April 2024 arXiv papers — page 176

Thinh Hung Truong, Yulia Otmakhova, Karin Verspoor, Trevor Cohn

In this work, we measure the impact of affixal negation on modern English large language models (LLMs). In affixal negation, the negated meaning is expressed through a negative morpheme, which is potentially challenging for LLMs as their tokenizers are often not morphologically plausible. We conduct extensive experiments using LLMs with different subword tok

Lu Lv, Lihong Hu, Weikang Dong, Jingyi Duan

Two-dimensional (2D) materials provide an ideal platform to explore novel superconducting behavior including Ising superconductivity, topological superconductivity and Majorana bound states in different 2D stoichiometric Ta-, Nb-, and Fe-based crystals. However, tuning the element content in 2D compounds for regulating their superconductivity has not been re

Y. H. M. Hendy, I. Zead, A. E. Abdelaziz, A. Takey

We present the first CCD observations of an eclipsing binary, ZTFJ015003.88+534734.1, which is a member in the open star cluster UBC 188. The observations were taken by the 1.88 m telescope at the Kottamia Astronomical Observatory (KAO) in SDSS griz bands. The latest version of the Wilson- Devinney (W-D) code was employed for photometric analysis and light c

Jennifer Hu, Michael C. Frank

Developmental psychologists have argued about when cognitive capacities such as language understanding or theory of mind emerge. These debates often hinge on the concept of "task demands" -- the auxiliary challenges associated with performing a particular evaluation -- that may mask the child's underlying ability. The same issues arise when measuring the cap

Anthony Gruber, Irina Tezaur

Though ubiquitous as first-principles models for conservative phenomena, Hamiltonian systems present numerous challenges for model reduction even in relatively simple, linear cases. Here, we present a method for the projection-based model reduction of canonical Hamiltonian systems that is variationally consistent for any choice of linear reduced basis: Hamil

Jiawei Zhang, Chejian Xu, Yu Gai, Freddy Lecue

This paper introduces KnowHalu, a novel approach for detecting hallucinations in text generated by large language models (LLMs), utilizing step-wise reasoning, multi-formulation query, multi-form knowledge for factual checking, and fusion-based detection mechanism. As LLMs are increasingly applied across various domains, ensuring that their outputs are not h

Khiem Hong Phan, Dzung Tri Tran, Thanh Huy Nguyen

We present one-loop formulas for the decay of CP-odd Higgs $A^0 \rightarrow \ell \bar{\ell} V$ with $\ell \equiv e, \mu$ and $V\equiv \gamma, Z$ in Higgs Extensions of the Standard Model, considering two higgs doublet model with a complex (and real) scalar, two higgs doublet model as well as triplet higgs model. Analytic results for one-loop amplitudes are e

Yuanyuan Zhang, Yingdong Wang, Chaoyong Wu, Lingmin Zhana

Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Al

Daewon Lee, Sam Oaks-Leaf, Sophia B. Betzler, Yifeng Shi

Solute-intercalation-induced phase separation creates spatial heterogeneities in host materials, a phenomenon ubiquitous in batteries, hydrogen storage, and other energy devices. Despite many efforts, probing intercalation processes at the atomic scale has been a significant challenge. We study hydrogen (de)intercalation in palladium nanocrystals as a model

Weichao Lan, Yiu-ming Cheung, Qing Xu, Buhua Liu

Knowledge distillation (KD) has become a widely used technique in the field of model compression, which aims to transfer knowledge from a large teacher model to a lightweight student model for efficient network development. In addition to the supervision of ground truth, the vanilla KD method regards the predictions of the teacher as soft labels to supervise

Anthony Meng Huat Tiong, Junqi Zhao, Boyang Li, Junnan Li

Vision-language (VL) models, pretrained on colossal image-text datasets, have attained broad VL competence that is difficult to evaluate. A common belief is that a small number of VL skills underlie the variety of VL tests. In this paper, we perform a large-scale transfer learning experiment aimed at discovering latent VL skills from data. We reveal interest

Zherui Chen, Giacomo Nannicini

We study the complexity of estimating the partition function $\mathsf{Z}(\beta)=\sum_{x\in\chi} e^{-\beta H(x)}$ for a Gibbs distribution characterized by the Hamiltonian $H(x)$. We provide a simple and natural lower bound for quantum algorithms that solve this task by relying on reflections through the coherent encoding of Gibbs states. Our primary contribu

