April 2024 arXiv papers — page 7

Thomas Saigre, Christophe Prud'Homme, Marcela Szopos, Vincent Chabannes

Understanding human eye behavior involves intricate interactions between physical phenomena such as heat transfer and fluid dynamics. Accurate computational models are vital for comprehending ocular diseases and therapeutic interventions.This work focuses on modeling and simulating aqueous humor flow in the anterior and posterior chambers of the eye, coupled

Matthias Raba, Sébastien Triqueneaux, James Butterworth, David Schmoranzer

Many laboratories routinely cool samples to 10 mK, but relatively few can cool condensed matter below 1 mK. Easy access to the microkelvin range would propel fields such as quantum sensors and quantum materials. Such temperatures are achieved with adiabatic nuclear demagnetization. Existing nuclear demagnetization refrigerators (NDR) are ``single-shot'', and

Ian W. McBrearty, Paul Segall

During the three month long eruption of Kilauea volcano, Hawaii in 2018, the pre-existing summit caldera collapsed in over 60 quasi-periodic failure events. The last 40 of these events, which generated Mw >5 very long period (VLP) earthquakes, had inter-event times between 0.8 - 2.2 days. These failure events offer a unique dataset for testing methods for pr

Oleksandr Zinenko

Transform Dialect in MLIR provides operations that can be used to control transformation of the Intermediate Representation (IR) using a different portion of the IR. It refers to the IR being transformed as payload IR, and to the IR guiding the transformation as transform IR. The main use case for this dialect is orchestrating fine-grain transformations on i

Benjamin Alt, Johannes Zahn, Claudius Kienle, Julia Dvorak

While recent advances in deep learning have demonstrated its transformative potential, its adoption for real-world manufacturing applications remains limited. We present an Explanation User Interface (XUI) for a state-of-the-art deep learning-based robot program optimizer which provides both naive and expert users with different user experiences depending on

Chen Ling, Liqun Qi

Dual quaternion/complex matrices have important applications in brain science and multi-agent formation control. In this paper, we first study some basic properties of determinants of dual complex matrices, including Sturm theorem and Bloomfield-Watson inequality for dual complex matrices. Then, we show that every eigenvalue of a dual complex matrix must be

A. Chauhan, C. Sasmal

This study employs extensive three-dimensional direct numerical simulations (DNS) to investigate the influence of blood non-Newtonian behaviors on the hemodynamics around a bileaflet mechanical heart valve under both steady inflow and physiologically realistic pulsatile flow conditions. Under steady inflow conditions, the study reveals that blood rheology im

Chenjia Bai, Lingxiao Wang, Jianye Hao, Zhuoran Yang

Offline Reinforcement Learning (RL) has shown promising results in learning a task-specific policy from a fixed dataset. However, successful offline RL often relies heavily on the coverage and quality of the given dataset. In scenarios where the dataset for a specific task is limited, a natural approach is to improve offline RL with datasets from other tasks

Kemal Selcuk, Saleh Bunaiyan, Nihal Sanjay Singh, Shehrin Sayed

An emerging paradigm in modern electronics is that of CMOS + $\sf X$ requiring the integration of standard CMOS technology with novel materials and technologies denoted by $\sf X$. In this context, a crucial challenge is to develop accurate circuit models for $\sf X$ that are compatible with standard models for CMOS-based circuits and systems. In this perspe

Andreas Anastasiou, Sophia Loizidou

In this paper, a new data-adaptive method, called DAIS (Data Adaptive ISolation), is introduced for the estimation of the number and the location of change-points in a given data sequence. The proposed method can detect changes in various different signal structures; we focus on the examples of piecewise-constant and continuous, piecewise-linear signals. The

Christian Tantardini, Darina Fazylbekova, Sergey Levchenko, Ivan S. Novikov

Transition-metal compounds represent a fascinating playground for exploring the intricate relationship between structural distortions, electronic properties, and magnetic behaviour, holding significant promise for technological advancements. Among these compounds, YBaCo$_4$O$_{7}$ (Y114) is attractive due to its manifestation of a ferrimagnetic component at

Takafumi Aoki, Masahiro Ibe, Satoshi Shirai, Keiichi Watanabe

This paper investigates the impact of small instanton effects on the axion mass in composite axion models. In particular, we focus on the Composite Accidental Axion (CAA) models, which are designed to address the axion quality problem, and where the Peccei-Quinn (PQ) symmetry emerges accidentally. In the CAA models, the QCD gauge symmetry is embedded in a la

