August 2022 arXiv papers — page 72

Nick Polson, Vadim Sokolov, Jianeng Xu

Quantum Bayesian Computation (QBC) is an emerging field that levers the computational gains available from quantum computers to provide an exponential speed-up in Bayesian computation. Our paper adds to the literature in two ways. First, we show how von Neumann quantum measurement can be used to simulate machine learning algorithms such as Markov chain Monte

Yifan Zhang, Michael Sandborn, Stefan Larson, Yu Huang

Large Language Models (LLMs) are transforming software engineering tasks, including code vulnerability detection-a critical area of software security. However, existing methods often rely on resource-intensive models or graph-based techniques, limiting their accessibility and practicality. This paper introduces K-ASTRO, a lightweight Transformer model that c

Wei Jiang, Zhen Zhang, Zeyu Zhou

By introducing height dependency in the surface energy density, we propose a novel regularized variational model to simulate wetting/dewetting problems. The regularized model leads to the appearance of a precursor layer which covers the bare substrate, with the precursor height depending on the regularization parameter $\varepsilon$. The new model enjoys lot

Donglai Ma, Jacob Bortnik, Xiangning Chu, Seth G. Claudepierre

Many Machine Learning (ML) systems, especially neural networks, are fundamentally regarded as black boxes since it is difficult to grasp how they function once they have been trained. Here, we tackle the issue of the interpretability of a high-accuracy ML model created to model the flux of Earth's radiation belt electrons. The Outer RadIation belt Electron N

Nima S. Hejazi, Mark J. van der Laan

About forty years ago, in a now--seminal contribution, Rosenbaum & Rubin (1983) introduced a critical characterization of the propensity score as a central quantity for drawing causal inferences in observational study settings. In the decades since, much progress has been made across several research fronts in causal inference, notably including the re-weigh

John Preskill

Rapid ongoing progress in quantum information science makes this an apt time for a Solvay Conference focused on The Physics of Quantum Information. Here I review four intertwined themes encompassed by this topic: Quantum computer science, quantum hardware, quantum matter, and quantum gravity. Though the time scale for broad practical impact of quantum comput

Guangxuan Xu, Paulina Toro Isaza, Moshi Li, Akintoye Oloko

To understand a narrative, it is essential to comprehend the temporal event flows, especially those associated with main characters; however, this can be challenging with lengthy and unstructured narrative texts. To address this, we introduce NECE, an open-access, document-level toolkit that automatically extracts and aligns narrative events in the temporal

Suyeon Son, Minjin Kim, Luis C. Ho, Dohyeong Kim

We report an exceptional mid-infrared flare in the Seyfert 1.8 NGC 3786. In the multi-epoch data from the Wide-field Infrared Survey Explorer, the nuclear mid-infrared brightness of NGC 3786 appears to vary substantially up to $0.5-0.8$ mag around mid-2020. However, there is no evidence of significant variation in the corresponding light curve of the optical

Faranak Tohidi, Manoranjan Paul, Anwaar Ulhaq

With the fast growth of immersive video sequences, achieving seamless and high-quality compressed 3D content is even more critical. MPEG recently developed a video-based point cloud compression (V-PCC) standard for dynamic point cloud coding. However, reconstructed point clouds using V-PCC suffer from different artifacts, including losing data during pre-pro

Wenjie Liu, Shi Hu, Li Zhang, Yongguan Ke

Thouless pumping, not only achieving quantized transport but also immune to moderate disorder, has attracted growing attention in both experiments and theories. Here, we explore how particle-particle interactions affect topological transport in a periodically-modulated and tilted optical lattice. Not limited to wannier states, our scheme ensures a dispersion

Vitaly Bergelson, Younghwan Son

For $i = 0, 1, 2, \dots, k$, let $\mu_i$ be a Borel probability measure on $[0,1]$ which is equivalent to Lebesgue measure $\lambda$ and let $T_i:[0,1] \rightarrow [0,1]$ be $\mu_i$-preserving ergodic transformations. We say that transformations $T_0, T_1, \dots, T_k$ are uniformly jointly ergodic with respect to $(\lambda; \mu_0, \mu_1, \dots, \mu_k)$ if fo

Ji Xu, Gang Ren, Yao Xiao, Shaobo Li

The centrality and diversity of the labeled data are very influential to the performance of semi-supervised learning (SSL), but most SSL models select the labeled data randomly. This study first construct a leading forest that forms a partially ordered topological space in an unsupervised way, and select a group of most representative samples to label with o