Taekyun Kim, Dae San Kim

In 2008, Spivey found a recurrence relation for the Bell numbers. We consider the probabilistic r-Bell polynomials associated with which are a probabilistic extension of the r-Bell polynomials. Here Y is a random variable whose moment generating function exists in some neighborhood of the origin . The aim of this paper is to generalize the relation for the B

Ryan Arbon, Jacob Bedrossian

We prove a stability threshold theorem for 2D Navier-Stokes on three unbounded domains: the whole plane $\mathbb{R} \times \mathbb{R}$, the half plane $\mathbb{R} \times [0,\infty)$ with Navier boundary conditions, and the infinite channel $\mathbb{R} \times [-1, 1]$ with Navier boundary conditions. Starting with the Couette shear flow, we consider initial p

Zeyu Zhao, Nan Gao, Zhi Zeng, Guixuan Zhang

Diffusion models have shown great success in generating high-quality co-speech gestures for interactive humanoid robots or digital avatars from noisy input with the speech audio or text as conditions. However, they rarely focus on providing rich editing capabilities for content creators other than high-level specialized measures like style conditioning. To r

Cheng Zhao, Su Sun, Ruoyu Wang, Yuliang Guo

Most 3D Gaussian Splatting (3D-GS) based methods for urban scenes initialize 3D Gaussians directly with 3D LiDAR points, which not only underutilizes LiDAR data capabilities but also overlooks the potential advantages of fusing LiDAR with camera data. In this paper, we design a novel tightly coupled LiDAR-Camera Gaussian Splatting (TCLC-GS) to fully leverage

Anthony Bonato, Florian Lehner, Trent G. Marbach, JD Nir

We study the Localization game on locally finite graphs trees, where each of the countably many vertices have finite degree. In contrast to the finite case, we construct a locally finite tree with localization number $n$ for any choice of positive integer $n$. Our examples have uncountably many ends, and we show that this is necessary by proving that locally

Zaid Sheikh, Antonios Anastasopoulos, Shruti Rijhwani, Lindia Tjuatja

Effectively using Natural Language Processing (NLP) tools in under-resourced languages requires a thorough understanding of the language itself, familiarity with the latest models and training methodologies, and technical expertise to deploy these models. This could present a significant obstacle for language community members and linguists to use NLP tools.

Xiangyuan Zhang, Weichao Mao, Haoran Qiu, Tamer Başar

Closed-loop control of nonlinear dynamical systems with partial-state observability demands expert knowledge of a diverse, less standardized set of theoretical tools. Moreover, it requires a delicate integration of controller and estimator designs to achieve the desired system behavior. To establish a general controller synthesis framework, we explore the De

Yunzhuo Hao, Wenkai Yang, Yankai Lin

Recent researches have shown that Large Language Models (LLMs) are susceptible to a security threat known as Backdoor Attack. The backdoored model will behave well in normal cases but exhibit malicious behaviours on inputs inserted with a specific backdoor trigger. Current backdoor studies on LLMs predominantly focus on instruction-tuned LLMs, while neglecti

Jeffy Yu, Maximilian Huber, Kevin Tang

This paper investigates the ethical implications of aligning Large Language Models (LLMs) with financial optimization, through the case study of GreedLlama, a model fine-tuned to prioritize economically beneficial outcomes. By comparing GreedLlama's performance in moral reasoning tasks to a base Llama2 model, our results highlight a concerning trend: GreedLl

Ho-Joong Kim, Jung-Ho Hong, Heejo Kong, Seong-Whan Lee

In this paper, we investigate that the normalized coordinate expression is a key factor as reliance on hand-crafted components in query-based detectors for temporal action detection (TAD). Despite significant advancements towards an end-to-end framework in object detection, query-based detectors have been limited in achieving full end-to-end modeling in TAD.

Asad Ur Rehman Khan, Muhammad Ramzan, Muhammad Faisal Iqbal, Muhammad Hafeez

Zinc sulfide ZnS thin films with variable structural, optical, electrical, and thermoelectric properties were obtained by changing the source to substrate SSD distance in the physical vaporthermal coating PVTC system. The films crystallized into a zinc blede cubic structure with 111 preferred orientation.

