March 2023 arXiv papers — page 118

Yan Lei, Tiantian Wen, Huan Xie, Lingfeng Fu

Fault localization (FL) analyzes the execution information of a test suite to pinpoint the root cause of a failure. The class imbalance of a test suite, i.e., the imbalanced class proportion between passing test cases (i.e., majority class) and failing ones (i.e., minority class), adversely affects FL effectiveness. To mitigate the effect of class imbalance

Eduardo Calò, Jordi Levy

The minimization of propositional formulae is a classical problem in logic, whose first algorithms date back at least to the 1950s with the works of Quine and Karnaugh. Most previous work in the area has focused on obtaining minimal, or quasi-minimal, formulae in conjunctive normal form (CNF) or disjunctive normal form (DNF), with applications in hardware de

Wim Vanderbauwhede

The current emissions from computing are almost 4% of the world total. This is already more than emissions from the airline industry and are projected to rise steeply over the next two decades. By 2040 emissions from computing alone will account for more than half of the emissions budget to keep global warming below 1.5$^\circ$C. Consequently, this growth in

Weiquan Liu, Shijun Zheng, Cheng Wang

As the key technology of augmented reality (AR), 3D recognition and tracking are always vulnerable to adversarial examples, which will cause serious security risks to AR systems. Adversarial examples are beneficial to improve the robustness of the 3D neural network model and enhance the stability of the AR system. At present, most 3D adversarial attack metho

Mikolaj Spytek, Weronika Hryniewska-Guzik, Jaroslaw Zygierewicz, Jacek Rogala

The prediction of age is a challenging task with various practical applications in high-impact fields like the healthcare domain or criminology. Despite the growing number of models and their increasing performance, we still know little about how these models work. Numerous examples of failures of AI systems show that performance alone is insufficient, thus,

Jascha Rolf, Julian Handke, Frank Burzinski, Stephan Luetz

Cell-free protein synthesis (CFPS) systems are an attractive to complement the usual cell-based synthesis of proteins, especially for screening approaches. The literature describes a wide variety of CFPS systems, but their performance is difficult to compare since the reaction components are often used at different concentrations. Therefore, we have develope

Thomas Dagès, Michael Lindenbaum, Alfred M. Bruckstein

Neural networks are omnipresent, but remain poorly understood. Their increasing complexity and use in critical systems raises the important challenge to full interpretability. We propose to address a simple well-posed learning problem: estimating the radius of a centred pulse in a one-dimensional signal or of a centred disk in two-dimensional images using a

Mehrasa Ahmadipour, Michèle Wigger, Shlomo Shamai

We study the state-dependent wiretap channel with non-causal channel state informations at the encoder in an integrated sensing and communications (ISAC) scenario. In this scenario, the transmitter communicates a message and a state sequence to a legitimate receiver while keeping the message and state-information secret from an external eavesdropper. This pa

Mehrasa Ahmadipour, Michele Wigger, Shlomo Shamai

The paper characterizes the fundamental limits of integrated sensing and communication (ISAC) systems with a bi-static radar, where the radar receiver is located close to the transmitter and estimates or detects the state based on the transmitter's channel inputs and the backscattered signals. Two models are considered. In the first model, the memoryless sta

Haofei Zhang, Mengqi Xue, Xiaokang Liu, Kaixuan Chen

In this paper, we study a novel inference paradigm, termed as schema inference, that learns to deductively infer the explainable predictions by rebuilding the prior deep neural network (DNN) forwarding scheme, guided by the prevalent philosophical cognitive concept of schema. We strive to reformulate the conventional model inference pipeline into a graph mat

Elad Steinberg, Shay I. Heizler

This work generalizes the discrete implicit Monte-Carlo (DIMC) method for modeling the radiative transfer equation from a gray treatment to an frequency-dependent one. The classic implicit Monte-Carlo (IMC) algorithm, that has been used for several decades, suffers from a well-known numerical problem, called teleportation, where the photons might propagate f

Antonina K. Begicheva, Irina A. Lomazova, Roman A. Nesterov

Process mining is a field of computer science that deals with discovery and analysis of process models based on automatically generated event logs. Currently, many companies use this technology for optimization and improving their processes. However, a discovered process model may be too detailed, sophisticated and difficult for experts to understand. In thi

Lajos Diósi

When about half a century ago the concept of universal spontaneous collapse of the wave function was conceived it was an attempt to alter standard non-relativistic quantum physics. As such, it was largely ignored by relativistic field theory and quantum gravity communities. A central motivation of spontaneous collapse community has been to replace the standa