Yang Zhang, Xuan Ye

The Maxwell field with a general gauge fixing (GF) term is nontrivial, not only the longitudinal and temporal modes are mixed up in the field equations, but also unwanted consequences might arise from the GF term. We derive the complete set of solutions in de Sitter space, and implement the covariant canonical quantization which restricts the residual gauge

Jason Xiaotian Dou, Alvin Qingkai Pan, Runxue Bao, Haiyi Harry Mao

Sampling is ubiquitous in machine learning methodologies. Due to the growth of large datasets and model complexity, we want to learn and adapt the sampling process while training a representation. Towards achieving this grand goal, a variety of sampling techniques have been proposed. However, most of them either use a fixed sampling scheme or adjust the samp

Zhangjie Peng, Xianzhe Chen, Cunhua Pan, Maged Elkashlan

The paper studies a reconfigurable intelligent surface (RIS)-assisted multi-user uplink massive multiple-input multiple-output (MIMO) system with imperfect hardware. At the RIS, the paper considers phase noise, while at the base station, the paper takes into consideration the radio frequency impairments and low-resolution analog-to-digital converters. The pa

Heng Zhang, Haoyi Song, Wenhang Liu, Xinjun Sheng

Multiple mobile manipulators show superiority in the tasks requiring mobility and dexterity compared with a single robot, especially when manipulating/transporting bulky objects. However, closed-chain of the system, redundancy of each mobile manipulator and obstacles in the environment bring challenges to the motion planning problem. In this paper, we propos

Xukun Luo, Ping Wang

Relation Extraction (RE) refers to extracting the relation triples in the input text. Existing neural work based systems for RE rely heavily on manually labeled training data, but there are still a lot of domains where sufficient labeled data does not exist. Inspired by the distance-based few-shot named entity recognition methods, we put forward the definiti

Kuofeng Gao, Jiawang Bai, Baoyuan Wu, Mengxi Ya

With the thriving of deep learning in processing point cloud data, recent works show that backdoor attacks pose a severe security threat to 3D vision applications. The attacker injects the backdoor into the 3D model by poisoning a few training samples with trigger, such that the backdoored model performs well on clean samples but behaves maliciously when the

Wanjun Huang, Changhong Zhao

Power distribution networks are approaching their voltage stability boundaries due to the severe voltage violations and the inadequate reactive power reserves caused by the increasing renewable generations and dynamic loads. In the broad endeavor to resolve this concern, we focus on enhancing voltage stability through stochastic distribution network reconfig

G. M. Fuller, W. C. Haxton

The physics of the mysterious and stealthy neutrino is at the heart of many phenomena in the cosmos. These particles interact with matter and with each other through the aptly named weak interaction. At typical astrophysical energies the weak interaction is some twenty orders of magnitude weaker than the electromagnetic interaction. However, in the early uni

Cheng Peng, Rama Chellappa

We present Progressively Deblurring Radiance Field (PDRF), a novel approach to efficiently reconstruct high quality radiance fields from blurry images. While current State-of-The-Art (SoTA) scene reconstruction methods achieve photo-realistic rendering results from clean source views, their performances suffer when the source views are affected by blur, whic

Cheng Peng, Haofu Liao, S. Kevin Zhou, Rama Chellappa

It is a long-standing challenge to reconstruct Cone Beam Computed Tomography (CBCT) of the lung under respiratory motion. This work takes a step further to address a challenging setting in reconstructing a multi-phase}4D lung image from just a single}3D CBCT acquisition. To this end, we introduce REpiratory-GAted Synthesis of views, or REGAS. REGAS proposes

Sachith Seneviratne, Jasper S. Wijnands, Kerry Nice, Haifeng Zhao

Analysis of overhead imagery using computer vision is a problem that has received considerable attention in academic literature. Most techniques that operate in this space are both highly specialised and require expensive manual annotation of large datasets. These problems are addressed here through the development of a more generic framework, incorporating

Yang He, Raymond Lopez-Rios, Usman A. Javid, Jingwei Ling

Microwave signal generation with fast frequency tuning underlies many applications including sensing, imaging, ranging, time keeping, wireless communication, and high-speed electronics. Soliton microcombs are a promising new approach for photonic-based microwave signal synthesis. To date, however, tuning rate has been limited in microcombs (and in frequency