Soham Mitra, Atri Sukul, Swalpa Kumar Roy, Pravendra Singh

Deep learning models have achieved remarkable success across diverse domains. However, the intricate nature of these models often impedes a clear understanding of their decision-making processes. This is where Explainable AI (XAI) becomes indispensable, offering intuitive explanations for model decisions. In this work, we propose a simple yet highly effectiv

Yun-Peng Li, Zhi-Lin Chen, Da-Bin Lin, En-Wei Liang

The merger of neutron star (NS)-NS binary can form different production of the compact remnant, among which the supramassive NS (SMNS) could create an internal plateau and the followed steep decay marks the collapse of the SMNS. The proportion of SMNS and the corresponding collapse-time are often used to constrain the NS equation of state (EoS). This paper r

Matteo Furci, Giovanni Marini, Matteo Calandra

Photoexcited GeTe undergoes a non-thermal phase transition from a rhombohedral to a rocksalt crystalline phase. The microscopic mechanism and the nature of the transition are unclear. By using constrained density functional perturbation theory and by accounting for quantum anharmonicity within the stochastic self-consistent harmonic approximation, we show th

Ulrich Ellwanger, Cyril Hugonie, Stephen F. King, Stefano Moretti

The observed excesses in the search for neutralinos and charginos by ATLAS and CMS can be fitted simultaneously in the minimal supersymmetric standard model (MSSM) assuming a light higgsino mass, of magnitude less than about 250 GeV, and a compressed higgsino dominated neutralino and chargino spectrum, with $5-10\%$ mass splittings. However, light higgsinos

Uwe M. Borghoff, Eberhard Pfeiffer, Peter Rödig

The long-term usability of digital building documents is essential for the maintenance and optimization of infrastructure portfolios. It supports the preservation of building-specific knowledge and the cultural heritage hidden within. However, having to do this throughout the lifecycle of a building - or even indefinitely - remains a major challenge. This is

Yanggyu Lee, Suchae Jeong, Jihie Kim

LLMs trained in the understanding of programming syntax are now providing effective assistance to developers and are being used in programming education such as in generation of coding problem examples or providing code explanations. A key aspect of programming education is understanding and dealing with error message. However, 'logical errors' in which the

Frédéric Moulin

The Riemann tensor is the cornerstone of general relativity, but as everyone knows it does not appear explicitly in Einstein's equation of gravitation. This suggests that the latter may not be the most general equation. We propose here for the first time, following a rigorous mathematical treatment based on the variational principle, that there exists a gene

Xiaoming Liu, Chen Liu, Zhaohan Zhang, Chengzhengxu Li

Large language models have shown their ability to become effective few-shot learners with prompting, revolutionizing the paradigm of learning with data scarcity. However, this approach largely depends on the quality of prompt initialization, and always exhibits large variability among different runs. Such property makes prompt tuning highly unreliable and vu

Minghao Han, Xukun Zhang, Dingkang Yang, Tao Liu

Survival prediction is a complex ordinal regression task that aims to predict the survival coefficient ranking among a cohort of patients, typically achieved by analyzing patients' whole slide images. Existing deep learning approaches mainly adopt multiple instance learning or graph neural networks under weak supervision. Most of them are unable to uncover t

Y. Ben-Aryeh

The EM waves transmitted through a thin object with fine structures is observed, by microsphere located above the object. While the waves include both evanescent and propagating waves, the high resolution is obtained by the evanescent ones, including the information on the fine structures of the object. Description of this process is divided into two parts:

S. I. Dimitrov

In this paper we establish lower bounds on several expressions dependent on functions $\varphi(n)$, $\psi(n)$ and $\sigma(n)$.

Fareed Qararyah, Muhammad Waqar Azhar, Mohammad Ali Maleki, Pedro Trancoso

Depthwise and pointwise convolutions have fewer parameters and perform fewer operations than standard convolutions. As a result, they have become increasingly used in various compact DNNs, including convolutional neural networks (CNNs) and vision transformers (ViTs). However, they have a lower compute-to-memory-access ratio than standard convolutions, making

Zhanwei Zhang, Zishuo Hua, Minghao Chen, Wei Lu

Predicting future trajectories of traffic agents accurately holds substantial importance in various applications such as autonomous driving. Previous methods commonly infer all future steps of an agent either recursively or simultaneously. However, the recursive strategy suffers from the accumulated error, while the simultaneous strategy overlooks the constr