Soujanya Narayana, Ramanathan Subramanian, Ibrahim Radwan, Roland Goecke

While emotion and mood interchangeably used, they differ in terms of duration, intensity and attributes. Even as multiple psychology studies examine the mood-emotion relationship, mood prediction has barely been studied. Recent machine learning advances such as the attention mechanism to focus on salient parts of the input data, have only been applied to inf

Zhentong Zhang, Ionut Danaila, Emmanuel Lévêque, Luminita Danaila

The Hall-Vinen-Bekharevich-Khalatnikov (HVBK) model is widely used to numerically study quantum turbulence in superfluid helium. Based on the two-fluid model of Tisza and Landau, the HVBK model describes the normal (viscous) and superfluid (inviscid) components of the flow using two Navier-Stokes type equations, coupled through a mutual friction force term.

Ruiming Zhang

In this work we apply Hausdorff moment problem to prove a necessary and sufficient condition for a complex sequence to be positive. Then we apply it to a subclass of genus $0$ entire functions $f(z)$ to obtain an infinite family of necessary and sufficient conditions for $f(z)$ to have only negative roots, which in turn to give infinite many necessary and su

P. P. Avelino

Gravitational vacuum condensate stars, also known as gravastars, have been proposed as an alternative to black holes. Their interior contains a perfect fluid with an equation of state akin to that of a cosmological constant. For this reason, they have recently been considered as a possible astrophysical source of dark energy. In this work we argue that gravi

Xiuzhan Guo, Arthur Berrill, Ajinkya Kulkarni, Kostya Belezko

Benjelloun et al. \cite{BGSWW} considered the Entity Resolution (ER) problem as the generic process of matching and merging entity records judged to represent the same real world object. They treated the functions for matching and merging entity records as black-boxes and introduced four important properties that enable efficient generic ER algorithms. In th

Zangwei Zheng, Mingyuan Ma, Kai Wang, Ziheng Qin

Continual learning (CL) can help pre-trained vision-language models efficiently adapt to new or under-trained data distributions without re-training. Nevertheless, during the continual training of the Contrastive Language-Image Pre-training (CLIP) model, we observe that the model's zero-shot transfer ability significantly degrades due to catastrophic forgett

Amjad Ashoorioon, Zahra Davari

We study varying forms of viscous dark matter and try to address the intriguing tensions of the standard model of cosmology with recent cosmological data, including the Hubble and $S_8$ tensions. We note that by assuming the dark matter viscosity depends on the Hubble parameter, dark matter density, or both, one can improve the statistics. Although the model

Yuzuru Inahama, Yong Xu, Xiaoyu Yang

This work focuses on a slow-fast system perturbed by mixed fractional Brownian motion with Hurst parameter $H\in(1/2,1)$. The integral with respect to fractional Brownian motion is the generalized Riemann-Stieltjes integral and the integral with respect to Brownian motion is the standard It\^o integral. Our approach is based on the variational framework and

Oliviu Şugar-Gabor

The Active Flux scheme is a Finite Volume scheme with additional degrees of freedom. It makes use of a continuous reconstruction and does not require a Riemann solver. An evolution operator is used for the additional degrees of freedom on the cell boundaries. This paper presents progress towards the computation of one-dimensional, viscous, compressible flows

Bingyi Xia, Hao Luan, Ziqi Zhao, Xuheng Gao

Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an e

Joshua Tanner, Jacob Hoffman

Recent approaches to word sense disambiguation (WSD) utilize encodings of the sense gloss (definition), in addition to the input context, to improve performance. In this work we demonstrate that this approach can be adapted for use in multiword expression (MWE) identification by training models which use gloss and context information to filter MWE candidates

Jaak Peetre, Per Nilsson

This note is an (exact) copy of the report of Jaak Peetre, "Banach Couples. I. Elementary Theory". Published as Technical Report, Lund (1971). Some more recent general references have been added and some references updated though

Xingjun Wu, Jianhuan Wang, Miaoling Huang, Shili Yan

InAs/GaSb double quantum wells (QWs) separated by a 100 \AA\ AlSb middle barrier are grown by molecular beam epitaxy. We report a nanofabrication technique that utilizes the surface Fermi level pinning position in InAs $[E_f^s(\rm InAs)]$ for realizing independent electric contacts to each well. In particular, separate ohmic contacts to the upper InAs quantu