Jiankun Zhang, Hao Wang, Jing Qian, Zhenxing Gao

Soft demodulation of received symbols into bit log-likelihood ratios (LLRs) is at the very heart of multiple-input-multiple-output (MIMO) detection. However, the optimal maximum a posteriori (MAP) detector is complicated and infeasible to be used in a practical system. In this paper, we propose a soft MIMO detection algorithm based on marginal posterior prob

Wonho Song, Jung-Yong Lee, Junhyung Kim, Jinyoung Park

The effective work-function of metal electrode is one of the major factors to determine the threshold voltage of metal/oxide/semiconductor junction. In this work, we demonstrate experimentally that the effective work-function of Aluminum (Al) electrode in Al/SiO$_2$/n-Si junction increases significantly by $\sim$1.04 eV with the graphene interlayer inserted

Gen Li, Yong Lei

Owing to the limitation of traditional analytical methods, the coloration mechanism of copper red glaze has been disputed in the academic field for a long time, which mainly focuses on whether the color agent is metallic copper nanoparticles or cuprous oxide (Cu2O) nanoparticles. Based on Mie scattering theory, this work calculated the reflection spectra of

Gaofeng Cheng, Yifan Chen, Runyan Yang, Qingxuan Li

The conversation scenario is one of the most important and most challenging scenarios for speech processing technologies because people in conversation respond to each other in a casual style. Detecting the speech activities of each person in a conversation is vital to downstream tasks, like natural language processing, machine translation, etc. People refer

John Willes, Cody Reading, Steven L. Waslander

3D multi-object tracking (MOT) is a key problem for autonomous vehicles, required to perform well-informed motion planning in dynamic environments. Particularly for densely occupied scenes, associating existing tracks to new detections remains challenging as existing systems tend to omit critical contextual information. Our proposed solution, InterTrack, int

Jie Wen, Zheng Zhang, Lunke Fei, Bob Zhang

Conventional multi-view clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failur

Alexandros-Apostolos A. Boulogeorgos, Edwin Yaqub, Rachana Desai, Tachporn Sanguanpuak

Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. However, due to the directional nature and the line-of-sight demand of THz links, as well as the ultra-dense deployment of THz networks, a number of challenges that the medium access control (MAC) layer needs to face are created. In more detail, the need of reth

Xiangyan An, Min Chen, Zheng-Ming Sheng, Jie Zhang

To include the bound electron effects in particle-in-cell (PIC) simulation, we propose a model in which the response of the dipole components of partially ionized ions to external electromagnetic fields can be included. Instead of treating the macro-ion particle as a single particle without an internal structure, the ions are considered to have a structure c

Zhaoyun Jiang, Jiaqi Guo, Shizhao Sun, Huayu Deng

Conditional graphic layout generation, which generates realistic layouts according to user constraints, is a challenging task that has not been well-studied yet. First, there is limited discussion about how to handle diverse user constraints flexibly and uniformly. Second, to make the layouts conform to user constraints, existing work often sacrifices genera

Yajian Hu, Soichiro Yamane, Giordano Mattoni, Kanae Yada

The Kagome lattice exhibits rich quantum phenomena owing to its unique geometric properties. Appealing realizations are the Kagome metals AV$_3$Sb$_5$ (A = K, Rb, Cs), where unconventional charge density wave (CDW) is intertwined with superconductivity and non-trivial band topology. Several experiments suggest that this CDW is a rare occurrence of chiral CDW

Bingbing Wen, Xiaoning Bu, Chirag Shah

Growing attention has been paid in Conversational Recommendation System (CRS), which works as a conversation-based and recommendation task-oriented tool to provide items of interest and explore user preference. However, existing work in CRS fails to explicitly show the reasoning logic to users and the whole CRS still remains a black box. Therefore we propose

Neşet Ünver Akmandor, Hongyu Li, Gary Lvov, Eric Dusel

This paper presents a Deep Reinforcement Learning based navigation approach in which we define the occupancy observations as heuristic evaluations of motion primitives, rather than using raw sensor data. Our method enables fast mapping of the occupancy data, generated by multi-sensor fusion, into trajectory values in 3D workspace. The computationally efficie

Andrea Bourque, Anton M. Zeitlin

We study the moduli space of flat $GL(1|1)$-connections on a punctured surface from the point of view of graph connections. To each fatgraph, a system of coordinates is assigned, which involves two bosonic and two fermionic variables per edge, subject to certain relations. In the case of trivalent graphs, we provide a closed explicit formula for the Whitehea

Anh D. Phan

We propose a theoretical framework for the dynamics of bulk isotropic hard-sphere systems in the presence of randomly pinned particles and apply this theory to supercooled water to validate it. Structural relaxation is mainly governed by local and non-local activated process. As the pinned fraction grows, a local caging constraint becomes stronger and the lo

Peter Koroteev, Anton M. Zeitlin

We investigate various spaces of $SL(r+1)$-opers and their deformations. For each type of such opers, we study the quantum/classical duality, which relates quantum integrable spin chains with classical solvable many body systems. In this context, quantum/classical dualities serve as an interplay between two different coordinate systems on the space of opers.