Thomas Constum, Lucas Preel, Théo Larcher, Pierrick Tranouez

The EXO-POPP project aims to establish a comprehensive database comprising 300,000 marriage records from Paris and its suburbs, spanning the years 1880 to 1940, which are preserved in over 130,000 scans of double pages. Each marriage record may encompass up to 118 distinct types of information that require extraction from plain text. In this paper, we introd

Luise Häuser, Gerhard Jäger, Alexandros Stamatakis

Working with cognate data involves handling synonyms, that is, multiple words that describe the same concept in a language. In the early days of language phylogenetics it was recommended to select one synonym only. However, as we show here, binary character matrices, which are used as input for computational methods, do allow for representing the entire data

Feliciano Solano-Farías, Matilde García-Valdecasas Ojeda, David Donaire-Montaño, Juan José Rosa-Cánovas

Convection-permitting models (CPMs) enable the representation of meteorological variables at horizontal high resolution spatial scales (higher than 4 km), where convection plays a significant role. Physical schemes need to be evaluated considering factors in the studied region such as orography and climate variability. This study investigates the sensitivity

Lingyi Hong, Zhongying Liu, Wenchao Chen, Chenzhi Tan

Video object segmentation (VOS) aims to distinguish and track target objects in a video. Despite the excellent performance achieved by off-the-shell VOS models, existing VOS benchmarks mainly focus on short-term videos lasting about 5 seconds, where objects remain visible most of the time. However, these benchmarks poorly represent practical applications, an

Christian Bartels, Martina Scauda, Neva Coello, Thomas Dumortier

Non-linear mixed effects modeling and simulation (NLME M&S) is evaluated to be used for standardization with longitudinal data in presence of confounders. Standardization is a well-known method in causal inference to correct for confounding by analyzing and combining results from subgroups of patients. We show that non-linear mixed effects modeling is a part

Hulusi Mehmet Tanrikulu, Hakan Pabuccu

Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A small improvement in forecasting performance can lead to increased profitability; therefore, obtaining a realistic forecast is very important for investors. Successful forecasting provides traders with effective buy-or-hold strategies, al

Hemanth Manikantan, Biswajit Paul, Rahul Sharma, Pragati Pradhan

We present the results from an investigation of the energy dependence of Quasi-Periodic Oscillations (QPOs) exhibited by accreting X-ray pulsars using data from archival \textit{XMM-Newton}, \textit{NuSTAR}, \textit{RXTE}, and \textit{NICER} observations. In a search for the presence of QPOs in 99 \textit{XMM-Newton} and \textit{NuSTAR} observations, we dete

Eleonora Di Valentino, Stefano Gariazzo, Olga Mena

Neutrinos are the least known particle in the Standard Model of elementary particle physics. They play a crucial role in cosmology, governing the universe's evolution and shaping the large-scale structures we observe today. In this chapter, we review crucial topics in neutrino cosmology, such as the neutrino decoupling process in the very early universe. We

Mingchao Liu, Weining Mao, Yiqiu Zhao, Qin Xu

The strain-rate sensitivity of confined granular materials has been widely explored, with most findings exhibiting rate-strengthening behaviors. This study, however, reveals a distinct rate-softening behavior across a certain strain rate range based on triaxial tests on particle clusters of various materials with different surface properties, particle sizes,

Bin Shen, Franziska Breitner, Philipp Gegenwart, Anton Jesche

The performance of permanent magnets is intricately tied to their magnetic hysteresis loop. In this study, we investigate the heavy-fermion ferromagnet CeAgSb$_2$ through magnetization measurements under uniaxial stress. We observe a 2400 % increase in magnetic coercivity with just a modest stress of approximately 1 kbar. This effect persists even after pres

Minh Duc Bui, Fabian David Schmidt, Goran Glavaš, Katharina von der Wense

Compared to standard language model (LM) pretraining (i.e., from scratch), Knowledge Distillation (KD) entails an additional forward pass through a teacher model that is typically substantially larger than the target student model. As such, KD in LM pretraining materially slows down throughput of pretraining instances vis-a-vis pretraining from scratch. Scal

Yuvraj Virk, Premkumar Devanbu, Toufique Ahmed

A brief, fluent, and relevant summary can be helpful during program comprehension; however, such a summary does require significant human effort to produce. Often, good summaries are unavailable in software projects, which makes maintenance more difficult. There has been a considerable body of research into automated AI-based methods, using Large Language mo