J. Garza, Steven Swanson

Electronics prototyping using breakout boards allows designers with and without an engineering background to rapidly create interactive prototypes. However, when it comes to transition to a production-ready PCB design, stagnation exists due to the high skill floor required for PCB design. While PCB design automation has been used successfully in recent resea

Joseph Newton

We classify all quotients $W/W_J$ up to isomorphism in Bruhat order, with $(W,S)$ a Coxeter system and $W_J$ a parabolic subgroup of $W$. In particular, the non-trivial isomorphisms fall into a small number of cases which are highly restricted; all have $W$ finite and $W_J$ a maximal parabolic. This has the immediate application of classifying dominant and a

A. R. Gogate, M. A. W. Verheijen, J. M. van der Hulst, Y. L. Jaffé

We present HI-based B- and R-band Tully-Fisher relations (TFRs) and the Baryonic TFR (BTFR) at z=0.2 using direct HI detections from the Blind Ultra-Deep HI Environmental Survey (BUDHIES). Deep photometry from the Isaac Newton Telescope was used for 36 out of 166 HI sources, matching the quality criteria required for a robust TFR analysis. Two velocity defin

Zetao Fei, Hai Zhang

We consider the problem of reconstructing one-dimensional point sources from their Fourier measurements in a bounded interval $[-\Omega, \Omega]$. This problem is known to be challenging in the regime where the spacing of the sources is below the Rayleigh length $\frac{\pi}{\Omega}$. In this paper, we propose a super-resolution algorithm, called Iterative Fo

Zhihe Zhang, Bin Yue, Yidong Xu, Yin-Zhe Ma

The presence of an extra radio background besides the cosmic microwave background has important implications for the observation of the 21-cm signal during the cosmic Dark Ages, Cosmic Dawn, and epoch of Reionization. The strong absorption trough found in the 21-cm global spectrum measured by the EDGES experiment, which has a much greater depth than the stan

Gangwei Xu, Xianqi Wang, Xiaohuan Ding, Xin Yang

Recurrent All-Pairs Field Transforms (RAFT) has shown great potentials in matching tasks. However, all-pairs correlations lack non-local geometry knowledge and have difficulties tackling local ambiguities in ill-posed regions. In this paper, we propose Iterative Geometry Encoding Volume (IGEV-Stereo), a new deep network architecture for stereo matching. The

Cong Lu, Philip J. Ball, Yee Whye Teh, Jack Parker-Holder

A key theme in the past decade has been that when large neural networks and large datasets combine they can produce remarkable results. In deep reinforcement learning (RL), this paradigm is commonly made possible through experience replay, whereby a dataset of past experiences is used to train a policy or value function. However, unlike in supervised or self

Shōta Inoue

In this note, we prove Selberg's announced result on $r$-gaps between zeros of the Riemann zeta-function $\zeta$. Our proof uses a result on variations of $\arg\zeta$ by Tsang based on Selberg's method. The same result with explicit constants under the Riemann Hypothesis has been obtained by Conrey and Turnage-Butterbaugh using a different method. We explain

Yu. M. Bilinsky, P. I. Arseyev, N. S. Maslova

We investigate the role of gap states in processes of perturbation transmission along finite superconducting Kitaev chain. We look at this problem on the general ground and use the formalism of non-stationary Greens functions, which contain full information about the non-equilibrium and non stationary properties of the system. We discuss tunneling current an

Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu

Historical user-item interaction datasets are essential in training modern recommender systems for predicting user preferences. However, the arbitrary user behaviors in most recommendation scenarios lead to a large volume of noisy data instances being recorded, which cannot fully represent their true interests. While a large number of denoising studies are e

Prem Prakash Pandey

It is conjectured that all separable polynomials with integers coefficients, satisfying some local conditions, take infinitely many square free values on integer arguments. But not a single polynomial of degree greater than $3$ is proven to exhibit this property. In this article, we propose a method to show that ``cyclotomic polynomials $\Phi_{\ell}(X)$ take

Paul Bastide, Linda Cook, Jeff Erickson, Carla Groenland

We introduce a new model of indeterminacy in graphs: instead of specifying all the edges of the graph, the input contains all triples of vertices that form a connected subgraph. In general, different (labelled) graphs may have the same set of connected triples, making unique reconstruction of the original graph from the triples impossible. We identify some f