Kai Xu, Yiling Yang, Engui Fan

Based on the $\overline\partial$-generalization of the Deift-Zhou steepest descent method, we extend the long-time and Painlev\'e asymptotics for the Camassa-Holm (CH) equation to the solutions with initial data in a weighted Sobolev space $ H^{4,2}(\mathbb{R})$. With a new scale $(y,t)$ and a RH problem associated with the initial value problem,we derive di

Huijun Liu, Jie Yu, Shasha Li, Jun Ma

Textual adversarial attacks expose the vulnerabilities of text classifiers and can be used to improve their robustness. Existing context-aware methods solely consider the gold label probability and use the greedy search when searching an attack path, often limiting the attack efficiency. To tackle these issues, we propose PDBS, a context-aware textual advers

Mingjian Tuo, Xingpeng Li

With the goal of electricity system decarbonization, conventional synchronous generators are gradually replaced by converter-interfaced renewable generations. Such transition is causing concerns over system frequency and rate-of-change-of-frequency (RoCoF) security due to significant reduction in system inertia. Existing efforts are mostly derived from unifo

Fatimah Alzubaidi, Peyman Mostaghimi, Yufu Niu, Ryan T. Armstrong

Based on the phenomenological extension of Darcy's law, two-fluid flow is dependent on a relative permeability function of saturation only that is process/path dependent with an underlying dependency on pore structure. For applications, fuel cells to underground $CO_2$ storage, it is imperative to determine the effective phase permeability relationships wher

Stephen C. Anco, Almudena P. Marquez, Tamara M. Garrido, Maria L. Gandarias

All low-order conservation laws are found for a general class of nonlinear wave equations in one dimension with linear damping which is allowed to be time-dependent. Such equations arise in numerous physical applications and have attracted much attention in analysis. The conservation laws describe generalized momentum and boost momentum, conformal momentum,

Mulong Luo, Wenjie Xiong, Geunbae Lee, Yueying Li

The aggressive performance optimizations in modern microprocessors can result in security vulnerabilities. For example, timing-based attacks in processor caches can steal secret keys or break randomization. So far, finding cache-timing vulnerabilities is mostly performed by human experts, which is inefficient and laborious. There is a need for automatic tool

Shengyu Zhang, Bofang Li, Dong Yao, Fuli Feng

Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial recommendations (e.g., popular items) or even weird ones that are far beyond users' interests. Contrastive learning is an emergent technique for learning discriminating representations

Bin Ji, Shasha Li, Shaoduo Gan, Jie Yu

Few-shot named entity recognition (NER) enables us to build a NER system for a new domain using very few labeled examples. However, existing prototypical networks for this task suffer from roughly estimated label dependency and closely distributed prototypes, thus often causing misclassifications. To address the above issues, we propose EP-Net, an Entity-lev

Feng Lin, Valery Levitas, Krishan Pandey, Sorb Yesudhas

Study of the plastic flow and strain-induced phase transformations (PTs) under high pressure with diamond anvils is important for material and geophysics. We introduce rough diamond anvils and apply them to Zr, which drastically change the plastic flow, microstructure, and PTs. Multiple steady microstructures independent of pressure, plastic strain, and stra

Shaojie Tang, Jing Yuan

Many sequential decision making problems can be formulated as an adaptive submodular maximization problem. However, most of existing studies in this field focus on pool-based setting, where one can pick items in any order, and there have been few studies for the stream-based setting where items arrive in an arbitrary order and one must immediately decide whe

Nicole F. Bell, James B. Dent, Bhaskar Dutta, Jason Kumar

We revisit the detection of luminous dark matter in direct detection experiments. In this scenario, dark matter scatters endothermically to produce an excited state, which decays to produce a photon. We explore ways in which the electron recoil signal from the decay photon can be differentiated from other potential electron recoil signals with a narrow spect