Solène Tarride, Christopher Kermorvant

In recent advances in automatic text recognition (ATR), deep neural networks have demonstrated the ability to implicitly capture language statistics, potentially reducing the need for traditional language models. This study directly addresses whether explicit language models, specifically n-gram models, still contribute to the performance of state-of-the-art

Sheng Ouyang, Jianzong Wang, Yong Zhang, Zhitao Li

Extractive Question Answering (EQA) in Machine Reading Comprehension (MRC) often faces the challenge of dealing with semantically identical but format-variant inputs. Our work introduces a novel approach, called the ``Query Latent Semantic Calibrator (QLSC)'', designed as an auxiliary module for existing MRC models. We propose a unique scaling strategy to ca

Zachary William Hopton, Noëmi Aepli

Effectively normalizing textual data poses a considerable challenge, especially for low-resource languages lacking standardized writing systems. In this study, we fine-tuned a multilingual model with data from several Occitan dialects and conducted a series of experiments to assess the model's representations of these dialects. For evaluation purposes, we co

Sébastien Martin, Youcef Magnouche, Paolo Medagliani, Jérémie Leguay

In backbone networks, it is fundamental to quickly protect traffic against any unexpected event, such as failures or congestions, which may impact Quality of Service (QoS). Standard solutions based on Segment Routing (SR), such as Topology-Independent Loop-Free Alternate (TI-LFA), are used in practice to handle failures, but no distributed solutions exist fo

Adrisha Sarkar, Zachary Jones, Madhur Parashar, Emanuel Druga

We report on a novel flow-based method for high-precision chemical detection that integrates quantum sensing with droplet microfluidics. We deploy nanodiamond particles hosting fluorescent nitrogen vacancy defects as quantum sensors in flowing, monodisperse, picoliter-volume microdroplets containing analyte molecules. ND motion within these microcompartments

Xin Zhang, Yuexian Hou

Quantum neural networks (QNNs) is a parameterized quantum circuit model, which can be trained by gradient-based optimizer, can be used for supervised learning, regression tasks, combinatorial optimization, etc. Although many works have demonstrated that QNNs have better learnability, generalizability, etc. compared to classical neural networks. However, as w

Wang Zhang, Tingting Li, Yuntian Zhang, Gensheng Pei

Matching visible and near-infrared (NIR) images remains a significant challenge in remote sensing image fusion. The nonlinear radiometric differences between heterogeneous remote sensing images make the image matching task even more difficult. Deep learning has gained substantial attention in computer vision tasks in recent years. However, many methods rely

Eyal Liron Dolev, Clemens Fidel Lutz, Noëmi Aepli

Whisper is a state-of-the-art automatic speech recognition (ASR) model (Radford et al., 2022). Although Swiss German dialects are allegedly not part of Whisper's training data, preliminary experiments showed that Whisper can transcribe Swiss German quite well, with the output being a speech translation into Standard German. To gain a better understanding of

Liad Doniza, Mitchel Lee, Tamar Blumenfeld Katzir, Moran Artzi

Quantitative Susceptibility Mapping (QSM) is a technique for measuring magnetic susceptibility of tissues, aiding in the detection of pathologies like traumatic brain injury and multiple sclerosis by analyzing variations in substances such as iron and calcium. Despite its clinical value, achieving high-resolution QSM (voxel sizes < 1 mm3) reduces signal-to-n

Lin Zhang, Ming-Jing Zhao, Lin Chen, Hua Xiang

Although quantum entanglement is an important resource, its characterization is quite challenging. The partial transposition is a common method to detect bipartite entanglement. In this paper, the authors study the partial-transpose(PT)-moments of two-qubit states,and completely describe the whole region, composed of the second and third PT-moments, for all

Mark Huasong Meng, Hao Guan, Liuhuo Wan, Sin Gee Teo

We present PAODING, a toolkit to debloat pretrained neural network models through the lens of data-free pruning. To preserve the model fidelity, PAODING adopts an iterative process, which dynamically measures the effect of deleting a neuron to identify candidates that have the least impact to the output layer. Our evaluation shows that PAODING can significan

Han Hu, Han Wang, Ruiqi Dong, Xiao Chen

Mobile apps are ubiquitous in our daily lives for supporting different tasks such as reading and chatting. Despite the availability of many GUI testing tools, app testers still struggle with low testing code coverage due to tools frequently getting stuck in loops or overlooking activities with concealed entries. This results in a significant amount of testin

