arXiv papers from March 2024

Frederick V. Qiu, S. Matthew Weinberg

We consider truthful combinatorial auctions with items $M = [m]$ for sale to $n$ bidders, where each bidder $i$ has a private monotone valuation $v_i : 2^M \to R_+$. Among truthful mechanisms, maximal-in-range (MIR) mechanisms achieve the best-known approximation guarantees among all poly-communication deterministic truthful mechanisms in all previously-stud

Hojune Kim, Hyesu Jang, Ayoung Kim

The interest in single-chip mmWave Radar is driven by their compact form factor, cost-effectiveness, and robustness under harsh environmental conditions. Despite its promising attributes, the principal limitation of mmWave radar lies in its capacity for autonomous yaw rate estimation. Conventional solutions have often resorted to integrating inertial measure

Anneliese Brei, Chao Zhao, Snigdha Chaturvedi

Human writers often bookend their writing with ending sentences that relate back to the beginning sentences in order to compose a satisfying narrative that "closes the loop." Motivated by this observation, we propose RENarGen, a controllable story-generation paradigm that generates narratives by ensuring the first and last sentences are related and then infi

Zhuotong Chen, Zihu Wang, Yifan Yang, Qianxiao Li

Despite the effectiveness of deep neural networks in numerous natural language processing applications, recent findings have exposed the vulnerability of these language models when minor perturbations are introduced. While appearing semantically indistinguishable to humans, these perturbations can significantly reduce the performance of well-trained language

Orchid Chetia Phukan, Ankita Das, Arun Balaji Buduru, Rajesh Sharma

Stress recognition through physiological signals such as Electrocardiogram (ECG) signals has garnered significant attention. Traditionally, research in this field predominantly focused on utilizing handcrafted features or raw signals as inputs for learning algorithms. However, there is now a burgeoning interest within the community in leveraging large-scale

Yujuan Fu, Giridhar Kaushik Ramachandran, Nicholas J Dobbins, Namu Park

Social determinants of health (SDoH) play a critical role in shaping health outcomes, particularly in pediatric populations where interventions can have long-term implications. SDoH are frequently studied in the Electronic Health Record (EHR), which provides a rich repository for diverse patient data. In this work, we present a novel annotated corpus, the Pe

Nolan Alexander, William Scherer

We propose a novel method to improve estimation of asset returns for portfolio optimization. This approach first performs a monthly directional market forecast using an online decision tree. The decision tree is trained on a novel set of features engineered from portfolio theory: the efficient frontier functional coefficients. Efficient frontiers can be deco

Clara Lage, Nelly Pustelnik, Benjamin Audit, Jean-Michel Arbona

DNA replication stands as one of the fundamental biological processes crucial for cellular functioning. Recent experimental developments enable the study of replication dynamics at the single-molecule level for complete genomes, facilitating a deeper understanding of its main parameters. In these new data, replication dynamics is reported by the incorporatio

Katsunori Kubo

A tight-binding model for $e_g$ orbitals on a square lattice is investigated. We consider only the nearest-neighbor hopping and the model is characterized by two hopping parameters, $t_1$ and $t_2$. There are Dirac points in the electronic band structure and the type of the Dirac points (type-I or type-II) depends on the ratio $t_2/t_1$. For the case of the

Francisco J. Perez-Reche

The process of infection of a host is complex, influenced by factors such as microbial variation within and between hosts as well as differences in dose across hosts. This study uses dose-response and within-host microbial infection models to delve into the impact of these factors on infection probability. It is rigorously demonstrated that within-host heter

Sandro Maludze, Mahdi Mirzakhani, William Baker, Matthew Lee

The phonon-mediated hybrid detector is made out of a monolithic silicon crystal characterized by two interconnected regions linked through a narrow neck. Operating solely on phonon signal measurements, the hybrid design facilitates the differentiation between electron recoil and nuclear recoil events, effectively discerning two types of interaction down to l

Feng Yang

Ferroelectric domain walls hold great promise for innovative applications in ferroelectric devices. However, the underlying mechanisms behind the observed giant conductance of charged domain walls remain poorly understood. Using a first-principles approach that incorporates Boltzmann transport theory and the relaxation time approximation, we determine the ca

