March 2023 arXiv papers — page 101

V. D. Burkert, L. Elouadrhiri, F. X. Girod, C. Lorce

The physics of the gravitational form factors of the proton, and their understanding within quantum chromodynamics, has advanced significantly in the past two decades through both theory and experiment. This Colloquium provides an overview of this progress, highlights the physical insights unveiled by studies of gravitational form factors, and reviews their

Yuhan Quan, Jingtao Ding, Chen Gao, Lingling Yi

Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations. However, in terms of both effectiveness and efficiency of recommendation, a large portion of social relations can be redundant or even noisy, e.g., it is quite nor

Yulin Pan, Xiangteng He, Biao Gong, Yiliang Lv

Video temporal grounding aims to pinpoint a video segment that matches the query description. Despite the recent advance in short-form videos (\textit{e.g.}, in minutes), temporal grounding in long videos (\textit{e.g.}, in hours) is still at its early stage. To address this challenge, a common practice is to employ a sliding window, yet can be inefficient a

Dan Qiu, Hao Tian, Jing Li, Chao Liu

A catalog of more than 43,000 M giant stars has been selected by Li et al. from the ninth data release of LAMOST. Using the data-driven method SLAM, we obtain the stellar parameters (Teff, logg, [M/H], [$\alpha$/M]) for all the M giant stars with uncertainties of 57 K, 0.25 dex, 0.16 dex and 0.06 dex at SNR > 100, respectively. With those stellar parameters,

Steven M. Hernandez, Ding Zhao, Shaojin Ding, Antoine Bruguier

Continued improvements in machine learning techniques offer exciting new opportunities through the use of larger models and larger training datasets. However, there is a growing need to offer these new capabilities on-board low-powered devices such as smartphones, wearables and other embedded environments where only low memory is available. Towards this, we

Eugene Ivanov, Michael Tobar

We studied how the cryogenic sapphire resonator responds to fast variations of the dissipated microwave power. The experiments were carried out with sapphire resonators cooled to 6 K at frequencies around 11 GHz. We found that the power-to-frequency conversion of the resonator depends on Fourier frequency as the transfer function of the 1st-order low-pass fi

Kenneth Ooi, Karn N. Watcharasupat, Bhan Lam, Zhen-Ting Ong

Autonomous soundscape augmentation systems typically use trained models to pick optimal maskers to effect a desired perceptual change. While acoustic information is paramount to such systems, contextual information, including participant demographics and the visual environment, also influences acoustic perception. Hence, we propose modular modifications to a

Bao-Ming Xu, Chao-Quan Wang

Quantum coherence will undoubtedly play a fundamental role in understanding of the dynamics of quantum many-body systems, thereby to reveal its genuine contribution is of great importance. In this paper, we specialize our discussions to the one-dimensional transverse field quantum Ising model initialized in the coherent Gibbs state. After quenching the stren

Xiaoyu Shi, Zhaoyang Huang, Weikang Bian, Dasong Li

We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to previous methods that learn to estimate optical flow from two frames, VideoFlow concurrently estimates bi-directional optical flows for multiple frames that are available in videos by sufficiently exploiting temporal cues. We first propose a TRi-frame Optical Flow (T

Angus Southwell, Nicholas Wormald

We study a random graph $G$ with given degree sequence $\boldsymbol{d}$, with the aim of characterising the degree sequence of the subgraph induced on a given set $S$ of vertices. For suitable $\boldsymbol{d}$ and $S$, we show that the degree sequence of the subgraph induced on $S$ is essentially concentrated around a sequence that we can deterministically d

Moeen Mostafavi, Michael D. Porter

This paper focuses on the EmoWoz dataset, an extension of MultiWOZ that provides emotion labels for the dialogues. MultiWOZ was partitioned initially for another purpose, resulting in a distributional shift when considering the new purpose of emotion recognition. The emotion tags in EmoWoz are highly imbalanced and unevenly distributed across the partitions,

Till Hoffmann

Large-scale network data can pose computational challenges, be expensive to acquire, and compromise the privacy of individuals in social networks. We show that the locations and scales of latent space cluster models can be inferred from the number of connections between groups alone. We demonstrate this modelling approach using synthetic data and apply it to