Hung T. Nguyen, Steven Bottone, Kwang Taik Kim, Mung Chiang

Symbol detection is a fundamental and challenging problem in modern communication systems, e.g., multiuser multiple-input multiple-output (MIMO) setting. Iterative Soft Interference Cancellation (SIC) is a state-of-the-art method for this task and recently motivated data-driven neural network models, e.g. DeepSIC, that can deal with unknown non-linear channe

Anton M. Zeitlin

We discuss the $qq$-systems, the functional form of the Bethe ansatz equations for the twisted Gaudin model from a new geometric point of view. We use a concept of $G$-Wronskians, which are certain meromorphic sections of principal $G$-bundles on the projective line. In this context, the $qq$-system, similar to its difference analog, is realized as the relat

Bingbing Wen, Yunhe Feng, Yongfeng Zhang, Chirag Shah

As recommender systems become increasingly sophisticated and complex, they often suffer from lack of fairness and transparency. Providing robust and unbiased explanations for recommendations has been drawing more and more attention as it can help address these issues and improve trustworthiness and informativeness of recommender systems. However, despite the

Tatsuro Kawakami, Teppei Takamatsu, Hiromu Tanaka, Jakub Witaszek

We develop the theory of quasi-$F$-splittings in the context of birational geometry. Amongst other things, we obtain results on liftability of sections and establish a criterion for whether a scheme is quasi-$F$-split employing the higher Cartier operator. As one of the applications of our theory, we prove that three-dimensional klt singularities in large ch

Huali Xu, Shuaifeng Zhi, Li Liu

The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target dataset with limited labelled data by exploiting the knowledge of a richly labelled auxiliary dataset, despite the differences between the domains of the two datasets. Some existing approaches require labelled samples from multiple domains for model training. However,

Lingwei Li, Li Yang, Huaxi Jiang, Jun Yan

Code review is one of the best practices as a powerful safeguard for software quality. In practice, senior or highly skilled reviewers inspect source code and provide constructive comments, considering what authors may ignore, for example, some special cases. The collaborative validation between contributors results in code being highly qualified and less ch

Wei-Lin Mu, Xiao-Xuan Li, Xiao-Qiang Shao

We propose a cooling scheme to prepare stationary entanglement of neutral atoms in the Rydberg blockade regime by combination of periodically collective laser pumping and dissipation. In each cycle, the controlled unitary dynamics process can selectively pump atoms away from the non-target state while maintaining the target state unchanged. The subsequent di

Sung Hwan Mun, Min Hyun Han, Minchan Kim, Dongjune Lee

Domain mismatch problem caused by speaker-unrelated feature has been a major topic in speaker recognition. In this paper, we propose an explicit disentanglement framework to unravel speaker-relevant features from speaker-unrelated features via mutual information (MI) minimization. To achieve our goal of minimizing MI between speaker-related and speaker-unrel

Shengyu Zhang, Lingxiao Yang, Dong Yao, Yujie Lu

Effectively representing users lie at the core of modern recommender systems. Since users' interests naturally exhibit multiple aspects, it is of increasing interest to develop multi-interest frameworks for recommendation, rather than represent each user with an overall embedding. Despite their effectiveness, existing methods solely exploit the encoder (the

Zhihua Jin, Xingbo Wang, Furui Cheng, Chunhui Sun

Benchmark datasets play an important role in evaluating Natural Language Understanding (NLU) models. However, shortcuts -- unwanted biases in the benchmark datasets -- can damage the effectiveness of benchmark datasets in revealing models' real capabilities. Since shortcuts vary in coverage, productivity, and semantic meaning, it is challenging for NLU exper

Rakpong Kaewpuang, Minrui Xu, Dusit Niyato, Han Yu

Space-air-ground integrated networks (SAGIN) are one of the most promising advanced paradigms in the sixth generation (6G) communication. SAGIN can support high data rates, low latency, and seamless network coverage for interconnected applications and services. However, communications in SAGIN are facing tremendous security threats from the ever-increasing c

Fei Yao, Lisa Walter, Jiunn-Wei Chen, Jun Hua

We report a state-of-the-art lattice QCD calculation of the isovector quark transversity distribution of the proton in the continuum and physical mass limit using large-momentum effective theory. The calculation is done at four lattice spacings $a=\{0.098,0.085,0.064,0.049\}$~fm and various pion masses ranging between $220$ and $350$ MeV, with proton momenta