Amirhossein Abaskohi, Sara Baruni, Mostafa Masoudi, Nesa Abbasi

This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for more low-resource languages remains an open question. We present the first comprehensive benchmarking study of LLMs across diverse Persian language tasks. Our primary focus is on GP

Md. Kowsher, Ritesh Panditi, Nusrat Jahan Prottasha, Prakash Bhat

Conversational modeling using Large Language Models (LLMs) requires a nuanced understanding of context to generate coherent and contextually relevant responses. In this paper, we present Token Trails, a novel approach that leverages token-type embeddings to navigate the intricate contextual nuances within conversations. Our framework utilizes token-type embe

Setsuo Taniguchi

Two-way relationships between transformations and quadratic forms on Wiener spaces are investigated with the help of change of variables formulas on Wiener spaces. Further the evaluation of Laplace transforms of quadratic forms via Riccati or linear second order ODEs will be shown.

Mark Mandelkern

An age-old controversy in mathematics concerns the necessity and the possibility of constructive proofs. The controversy has been rekindled by recent advances which demonstrate the feasibility of a fully constructive mathematics. This nontechnical article discusses the motivating ideas behind the constructive approach to mathematics and the implications of c

Zhaolin Li, Artem Prokhorov

Many management decisions involve accumulated random realizations for which only the first and second moments of their distribution are available. The sharp Chebyshev-type bound for the tail probability and Scarf bound for the expected loss are widely used in this setting. We revisit the tail behavior of such quantities with a focus on independence. Conventi

H. P. Zhang, Z. Song

In the Hermitian regime, the Wannier-Stark ladder characterizes the eigenstates of an electron in a periodic potential with an applied static electric field. In this work, we extend this concept to the complex regime for a periodic non-Hermitian system under a linear potential. We show that although the energy levels can be complex, they are still equally sp

Muhammad Farooq Saleem, Ghulam Abbas Ashraf, Muhammad Faisal Iqbal, Rashid Khan

InGaN quantum wells (QWs) grown on c-plane sapphire substrate experience strain due to the lattice mismatch. The strain generates a strong piezoelectric field in QWs that contributes to THz emission under ultrafast excitation. Physical parameters such as QW width, period number, and Indium concentration can affect the strength of the piezoelectric field and

João Paulo S. Melo, José A. Helayël-Neto

This paper focuses on additional inspections concerning the fermionic sector of the Standard Model Extension (SME). In this context, our main effort in this contribution is to investigate effects of Lorentz-symmetry violation (LSV) on the Klein Paradox, the Zitterbewegung and its phenomenology in connection to Condensed Matter Physics, Atomic Physics, and As

Yi-fan Shen, Wei Chen, Xiang-yu Wu, Kun Xu

In the SMASH-CLVisc-hybrid framework, including SMASH for the initial conditions and the hadronic rescattering stage, and CLVisc for the quark gluon plasma (QGP) evolution, we investigate net baryon number fluctuations via considering the equation of state (EoS) with and without a critical end point (CEP) in the QCD phase transition. Specifically, two distin

Yukun Li, Liping Liu

Diffusion models have been popular for point cloud generation tasks. Existing works utilize the forward diffusion process to convert the original point distribution into a noise distribution and then learn the reverse diffusion process to recover the point distribution from the noise distribution. However, the reverse diffusion process can produce samples wi

Jaeyoung Song, Sang-Woon Jeon

Federated learning aims to construct a global model that fits the dataset distributed across local devices without direct access to private data, leveraging communication between a server and the local devices. In the context of a practical communication scheme, we study the completion time required to achieve a target performance. Specifically, we analyze t

Huajun Zhou, Fengtao Zhou, Hao Chen

Recently, we have witnessed impressive achievements in cancer survival analysis by integrating multimodal data, e.g., pathology images and genomic profiles. However, the heterogeneity and high dimensionality of these modalities pose significant challenges for extracting discriminative representations while maintaining good generalization. In this paper, we p

Jun Wang, Qiongkai Xu, Xuanli He, Benjamin I. P. Rubinstein

While multilingual machine translation (MNMT) systems hold substantial promise, they also have security vulnerabilities. Our research highlights that MNMT systems can be susceptible to a particularly devious style of backdoor attack, whereby an attacker injects poisoned data into a low-resource language pair to cause malicious translations in other languages

Antoine Nzeyimana

Morphological modeling in neural machine translation (NMT) is a promising approach to achieving open-vocabulary machine translation for morphologically-rich languages. However, existing methods such as sub-word tokenization and character-based models are limited to the surface forms of the words. In this work, we propose a framework-solution for modeling com

Perpetual Hope Akwensi, Akshay Bharadwaj, Ruisheng Wang

The benefits of having digital twins of urban buildings are numerous. However, a major difficulty encountered in their creation from airborne LiDAR point clouds is the effective means of accurately reconstructing significant occlusions amidst point density variations and noise. To bridge the noise/sparsity/occlusion gap and generate high fidelity 3D building

Shouhei Honda, Artem Nepechiy

The goal of this note is to demonstrate how existing results can be adapted to establish the following result: A locally metric measure homogeneous $\mathrm{RCD}(K,N)$ space is isometric to, after multiplying a positive constant to the reference measure, a smooth Riemannian manifold with the Riemannian volume measure.