Zakariae Jorti, Qi Tang, Konstantin Lipnikov, Xian-Zhu Tang

Force-free plasmas are a good approximation where the plasma pressure is tiny compared with the magnetic pressure, which is the case during the cold vertical displacement event (VDE) of a major disruption in a tokamak. On time scales long compared with the transit time of Alfven waves, the evolution of a force-free plasma is most efficiently described by the

Tongyu Zong, Yixiang Mao, Chen Li, Yong Liu

Many XR applications require the delivery of volumetric video to users with six degrees of freedom (6-DoF) movements. Point Cloud has become a popular volumetric video format. A dense point cloud consumes much higher bandwidth than a 2D/360 degree video frame. User Field of View (FoV) is more dynamic with 6-DoF movement than 3-DoF movement. To save bandwidth

Qianqian Xie, Jiayu Zhou, Yifan Peng, Fei Wang

Automatic radiology report summarization is a crucial clinical task, whose key challenge is to maintain factual accuracy between produced summaries and ground truth radiology findings. Existing research adopts reinforcement learning to directly optimize factual consistency metrics such as CheXBert or RadGraph score. However, their decoding method using greed

Tomoaki Okayama, Shota Ogawa

The Sinc approximation applied to double-exponentially decaying functions is referred to as the DE-Sinc approximation. Because of its high efficiency, this method has been used in various applications. In the Sinc approximation, the mesh size and truncation numbers should be optimally selected to achieve its best performance. However, the standard selection

Jiayu Zou, Zheng Zhu, Yun Ye, Xingang Wang

BEV perception is of great importance in the field of autonomous driving, serving as the cornerstone of planning, controlling, and motion prediction. The quality of the BEV feature highly affects the performance of BEV perception. However, taking the noises in camera parameters and LiDAR scans into consideration, we usually obtain BEV representation with har

W. Horiuchi, T. Sato, Y. Uesaka, K. Yoshida

We present complete formulas of the allowed and first-forbidden transitions of the nuclear beta decay taking into account the recoil-order and induced currents up to the next-to-leading order (NLO). The longitudinal part of the vector current is cleared away by the use of the conservation of the vector current for the multipole operators of the natural-parit

Gen Li, Jie Ji, Minghai Qin, Wei Niu

As deep convolutional neural networks (DNNs) are widely used in various fields of computer vision, leveraging the overfitting ability of the DNN to achieve video resolution upscaling has become a new trend in the modern video delivery system. By dividing videos into chunks and overfitting each chunk with a super-resolution model, the server encodes videos be

John Chae

This is a companion paper to earlier work of the author, which generalizes to an infinite family of $(2,2w+1)$-cabling of the figure eight knot ($|w|>3$) and proposes general formulas for the two-variable series invariant of the family of the cable knots. The formulas provide an insight into the cabling operation. We verify the conjecture through explicit ex

Suhee Jo, Younggun Lee, Yookyung Shin, Yeongtae Hwang

In recent years, emotional text-to-speech has shown considerable progress. However, it requires a large amount of labeled data, which is not easily accessible. Even if it is possible to acquire an emotional speech dataset, there is still a limitation in controlling emotion intensity. In this work, we propose a novel method for cross-speaker emotion transfer

Aurélien Stcherbinine, Christopher S. Edwards, Michael D. Smith, Michael J. Wolff

Condensation and sublimation of ices at the surface of the planet is a key part of both the Martian H$_2$O and CO$_2$ cycles, either from a seasonal or diurnal aspect. While most of the ice is located within the polar caps, surface frost is known to be formed during nighttime down to equatorial latitudes. Here, we use data from the Emirates Mars Infrared Spe

Yuanbo Li, Yiqin Wang, Yi Chen, Ziming Yu

Due to large reflection and diffraction losses in the THz band, it is arguable to achieve reliable links in the none-line-of-sight (NLoS) cases. Intelligent reflecting surfaces, although are expected to solve the blockage problem and enhance the system connectivity, suffer from fabrication difficulty and operation complexity. In this work, non-intelligent re

Kangqiao Liu, Masaya Nakagawa, Masahito Ueda

While most of the existing quantum information engines assisted by Maxwell's demon harness thermal fluctuations, those that rectify only quantum fluctuations have recently been constructed. We propose an alternative type of quantum information engine that harnesses only quantum fluctuations to achieve cumulative energy storage and unidirectional transport of

