG resolution of the finite-dimensional simple module of the nilBrauer algebra by using infinity-categorical methods following the reconstruction-from-stratification philosophy, e.g. appearing in Ayala-Mazel-Gee-Rozenblyum. To do so, we prove a fact of independent interest, that half of the nilBrauer a

Guanglian Li, Jing Li, Julie Merten, Yifeng Xu

In this paper, we propose an adaptive finite element method for computing the first eigenpair of the $p$-Laplacian problem. We prove that starting from a fine initial mesh our proposed adaptive algorithm produces a sequence of discrete first eigenvalues that converges to the first eigenvalue of the continuous problem and the distance between discrete eigenfu

Jialu Li, Mark Hasegawa-Johnson, Nancy L. McElwain

To understand why self-supervised learning (SSL) models have empirically achieved strong performances on several speech-processing downstream tasks, numerous studies have focused on analyzing the encoded information of the SSL layer representations in adult speech. Limited work has investigated how pre-training and fine-tuning affect SSL models encoding chil

Solmaz Azimi, Emilio Gallicchio

We present receptor hopping and receptor swapping free energy estimation protocols based on the Alchemical Transfer Method (ATM) to model the binding selectivity of a set of ligands to two arbitrary receptors. The receptor hopping protocol, where a ligand is alchemically transferred from one receptor to another in one simulation, directly yields the ligand's

Han Shen, Zhuoran Yang, Tianyi Chen

Bilevel optimization has been recently applied to many machine learning tasks. However, their applications have been restricted to the supervised learning setting, where static objective functions with benign structures are considered. But bilevel problems such as incentive design, inverse reinforcement learning (RL), and RL from human feedback (RLHF) are of

Parisa Salmanian, Angelos Chatzimparmpas, Ali Can Karaca, Rafael M. Martins

Dimensionality Reduction (DR) techniques such as t-SNE and UMAP are popular for transforming complex datasets into simpler visual representations. However, while effective in uncovering general dataset patterns, these methods may introduce artifacts and suffer from interpretability issues. This paper presents DimVis, a visualization tool that employs supervi

Shariq Murtuza

With the release of OpenAI's ChatGPT, the field of large language models (LLM) saw an increase of academic interest in GPT based chat assistants. In the next few months multiple accesible large language models were released that included Meta's LLama models and Mistral AI's Mistral and Mixtral MoE models. These models are available openly for a wide array of

Kang Du, Yu Xiang

We study the data-generating mechanism for reconstructive SSL to shed light on its effectiveness. With an infinite amount of labeled samples, we provide a sufficient and necessary condition for perfect linear approximation. The condition reveals a full-rank component that preserves the label classes of Y, along with a redundant component. Motivated by the co

Takahiro Inayama, Shin-ichi Matsumura

This paper studies the approximation of singular Hermitian metrics on vector bundles using smooth Hermitian metrics with Nakano semi-positive curvature on Zariski open sets. We show that singular Hermitian metrics capable of this approximation satisfy Nakano semi-positivity as defined through the $\overline{\partial} $-equation with optimal $L^2$-estimates.

Colin J. Burke, Yichen Liu, Charlotte A. Ward, Xin Liu

We study the black hole mass $-$ host galaxy stellar mass relation, $M_{\rm{BH}}-M_{\ast}$, of a sample of $z<4$ optically-variable AGNs in the COSMOS field. The parent sample of 491 COSMOS AGNs were identified by optical variability from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) program. Using publicly-available catalogs and spectra, we conso

Jamison R. Ebert, Jean-Francois Chamberland, Krishna R. Narayanan

Novel sparse regression LDPC (SR-LDPC) codes exhibit excellent performance over additive white Gaussian noise (AWGN) channels in part due to their natural provision of shaping gains. Though SR-LDPC-like codes have been considered within the context of single-user error correction and massive random access, they are yet to be examined as candidates for coordi

Zheyu Wen, Ali Ghafouri, George Biros

We propose a method that uses a two-species ordinary differential equation (ODE) model to characterize misfolded tau (or simply tau) protein spreading in Alzheimer's disease (AD) and calibrates it from clinical data. The unknown model parameters are the initial condition (IC) for tau and three scalar parameters representing the migration, proliferation, and

Yongqing Xu, Yong Li, Tony Q. S. Quek

Cognitive radio (CR) and integrated sensing and communication (ISAC) are both critical technologies for the sixth generation (6G) wireless networks. However, their interplay has yet to be explored. To obtain the mutual benefits between CR and ISAC, we focus on a reconfigurable intelligent surface (RIS)-enhanced cognitive ISAC system and explore using the add

Yumu Yang, Mauricio Hippert, Enrico Speranza, Jorge Noronha

In neutron stars, flavor-changing weak interactions determine the equilibrium fraction of protons over neutrons. In binary neutron-star mergers, violent changes in density modify this equilibrium value at timescales of milliseconds, comparable to those required for weak interactions to take place. As a result, the fraction of protons evolves out of phase wit

S. M. Croom, J. van de Sande, S. P. Vaughan, T. H. Rutherford

We use the SAMI Galaxy Survey to examine the drivers of galaxy spin, $\lambda_{R_e}$, in a multi-dimensional parameter space including stellar mass, stellar population age (or specific star formation rate) and various environmental metrics (local density, halo mass, satellite vs. central). Using a partial correlation analysis we consistently find that age or

Iker de las Heras, Benjamin Klopsch, Anitha Thillaisundaram

Let $G$ be a $p$-adic analytic pro-$p$ group of dimension $d$. We produce an approximate series which descends regularly in strata and whose terms deviate from the lower $p$-series in a uniformly bounded way. This brings to light a new set of rational invariants, canonically associated to $G$, that yield the aforementioned uniform bound and that restrict the

Mengqi Wu, Lintao Zhang, Pew-Thian Yap, Hongtu Zhu

Brain magnetic resonance imaging (MRI) has been extensively employed across clinical and research fields, but often exhibits sensitivity to site effects arising from non-biological variations such as differences in field strength and scanner vendors. Numerous retrospective MRI harmonization techniques have demonstrated encouraging outcomes in reducing the si

Te-Chun Wang

We consider the mollified versions of the Kardar-Parisi-Zhang (KPZ) equation and the stochastic heat equation (SHE) in high dimensions $d\geq 3$ and analyze their probability distributions as the mollification is removed. Up to the $L^2$-criticality, we prove Gaussian limits, possibly with random perturbations, for the space-time fluctuations of the mollifie