Maren E. Mossman, Ryan A. Corbin, Michael McNeil Forbes, Peter Engels

Interferometry is a prime technique for modern precision measurements. Atoms, unlike light, have significant interactions with electric, magnetic, and gravitational fields, making their use in interferometric applications particularly versatile. Here, we demonstrate atom interferometry to image optical and magnetic potential landscapes over an area exceeding

Haosen Ge, In Young Park, Xuancheng Qian, Grace Zeng

High-quality text data has become an important data source for social scientists. We have witnessed the success of pretrained deep neural network models, such as BERT and RoBERTa, in recent social science research. In this paper, we propose a compact pretrained deep neural network, Transformer Encoder for Social Science (TESS), explicitly designed to tackle

Tesi Xiao, Xia Xiao, Ming Chen, Youlong Chen

Feature embeddings are one of the most essential steps when training deep learning based Click-Through Rate prediction models, which map high-dimensional sparse features to dense embedding vectors. Classic human-crafted embedding size selection methods are shown to be "sub-optimal" in terms of the trade-off between memory usage and model capacity. The trendi

Yihao Xue, Kyle Whitecross, Baharan Mirzasoleiman

Increasing the size of overparameterized neural networks has been a key in achieving state-of-the-art performance. This is captured by the double descent phenomenon, where the test loss follows a decreasing-increasing-decreasing pattern (or sometimes monotonically decreasing) as model width increases. However, the effect of label noise on the test loss curve

Alejandro Martin-Gomez, Haowei Li, Tianyu Song, Sheng Yang

The use of Augmented Reality (AR) for navigation purposes has shown beneficial in assisting physicians during the performance of surgical procedures. These applications commonly require knowing the pose of surgical tools and patients to provide visual information that surgeons can use during the task performance. Existing medical-grade tracking systems use i

Shili Sheng, Erfan Pakdamanian, Kyungtae Han, Ziran Wang

Automated vehicles are gradually entering people's daily life to provide a comfortable driving experience for the users. The generic and user-agnostic automated vehicles have limited ability to accommodate the different driving styles of different users. This limitation not only impacts users' satisfaction but also causes safety concerns. Learning from user

Kengo Matsumoto

In this paper, we study the strong extension groups of Cuntz--Krieger algebras, and present a formula to compute the groups. We also detect the position of the Toeplitz extension of a Cuntz--Krieger algebra in the strong extension group and in the weak extension group to see that the weak extension group with the position of the Toeplitz extension is a compl

Emad Elwany, Allison Hegel, Marina Shah, Brendan Roof

Weak supervision has been applied to various Natural Language Understanding tasks in recent years. Due to technical challenges with scaling weak supervision to work on long-form documents, spanning up to hundreds of pages, applications in the document understanding space have been limited. At Lexion, we built a weak supervision-based system tailored for long

Tanmay Ghai, Yixiang Yao, Srivatsan Ravi, Pedro Szekely

Entity resolution is the task of disambiguating records that refer to the same entity in the real world. In this work, we explore adapting one of the most efficient and accurate Jaccard-based entity resolution algorithms - PPJoin, to the private domain via homomorphic encryption. Towards this, we present our precise adaptation of PPJoin (HE-PPJoin) that deta

Alex Warstadt, Samuel R. Bowman

Rapid progress in machine learning for natural language processing has the potential to transform debates about how humans learn language. However, the learning environments and biases of current artificial learners and humans diverge in ways that weaken the impact of the evidence obtained from learning simulations. For example, today's most effective neural

Jiaxiang Jiang, Amil Khan, S. Shailja, Samuel A. Belteton

This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for f

Chao Ma, Lexing Ying

A general framework with a series of different methods is proposed to improve the estimate of convex function (or functional) values when only noisy observations of the true input are available. Technically, our methods catch the bias introduced by the convexity and remove this bias from a baseline estimate. Theoretical analysis are conducted to show that th

Tom Bachmann, Adeel A. Khan, Charanya Ravi, Vladimir Sosnilo

We introduce a notion of Milnor square of stable $\infty$-categories and prove a criterion under which algebraic K-theory sends such a square to a cartesian square of spectra. We apply this to prove Milnor excision and proper excision theorems in the K-theory of algebraic stacks with affine diagonal and nice stabilizers. This yields a generalization of Weibe