Ajinkya Jayawant, Antonio Ortega

In numerous graph signal processing applications, data is often missing for a variety of reasons, and predicting the missing data is essential. In this paper, we consider data on graphs modeled as bandlimited graph signals. Predicting or reconstructing the unknown signal values for such a model requires an estimate of the signal bandwidth. In this paper, we

Van-Vi Vo, Duc-Tai Le, Hyunseung Choo

Data aggregation is a fundamental technique in wireless sensor networks (WSNs) in which sensory data collected by intermediate nodes is merged by in-network computation using maximum, average, or sum functions. Because sensors run on batteries, energy conservation is a critical issue. Duty cycle is a well-known energy-saving mechanism in WSNs, but it causes

Baruch Meerson, Arkady Vilenkin

We study large deviations of the one-point height distribution, $\mathcal{P}(H,T)$, of a stochastic interface, governed by the Golubovi\'{c}-Bruinsma equation $$ \partial_{t}h=-\nu\partial_{x}^{4}h+\frac{\lambda}{2}\left(\partial_{x}h\right)^{2}+\sqrt{D}\,\xi(x,t)\,, $$ where $h(x,t)$ is the interface height at point $x$ and time $t$, and $\xi(x,t)$ is the G

Tengtao Song, Nuo Chen, Ji Jiang, Zhihong Zhu

Multi-turn response selection is a challenging task due to its high demands on efficient extraction of the matching features from abundant information provided by context utterances. Since incorporating syntactic information like dependency structures into neural models can promote a better understanding of the sentences, such a method has been widely used i

X. N. Feng, D. He, L. F. Wei

Entanglement is an important quantum resource to achieve high sensitive quantum metrology. However, the rapid decoherence of quantum entangled states, due to the unavoidable environment noise, result in practically the unwanted sharp drop of the measurement sensitivity. To overcome such a difficulty, here we propose a spin-oscillator hybrid quantum interfero

Silvia Bartolucci, Fabio Caccioli, Francesco Caravelli, Pierpaolo Vivo

This paper is concerned with upstreamness and downstreamness of industries and countries. Upstreamness and downstreamness measure respectively the average distance of an industrial sector from final consumption and from primary inputs. Recently, Antr\`as and Chor reported a puzzling and counter-intuitive finding in data from the period 1995-2011, namely that

Soroush Sadeghnejad, Farshad Khadivar, Mojtaba Esfandiari, Golchehr Amirkhani

Haptic training simulators generally consist of three major components, namely a human operator, a haptic interface, and a virtual environment. Appropriate dynamic modeling of each of these components can have far-reaching implications for the whole system's performance improvement in terms of transparency, the analogy to the real environment, and stability.

Siquan Huang, Yijiang Li, Chong Chen, Leyu Shi

The decentralized and privacy-preserving nature of federated learning (FL) makes it vulnerable to backdoor attacks aiming to manipulate the behavior of the resulting model on specific adversary-chosen inputs. However, most existing defenses based on statistical differences take effect only against specific attacks, especially when the malicious gradients are

Antonios Argyriou

Passive emitter tracking (PET) algorithms can estimate both the range and Doppler of a wireless emitter when it uses orthogonal frequency division multiplexing (OFDM). In this paper we are interested to prevent this from happening by an unauthorized receiver (URx). To accomplish that we introduce in the transmitted signal a \textit{spoofing signal} that vari

Tianyun Tang, Kim-Chuan Toh

In this paper, we consider an SDP relaxation of the quadratic knapsack problem (QKP). After using the Burer-Monteiro factorization, we get a non-convex optimization problem, whose feasible region is an algebraic variety. Although there might be non-regular points on the algebraic variety, we prove that the algebraic variety is a smooth manifold except for a

Tzvetelina A. Dimitrova, Nathaniel R. Butler, Srihari Ravi

Starting with models for the compact object merger event rate, the short-duration Gamma-ray Burst (sGRB) luminosity function, and the Swift/BAT detector, we calculate the observed Swift sGRB rate and its uncertainty. Our probabilistic sGRB world model reproduces the observed number distributions in redshift and flux for 123 Swift/BAT detected sGRBs and can b

Mousa Rezaee, Saeed Lotfan, Vahid A. Maleki

In this article, the model presented by Shen and Pierre to investigate the transverse vibration behavior of a simply supported beam has been revised. This is done by applying more realistic assumptions. The crack is modeled as a continuous disturbance and the disturbance function is provided based on fracture mechanics. Next, the natural frequencies correspo