Zikang Xu, Shang Zhao, Quan Quan, Qingsong Yao

Deep learning is becoming increasingly ubiquitous in medical research and applications while involving sensitive information and even critical diagnosis decisions. Researchers observe a significant performance disparity among subgroups with different demographic attributes, which is called model unfairness, and put lots of effort into carefully designing ele

Ling-Fang Lin, Yang Zhang, Gonzalo Alvarez, Michael A. McGuire

Recently, it was argued that a ferromagnetic (FM) insulating phase can be induced by a novel {\it interorbital} hopping mechanism. Here, we study the stability range of this novel FM phase under modifications in the crystal fields and electronic correlation strength, constructing a theoretical phase diagram. A plethora of states is unveiled, including the FM

Seyyed A. Fatemi, June Zhang

Graph dynamical systems (GDS) model dynamic processes on a (static) graph. Stochastic GDS has been used for network-based epidemics models such as the contact process and the reversible contact process. In this paper, we consider stochastic GDS that are also continuous-time Markov processes (CTMP), whose transition rates are linear functions of some dynamics

Bing Luo, Xiaomin Ouyang, Peng Sun, Pengchao Han

With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the evolving 6G network will adopt advanced artificial intelligence (AI) technologies to collect, transmit, and learn this valuable data for inn

Adrian Reich, Erez Berg, Jörg Schmalian, Alexander Shnirman

We study a long topological Josephson junction with a ferromagnetic strip between two superconductors. The low-energy theory exhibits a non-local in time and space interaction between chiral Majorana fermions, mediated by the magnonic excitations in the ferromagnet. While short ranged interactions turn out to be irrelevant by power counting, we show that suf

Zhengxiong Luo, Dayou Chen, Yingya Zhang, Yan Huang

A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution. Despite its recent success in image synthesis, applying DPMs to video generation is still challenging due to high-dim

Yiming Cui, Linjie Yang

Video object detection needs to solve feature degradation situations that rarely happen in the image domain. One solution is to use the temporal information and fuse the features from the neighboring frames. With Transformerbased object detectors getting a better performance on the image domain tasks, recent works began to extend those methods to video objec

Xiao Wang, Tian Gan, Yinwei Wei, Jianlong Wu

The last decade has witnessed the proliferation of micro-videos on various user-generated content platforms. According to our statistics, around 85.7\% of micro-videos lack annotation. In this paper, we focus on annotating micro-videos with tags. Existing methods mostly focus on analyzing video content, neglecting users' social influence and tag relation. Me

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Based on $(448.1 \pm 2.9 )\times 10^6$ $\psi(3686)$ events collected with the BESIII detector operating at the BEPCII collider, the decay $\psi(3686)\rightarrow \phi K_{S}^{0} K_{S}^{0}$ is observed for the first time. Taking the interference between $\psi(3686)$ decay and continuum production into account, the branching fraction of this decay is measured to

Chenhang He, Ruihuang Li, Yabin Zhang, Shuai Li

Point cloud sequences are commonly used to accurately detect 3D objects in applications such as autonomous driving. Current top-performing multi-frame detectors mostly follow a Detect-and-Fuse framework, which extracts features from each frame of the sequence and fuses them to detect the objects in the current frame. However, this inevitably leads to redunda

Chuan-Sheng Chen, Ren Wang, Jin-Pin Liu, Bing-Zhong Wang

Existing electromagnetic inverse design methods are often established in the spacial domain. This communication presents an inverse design method, which can design aperture field for the desired phaseless radiation pattern, from the spectral domain perspective. In addition, it naturally adapts to the polarization constraint. Specifically, the inverse design

Minhyeok Lee, Suhwan Cho, Dogyoon Lee, Chaewon Park

Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However, the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue, we propose a guided slot attention network to reinforce spatial structural information and obtain better foreground--background s

Haoran Wang, Jun Zhang, Hekun Li, Cong Liu

Weak lensing provides a direct way of mapping the density distribution in the universe. To reconstruct the density field from the shear catalog, an important step is to build the shear field from the shear catalog, which can be quite nontrivial due to the inhomogeneity of the background galaxy distribution and the shape noise. We propose the PDF-Folding meth

Xinliang Zhang, Mojtaba Vaezi, Lizhong Zheng

A deep autoencoder (DAE)-based end-to-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The design is for imperfect channel state information (CSI) where both estimation and quantization errors exist. The proposed structure jointly optimizes the encoders and decoders to generate interferen