Swayamjit Saha

Wind speed is a powerful source of renewable energy, which can be used as an alternative to the non-renewable resources for production of electricity. Renewable sources are clean, infinite and do not impact the environment negatively during production of electrical energy. However, while eliciting electrical energy from renewable resources viz. solar irradia

Heinz-Jürgen Schmidt

This study addresses the often underestimated importance of physical dimensions and units in the formal reconstruction of physical theories, focusing on structuralist approaches that use the concept of ``species of structure" as a meta-mathematical tool. Similar approaches also play a role in current philosophical debates on the metaphysical status of physic

Hiroko Tomoda, Akihiro Machinaga, Kan Takase, Jun Harada

In optical quantum information processing with continuous variables, optical non-Gaussian quantum states are essential for universal and fault-tolerant quantum computation. Experimentally, their most typical generation method is photon subtraction (PS), where single-photon detection by an on/off detector probabilistically heralds the generation of squeezed s

Shannon L. Walston, Hiroshi Seki, Hirotaka Takita, Yasuhito Mitsuyama

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. With such history comes a set of terminology that has a specific way in which it is applied. However, when two distinct fields with overlapping terminology start to collaborate, miscommunication and misunderstandings

K. Chakrabarti, J. Zs Mezei, O. Motapon, A. Faure

The reactive collisions of the CH$^+$ molecular ion with electrons is studied in the framework of the multichannel quantum defect theory, taking into account the contribution of the core-excited Rydberg states. In addition to the $X^1\Sigma^+$ ground state of the ion, we also consider the contribution to the dynamics of the $a^3\Pi$ and $A^1\Pi$ excited stat

Valentina Ghidini

In this paper, we propose standard statistical tools as a solution to commonly highlighted problems in the explainability literature. Indeed, leveraging statistical estimators allows for a proper definition of explanations, enabling theoretical guarantees and the formulation of evaluation metrics to quantitatively assess the quality of explanations. This app

Daiki Miura, Shogo B. Kobayashi, Hiroya Yamaguchi

Discovery of coherent pulsations from several ultraluminous X-ray pulsars (ULXPs) has provided direct evidence of super-critical accretion flow. However, geometrical structure of such accretion flow onto the central neutron star remains poorly understood. NGC 5907 ULX1 is one of the most luminous ULXPs with the luminosity exceeding $10^{41}~{\rm erg~s^{-1}}$

Sungjune Park, Hyunjun Kim, Yong Man Ro

Pedestrian detection is a crucial field of computer vision research which can be adopted in various real-world applications (e.g., self-driving systems). However, despite noticeable evolution of pedestrian detection, pedestrian representations learned within a detection framework are usually limited to particular scene data in which they were trained. Theref

F. Iacob, T. Meltzer, J. Zs. Mezei, I. F. Schneider

The bound and resonance states along with corresponding autoionization widths for nitrogen sulphide (NS) molecule are determined using electron NS$^+$ cation scattering calculations. The calculations are performed for $^2{\Sigma}^+$, $^2{\Pi}$ and $^2\Delta$ total symmetries using the ab initio R-matrix method for both bound and continuum states. Calculation

Alexander Marinek, Xuesong Cai, Lieven De Strycker, Fredrik Tufvesson

Millimeter-wave (mmWave) technology holds the potential to revolutionize head-mounted displays (HMDs) by enabling high-speed wireless communication with nearby processing nodes, where complex video rendering can take place. However, the sparse angular profile of mmWave channels, coupled with the narrow field of view (FoV) of patch-antenna arrays and frequent

Xinyi Li, Yongfeng Zhang, Edward C. Malthouse

In the current digital era, the rapid spread of misinformation on online platforms presents significant challenges to societal well-being, public trust, and democratic processes, influencing critical decision making and public opinion. To address these challenges, there is a growing need for automated fake news detection mechanisms. Pre-trained large languag

Yanming Hu, Yifan Fei, Xiao-Long Chen, Yunbo Zhang

The collisional dynamics of two symmetric droplets with equal intraspecies scattering lengths and particle number density for each component is studied by solving the corresponding extended Gross-Pitaevskii equation in two dimensions by including a logarithmic correction term in the usual contact interaction. We find the merging droplet after collision exper

Jinyoung Jun, Jae-Han Lee, Chang-Su Kim

The main function of depth completion is to compensate for an insufficient and unpredictable number of sparse depth measurements of hardware sensors. However, existing research on depth completion assumes that the sparsity -- the number of points or LiDAR lines -- is fixed for training and testing. Hence, the completion performance drops severely when the nu