Weizhi Li, Haotai Liang, Chen Dong, Xiaodong Xu

Semantic communication serves as a novel paradigm and attracts the broad interest of researchers. One critical aspect of it is the multi-user semantic communication theory, which can further promote its application to the practical network environment. While most existing works focused on the design of end-to-end single-user semantic transmission, a novel no

Jiayang Ao, Qiuhong Ke, Krista A. Ehinger

Images of realistic scenes often contain intra-class objects that are heavily occluded from each other, making the amodal perception task that requires parsing the occluded parts of the objects challenging. Although important for downstream tasks such as robotic grasping systems, the lack of large-scale amodal datasets with detailed annotations makes it diff

Jiajin Li, Jianheng Tang, Lemin Kong, Huikang Liu

In this work, we present the Bregman Alternating Projected Gradient (BAPG) method, a single-loop algorithm that offers an approximate solution to the Gromov-Wasserstein (GW) distance. We introduce a novel relaxation technique that balances accuracy and computational efficiency, albeit with some compromises in the feasibility of the coupling map. Our analysis

Deyao Zhu, Jun Chen, Kilichbek Haydarov, Xiaoqian Shen

Asking insightful questions is crucial for acquiring knowledge and expanding our understanding of the world. However, the importance of questioning has been largely overlooked in AI research, where models have been primarily developed to answer questions. With the recent advancements of large language models (LLMs) like ChatGPT, we discover their capability

Jianduo Chai, Shaoming He, Hyo-Sang Shin

This paper investigates the problem of traffic surveillance using an unmanned aerial vehicle (UAV) and proposes a domain-knowledge-aided airborne ground moving targets tracking algorithm. To improve the accuracy of multiple targets tracking, the proposed algorithm incorporates domain knowledge into the joint probabilistic data association (JPDA) filter as st

Gideon Yoffe, Roy van Boekel, Aigen Li, L. B. F. M Waters

We use the long-slit spectroscopy mode of the VISIR-NEAR experiment to perform diffraction-limited observations of eight nearby Herbig Ae protoplanetary disks. We extract spectra for various locations along the slit with a spectral resolution of R = 300 and perform a compositional fit at each spatial location using spectral templates of silicates and the fou

Ludan Ruan, Anwen Hu, Yuqing Song, Liang Zhang

Multimodal processing has attracted much attention lately especially with the success of pre-training. However, the exploration has mainly focused on vision-language pre-training, as introducing more modalities can greatly complicate model design and optimization. In this paper, we extend the stateof-the-art Vision-Language model CLIP to accommodate the audi

STAR Collaboration, M. I. Abdulhamid, B. E. Aboona, J. Adam

We report a new measurement of the production of electrons from open heavy-flavor hadron decays (HFEs) at mid-rapidity ($|y|<$ 0.7) in Au+Au collisions at $\sqrt{s_{\rm NN}}=200$ GeV. Invariant yields of HFEs are measured for the transverse momentum range of $3.5 < p_{\rm T} < 9$ GeV/$c$ in various configurations of the collision geometry. The HFE yields in

Yifan Wang, Wei Cai

Cross slip of screw dislocations in crystalline solids is a stress-driven thermally activated process essential to many phenomena during plastic deformation, including dislocation pattern formation, strain hardening, and dynamic recovery. Molecular dynamics (MD) simulation has played an important role in determining the microscopic mechanisms of cross slip.

M. H. Maqbool, Umar Farooq, Adib Mosharrof, A. B. Siddique

Recommender systems have become ubiquitous in our digital lives, from recommending products on e-commerce websites to suggesting movies and music on streaming platforms. Existing recommendation datasets, such as Amazon Product Reviews and MovieLens, greatly facilitated the research and development of recommender systems in their respective domains. While the

Can He, Lingxiao Meng, Zhirui Sun, Jiankun Wang

Autonomous fabric manipulation is a challenging task due to complex dynamics and potential self-occlusion during fabric handling. An intuitive method of fabric folding manipulation first involves obtaining a smooth and unfolded fabric configuration before the folding process begins. However, the combination of quasi-static actions such as pick & place and dy

Moghis Fereidouni, Adib Mosharrof, Umar Farooq, AB Siddique

Mobile app stores produce a tremendous amount of data in the form of user reviews, which is a huge source of user requirements and sentiments; such reviews allow app developers to proactively address issues in their apps. However, only a small number of reviews capture common issues and sentiments which creates a need for automatically identifying prominent