Ruth M. Birch, James O. Douglas, T. Ben Britton

Zircaloy-4 is used as a fuel cladding material for water reactors, as it has good mechanical properties, corrosion resistance, and a low thermal neutron absorption cross section. However, the mechanical performance of Zircaloy-4 can be reduced during service due to hydrogen uptake and hydride formation. These hydrides are brittle, and often reduce the streng

Kimi Wenzel, Geoff Kaufman

We take a critical lens toward the pursuit of racially inclusive language technologies and identify several areas necessitating future work. We discuss the potential harms of conversational technologies, outline three challenges that arise in inclusive design, and lastly, argue that conversational user interface designers and researchers should go beyond rac

Xi Yang, Shui-Jing Tang, Jia-Wei Meng, Pei-Ji Zhang

Liquid-crystal microcavity lasers have attracted considerable attention because of their extraordinary tunability and sensitive response to external stimuli, and they operate generally within a specific phase. Here, we demonstrate a liquid-crystal microcavity laser operated in the phase transition, in which the reorientation of liquid-crystal molecules occur

Steven Sagona-Stophel

We propose a set of simple quantum optics experiments that test for an entirely new domain of physical laws that govern how an individual's conscious experience traverses the multiverse within Everett's many worlds interpretation of quantum mechanics. These experiments imply an exception to the Born rule in a proposed ''observer-specific'' reference frame. T

Zachary Cox, Douglas M. Gingrich

Greybody factors are computed for massless fields of spin 0, 1/2, 1, and 2 emitted from higher-dimensional non-commutative geometry inspired black holes. Short-range potentials are used with path-ordered matrix exponentials to numerically calculate transmission coefficients. The resulting absorption cross sections and emission spectra are computed on the bra

Li Lyna Zhang, Xudong Wang, Jiahang Xu, Quanlu Zhang

The combination of Neural Architecture Search (NAS) and quantization has proven successful in automatically designing low-FLOPs INT8 quantized neural networks (QNN). However, directly applying NAS to design accurate QNN models that achieve low latency on real-world devices leads to inferior performance. In this work, we find that the poor INT8 latency is due

Ariyan Bighashdel, Daan de Geus, Pavol Jancura, Gijs Dubbelman

Learning anticipation is a reasoning paradigm in multi-agent reinforcement learning, where agents, during learning, consider the anticipated learning of other agents. There has been substantial research into the role of learning anticipation in improving cooperation among self-interested agents in general-sum games. Two primary examples are Learning with Opp

Tongwen Huang, Xihua Li, Chao Yi, Xuemin Zhao

When students make a mistake in an exercise, they can consolidate it by ``similar exercises'' which have the same concepts, purposes and methods. Commonly, for a certain subject and study stage, the size of the exercise bank is in the range of millions to even tens of millions, how to find similar exercises for a given exercise becomes a crucial technical pr

Paul C. Kainen, Shannon Overbay

We show that extending an embedding of a graph $\Gamma$ in a surface to an embedding of a Hamiltonian supergraph can be blocked by certain planar subgraphs but, for some subdivisions of $\Gamma$, Hamiltonian extensions must exist.

Thomas P. Lyons, Jorge Puebla, Kei Yamamoto, Russell S. Deacon

Harnessing the causal relationships between mechanical and magnetic properties of van der Waals materials presents a wealth of untapped opportunity for scientific and technological advancement, from precision sensing to novel memories. This can, however, only be exploited if the means exist to efficiently interface with the magnetoelastic interaction. Here,

Jeng Yi Lee, Hao-Yu Lu, Ray-Kuang Lee

We find that there exists a universal law of coiling not only for a long elastic strip contacting within a tube but also for a short one. Here the elastic strip we consider has the ratio of $2 < L/R \le 2\pi$ for its length $L$ to the tube radius $R$. By varying the ratio of $L/R$, we identify four types of deformation for such a short elastic strip, namely,

Wei Zhu, Runtao Zhou, Yao Yuan, Campbell Timothy

Recently, deep learning has produced encouraging results for kidney stone classification using endoscope images. However, the shortage of annotated training data poses a severe problem in improving the performance and generalization ability of the trained model. It is thus crucial to fully exploit the limited data at hand. In this paper, we propose SegPrompt