Arturo Erdely, Manuel Rubio-Sanchez

Scatter plots are widely recognized as fundamental tools for illustrating the relationship between two numerical variables. Despite this, based on solid theoretical foundations, scatter plots generated from pairs of continuous random variables may not serve as reliable tools for assessing dependence. Sklar's Theorem implies that scatter plots created from ra

Sihao Wu, Weijie Du, Xingbo Zhao, James P. Vary

We present an efficient and precise framework to quantum simulate the dynamics of the ultra-relativistic quark-nucleus scattering. This framework employs the eigenbasis of the asymptotic scattering system and implements a compact scheme for encoding this basis upon lattice discretization. It exploits the operator structure of the light-front Hamiltonian of t

Huiling Zhong, Fanrong Xu, Hai-Yang Cheng

We perform a global fit to the experimental data of two-body charmed baryon decays based on the topological diagrammatic approach (TDA) and take into account the phase shifts between $S$- and $P$-wave amplitudes as inspired by the recent BESIII measurement of the decay asymmetry in the decay $\Lambda_c^+\to \Xi^0K^+$. The TDA has the advantage that it is mor

Alexey A. Kovalev, Bo Li, Edward Schwartz

We study superfluidlike spin transport facilitated by thermal diffusion of magnetic domain walls, where the positive and negative chiralities of domain walls act as opposite topological charges. The topological charge conservation leads to algebraic decay of spin current carried by domain walls, allowing for the transport of spin over extended distances. We

Paweł Maczuga, Marcin Łoś, Eirik Valseth, Albert Oliver Serra

The Northern European Enclosure Dam (NEED) is a hypothetical project to prevent flooding in European countries following the rising ocean level due to melting arctic glaciers. This project involves the construction of two large dams between Scotland and Norway, as well as England and France. The anticipated cost of this project is 250 to 500 billion euros. I

Yuno Iwasaki, Andi Tan, Christopher G. Tully

The novel electron spectrometry method proposed by the PTOLEMY relic neutrino experiment requires a real-time, non-destructive estimate of the parallel and transverse momentum splits of tritium $\beta$-decay electrons. The collaboration has proposed to obtain this estimate using cyclotron-radiation emission spectroscopy (CRES), in which the kinetic energy of

Zhibo Chu, Zichong Wang, Wenbin Zhang

Large Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations. Consequently, they may lead to discriminatory outcomes against certain communities, particularly marginalized populations, prompting

Yue Zhang, Yuntian He, Saket Gurukar, Srinivasan Parthasarathy

Heterogeneous graphs are ubiquitous in real-world applications because they can represent various relationships between different types of entities. Therefore, learning embeddings in such graphs is a critical problem in graph machine learning. However, existing solutions for this problem fail to scale to large heterogeneous graphs due to their high computati

Haoxi Ran, Vitor Guizilini, Yue Wang

Diffusion models (DMs) excel in photo-realistic image synthesis, but their adaptation to LiDAR scene generation poses a substantial hurdle. This is primarily because DMs operating in the point space struggle to preserve the curve-like patterns and 3D geometry of LiDAR scenes, which consumes much of their representation power. In this paper, we propose LiDAR

Aditya Singh, Zeyuan Feng, Somil Bansal

Hamilton-Jacobi (HJ) reachability analysis is a widely adopted verification tool to provide safety and performance guarantees for autonomous systems. However, it involves solving a partial differential equation (PDE) to compute a safety value function, whose computational and memory complexity scales exponentially with the state dimension, making its direct

Adedibu Sunny Akingboye

This comprehensive review examines electrical and seismic refraction methods, emphasizing their advanced applications in electrical resistivity tomography (ERT) and seismic refraction tomography (SRT). These techniques are crucial for understanding surface-subsurface crustal dynamics, offering critical insights into resistivity and velocity structures for ge

Yuting Fang, Lianna Hambardzumyan, Nathaniel Harms, Pooya Hatami

We prove that the class of communication problems with public-coin randomized constant-cost protocols, called $BPP^0$, does not contain a complete problem. In other words, there is no randomized constant-cost problem $Q \in BPP^0$, such that all other problems $P \in BPP^0$ can be computed by a constant-cost deterministic protocol with access to an oracle fo

Archer Amon, Zhipeng Yin, Zichong Wang, Avash Palikhe

Generative AI is becoming increasingly prevalent in creative fields, sparking urgent debates over how current copyright laws can keep pace with technological innovation. Recent controversies of AI models generating near-replicas of copyrighted material highlight the need to adapt current legal frameworks and develop technical methods to mitigate copyright in