Mohamed Nabih Ali, Alessio Brutti, Daniele Falavigna

Intent classification is a fundamental task in the spoken language understanding field that has recently gained the attention of the scientific community, mainly because of the feasibility of approaching it with end-to-end neural models. In this way, avoiding using intermediate steps, i.e. automatic speech recognition, is possible, thus the propagation of er

Sullivan F. MacDonald

In the process of proving a sharpened form of G\r{a}rding's inequality, Fefferman & Phong demonstrated that every non-negative function $f\in C^{3,1}(\mathbb{R}^n)$ can be written as a finite sum of squares of functions in $C^{1,1}(\mathbb{R}^n)$. In this thesis, we generalize their decomposition result to show that if $f\in C^{k,\alpha}(\mathbb{R}^n)$ is no

H. T. Cui, Y. A. Yan, M. Qin, X. X. Yi

The discretization approximation method commonly used to simulate the dynamics of quantum system coupled to the environment in continuum often suffers from the periodically partial recovery of initial state because of the effect of finite dimension, dubbed the recurrence. To address this issue, we proposes a generalization of the discretization approximation

Haijian Chen, Wendong Zhang, Yunbo Wang, Xiaokang Yang

Masked image modeling is a promising self-supervised learning method for visual data. It is typically built upon image patches with random masks, which largely ignores the variation of information density between them. The question is: Is there a better masking strategy than random sampling and how can we learn it? We empirically study this problem and initi

Keyvan Majd, Geoffrey Clark, Tanmay Khandait, Siyu Zhou

Guaranteeing safety in human-centric applications is critical in robot learning as the learned policies may demonstrate unsafe behaviors in formerly unseen scenarios. We present a framework to locally repair an erroneous policy network to satisfy a set of formal safety constraints using Mixed Integer Quadratic Programming (MIQP). Our MIQP formulation explici

Neal Livesay, Daniel S. Sage, Bach Nguyen

The Deligne--Simpson problem is an existence problem for connections with specified local behavior. Almost all previous work on this problem has restricted attention to connections with regular or unramified singularities. Recently, the authors, together with Kulkarni and Matherne, formulated a version of the Deligne--Simpson problem where certain ramified s

Huahui Yi, Ziyuan Qin, Qicheng Lao, Wei Xu

Inevitable domain and task discrepancies in real-world scenarios can impair the generalization performance of the pre-trained deep models for medical data. Therefore, we audaciously propose that we should build a general-purpose medical AI system that can be seamlessly adapted to downstream domains/tasks. Since the domain/task adaption procedures usually inv

Taisuke Hosaka, Norio Konno

The Parrondo game, devised by Parrondo, means that winning strategy is constructed a combination of losing strategy. This situation is called the Parrondo paradox. The Parrondo game based on quantum walk and the search algorithm via quantum walk have been widely studied, respectively. This paper newly presents a Parrondo game of quantum search based on quant

Ya-Guang Wang, Meng Zhao

We study the asymtotic behavior of solutions to the two-dimensional stochasitc Navier-Stokes (SNS) equation in the small viscosity limit. The SNS equation is supplemented with no-slip boundary condition, in which a strong boundary layer shall appear in the limit due to the mismatch of the boundary conditions of the SNS equation and the corresponding limit pr

Siyu Tang, Liang Zheng, Xiaoming Zhang, Renzhuo Wan

The elliptic azimuthal anisotropy coefficient ($v_{2}$) of the identified particles at midrapidity ($|\eta|<0.8$) was investigated in p--Pb collisions at $\sqrt{s_\mathrm{NN}}=$ 5.02 TeV using a multi-phase transport model (AMPT). The calculations of differential $v_{2}$ based on the advanced flow extraction method of light flavor hadrons (pions, kaons, prot

E. Grohs, A. B. Balantekin

The mechanism for generating neutrino masses remains a puzzle in particle physics. If neutrino masses follow from a Dirac mass term, then neutrino states exist with opposite chirality compared to their weakly-interacting counterparts. These inactive states do not interact with their active counterparts at measurable scales in the standard model. However, the

Hui Li, Xuyang Yao, Wuyuan Xie, Miaohui Wang

Deep high dynamic range (HDR) imaging as an image translation issue has achieved great performance without explicit optical flow alignment. However, challenges remain over content association ambiguities especially caused by saturation and large-scale movements. To address the ghosting issue and enhance the details in saturated regions, we propose a scale-aw