Zhewei Yao, Xiaoxia Wu, Cheng Li, Stephen Youn

Post-training quantization (PTQ) has emerged as a promising technique for mitigating memory consumption and computational costs in large language models (LLMs). However, a systematic examination of various quantization schemes, model families, and quantization bit precision has been absent from the literature. In this paper, we conduct a comprehensive analys

Ze Mao, Yang Xu, Erick Suarez

The quality of the data in a dataset can have a substantial impact on the performance of a machine learning model that is trained and/or evaluated using the dataset. Effective dataset management, including tasks such as data cleanup, versioning, access control, dataset transformation, automation, integrity and security, etc., can help improve the efficiency

Hossein Hassani, Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif

The performance of fault diagnosis systems is highly affected by data quality in cyber-physical power systems. These systems generate massive amounts of data that overburden the system with excessive computational costs. Another issue is the presence of noise in recorded measurements, which prevents building a precise decision model. Furthermore, the diagnos

Viktor V. Dodonov, Alexandre V. Dodonov

We study the evolution of the energy of a harmonic oscillator when its frequency slowly varies with time and passes through zero value. We consider both the classical and quantum descriptions of the system. We show that after a single frequency passage through zero value, the famous adiabatic invariant ratio of energy to frequency (which does not hold for ze

Juliana Fernandes, Liliane A. Maia

The present paper analyses the behavior of solutions to a degenerate logistic equation with a nonlinear term of the form b(x)f(u), where the weight function b is assumed to be nonpositive. We exploit variational techniques and comparison principle in order to study the evolutionary dynamics. A crucial role is then played by the Nehari manifold, as we note ho

Jiho Park, Junghee Ryu, Heonoh Kim, Han Seb Moon

A Greenberger Horne Zeilinger (GHZ) entangled state with a phase is crucial for realizing desired multipartite quantum states for practical applications. Here, we report violations of the general Bell inequality (GBI) introduced in [1] using the four photon polarization entangled phase GHZ state realized via intrinsic polarization correlation and collective

Jianjin Wang, Chi Xiong, Daxing Xiong

The amorphous solids can be theoretically modeled by anharmonic disordered lattices. However, most of theoretical studies on thermal conductivity in anharmonic disordered lattices only focus on the potentials of hard-type (HT) anharmonicity. Here we study the thermal conductivity $\kappa$ of one-dimensional (1D) disordered lattices with both hard- and soft-t

Apoorv Singh

To plan a safe and efficient route, an autonomous vehicle should anticipate future trajectories of other agents around it. Trajectory prediction is an extremely challenging task which recently gained a lot of attention in the autonomous vehicle research community. Trajectory-prediction forecasts future state of all the dynamic agents in the scene given their

Yao Ge, Chong Tang, Haobo Li, Zikang Zhang

Nowadays, non-privacy small-scale motion detection has attracted an increasing amount of research in remote sensing in speech recognition. These new modalities are employed to enhance and restore speech information from speakers of multiple types of data. In this paper, we propose a dataset contains 7.5 GHz Channel Impulse Response (CIR) data from ultra-wide

Priya J. Nadkarni, Praveen Jayakumar, Arpit Behera, Shayan Srinivasa Garani

We present the construction of standard entanglement-assisted (EA) qubit Reed-Muller (RM) codes and their tensor product variants from classical RM codes. We show that the EA RM codes obtained using the CSS construction have zero coding rate and negative catalytic rate. We further show that EA codes constructed from these same classical RM codes using the te

Yan-Yan Hou, Jian Li, Xiu-Bo Chen, Chong-Qiang Ye

Metric learning plays an essential role in image analysis and classification, and it has attracted more and more attention. In this paper, we propose a quantum adversarial metric learning (QAML) model based on the triplet loss function, where samples are embedded into the high-dimensional Hilbert space and the optimal metric is obtained by minimizing the tri

Sean Breckling, Malena I. Español, Victoria Uribe, Chrisitan Bobmara

We present a note on the implementation and efficacy of a box-constrained $L_1/L_2$ regularization in numerical optimization approaches to performing tomographic reconstruction from a single projection view. The constrained $L_1/L_2$ minimization problem is constructed and solved using the Alternating Direction Method of Multipliers (ADMM). We include brief

Aparna S. Varde, Jianyu Liang

It is important to develop sustainable processes in materials science and manufacturing that are environmentally friendly. AI can play a significant role in decision support here as evident from our earlier research leading to tools developed using our proposed machine learning based approaches. Such tools served the purpose of computational estimation and e