Jaehoon Kim, Alexandr Kostochka, Ruth Luo

The famous Dirac's Theorem states that for each $n\geq 3$ every $n$-vertex graph $G$ with minimum degree $\delta(G)\geq n/2$ has a hamiltonian cycle. When $\delta(G)< n/2$, this cannot be guaranteed, but the existence of some other specific subgraphs can be provided. Gargano, Hell, Stacho and Vaccaro proved that every connected $n$-vertex graph $G$ with $\de

Marta Lazzaretti, Claudio Estatico, Alejandro Melero, Luca Calatroni

Off-the-grid regularisation has been extensively employed over the last decade in the context of ill-posed inverse problems formulated in the continuous setting of the space of Radon measures $\mathcal{M}(\mathcal{X})$. These approaches enjoy convexity and counteract the discretisation biases as well the numerical instabilities typical of their discrete coun

Orchid Chetia Phukan, Gautam Siddharth Kashyap, Arun Balaji Buduru, Rajesh Sharma

In this work, we investigate multilingual speech Pre-Trained models (PTMs) for Audio deepfake detection (ADD). We hypothesize that multilingual PTMs trained on large-scale diverse multilingual data gain knowledge about diverse pitches, accents, and tones, during their pre-training phase and making them more robust to variations. As a result, they will be mor

Rushang Karia, Jayesh Nagpal, Daksh Dobhal, Pulkit Verma

Understanding how robots plan and execute tasks is crucial in today's world, where they are becoming more prevalent in our daily lives. However, teaching non-experts, such as K-12 students, the complexities of robot planning can be challenging. This work presents an open-source platform, JEDAI.Ed, that simplifies the process using a visual interface that abs

Youssef Mansour, Reinhard Heckel

Deep learning-based methods have shown remarkable success for various image restoration tasks such as denoising and deblurring. The current state-of-the-art networks are relatively deep and utilize (variants of) self attention mechanisms. Those networks are significantly slower than shallow convolutional networks, which however perform worse. In this paper,

Sara Fish, Yannai A. Gonczarowski, Ran I. Shorrer

We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs). In oligopoly settings, LLM-based pricing agents quickly and autonomously reach supracompetitive prices and profits. Variation in seemingly innocuous phrases in LLM instructions ("prompts") substantially influence the degree of supracompetitive pricing. We develop no

Nawaf Nazir, Bowen Huang, Shant Mahserejian

In this paper, we present the mathematical formulation of an OpenStreetMaps (OSM) based tool that compares the costs and emissions of long-haul medium and heavy-duty (M&HD) electric and diesel freight trucks, and determines the spatial distribution of added energy demand due to M&HD EVs. The optimization utilizes a combination of information on routes from O

Gamaliel Cerda-Morales

Spinors are used in physics quite extensively. The goal of this study is also the spinor structure lying in the basis of the quaternion algebra. In this paper, first, we have introduced spinors mathematically. Then, we have defined Tribonacci spinors using the generalized Tribonacci quaternions. Later, we have established the structure of algebra for these s

Marie-Claude Arnaud, Vincent Humilière, Claude Viterbo

We extend to higher dimensions the notion of Birkhoff attractor of a dissipative map. We prove that this notion coincides with the classical Birkhoff attractor. We prove that for the dissipative system associated to the discounted Hamilton-Jacobi equation the graph of a solution is contained in the Birkhoff attractor. We also study what happens when we pertu

Alex Buchel

Thus far, the known wormholes in string theory connecting disjoint boundaries represented by finite volume quotients of hyperbolic spaces leak: they are non-perturbatively unstable towards brane-anti-brane nucleation in the flux backgrounds that support these wormholes. Turning on additional fluxes suppressed this instability, but would not completely elimin

Qiong Wang, Anan Ghrayeb, SeongHyeon Kim, Liuyang Cheng

Twisted and coiled polymer actuators (TCPAs) generate large contractile mechanical work mimicking natural muscles, which makes them suitable for robotics and health-assistive devices. Understanding the mechanism of nylon TCPA remains challenging due to the interplay between their intricate geometry, chirality, residual stresses, and material microstructure.