Yifan Li, Kun Zhou, Wayne Xin Zhao, Ji-Rong Wen

Non-autoregressive (NAR) text generation has attracted much attention in the field of natural language processing, which greatly reduces the inference latency but has to sacrifice the generation accuracy. Recently, diffusion models, a class of latent variable generative models, have been introduced into NAR text generation, showing an improved text generatio

Kelong Mao, Zhicheng Dou, Fengran Mo, Jiewen Hou

Precisely understanding users' contextual search intent has been an important challenge for conversational search. As conversational search sessions are much more diverse and long-tailed, existing methods trained on limited data still show unsatisfactory effectiveness and robustness to handle real conversational search scenarios. Recently, large language mod

Minting Pan, Wendong Zhang, Geng Chen, Xiangming Zhu

Learning physical dynamics in a series of non-stationary environments is a challenging but essential task for model-based reinforcement learning (MBRL) with visual inputs. It requires the agent to consistently adapt to novel tasks without forgetting previous knowledge. In this paper, we present a new continual learning approach for visual dynamics modeling a

Juncheng Li, Minghe Gao, Longhui Wei, Siliang Tang

Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-training models to adapt to downstream tasks in a parameter -- and data -- efficient way, by learning the ``soft prompts'' to condition frozen pre-training models. Though effective, it is particularly problematic in the few-shot scenario, where prompt tuning performance is

Hyeonhak Kim, Seokhie Hong

In previous research, quantum resources were concretely estimated for solving Elliptic Curve Discrete Logarithm Problem(ECDLP). In [1], the quantum algorithm was optimized for the binary elliptic curves and the main optimization target was the number of the logical qubits. The division algorithm was mainly optimized in [1] since every ancillary qubit is used

Milad Pooladsanj, Ketan Savla, Petros A. Ioannou

Ramp metering is one of the most effective tools to combat traffic congestion. In this paper, we present a ramp metering policy for a network of freeways with arbitrary number of on- and off-ramps, merge, and diverge junctions. The proposed policy is designed at the microscopic level and takes into account vehicle following safety constraints. In addition, e

Swapnil Kumar, Sundar V Atre

Design calculation and analysis have been performed for the brake disc along with the design calculations for the brake caliper for the system optimization, and design of experiments have been implemented for the brake disc in order to optimize the performance of the braking system. Ventilated disc brake with an outer diameter of 175 mm has been used for the

Hao Chen, Zhe-Ming Lu, Jie Liu

This paper focuses on proposing a deep learning-based monkey swing counting algorithm. Nowadays, there are very few papers on monkey detection, and even fewer papers on monkey swing counting. This research focuses on this gap and attempts to count the number of monkeys swinging their heads by deep learning. This paper further extends the traditional target d

Ross Cutler, Ando Saabas, Babak Naderi, Nicolae-Cătălin Ristea

The ICASSP 2023 Speech Signal Improvement Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems. The speech signal quality can be measured with SIG in ITU-T P.835 and is still a top issue in audio communication and conferencing systems. For example, in the ICASSP 2022 Deep Noise Suppression ch

Miao Li, Jianzhong Qi, Jey Han Lau

Multi-document summarization (MDS) aims to generate a summary for a number of related documents. We propose HGSUM, an MDS model that extends an encoder-decoder architecture, to incorporate a heterogeneous graph to represent different semantic units (e.g., words and sentences) of the documents. This contrasts with existing MDS models which do not consider dif

Aiwei Liu, Xuming Hu, Lijie Wen, Philip S. Yu

This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability. Given the recent emergence of large-scale conversational language model ChatGPT and its impressive capabilities in both conversational abilities and code generation, we sought to evaluate its Text-to-SQL performance. We conducted experiments on 12 benchmark datasets with d

Kyle Greenberg, Parag A. Pathak, Tayfun Sönmez

We present the proof-of-concept for minimalist market design (S\"{o}nmez, 2023) as an effective methodology to enhance an institution based on the desiderata of stakeholders with minimal interference. Four objectives-respecting merit, increasing retention, aligning talent, and enhancing trust-guided reforms to US Army's centralized branching process of cadet

Craig Hogan, Ohkyung Kwon, Nathaniel Selub

Scaling arguments are used to constrain the angular spectrum of distortions on boundaries of macroscopic causal diamonds, produced by Planck-scale vacuum fluctuations of causally-coherent quantum gravity. The small-angle spectrum of displacement is derived from a form of scale invariance: the variance and fluctuation rate of distortions normal to the surface