Eunbyeol Cho, Min Jae Lee, Kyunghoon Hur, Jiyoun Kim

Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain. Recent work presented a promising framework that embeds entire features in raw EHR data regardless of its form and medical code standards. The framework, however, only focuses on encoding EHR with minimal preprocessing

Haoran Wu, Wenxuan Wang, Yuxuan Wan, Wenxiang Jiao

ChatGPT is a cutting-edge artificial intelligence language model developed by OpenAI, which has attracted a lot of attention due to its surprisingly strong ability in answering follow-up questions. In this report, we aim to evaluate ChatGPT on the Grammatical Error Correction(GEC) task, and compare it with commercial GEC product (e.g., Grammarly) and state-o

Olukorede Fakorede, Ashutosh Nirala, Modeste Atsague, Jin Tian

Adversarial training (AT) methods have been found to be effective against adversarial attacks on deep neural networks. Many variants of AT have been proposed to improve its performance. Pang et al. [1] have recently shown that incorporating hypersphere embedding (HE) into the existing AT procedures enhances robustness. We observe that the existing AT procedu

Jingyu Wang, Jinfu Chen, Dongyuan Shi, Xianzhong Duan

Topology diagrams are widely seen in power system applications, but their automatic generation is often easier said than done. When facing power transmission systems with strongly-meshed structures, existing approaches can hardly produce topology diagrams catering to the aesthetics of readers. This paper proposes an integrated framework for generating aesthe

Valeria Ruscio, Valentino Maiorca, Fabrizio Silvestri

We analyze how large language models (LLMs) represent out-of-context words, investigating their reliance on the given context to capture their semantics. Our likelihood-guided text perturbations reveal a correlation between token likelihood and attention values in transformer-based language models. Extensive experiments reveal that unexpected tokens cause th

Edwin Pednault

Wire cutting is a technique for partitioning large quantum circuits into smaller subcircuits in such a way that observables for the original circuits can be estimated from measurements on the smaller subcircuits. Such techniques provide workarounds for the limited numbers of qubits that are available on near-term quantum devices. Wire cutting, however, intro

Anuradha Singh, Jyoti Yadav, Sarahana Shrestha, Aparna S. Varde

This is a study on the potential widespread usage of alternative fuel vehicles, linking them with the socio-economic status of the respective consumers as well as the impact on the resulting air quality index. Research in this area aims to leverage machine learning techniques in order to promote appropriate policies for the proliferation of alternative fuel

Dhruv Apte, Mingming Ge, Olivier Coutier-Delgosha

Cavitation is a complex multiphase phenomenon characterised by vapour bubbles forming due to a sudden pressure drop and is often accompanied by increased hull vibrations, increased radiated noise and decrease in propeller and impeller performance. Although the Reynolds-Averaged Navier-Stokes (RANS) method coupled with a cavitation model is still considered a

Yu Li, Wujie Shi

In this paper, we prove that if two finite groups G and H have isomorphic Burnside rings, then G and H are the same order type groups, and give an example to show that the Burnside rings of the same order type groups are not necessarily isomorphic. This result is related to the Thompson Problem which was raised in 1987.

Aamodh Suresh, Angelique Taylor, Laurel D. Riek, Sonia Martinez

Risky and crowded environments (RCE) contain abstract sources of risk and uncertainty, which are perceived differently by humans, leading to a variety of behaviors. Thus, robots deployed in RCEs, need to exhibit diverse perception and planning capabilities in order to interpret other human agents' behavior and act accordingly in such environments. To underst

Maziar Heidari, Théophile Gaichies, Ludwik Leibler, Matthieu Labousse

Reversible covalent or supramolecular bonds play an important role in materials science and in biological systems. The equilibrium between open and closed bonds and the association rate can be controlled thermally, chemically, by mechanical pulling, ultrasound or catalysts. In practice, these intrinsic equilibrium methods either suffer from a limited range o

Camden L. Lopez

Modern demands of the statistics profession call for reimagining statistics training. The discipline needs to attract and develop students who are effective as real-world problem solvers, interdisciplinary collaborators, communicators, leaders, and teachers. Demand for statistics professionals with broad technical and non-technical skills has grown in a vari