Ye Liu, Jixuan He, Wanhua Li, Junsik Kim

Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to ground relevant clips in untrimmed videos given natural language queries. Most existing VTG models are built upon frame-wise final-layer CLIP features, aided by additional temporal backbones (e.g., SlowFast) with sophisticated temporal reasoning mechanisms. In this work

M. Shahid, B. Chambers, S. Sankarasubramanian

NASA mandate is a human mission to Mars in the 2030s and sustained exploration of Mars requires in-situ resource utilization (ISRU). Exploiting the Martian water cycle (alongside perchlorate salts that depress the freezing point of water to less than 213K) and the available 95 volume percent atmospheric CO2, we detail an ultra-low temperature (255K) CO2-H2O

Thi Tam Dang, Trung Hau Hoang

This paper considers a class of noncoercive nonlinear elliptic problems with coefficients defined in Marcinkiewicz and Lorentz spaces. We prove the existence of a solution for the corresponding Dirichlet problem and investigate the higher integrability properties of the solution.

Uladzislau Yorsh, Martin Holeňa, Ondřej Bojar, David Herel

Transformers have revolutionized deep learning in numerous fields, including natural language processing, computer vision, and audio processing. Their strength lies in their attention mechanism, which allows for the discovering of complex input relationships. However, this mechanism's quadratic time and memory complexity pose challenges for larger inputs. Re

Eric Guiffo Kaigom

Metarobotics aims to combine next generation wireless communication, multi-sense immersion, and collective intelligence to provide a pervasive, itinerant, and non-invasive access and interaction with distant robotized applications. Industry and society are expected to benefit from these functionalities. For instance, robot programmers will no longer travel w

Bibek Poudel, Weizi Li, Shuai Li

Human-driven vehicles (HVs) exhibit complex and diverse behaviors. Accurately modeling such behavior is crucial for validating Robot Vehicles (RVs) in simulation and realizing the potential of mixed traffic control. However, existing approaches like parameterized models and data-driven techniques struggle to capture the full complexity and diversity. To addr

Zhi Ma, Cheng Wen, Jie Su, Ming Zhao

IP-based software design is a crucial research field that aims to improve efficiency and reliability by reusing complex software components known as intellectual property (IP) components. To ensure the reusability of these components, particularly in security-sensitive software systems, it is necessary to analyze the requirements and perform formal verificat

Jaehoon Kim, Hong Liu, Péter Pál Pach

Liu, Pach and S\'andor recently characterized all polynomials $p(z)$ such that the equation $x+y=p(z)$ is $2$-Ramsey, that is, any $2$-coloring of $\mathbb{N}$ contains infinitely many monochromatic solutions for $x+y=p(z)$. In this paper, we find asymptotically tight bounds for the following two quantitative questions. $\bullet$ For $n\in \mathbb{N}$, what

Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia

We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptuall

Ernest Y. -Z. Tan, Ramona Wolf

One of the main challenges in device-independent quantum key distribution (DIQKD) is achieving the required Bell violation over long distances, as the channel losses result in low overall detection efficiencies. Recent works have explored the concept of certifying nonlocal correlations over extended distances through the use of a local Bell test. Here, an ad

Inseon Jang, Haici Yang, Wootaek Lim, Seungkwon Beack

In this paper, we propose a personalized neural speech codec, envisioning that personalization can reduce the model complexity or improve perceptual speech quality. Despite the common usage of speech codecs where only a single talker is involved on each side of the communication, personalizing a codec for the specific user has rarely been explored in the lit

Mingyang Wang, Heike Adel, Lukas Lange, Jannik Strötgen

Continual learning aims at incrementally acquiring new knowledge while not forgetting existing knowledge. To overcome catastrophic forgetting, methods are either rehearsal-based, i.e., store data examples from previous tasks for data replay, or isolate parameters dedicated to each task. However, rehearsal-based methods raise privacy and memory issues, and pa

Kahyun Choi, Minje Kim

This paper provides a computational analysis of poetry reading audio signals at a large scale to unveil the musicality within professionally-read poems. Although the acoustic characteristics of other types of spoken language have been extensively studied, most of the literature is limited to narrative speech or singing voice, discussing how different they ar

Zihan Qian, Lu Tian, Miki Horiguchi, Hajime Uno

Given the limitations of using the Cox hazard ratio to summarize the magnitude of the treatment effect, alternative measures that do not have these limitations are gaining attention. One of the recently proposed alternative methods uses the average hazard with survival weight (AH). This population quantity can be interpreted as the average intensity of the e