Seyed Mehdi Kazemi Torbaghan, Keyvan Salehi, Salman Babayi

In this paper, we first study the $\alpha-$energy functional, Euler-Lagrange operator and $\alpha$-stress energy tensor. Second, it is shown that the critical points of $\alpha-$ energy functional are explicitly related to harmonic maps through conformal deformation. In addition, an $\alpha-$harmonic map is constructed from any smooth map between Riemannian

Prateek Verma, Jonathan Berger

Every sound that we hear is the result of successive convolutional operations (e.g. room acoustics, microphone characteristics, resonant properties of the instrument itself, not to mention characteristics and limitations of the sound reproduction system). In this work we seek to determine the best room in which to perform a particular piece using AI. Additio

Xiao Li, Weili Wu

Blockchain is an emerging decentralized data collection, sharing and storage technology, which have provided abundant transparent, secure, tamper-proof, secure and robust ledger services for various real-world use cases. Recent years have witnessed notable developments of blockchain technology itself as well as blockchain-adopting applications. Most existing

Qiao He, Yousheng Shi, Tonghai Yang

In this paper, we propose a modified Kudla-Rapoport conjecture for the Kr\"amer model of unitary Rapoport-Zink space at a ramified prime, which is a precise identity relating intersection numbers of special cycles to derivatives of Hermitian local density polynomials. We also introduce the notion of special difference cycles, which has surprisingly simple de

Yaotian Wang, Guofen Yan, Seiji Tanabe, Chang-Chia Liu

The brain is a high-dimensional directional network system consisting of many regions as network nodes that influence each other. The directional influence from one region to another is referred to as directional connectivity. Epilepsy is a directional network disorder, as epileptic activity spreads from a seizure onset zone (SOZ) to many other regions after

Zhi-Qing Zhang, Zhi-Lin Guan, Yan-Chao Zhao, Zi-Yu Zhang

We study the decays $B_{c,u,d}\to X(3872)P$ in the perturbative QCD (PQCD) approach, where the puzzling resonance $X(3872)$ is involved and $P$ represents a light pseudoscalar meson $K$ and $\pi$. Assuming the $X(3872)$ as a $1^{++}$ charmonium state, we find the following results: (a) The branching ratios for the decays $B^+_c\to X(3872)\pi^+$ and $B^+_c\to

Yasha Savelyev

We prove one deformation theoretic extension of the Gromov non-squeezing phenomenon to $lcs$ structures, or locally conformally symplectic structures, which suitably generalize both symplectic and contact structures. We also conjecture an analogue in $lcs$ geometry of contact non-squeezing of Eliashberg-Polterovich and discuss other related questions.

Mingyu Derek Ma, Alexander K. Taylor, Wei Wang, Nanyun Peng

Event extraction for the clinical domain is an under-explored research area. The lack of training data along with the high volume of domain-specific terminologies with vague entity boundaries makes the task especially challenging. In this paper, we introduce DICE, a robust and data-efficient generative model for clinical event extraction. DICE frames event e

Qiao He, Chao Li, Yousheng Shi, Tonghai Yang

We prove the Kudla--Rapoport conjecture for Kr\"amer models of unitary Rapoport--Zink spaces at ramified places. It is a precise identity between arithmetic intersection numbers of special cycles on Kr\"amer models and modified derived local densities of hermitian forms. As an application, we relax the local assumptions at ramified places in the arithmetic S

Ruojing Peng, Johnnie Gray, Garnet Kin-Lic Chan

Many computational problems can be formulated in terms of high-dimensional functions. Simple representations of such functions and resulting computations with them typically suffer from the "curse of dimensionality", an exponential cost dependence on dimension. Tensor networks provide a way to represent certain classes of high-dimensional functions with poly

Ioan Baldea

The pharmaceutical success of atorvastatin (ATV), a widely employed drug against the "bad" cholesterol (LDL) and cardiovascular diseases, traces back to its ability to scavenge free radicals. Unfortunately, information on its antioxidant properties is missing or unreliable. Here, we report detailed quantum chemical results for ATV and its ortho- and para-hyd

Youssef El Amrani, Saad Motahhir, Abdelaziz El Ghzizal

An Electric Vehicle usually refers to any vehicle that is partially or fully powered by a battery that can be directly plugged into the mains. Therefore, the new vehicles provide various benefits, including convenience, efficiency, sustainability, and economy. The present study concerns a comprehensive review of vehicle electrification solutions. Indeed, the