Yutong Shao, Ndapa Nakashole

Structured data, prevalent in tables, databases, and knowledge graphs, poses a significant challenge in its representation. With the advent of large language models (LLMs), there has been a shift towards linearization-based methods, which process structured data as sequential token streams, diverging from approaches that explicitly model structure, often as

Townim Faisal Chowdhury, Kewen Liao, Vu Minh Hieu Phan, Minh-Son To

Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability. Class activation maps (CAMs) and recent variants provide ways to visually explain the DNN decision-making process by displaying 'attention' heatmaps of the DNNs. Nevertheles

Xinye Tang, Amir H. Abdi, Jeremias Eichelbaum, Mahan Das

Data is growing rapidly in volume and complexity. Proficiency in database query languages is pivotal for crafting effective queries. As coding assistants become more prevalent, there is significant opportunity to enhance database query languages. The Kusto Query Language (KQL) is a widely used query language for large semi-structured data such as logs, telem

Hwiwoo Park, Jun H. Park, Jungseek Hwang

We propose the regularized recurrent inference machine (rRIM), a novel machine-learning approach to solve the challenging problem of deriving the pairing glue function from measured optical spectra. The rRIM incorporates physical principles into both training and inference and affords noise robustness, flexibility with out-of-distribution data, and reduced d

Edward Parker

Very little previous literature has considered the *exact* solution to Maxwell's equations for an infinite ideal cylindrical solenoid with an arbitrary time-dependent azimuthal surface current $K(t) \hat{\bf \phi}$. Most of the previous literature has focused on special cases and has approached the problem by calculating the magnetic vector potential ${\bf A

Aryadine F. de Sousa, Gabriella G. Damas, Norton G. de Almeida

Cyclic classical and quantum thermal machines show higher efficiency when the strokes are carried out quasi-statically. Recent theoretical and experimental work on figures of merit for thermal machines show that they have an advantage when operating in environments with negative temperatures. In an experimental proof of concept [Phys. Rev. Lett. 122, 240602

Hui Xue, Rhodri H Davies, James Howard, Hunain Shiwani

Cardiac Magnetic Resonance (CMR) is established as a non-invasive imaging technique for evaluation of heart function, anatomy, and myocardial tissue characterization. Quantitative biomarkers are central for diagnosis and management of heart disease. Deep learning (DL) is playing an ever more important role in extracting these quantitative measures from CMR i

Dongliang Jing, Lin Lin, Andrew W. Eckford

In molecular communication (MC), molecules are released from the transmitter to convey information. This paper considers a realistic molecule shift keying (MoSK) scenario with two species of molecule in two reservoirs, where the molecules are harvested from the environment and placed into different reservoirs, which are purified by exchanging molecules betwe

Hui Xue, Sarah Hooper, Azaan Rehman, Iain Pierce

The ability to recover MRI signal from noise is key to achieve fast acquisition, accurate quantification, and high image quality. Past work has shown convolutional neural networks can be used with abundant and paired low and high-SNR images for training. However, for applications where high-SNR data is difficult to produce at scale (e.g. with aggressive acce

Vadim Kaloshin, Illya Koval, Amir Vig

This paper is part I of a series in which we aim to show that the singular support of the wave trace and the length spectrum of a smooth, strictly convex, and bounded planar billiard table are generally distinct objects. We derive an asymptotic trace formula for the regularized resolvent which is dual to the wave trace and contains the same information. To d

Eliseu Venites Filho, Roberto da Silva, José Roberto Drugowich de Felício

Random matrix theory, particularly using matrices akin to the Wishart ensemble, has proven successful in elucidating the thermodynamic characteristics of critical behavior in spin systems across varying interaction ranges. This paper explores the applicability of such methods in investigating critical phenomena and the crossover to tricritical points within