Jeongwook Seo, Shrihari Sankarasubramanian, Nikhilendra Singh, Fuminori Mizuno

The kinetics of the oxygen reduction reaction (ORR) on the practical air cathode in a Lithium air cell, which is conventionally composed of porous carbon with or without catalysts supported on it, was investigated. The mechanism and kinetics of the oxygen reduction reaction (ORR) was studied on a porous carbon electrode in an oxygen saturated solution of 0.1

Gus Henry Smith, Zachary D. Sisco, Thanawat Techaumnuaiwit, Jingtao Xia

EDA toolchains are notoriously unpredictable, incomplete, and error-prone; the generally-accepted remedy has been to re-imagine EDA tasks as compilation problems. However, any compiler framework we apply must be prepared to handle the wide range of EDA tasks, including not only compilation tasks like technology mapping and optimization (the "there"} in our t

Jakaria Rabbi, Johannes Kiechle, Christian Beaulieu, Nilanjan Ray

This paper presents a comprehensive study focused on disentangling hippocampal shape variations from diffusion tensor imaging (DTI) datasets within the context of neurological disorders. Leveraging a Mesh Variational Autoencoder (VAE) enhanced with Supervised Contrastive Learning, our approach aims to improve interpretability by disentangling two distinct la

Benjamin Davies

I derive the pointwise conditional means and variances of an arbitrary Gauss-Markov process, given noisy observations of points on a sample path. These moments depend on the process's mean and covariance functions, and on the conditional moments of the sampled points. I study the Brownian motion and bridge as special cases.

Eric Guiffo Kaigom

As a digital environment of interconnected virtual ecosystems driven by measured and synthesized data, the Metaverse has so far been mostly considered from its gaming perspective that closely aligns with online edutainment. Although it is still in its infancy and more research as well as standardization efforts remain to be done, the Metaverse could provide

Ovidiu Popescu, Cristina Maria Păcurar

We introduce a new type of mappings in metric space which are three-point analogue of the well-known Chatterjea type mappings, and call them generalized Chatterjea type mappings. It is shown that such mappings can be discontinuous as is the case of Chatterjea type mappings and this new class includes the class of Chatterjea type mappings. The fixed point the

Mohamed Elsayed, A. Rupam Mahmood

Deep representation learning methods struggle with continual learning, suffering from both catastrophic forgetting of useful units and loss of plasticity, often due to rigid and unuseful units. While many methods address these two issues separately, only a few currently deal with both simultaneously. In this paper, we introduce Utility-based Perturbed Gradie

Shudi Weng, Chengxi Li, Ming Xiao, Mikael Skoglund

Stragglers' effects are known to degrade FL performance. In this paper, we investigate federated learning (FL) over wireless networks in the presence of communication stragglers, where the power-constrained clients collaboratively train a global model by iteratively optimizing a local objective function with their local datasets and transmitting local model

Jalil Varela-Manjarres, Ali Kefayati, M. Benjamin Jungfleisch, John Q. Xiao

The surprising discovery of ultrafast demagnetization -- where electric field of femtosecond laser pulse couples to electrons of a ferromagnetic (FM) layer causing its magnetization vector {\em to shrink while not rotating}, is also assumed to be accompanied by generation of spin current in the direction orthogonal to electric field. However, understanding o

Chongying Dong, Li Ren, Feng Xu

Categorical coset constructions are investigated and Kac-Wakimoto Hypothesis associated with pseudo unitary modular tensor categories is proved. In particular, the field identifications are obtained. These results are applied to the coset constructions in the theory of vertex operator algebra.

Jhon Lopez, Carlos Hinojosa, Henry Arguello, Bernard Ghanem

The modern surge in camera usage alongside widespread computer vision technology applications poses significant privacy and security concerns. Current artificial intelligence (AI) technologies aid in recognizing relevant events and assisting in daily tasks in homes, offices, hospitals, etc. The need to access or process personal information for these purpose

Weihua Hu, Yiwen Yuan, Zecheng Zhang, Akihiro Nitta

We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based data structure to handle complex tabular data, introducing a model abstraction to enable modular implementation of tabular models, and allowing external foundation models to be incorp

Maarten Grachten

An increasing number of generative music models can be conditioned on an audio prompt that serves as musical context for which the model is to create an accompaniment (often further specified using a text prompt). Evaluation of how well model outputs adhere to the audio prompt is often done in a model or problem specific manner, presumably because no generic