Xiaojun Guo, Yifei Wang, Tianqi Du, Yisen Wang

Oversmoothing is a common phenomenon in a wide range of Graph Neural Networks (GNNs) and Transformers, where performance worsens as the number of layers increases. Instead of characterizing oversmoothing from the view of complete collapse in which representations converge to a single point, we dive into a more general perspective of dimensional collapse in w

Zhengan Chen, Yuqing Li, Tao Luo, Zhangchen Zhou

The phenomenon of distinct behaviors exhibited by neural networks under varying scales of initialization remains an enigma in deep learning research. In this paper, based on the earlier work by Luo et al.~\cite{luo2021phase}, we present a phase diagram of initial condensation for two-layer neural networks. Condensation is a phenomenon wherein the weight vect

Minghao Chen, Yingchun Zhou

Causal mediation analysis is increasingly abundant in biology, psychology, and epidemiology studies, etc. In particular, with the advent of the big data era, the issue of high-dimensional mediators is becoming more prevalent. In neuroscience, with the widespread application of magnetic resonance technology in the field of brain imaging, studies on image bein

William Alvarez-Giron, Pablo Solano, Kanu Sinha, Pablo Barberis-Blostein

We investigate the collective non-Markovian dynamics of two fully excited two-level atoms coupled to a one-dimensional waveguide in the presence of delay. We demonstrate that analogous to the well-known superfluorescence phenomena, where an inverted atomic ensemble synchronizes to enhance its emission, there is a `subfluorescence' effect that synchronizes th

Siran Li

We show that the Gaussian kernel $\exp\left\{-\lambda d_g^2(\bullet, \bullet)\right\}$ on any non-simply-connected closed Riemannian manifold $(\mathcal{M},g)$, where $d_g$ is the geodesic distance, is not positive definite for any $\lambda > 0$, combining analyses in the recent preprint~[9] by Da Costa--Mostajeran--Ortega and classical comparison theorems i

Yuran Sun, Shih-Kai Huang, Xilei Zhao

The aggravating effects of climate change and the growing population in hurricane-prone areas escalate the challenges in large-scale hurricane evacuations. While hurricane preparedness and response strategies vastly rely on the accuracy and timeliness of the predicted households' evacuation decisions, current studies featuring psychological-driven linear mod

Jun Wang, Klaus Mueller

Current work on using visual analytics to determine causal relations among variables has mostly been based on the concept of counterfactuals. As such the derived static causal networks do not take into account the effect of time as an indicator. However, knowing the time delay of a causal relation can be crucial as it instructs how and when actions should be

Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li

This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning diffusion models for marginal, conditional, and joint distributions can be unified as predicting the noise in the perturbed data, where the perturbation levels (i.e. timesteps) can

Mingfeng Liu, Xiangyang Liu, Jiangxu Li, Jiaxi Liu

Recently, near-ambient superconductivity has been experimentally evidenced in a nitrogen-doped lutetium hydride by Dasenbrock-Gammon \emph{et al.} [Nature 615, 244 (2023)], which yields a remarkable maximum $T_c$ of 294 K at just 10 kbar. However, due to the difficulty of x-ray diffraction (XRD) in identifying light elements such as hydrogen and nitrogen, th

Dawei Shen, Yaru Wu

The magnitude for algebras is a generalization of the Euler characteristic. We investigate the magnitude for Nakayama algebras. Using Ringel's resolution quiver, the existence and the value of rational magnitude is given. As a result, we show that the finite global dimension criteria for Nakayama algebras of Madsen and the first author are equivalent.

Michael Rotman, Lior Wolf

We consider a Multi-Armed Bandit problem in which the rewards are non-stationary and are dependent on past actions and potentially on past contexts. At the heart of our method, we employ a recurrent neural network, which models these sequences. In order to balance between exploration and exploitation, we present an energy minimization term that prevents the

Zhirui Sun, Weinan Chen, Jiankun Wang, Max Q. -H. Meng

This article addresses the localization problem in robotic autonomous luggage trolley collection at airports and provides a systematic evaluation of different methods to solve it. The robotic autonomous luggage trolley collection is a complex system that involves object detection, localization, motion planning and control, manipulation, etc. Among these comp

Qian Li, Yunguan Fu, Qianye Yang, Zhijiang Du

Graph neural networks (GNNs) have been proposed for medical image segmentation, by predicting anatomical structures represented by graphs of vertices and edges. One such type of graph is predefined with fixed size and connectivity to represent a reference of anatomical regions of interest, thus known as templates. This work explores the potentials in these G