Xiao-Long Chen, Aixi Chen, Shi-Guo Peng

We study the problem of two harmonically trapped atoms in the presence of spin-orbital-angular-momentum (SOAM) coupling. The two-body energy spectrum is numerically calculated by utilizing the exact diagonalization method. We analyze how the degeneracy of energy levels is lifted under the interplay between the interatomic interaction and SOAM coupling. The e

Qiaosheng Zhang, Chenjia Bai, Shuyue Hu, Zhen Wang

This work designs and analyzes a novel set of algorithms for multi-agent reinforcement learning (MARL) based on the principle of information-directed sampling (IDS). These algorithms draw inspiration from foundational concepts in information theory, and are proven to be sample efficient in MARL settings such as two-player zero-sum Markov games (MGs) and mult

Jason Dekarske, Zhaodan Kong, Sanjay Joshi

Objective We model the dynamic trust of human subjects in a human-autonomy-teaming screen-based task. Background Trust is an emerging area of study in human-robot collaboration. Many studies have looked at the issue of robot performance as a sole predictor of human trust, but this could underestimate the complexity of the interaction. Method Subjects were pa

Svetlana Boyarchenko, Sergei Levendorskiĭ

We suggest new closely related methods for numerical inversion of $Z$-transform and Wiener-Hopf factorization of functions on the unit circle, based on sinh-deformations of the contours of integration, corresponding changes of variables and the simplified trapezoid rule. As applications, we consider evaluation of high moments of probability distributions and

Yun-Hao Cao, Jianxin Wu

Self-supervised learning (SSL) has developed rapidly in recent years. However, most of the mainstream methods are computationally expensive and rely on two (or more) augmentations for each image to construct positive pairs. Moreover, they mainly focus on large models and large-scale datasets, which lack flexibility and feasibility in many practical applicati

Ryoma Sato

We propose training-free graph neural networks (TFGNNs), which can be used without training and can also be improved with optional training, for transductive node classification. We first advocate labels as features (LaF), which is an admissible but not explored technique. We show that LaF provably enhances the expressive power of graph neural networks. We d

Wanqi Zhou, Shuanghao Bai, Danilo P. Mandic, Qibin Zhao

Pretrained vision-language models (VLMs) like CLIP exhibit exceptional generalization across diverse downstream tasks. While recent studies reveal their vulnerability to adversarial attacks, research to date has primarily focused on enhancing the robustness of image encoders against image-based attacks, with defenses against text-based and multimodal attacks

Shuanghao Bai, Yuedi Zhang, Wanqi Zhou, Zhirong Luan

Large pre-trained vision language models (VLMs) have shown impressive zero-shot ability on downstream tasks with manually designed prompt. To further adapt VLMs to downstream tasks, soft prompt is proposed to replace manually designed prompt, which undergoes fine-tuning based on specific domain data. Prior prompt learning methods primarily learn a fixed prom

Yi-Min Huang, Amitava Bhattacharjee

Plasmoid instability accelerates reconnection in collisional plasmas by transforming a laminar reconnection layer into numerous plasmoids connected by secondary current sheets in two dimensions (2D) and by fostering self-generated turbulent reconnection in three dimensions (3D). In large-scale astrophysical and space systems, plasmoid instability likely init

Ben Harwood, Amir Dezfouli, Iadine Chades, Conrad Sanderson

Approximate k-Nearest Neighbour (ANN) methods are often used for mining information and aiding machine learning on large scale high-dimensional datasets. ANN methods typically differ in the index structure used for accelerating searches, resulting in various recall/runtime trade-off points. For applications with static datasets, runtime constraints and datas

Marlon Steiner, Marvin Klemp, Christoph Stiller

There is a gap in risk assessment of trajectories between the trajectory information coming from a traffic motion prediction module and what is actually needed. Closing this gap necessitates advancements in prediction beyond current practices. Existing prediction models yield joint predictions of agents' future trajectories with uncertainty weights or margin

Xiruo Jiang, Yazhou Yao, Sheng Liu, Fumin Shen

Loss functions and sample mining strategies are essential components in deep metric learning algorithms. However, the existing loss function or mining strategy often necessitate the incorporation of additional hyperparameters, notably the threshold, which defines whether the sample pair is informative. The threshold provides a stable numerical standard for d

Nahal Sharafi, Christoph Martin, Sarah Hallerberg

Neural networks have become a widely adopted tool for tackling a variety of problems in machine learning and artificial intelligence. In this contribution we use the mathematical framework of local stability analysis to gain a deeper understanding of the learning dynamics of feed forward neural networks. Therefore, we derive equations for the tangent operato