Philip Sun, David Simcha, Dave Dopson, Ruiqi Guo

This paper introduces SOAR: Spilling with Orthogonality-Amplified Residuals, a novel data indexing technique for approximate nearest neighbor (ANN) search. SOAR extends upon previous approaches to ANN search, such as spill trees, that utilize multiple redundant representations while partitioning the data to reduce the probability of missing a nearest neighbo

Krishna Jalan, Roji Pius, Manish Ramchander

The island paradigm for an AdS$_2$ eternal black hole in the Hartle-Hawking state coupled to a finite temperature non-gravitating bath asserts that after the Page time the operators in the black hole interior can be reconstructed using the bath degrees of freedom. We demonstrate this assertion by consideing a special operator $\mathbb{U}_{bath}$ that has non

Hing Ong, Da Yang

Poleward water vapor transport in the midlatitudes mainly occurs in meandering filaments of intense water vapor transport, spanning thousands of kilometers long and hundreds of kilometers wide and drifting eastward. The water vapor filaments are known as atmospheric rivers (ARs). They can cause extreme wind gusts, intense precipitation, and flooding along de

Savari Prabhu, T. Jenifer Janany, Sandi Klavžar

Metric dimension is a valuable parameter that helps address problems related to network design, localization, and information retrieval by identifying the minimum number of landmarks required to uniquely determine distances between vertices in a graph. Generalized Sierpi\'{n}ski graphs represent a captivating class of fractal-inspired networks that have gain

Zhancheng Yao, Martin Sandberg, David W. Abraham, David J. Bishop

Building a modular architecture with superconducting quantum computing chips is one of the means to achieve qubit scalability, allowing the screening, selection, replacement, and integration of individual qubit modules into large quantum systems. However, the nondestructive replacement of modules within a compact architecture remains a challenge. Liquid meta

Siming He, Yuezhan Tao, Igor Spasojevic, Vijay Kumar

Active perception approaches select future viewpoints by using some estimate of the information gain. An inaccurate estimate can be detrimental in critical situations, e.g., locating a person in distress. However the true information gained can only be calculated post hoc, i.e., after the observation is realized. We present an approach to estimate the discre

Samuel B. Hopkins, Anqi Li

We introduce and study the problem of posterior inference on tree-structured graphical models in the presence of a malicious adversary who can corrupt some observed nodes. In the well-studied broadcasting on trees model, corresponding to the ferromagnetic Ising model on a $d$-regular tree with zero external field, when a natural signal-to-noise ratio exceeds

Saksham Sharma, Adnan Mahmud, Giuseppe Tarabella, Panagiotis Mougoyannis

With an aim to build analog computers out of soft matter fluidic systems in future, this work attempts to invent a new information-theoretic language, in the form of two-dimensional Quick Response (QR) codes. This language is, effectively, a digital representation of the analog signals shown by the proteinoids. We use two different experimental techniques: (

Nil Stolt-Ansó, Vasiliki Sideri-Lampretsa, Maik Dannecker, Daniel Rueckert

Cardiac magnetic resonance (CMR) image acquisition requires subjects to hold their breath while 2D cine images are acquired. This process assumes that the heart remains in the same position across all slices. However, differences in breathhold positions or patient motion introduce 3D slice misalignments. In this work, we propose an algorithm that simultaneou

Puneet Mehrotra, Vaastav Anand, Daniel Margo, Milad Rezaei Hajidehi

Graph-structured data is prevalent in domains such as social networks, financial transactions, brain networks, and protein interactions. As a result, the research community has produced new databases and analytics engines to process such data. Unfortunately, there is not yet widespread benchmark standardization in graph processing, and the heterogeneity of e

Theo Diamandis, Guillermo Angeris, Alan Edelman

We introduce a general framework for flow problems over hypergraphs. In our problem formulation, which we call the convex flow problem, we have a concave utility function for the net flow at every node and a concave utility function for each edge flow. The objective is to maximize the sum of these utilities, subject to constraints on the flows allowed at eac

Jianqing Jia, Ashley Prater-Bennette, Lixin Shen, Erin E. Tripp

This paper introduces a nonconvex approach for sparse signal recovery, proposing a novel model termed the $\tau_2$-model, which utilizes the squared $\ell_1/\ell_2$ norms for this purpose. Our model offers an advancement over the $\ell_0$ norm, which is often computationally intractable and less effective in practical scenarios. Grounded in the concept of ef

David J. Aldous, Shi Feng

Consider a compact metric space $S$ and a pair $(j,k)$ with $k \ge 2$ and $1 \le j \le k$. For any probability distribution $\theta \in P(S)$, define a Markov chain on $S$ by: from state $s$, take $k$ i.i.d. ($\theta$) samples, and jump to the $j$'th closest. Such a chain converges in distribution to a unique stationary distribution, say $\pi_{j,k}(\theta)$.