Tom Benhamou, Fanxin Wu

We provide two types of guessing principles for ultrafilter ($\diamondsuit^{-}_{\lambda}(U), \ \diamondsuit^p_\lambda(U)$) on $\omega$ which form subclasses of Tukey-top ultrafilters, and construct such ultrafilters in $ZFC$. These constructions are essentially different from Isbell's construction \cite{Isbell65} of Tukey-top ultrafilters. We prove using the

Aaron Mishkin, Mert Pilanci, Mark Schmidt

We prove new convergence rates for a generalized version of stochastic Nesterov acceleration under interpolation conditions. Unlike previous analyses, our approach accelerates any stochastic gradient method which makes sufficient progress in expectation. The proof, which proceeds using the estimating sequences framework, applies to both convex and strongly c

Seham Alharbi, Dimitris Kolovos

Context: Application Programming Interface (API) code examples are an essential knowledge resource for learning APIs. However, a few user studies have explored how the structural characteristics of the source code in code examples impact their comprehensibility and reusability. Objectives: We investigated whether the (a) linearity and (b) length of the sourc

Ryan Wong, Arjun Tyagi, Sungjun Cho, Pratik Sampat

Computer science and related fields (e.g., computer engineering, computer hardware engineering, electrical engineering, electrical and computer engineering, computer systems engineering) often draw inspiration from other fields, areas, and the real world in order to describe topics in their area. One cross-domain example is the idea of a block. The idea of b

Taiwang Deng

In this article, we geometrically study the partial Bernstein-Zelevinsky operator introduced in the author's thesis, which generalizes the original Bernstein-Zelevinsky operator. We relate the partial Bernstein-Zelevinsky operator to the geometric induction of Lusztig and then perform explicit computations in special cases. Finally, we develop a symmetric re

Pradip Gatkine, Greg Sercel, Nemanja Jovanovic, Ronald Broeke

Broadband low-resolution near-infrared spectrographs in a compact form are crucial for ground- and space-based astronomy and other fields of sensing. Astronomical spectroscopy poses stringent requirements including high efficiency, broad band operation ($>$ 300 nm), and in some cases, polarization insensitivity. We present and compare experimental results fr

S M Rakib Hasan, Aakar Dhakal, Md Humaion Kabir Mehedi, Annajiat Alim Rasel

Efforts on the research and development of OCR systems for Low-Resource Languages are relatively new. Low-resource languages have little training data available for training Machine Translation systems or other systems. Even though a vast amount of text has been digitized and made available on the internet the text is still in PDF and Image format, which are

Milad Beikbabaei, Ali Mehrizi-Sani

Cyberattacks are becoming more frequent, and attackers can use different mechanisms, such as denial of service (DoS) and false data injection (FDI). Furthermore, multiple attack types can be launched simultaneously, known as hybrid attacks, to cause more damage. Volt-Var control algorithms are widely used in the distribution system to maintain the voltage wi

Courtney Klein, James S. Bullock, Jorge Moreno, Francisco J. Mercado

The observationally-inferred size versus stellar-mass relationship (SMR) for low-mass galaxies provides an important test for galaxy formation models. However, the relationship relies on assumptions that relate observed luminosity profiles to underlying stellar mass profiles. Here we use the Feedback in Realistic Environments simulations of low-mass galaxies

S M Rakib Hasan, Aakar Dhakal

In the era of the internet and smart devices, the detection of malware has become crucial for system security. Malware authors increasingly employ obfuscation techniques to evade advanced security solutions, making it challenging to detect and eliminate threats. Obfuscated malware, adept at hiding itself, poses a significant risk to various platforms, includ

Sakshi Jain, Carlangelo Liverani

We study piecewise injective, but not necessarily globally injective, contracting maps on a compact subset of \(\bR^d\). We prove that generically the attractor and the set of discontinuities of such a map are disjoint, and hence the attractor consists of periodic orbits. In addition, we prove that piecewise injective contractions are generically topological

Yunsoo Kim, Jinge Wu, Yusuf Abdulle, Yue Gao

Recent advancements in Computer Assisted Diagnosis have shown promising performance in medical imaging tasks, particularly in chest X-ray analysis. However, the interaction between these models and radiologists has been primarily limited to input images. This work proposes a novel approach to enhance human-computer interaction in chest X-ray analysis using V

Jozsef Balogh, Ethan Patrick White

Using probabilistic methods, we obtain grid-drawings of graphs without crossings with low volume and small aspect ratio. We show that every $D$-degenerate graph on $n$ vertices can be drawn in $[m]^3$ where $m^3 = O(D^2 n\log n)$. In particular, every graph of bounded maximum degree can be drawn in a grid with volume $O(n \log n)$.