Sagar Gupta, Akansel Cosgun

We present a multimodal traffic light state detection using vision and sound, from the viewpoint of a quadruped robot navigating in urban settings. This is a challenging problem because of the visual occlusions and noise from robot locomotion. Our method combines features from raw audio with the ratios of red and green pixels within bounding boxes, identifie

Aven Le Zhou

This paper introduces an interactive Mixed Reality (MR) experience and artistic inquiry entitled ``Surrealism Me'' which delves into Vil\'em Flusser's critique of media as mediators that often distort human perception of reality and diminish freedom, particularly within the context of MR technology. It engages with Flusser's theories by allowing participants

Tongtong Liu, Xinyi Wu, Kaisi Liu, Lei Liu

Recently, the solid-state frustrated Lewis pairs (FLPs) on the surface of CeO2 have been demonstrated to effectively catalyze the selective hydrogenation of unsaturated substrates, hence, the relationship between their intrinsic properties and H2 activation at the atomic scale has attracted great attention. In this work, the effects of Cu doping on the intri

Xingyu Song, Zhan Li, Shi Chen, Kazuyuki Demachi

3D human pose estimation is a vital task in computer vision, involving the prediction of human joint positions from images or videos to reconstruct a skeleton of a human in three-dimensional space. This technology is pivotal in various fields, including animation, security, human-computer interaction, and automotive safety, where it promotes both technologic

Y. Samukawa, M. Maeda, N. Jiang, R. Nakamura

Trigonal PtBi$_2$ is one of the attractive van-der-Waals materials because of the enhancement of its superconducting transition temperature $T_{\rm{c}}$ by doping chalcogen elements such as Se and Te. Recently, it has been reported that $T_{\rm{c}}$ of Pt(Bi$_{1-x}$Se$_x$)$_2$ is enhanced by a factor of 4, compared to the pristine PtBi$_2$, together with the

Wentao Lei, Li Liu, Jun Wang

Cued Speech (CS) is an advanced visual phonetic encoding system that integrates lip reading with hand codings, enabling people with hearing impairments to communicate efficiently. CS video generation aims to produce specific lip and gesture movements of CS from audio or text inputs. The main challenge is that given limited CS data, we strive to simultaneousl

Sairam VC Rebbapragada, Pranoy Panda, Vineeth N Balasubramanian

A vision-based drone-to-drone detection system is crucial for various applications like collision avoidance, countering hostile drones, and search-and-rescue operations. However, detecting drones presents unique challenges, including small object sizes, distortion, occlusion, and real-time processing requirements. Current methods integrating multi-scale feat

JingHong Li, Huy Phan, Wen Gu, Koichi Ota

Novice researchers often face difficulties in understanding a multitude of academic papers and grasping the fundamentals of a new research field. To solve such problems, the knowledge graph supporting research survey is gradually being developed. Existing keyword-based knowledge graphs make it difficult for researchers to deeply understand abstract concepts.

Kevin John, Yinan Li, Hasti Seifi

Mid-air ultrasound haptic technology can enhance user interaction and immersion in extended reality (XR) applications through contactless touch feedback. Yet, existing design tools for mid-air haptics primarily support creating tactile sensations (i.e., tactons) which cannot change at runtime. These tactons lack expressiveness in interactive scenarios where

Yuta Tamura, Masayuki Ohzeki

In statistical mechanics, computing the partition function is generally difficult. An approximation method using a variational autoregressive network (VAN) has been proposed recently. This approach offers the advantage of directly calculating the generation probabilities while obtaining a significantly large number of samples. The present study introduces a

Hiroyasu Izeki, Anders Karlsson

We show that finitely generated groups which are Liouville and without infinite finite-dimensional linear representations must have a global fixed point whenever they act by isometry on a finite-dimensional complete CAT(0)-space. This provides a partial answer to an old question in geometric group theory and proves partly a conjecture formulated by Norin, Os

Rameshan Thimmappa, Joey Neilsen, Daryl Haggard, Michael A. Nowak

The radio galaxy M87 is well known for its jet, which features a series of bright knots observable from radio to X-ray wavelengths. We analyze the X-ray image and flux variability of the knot HST-1 in the jet. Our analysis includes all 112 available Chandra ACIS-S observations from 2000-2021, with a total exposure time of $\sim$887 ks. We use de-convolved im