Gopal Yadav

In this paper, we construct the wedge holography for the de-Sitter space as a bulk theory. First, we discuss a more general mathematical construction of wedge holography in parallel with wedge holography construction for the AdS bulk, and then we construct the wedge holography in the extended static patch. In the first case, we prove that one can construct w

Cheng Wen, Jialun Cao, Jie Su, Zhiwu Xu

Formal verification provides a rigorous and systematic approach to ensure the correctness and reliability of software systems. Yet, constructing specifications for the full proof relies on domain expertise and non-trivial manpower. In view of such needs, an automated approach for specification synthesis is desired. While existing automated approaches are lim

K. Narayan, Hitesh K. Saini, Gopal Yadav

We study holographic volume complexity for various families of holographic cosmologies with Kasner-like singularities, in particular with $AdS$, hyperscaling violating and Lifshitz asymptotics. We find through extensive numerical studies that the complexity surface always bends in the direction away from the singularity and transitions from spacelike near th

Peng Shan, Dan Xie, Wenbin Yan

We construct a bijection between admissible representations for an affine Lie algebra $\mathfrak{g}$ at boundary admissible levels and $\mathbb{C}^\times$ fixed points in homogeneous elliptic affine Springer fibres for the Langlands dual affine Lie algebra $\mathfrak{g}^\vee$. Using this bijection, we relate the modularity of the characters of admissible rep

Taiwang Deng

In this article, we derived some consequences to the symmetrization process developed in \cite{Deng23}. This consists a geometric derivation of part of the properties which uniquely determines the Kazhdan-Lusztig polynomials of type $A_n$ as well as an interpretations of the last property by the decomposition theorem of \cite{BBD}. Finally, the relation of g

Josip Jukić, Jan Šnajder

Enhancing generalization and uncertainty quantification in pre-trained language models (PLMs) is crucial for their effectiveness and reliability. Building on machine learning research that established the importance of robustness for improving generalization, we investigate the role of representation smoothness, achieved via Jacobian and Hessian regularizati

Mikhail G. Katz, Stephane Sabourau

We show that every closed nonpositively curved surface satisfies Loewner's systolic inequality. The proof relies on a combination of the Gauss-Bonnet formula with an averaging argument using the invariance of the Liouville measure under the geodesic flow. This enables us to find a disk with large total curvature around its center yielding a large area.

Cristina Cornelio, Mohammed Diab

Recognizing failures during task execution and implementing recovery procedures is challenging in robotics. Traditional approaches rely on the availability of extensive data or a tight set of constraints, while more recent approaches leverage large language models (LLMs) to verify task steps and replan accordingly. However, these methods often operate offlin

Byung-Hoon Hwang, Myeong-Su Lee

In a recent paper [16], the authors proposed a BGK model for relativistic gas mixtures based on the Marle-type approximation, which satisfies the fundamental kinetic properties: non-negativity of distribution functions, conservation laws, H-theorem, and indifferentiability principle. In this paper, we are concerned with the stationary problems to the relativ

José F. Fontanari

Evolutionary game theory has impacted many fields of research by providing a mathematical framework for studying the evolution and maintenance of social and moral behaviors. This success is owed in large part to the demonstration that the central equation of this theory - the replicator equation - is the deterministic limit of a stochastic imitation (social

Jordan Bryan, Haibo Zhou, Didong Li

In the heteroscedastic linear model, the weighted least squares (WLS) estimate of the model coefficients is more efficient than the ordinary least squares (OLS) esti- mate. However, the practical application of WLS is challenging because it requires knowledge of the error variances. Feasible weighted least squares (FLS) estimates, which use approximations of