Leonardo Ferreira Guilhoto, Paris Perdikaris

Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper, we introduce NEON (Neural Epistemic Operator Networks), an architecture for generating predictions with uncertainty using a single operator network backbone, which presents orders

Leonardo Arrighi, Luca Pennella, Gabriel Marques Tavares, Sylvio Barbon Junior

Understanding the decisions of tree-based ensembles and their relationships is pivotal for machine learning model interpretation. Recent attempts to mitigate the human-in-the-loop interpretation challenge have explored the extraction of the decision structure underlying the model taking advantage of graph simplification and path emphasis. However, while thes

Sho Shimoyama

We explicitly construct parameter transformations between gradient flows in metric spaces, called curves of maximal slope, having different exponents when the associated function satisfies a suitable convexity condition. These transformations induce the uniqueness of gradient flows for all exponents under a natural assumption which is satisfied in many examp

Federico Bongiorno

We show that a formal Deligne--Mumford stack is formal-locally represented by a formal scheme. This is an analogue of Frobenius theorem for smooth foliations in any characteristic and without smoothness hypotheses on the ambient space.

Benjamin Enriquez, Federico Zerbini

Let $\mathcal E$ be a complex elliptic curve and $S$ be a non-empty finite subset of $\mathcal E$. We show that the functions $\tildeΓ$ introduced in arXiv:1712.07089 out of string theory motivations give rise to a basis of the minimal algebra $A_{\mathcal E\smallsetminus S}$ of holomorphic multivalued functions on $\mathcal E\smallsetminus S$ which is stabl

Simon Charles Ellis, Joss Bland-Hawthorn

Astrophotonics is a burgeoning field that lies at the interface of photonics and modern astronomical instrumentation. Here we provide a pedagogical review of basic photonic functions that enable modern instruments, and give an overview of recent and future applications. Traditionally, optical fibres have been used in innovative ways to vastly increase the mu

Russelle Guadalupe

Let $p\leq 23$ be a prime and $a_p(n)$ counts the number of partitions of $n$ where parts that are multiple of $p$ come up with $2$ colors. Using a result of Sussman, we derive the exact formula for $a_p(n)$ and obtain an asymptotic formula for $\log a_p(n)$. Our results partially extend the work of Mauth, who proved the asymptotic formula for $\log a_2(n)$

Rowan Killip, Zhimeng Ouyang, Monica Visan, Lei Wu

For slowly-varying initial data, solutions to the Ablowitz-Ladik system have been proven to converge to solutions of the cubic Schr\"odinger equation. In this paper we show that in the continuum limit, solutions to the Ablowitz-Ladik system with $H^1$ initial data may also converge to solutions of the modified Korteweg--de Vries equation. To exhibit this new

Tomoyuki Arakawa, Vyacheslav Futorny, Libor Krizka

The closure of a Diximier sheet is the image of a generalized Grothendieck's simultaneous resolution. We show that the associated variety of simple affine vertex algebras is contained in the closure of the Diximier sheet when a chiralization of generalized Grothendieck's simultaneous resolution exists. This generalizes in a conceptual manner the results obta

Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

Recent work of Klivans, Stavropoulos, and Vasilyan initiated the study of testable learning with distribution shift (TDS learning), where a learner is given labeled samples from training distribution $\mathcal{D}$, unlabeled samples from test distribution $\mathcal{D}'$, and the goal is to output a classifier with low error on $\mathcal{D}'$ whenever the tra

Bharti Arora, M. De Becker, Jeewan C. Pandey

Massive star winds are known to be responsible for X-ray emission arising from wind plasma heated by the strong shocks up to the temperature of 10$^6$--10$^7$ K in case of colliding wind binaries. We have investigated thermal and non-thermal X-ray emission from the massive O-type star HD93250 to unveil its binary orbital parameters independently. To meet our

Marcin Kolakowski, Jozef Modelski

In this paper a novel NLOS (Non-Line-of-Sight) identification technique is proposed. In comparison to other methods described in the literature, it discerns a situation when the delayed direct path component is available from when it's totally blocked and introduced biases are much higher and harder to mitigate. In the method, NLOS identification is performe