Clinten A. Graham, Frederic Marazzato, Peter R. Wolenski

The Elvis problem has been studied in [2], which proves existence of solutions. However, their computation in the non-smooth case remains unsolved. A bisection method is proposed to solve the Elvis problem in two space dimensions for general convex bounded velocity sets. The convergence rate is proved to be linear. Finally, numerical tests are performed on s

Takahiro Sumi, Naoki koshimoto, David P. Bennett, Nicholas J. Rattenbury

We present the first measurement of the mass function of free-floating planets (FFP) or very wide orbit planets down to an Earth mass, from the MOA-II microlensing survey in 2006-2014. Six events are likely to be due to planets with Einstein radius crossing times, $t_{\rm E}<0.5$days, and the shortest has $t_{\rm E} = 0.057\pm 0.016$days and an angular Einst

Naoki Koshimoto, Takahiro Sumi, David P. Bennett, Valerio Bozza

We report the discoveries of low-mass free-floating planet (FFP) candidates from the analysis of 2006-2014 MOA-II Galactic bulge survey data. In this dataset, we found 6,111 microlensing candidates and identified a statistical sample consisting of 3,535 high quality single lens events with Einstein radius crossing times in the range $0.057 < t_{\rm E}/{\rm d

Corentin Lunel, Arnaud de Mesmay

Knots are commonly represented and manipulated via diagrams, which are decorated planar graphs. When such a knot diagram has low treewidth, parameterized graph algorithms can be leveraged to ensure the fast computation of many invariants and properties of the knot. It was recently proved that there exist knots which do not admit any diagram of low treewidth,

Qian Zhang

In this paper we study global nonlinear stability for the Dirac-Klein-Gordon system in two and three space dimensions for small and regular initial data. In the case of two space dimensions, we consider the Dirac-Klein-Gordon system with a massless Dirac field and a massive scalar field, and prove global existence, sharp time decay estimates and linear scatt

Ofir Gorodetsky, Jared Duker Lichtman, Mo Dick Wong

In 1935, Erd\H{o}s proved that the sums $f_k=\sum_n 1/(n\log n)$, over integers $n$ with exactly $k$ prime factors, are bounded by an absolute constant, and in 1993 Zhang proved that $f_k$ is maximized by the prime sum $f_1=\sum_p 1/(p\log p)$. According to a 2013 conjecture of Banks and Martin, the sums $f_k$ are predicted to decrease monotonically in $k$.

Junxiang Huang, Herbert Levine, Dapeng Bi

We introduce an active version of the recently proposed finite Voronoi model of epithelial tissue. The resultant Active Finite Voronoi (AFV) model enables the study of both confluent and non-confluent geometries and transitions between them, in the presence of active cells. Our study identifies six distinct phases, characterized by aggregation-segregation, d

Tanner Mengel, Patrick Steffanic, Charles Hughes, Antonio Carlos Oliveira da Silva

Jet measurements in heavy ion collisions can provide constraints on the properties of the quark gluon plasma, but the kinematic reach is limited by a large, fluctuating background. We present a novel application of symbolic regression to extract a functional representation of a deep neural network trained to subtract the background for measurements of jets i

Zhening Huang, Xiaoyang Wu, Hengshuang Zhao, Lei Zhu

Current point cloud segmentation architectures suffer from limited long-range feature modeling, as they mostly rely on aggregating information with local neighborhoods. Furthermore, in order to learn point features at multiple scales, most methods utilize a data-agnostic sampling approach to decrease the number of points after each stage. Such sampling metho

Leila Ismail, Muhammad Danish Waseem

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequa

Akshay Subramanian, Kevin P. Greenman, Alexis Gervaix, Tzuhsiung Yang

Deep generative models have emerged as an exciting avenue for inverse molecular design, with progress coming from the interplay between training algorithms and molecular representations. One of the key challenges in their applicability to materials science and chemistry has been the lack of access to sizeable training datasets with property labels. Published

Merlijn Krale, Thiago D. Simão, Nils Jansen

We study Markov decision processes (MDPs), where agents have direct control over when and how they gather information, as formalized by action-contingent noiselessly observable MDPs (ACNO-MPDs). In these models, actions consist of two components: a control action that affects the environment, and a measurement action that affects what the agent can observe.