Bohan Ouyang

We consider a diffuse interface model that describes the macro- and micro-phase separation processes of a polymer mixture. The resulting system consists of a Cahn-Hilliard equation and a Cahn-Hilliard-Oono type equation endowed with the singular Flory-Huggins potential. For the initial boundary value problem in a bounded smooth domain of $\mathbb{R}^d$ ($d\i

Takahiro Ikeda, Yusei Kobayashi, Masashi Yamakawa

Patchy nanocubes are intriguing materials with simple shapes and space-filling and multidirectional bonding properties. Previous studies have revealed various mesoscopic structures such as colloidal crystals in the solid regime and rod-like or fractal-like aggregates in the liquid regime of the phase diagram. Recent studies have also shown that mesoscopic st

Shingo Kobayashi, Masatoshi Sato

A remarkable feature of topological superconductors is the emergence of Majorana fermions in electron systems. Whereas the emergent Majorana fermions share the self-anti-particle property with Majorana fermions in particle physics, they may have essentially different electromagnetic properties. In this paper, we argue the electromagnetic response of spinful

Diego Solano-Alfaro, Francisco Frutos-Alfaro

An study of the equatorial circular motion of photons and massive particles around a rotating compact body like a neutron star is presented. For this goal, we use an approximate Kerr-like metric with mass quadrupole as perturbation. The effect of this deformation on the photon sphere, and the innermost stable circular orbit, is determined via an effective po

Haobai Xue, Xian Liu

Bradford's law of bibliographic scattering is a fundamental principle in bibliometrics, offering valuable guidance for academic libraries in literature search and procurement. However, Bradford curves can exhibit various shapes over time, and predicting these shapes remains a challenge due to a lack of causal explanation. This paper attributes the deviations

Xia Zhao, Peibiao Zhao

The celebrated Minkowski problem for the torsional rigidity ($2$-torsional rigidity) was firstly studied by Colesanti and Fimiani \cite{CA} using variational method. Moreover, Hu, Liu and Ma \cite{HJ} also studied the Minkowski problem {\it w.r.t.} $2$-torsional rigidity by method of curvature flows and obtain the existence of smooth even solutions. Up to no

Zhenglin Li, Bo Guan, Yuanzhou Wei, Yiming Zhou

Generative Adversarial Networks (GANs) have significantly advanced image processing, with Pix2Pix being a notable framework for image-to-image translation. This paper explores a novel application of Pix2Pix to transform abstract map images into realistic ground truth images, addressing the scarcity of such images crucial for domains like urban planning and a

Xiaoyu Huang, Yufeng Chi, Ruofeng Wang, Zhongyu Li

This work introduces DiffuseLoco, a framework for training multi-skill diffusion-based policies for dynamic legged locomotion from offline datasets, enabling real-time control of diverse skills on robots in the real world. Offline learning at scale has led to breakthroughs in computer vision, natural language processing, and robotic manipulation domains. How

Nima Baniasadi, Rami Hijab, Ali Niknejad

This work presents mm-Wave and sub-THz chip-to-package transitions for communications systems. To date, reported transitions either have high loss, typically 3 to 4 dB, or require high cost packages to support very fine bump pitches and low loss materials. We analyze the impact of transitions on a high frequency, wide bandwidth communication system and prese

Alexandre F. Fonseca, Luiz Felipe C. Pereira

Research on the physical properties of materials at the nanoscale is crucial for the development of breakthrough nanotechnologies. One of the key properties to consider is the ability to conduct heat, i.e., its thermal conductivity. Graphene is a remarkable nanostructure with exceptional physical properties, including one of the highest thermal conductivitie

Atish Agarwala, Jeffrey Pennington

Recent empirical and theoretical work has shown that the dynamics of the large eigenvalues of the training loss Hessian have some remarkably robust features across models and datasets in the full batch regime. There is often an early period of progressive sharpening where the large eigenvalues increase, followed by stabilization at a predictable value known

Abir Chakraborty

In this work we investigate the capability of Graph Attention Network for extracting aspect and opinion terms. Aspect and opinion term extraction is posed as a token-level classification task akin to named entity recognition. We use the dependency tree of the input query as additional feature in a Graph Attention Network along with the token and part-of-spee

Taylor Archibald, Tony Martinez

Document semantic segmentation is a promising avenue that can facilitate document analysis tasks, including optical character recognition (OCR), form classification, and document editing. Although several synthetic datasets have been developed to distinguish handwriting from printed text, they fall short in class variety and document diversity. We demonstrat