Yusei Naito, Tomohiko Jimbo, Tadashi Odashima, Takamitsu Matsubara

Multi-robot cooperative transport is crucial in logistics, housekeeping, and disaster response. However, it poses significant challenges in environments where objects of various weights are mixed and the number of robots and objects varies. This paper presents Task-priority Intermediated Hierarchical Distributed Policies (TIHDP), a multi-agent Reinforcement

Tiago Fonseca, Luis Ferreira, Bernardo Cabral, Ricardo Severino

This paper investigates the increasing roles of Renewable Energy Sources (RES) and Electric Vehicles (EVs). While indicating a new era of sustainable energy, these also introduce complex challenges, including the need to balance supply and demand and smooth peak consumptions amidst rising EV adoption rates. Addressing these challenges requires innovative sol

Adamo Young, Fei Wang, David S Wishart, Bo Wang

Compound identification from tandem mass spectrometry (MS/MS) data is a critical step in the analysis of complex mixtures. Typical solutions for the MS/MS spectrum to compound (MS2C) problem involve comparing the unknown spectrum against a library of known spectrum-molecule pairs, an approach that is limited by incomplete library coverage. Compound to MS/MS

Sahiti Yerramilli, Jayant Sravan Tamarapalli, Jonathan Francis, Eric Nyberg

Multimodal machine learning has gained significant attention in recent years due to its potential for integrating information from multiple modalities to enhance learning and decision-making processes. However, it is commonly observed that unimodal models outperform multimodal models, despite the latter having access to richer information. Additionally, the

Elija Perrier, Christopher S. Jackson

Geometric methods have useful application for solving problems in a range of quantum information disciplines, including the synthesis of time-optimal unitaries in quantum control. In particular, the use of Cartan decompositions to solve problems in optimal control, especially lambda systems, has given rise to a range of techniques for solving the so-called $

Conor Green, Mithuna Thottethodi

Over the past few decades, network topology design for general purpose, shared memory multicores has been primarily driven by human experts who use their insights to arrive at network designs that balance the competing goals of performance requirements (e.g., latency, bandwidth) and cost constraints (e.g., router radix, router counts). On the other hand, the

Victoria Graf, Qin Liu, Muhao Chen

Data poisoning backdoor attacks can cause undesirable behaviors in large language models (LLMs), and defending against them is of increasing importance. Existing defense mechanisms often assume that only one type of trigger is adopted by the attacker, while defending against multiple simultaneous and independent trigger types necessitates general defense fra

Guixin Xu, Guojing Ren

This paper investigates quasi-selfadjoint extensions of dual pairs of linear relations in Hilbert spaces. Some properties of dual pairs of linear relations are given and an Hermitian linear relation associated with a dual pair of linear relations is introduced. Necessary and sufficient conditions for quasi-selfadjoint extensions of dual pairs of linear relat

Thomas Erlebach, Kleitos Papadopoulos

The problem of constructing optimal factoring automata arises in the context of unification factoring for the efficient execution of logic programs. Given an ordered set of $n$ strings of length $m$, the problem is to construct a trie-like tree structure of minimum size in which the leaves in left-to-right order represent the input strings in the given order

Sahiti Yerramilli, Jayant Sravan Tamarapalli, Tanmay Girish Kulkarni, Jonathan Francis

Deep Learning models are incredibly data-hungry and require very large labeled datasets for supervised learning. As a consequence, these models often suffer from overfitting, limiting their ability to generalize to real-world examples. Recent advancements in diffusion models have enabled the generation of photorealistic images based on textual inputs. Levera

Alon Duvall, Eduardo D. Sontag

In this paper, we study systems of time-invariant ordinary differential equations whose flows are non-expansive with respect to a norm, meaning that the distance between solutions may not increase. Since non-expansiveness (and contractivity) are norm-dependent notions, the topology of $\omega$-limit sets of solutions may depend on the norm. For example, and

Austin Eide

In the averaging process on a graph $G = (V, E)$, a random mass distribution $\eta$ on $V$ is repeatedly updated via transformations of the form $\eta_{v}, \eta_{w} \mapsto (\eta_{v} + \eta_{w})/2$, with updates made according to independent Poisson clocks associated to the edge set $E$. We study the averaging process when $G$ is the integer lattice $\mathbb