Nate Clause, Tamal K. Dey, Facundo Mémoli, Bei Wang

We first introduce the notion of meta-rank for a 2-parameter persistence module, an invariant that captures the information behind images of morphisms between 1D slices of the module. We then define the meta-diagram of a 2-parameter persistence module to be the M\"{o}bius inversion of the meta-rank, resulting in a function that takes values from signed 1-par

Praveen Kumar, Christophe G. Lambert

Positive and Unlabeled (PU) learning is a type of semi-supervised binary classification where the machine learning algorithm differentiates between a set of positive instances (labeled) and a set of both positive and negative instances (unlabeled). PU learning has broad applications in settings where confirmed negatives are unavailable or difficult to obtain

Xufeng Zhao, Mengdi Li, Cornelius Weber, Muhammad Burhan Hafez

Programming robot behavior in a complex world faces challenges on multiple levels, from dextrous low-level skills to high-level planning and reasoning. Recent pre-trained Large Language Models (LLMs) have shown remarkable reasoning ability in few-shot robotic planning. However, it remains challenging to ground LLMs in multimodal sensory input and continuous

Simon Riche, Cristian Vay

We construct a "Koszul duality" equivalence relating the (diagrammatic) Hecke category attached to a Coxeter system and a given realization to the Hecke category attached to the same Coxeter system and the dual realization. This extends a construction of Beilinson-Ginzburg-Soergel and Bezrukavnikov-Yun in a geometric context, and of the first author with Ach

Mariam Guizani, Aileen Abril Castro-Guzman, Anita Sarma, Igor Steinmacher

Company engagement in open source (OSS) is now the new norm. From large technology companies to startups, companies are participating in the OSS ecosystem by open-sourcing their technology, sponsoring projects through funding or paid developer time. However, our understanding of the OSS ecosystem is rooted in the 'old world' model where individual contributo

Victor P. Debattista, David J. Liddicott, Oscar A. Gonzalez, Leandro Beraldo e Silva

In Paper I we showed that clumps in high-redshift galaxies, having a high star formation rate density (\Sigma_SFR), produce disks with two tracks in the [Fe/H]-[\alpha/Fe] chemical space, similar to that of the Milky Way's (MW's) thin + thick disks. Here we investigate the effect of clumps on the bulge's chemistry. The chemistry of the MW's bulge is comprise

David Chanin, Anthony Hunter

Social norms underlie all human social interactions, yet formalizing and reasoning with them remains a major challenge for AI systems. We present a novel system for taking social rules of thumb (ROTs) in natural language from the Social Chemistry 101 dataset and converting them to first-order logic where reasoning is performed using a neuro-symbolic theorem

Nityananda Sahu, Konstantin Gaul, Anke Wilm, Melanie Schnell

Parity-violating differences in rotational constants of a chiral 5d transition metal complex, that was previously experimentally well-characterised by broad-band microwave spectroscopy, are predicted with a recently established efficient analytical derivative technique. Relative differences $\Delta X/X$ between rotational constants $X=A,B,C$ of enantiomers o

Cheng Peng, Xi Yang, Zehao Yu, Jiang Bian

Objective: To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehension (MRC) architecture with good generalizability for cross-institution applications. Methods: We formulate both clinical concept extraction and relation extraction using a unified

Ana Sanz Matias, Fabrice Roncoroni, Siddharth Sundararaman, David Prendergast

Divalent ions, such as Mg, Ca, and Zn, are being considered as competitive, safe, and earth-abundant alternatives to Li-ion electrochemistry. However, the challenge remains to match electrode and electrolyte materials that stably cycle with these new formulations, based primarily on controlling interfacial phenomena. We explore the formation of electroactive

Juan Omar Gómez

We introduce the stable module $\infty$-category for groups of type $\Phi$ as an enhancement of the stable category defined by N. Mazza and P. Symonds. For groups of type $\Phi$ which act on a tree, we show that the stable module $\infty$-category decomposes in terms of the associated graph of groups. For groups which admit a finite-dimensional cocompact mod

Aokun Chen, Zehao Yu, Xi Yang, Yi Guo

Objective: To develop a natural language processing (NLP) system to extract medications and contextual information that help understand drug changes. This project is part of the 2022 n2c2 challenge. Materials and methods: We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and con

P. O. Sukhachov, D. O. Oriekhov, E. V. Gorbar

We calculate optical conductivity for bilayer dice lattices in commensurate vertically aligned stackings. The interband optical conductivity reveals a rich activation behavior unique for each of the four stackings. We found that the intermediate energy band, which corresponds to the flat band of a single-layer dice lattice, plays a different role for differe