Atsumoto Ohashi, Ukyo Honda, Tetsuro Morimura, Yuu Jinnai

Minimum Bayes-risk (MBR) decoding has recently gained renewed attention in text generation. MBR decoding considers texts sampled from a model as pseudo-references and selects the text with the highest similarity to the others. Therefore, sampling is one of the key elements of MBR decoding, and previous studies reported that the performance varies by sampling

Niki Kiriakidou, Ioannis E. Livieris, Christos Diou

Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data. This knowledge is important in many safety-critical domains, where it often needs to be extracted from observational data. In this work, we propose a new causal inference model, nam

Paula Rescala, Manoel Horta Ribeiro, Tiancheng Hu, Robert West

The capabilities of large language models (LLMs) have raised concerns about their potential to create and propagate convincing narratives. Here, we study their performance in detecting convincing arguments to gain insights into LLMs' persuasive capabilities without directly engaging in experimentation with humans. We extend a dataset by Durmus and Cardie (20

Stéphane Vannitsem, Carlos A. Pires, David Docquier

The information entropy budget and the rate of information transfer between variables is studied in the context of a nonlinear reduced-order atmospheric model. The key ingredients of the dynamics are present in this model, namely the baroclinic and barotropic instabilities, the instability related to the presence of an orography, the dissipation related to t

Kashob Kumar Roy, Md Hasibul Haque Moon, Md Mahmudur Rahman, Chowdhury Farhan Ahmed

In this uncertain world, data uncertainty is inherent in many applications and its importance is growing drastically due to the rapid development of modern technologies. Nowadays, researchers have paid more attention to mine patterns in uncertain databases. A few recent works attempt to mine frequent uncertain sequential patterns. Despite their success, they

Venelin Kovatchev, Matthew Lease

In this paper we present an exploratory research on quantifying the impact that data distribution has on the performance and evaluation of NLP models. We propose an automated framework that measures the data point distribution across 6 different dimensions: ambiguity, difficulty, discriminability, length, noise, and perplexity. We use disproportional stratif

Felipe Avila, Edilson de Carvalho, Armando Bernui, Hanna Lima

The Baryon Acoustic Oscillations (BAO) phenomenon provides a unique opportunity to establish a standard ruler at any epoch in the history of the evolving universe. The key lies in identifying a suitable cosmological tracer to conduct the measurement. In this study, we focus on quantifying the sound horizon scale of BAO in the Local Universe. Our chosen cosmo

Kashob Kumar Roy, Md Hasibul Haque Moon, Md Mahmudur Rahman, Chowdhury Farhan Ahmed

Due to the rapid development of science and technology, the importance of imprecise, noisy, and uncertain data is increasing at an exponential rate. Thus, mining patterns in uncertain databases have drawn the attention of researchers. Moreover, frequent sequences of items from these databases need to be discovered for meaningful knowledge with great impact.

Claudio Quadrelli

Let $p$ be a prime. Following Snopce-Tanushevski, a pro-$p$ group $G$ is called Frattini-resistant if the function $H\mapsto\Phi(H)$, from the poset of all closed finitely-generated subgroups of $G$ into itself, is a poset embedding. We prove that for an oriented right-angled Artin pro-$p$ group (oriented pro-$p$ RAAG) $G$ associated to a directed graph the

Luca Amendola, Vrund Patel, Ziad Sakr, Elena Sellentin

The ratio of Bayesian evidences is a popular tool in cosmology to compare different models. There are however several issues with this method: Bayes' ratio depends on the prior even in the limit of non-informative priors, and Jeffrey's scale, used to assess the test, is arbitrary. Moreover, the standard use of Bayes' ratio is often criticized for being unabl

Carl M. Bender, Daniel W. Hook

Hamilton's equations of motion are local differential equations and boundary conditions are required to determine the solution uniquely. Depending on the choice of boundary conditions, a Hamiltonian may thereby describe several different physically observable phases, each exhibiting its own characteristic global symmetry.

Yi Xu, Yun Fu

Trajectory prediction plays an important role in various applications, including autonomous driving, robotics, and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets, typically employing a standardized input duration. However, a notable issue arises when these models

Qin Liu, Jaemin Cho, Mohit Bansal, Marc Niethammer

The goal of interactive image segmentation is to delineate specific regions within an image via visual or language prompts. Low-latency and high-quality interactive segmentation with diverse prompts remain challenging for existing specialist and generalist models. Specialist models, with their limited prompts and task-specific designs, experience high latenc