André LeClair

We propose a thermodynamical definition of the vacuum energy density $\rho_{\rm vac}$, defined as $\langle 0| T_{\mu\nu} |0\rangle = - \rho_{\rm vac} \, g_{\mu\nu}$, in quantum field theory in flat Minkowski space in $D$ spacetime dimensions, which can be computed in the limit of high temperature, namely in the limit $\beta = 1/T \to 0$. It takes the form $\

Marcin Kolakowski

In this paper a concept of hybrid Bluetooth Low Energy (BLE) Ultra-wideband (UWB) positioning system is presented. The system is intended to be energy efficient. Low energy BLE unit is used as a primary source of measurement data and for most of the time localization is calculated based on received signal strength (RSS). UWB technology is used less often. Ti

Kavian Khanjani, Seyed Rasoul Hosseini, Hamid Taheri, Shahrzad Shashaani

In 2019, the world faced a new challenge: a COVID-19 disease caused by the novel coronavirus, SARS-CoV-2. The virus rapidly spread across the globe, leading to a high rate of mortality, which prompted health organizations to take measures to control its transmission. Early disease detection is crucial in the treatment process, and computer-based automatic de

Luk R. Arnaut

Auto- and cross-spectral density functions for dynamic {random} fields and power are derived. These are based on first- and second-order Pad\'{e} approximants of correlation functions expanded in terms of spectral moments. The second-order approximant permits a characterization of stir noise observable {at high stir frequencies in the autospectral density}.

E. Castro-Avila, P. Malgaretti, J. Harting, J. D. Muñoz

We employ a lattice Boltzmann method to compute the acoustic radiation force produced by standing waves on a compressible object. Instead of simulating the fluid mechanics equations directly, the proposed method uses a lattice Boltzmann model that reproduces the wave equation, together with a kernel interpolation scheme, to compute the first order perturbati

Wanrong Zheng, Haidong Zhu, Zhaoheng Zheng, Ram Nevatia

Gait recognition aims to identify a person based on their walking sequences, serving as a useful biometric modality as it can be observed from long distances without requiring cooperation from the subject. In representing a person's walking sequence, silhouettes and skeletons are the two primary modalities used. Silhouette sequences lack detailed part inform

Sreenitha Kasarapu, Sanket Shukla, Rakibul Hassan, Avesta Sasan

One of the pivotal security threats for the embedded computing systems is malicious software a.k.a malware. With efficiency and efficacy, Machine Learning (ML) has been widely adopted for malware detection in recent times. Despite being efficient, the existing techniques require a tremendous number of benign and malware samples for training and modeling an e

Evangelia Dragazi, Shuaiqiang Liu, Antonis Papapantoleon

We consider the computation of model-free bounds for multi-asset options in a setting that combines dependence uncertainty with additional information on the dependence structure. More specifically, we consider the setting where the marginal distributions are known and partial information, in the form of known prices for multi-asset options, is also availabl

Haven Kim, Taketo Akama

In musical compositions that include vocals, lyrics significantly contribute to artistic expression. Consequently, previous studies have introduced the concept of a recommendation system that suggests lyrics similar to a user's favorites or personalized preferences, aiding in the discovery of lyrics among millions of tracks. However, many of these systems do

Olufunke O. Sarumi, Béla Neuendorf, Joan Plepi, Lucie Flek

Recent trends in natural language processing research and annotation tasks affirm a paradigm shift from the traditional reliance on a single ground truth to a focus on individual perspectives, particularly in subjective tasks. In scenarios where annotation tasks are meant to encompass diversity, models that solely rely on the majority class labels may inadve

Francis Duey, James Schombert, Stacy McGaugh, Federico Lelli

We present WISE W1 photometry of the SPARC (Spitzer Photometry and Accurate Rotation Curves) sample. The baseline of near-IR fluxes is established for use by stellar mass models, a key component to the baryonic Tully-Fisher relation and other kinematic galaxies scaling relations. We focus this paper on determination of the characteristics of the W1 fluxes co

Jasurbek Shukurov

The escalating volume of data involved in Android backup packages necessitates an innovative approach to compression beyond traditional methods like GZIP, which may not fully exploit the redundancy inherent in Android backups, particularly those containing extensive XML data. This paper introduces the PatternRank algorithm, a novel compression strategy speci

Samantha C. Phillips, Kathleen M. Carley, Kenneth Joseph

Social sorting, the alignment of social identities, affiliations, and/or preferences with partisan groups, can increase in-party attachment and decrease out-party tolerance. We propose that self-presentation afforded by social media profiles fosters subjective social sorting by shaping perceptions of alignments between non-political and political identifiers