Weili Wang, Chengchao Liang, Lun Tang, Halim Yanikomeroglu

Virtualized network slicing allows a multitude of logical networks to be created on a common substrate infrastructure to support diverse services. A virtualized network slice is a logical combination of multiple virtual network functions, which run on virtual machines (VMs) as software applications by virtualization techniques. As the performance of network

Alex Bie, Gautam Kamath, Vikrant Singhal

We initiate the study of differentially private (DP) estimation with access to a small amount of public data. For private estimation of d-dimensional Gaussians, we assume that the public data comes from a Gaussian that may have vanishing similarity in total variation distance with the underlying Gaussian of the private data. We show that under the constraint

Istvan Z. Kiss, Eben Kenah, Grzegorz A. Rempala

We prove that the exact closure of SIR pairwise epidemic equations on a configuration model network is possible if and only if the degree distribution is Poisson, Binomial, or Negative Binomial. The proof relies on establishing, for these specific degree distributions, the equivalence of the closed pairwise model and the so-called dynamical survival analysis

Peter Rottmann, Markus Wallinger, Annika Bonerath, Sven Gedicke

Visualizing sets of elements and their relations is an important research area in information visualization. In this paper, we present MosaicSets: a novel approach to create Euler-like diagrams from non-spatial set systems such that each element occupies one cell of a regular hexagonal or square grid. The main challenge is to find an assignment of the elemen

Preetam Anbukarasu, Shailesh Nanisetty, Ganesh Tata, Nilanjan Ray

The focus of this paper is a proof of concept, machine learning (ML) pipeline that extracts heart rate from pressure sensor data acquired on low-power edge devices. The ML pipeline consists an upsampler neural network, a signal quality classifier, and a 1D-convolutional neural network optimized for efficient and accurate heart rate estimation. The models wer

Iara Ota

Gravitational waves provide direct information about the nature of spacetime and the existence of black holes. The remnant of a binary black hole merger emits gravitational waves in the form of quasinormal modes, whose spectrum is known as the "fingerprints" of a black hole, as it depends only on the properties of the remnant. The quasinormal modes can be us

Ali Cox, Quntao Zhuang, Christos Gagatsos, Boulat Bash

Pre-shared entanglement can significantly boost communication rates in the high thermal noise and low-brightness transmitter regime. In this regime, for a lossy-bosonic channel with additive thermal noise, the ratio between the entanglement-assisted capacity and the Holevo capacity - the maximum reliable-communications rate permitted by quantum mechanics wit

Duy Phuong Nguyen, Sixing Yu, J. Pablo Muñoz, Ali Jannesari

Concerned with user data privacy, this paper presents a new federated learning (FL) method that trains machine learning models on edge devices without accessing sensitive data. Traditional FL methods, although privacy-protective, fail to manage model heterogeneity and incur high communication costs due to their reliance on aggregation methods. To address thi

Marwa H. Farag, Joydip Ghosh

Variational quantum eigensolver (VQE) is a hybrid quantum-classical technique that leverages noisy intermediate scale quantum (NISQ) hardware to obtain the minimum eigenvalue of a model Hamiltonian. VQE has so far been used to simulate condensed matter systems as well as quantum chemistry of small molecules. In this work, we employ VQE methods to obtain the

Jens Barosch, Larry R. Nittler, Jianhua Wang, Conel M. O'D. Alexander

We have conducted a NanoSIMS-based search for presolar material in samples recently returned from C-type asteroid Ryugu as part of JAXA's Hayabusa2 mission. We report the detection of all major presolar grain types with O- and C-anomalous isotopic compositions typically identified in carbonaceous chondrite meteorites: 1 silicate, 1 oxide, 1 O-anomalous super

Igor A. Maia, Guillaume Brès, Lutz Lesshafft, Peter Jordan

This study concerns a turbulent jet at Mach number $M_j=0.9$, subject to a uniform external flow stream at $M_f=0.15$. We assess the mechanisms that underpin the reduction in fluctuation energy that is known to occur when a jet is surrounded by a flight stream. The analysis combines experimental and numerical databases, spectral proper orthogonal decompositi

João Carlos da Motta Ferreira, Maria das Graças Bruno Marietto

Let $\mathcal{A}$ and $\mathcal{B}$ be two unital complex $\ast $-algebras such that $\mathcal{A}$ has a nontrivial projection. In this paper, we study the structure of bijective nonlinear maps $\Phi :\mathcal{A}\rightarrow \mathcal{B}$ preserving sum of triple products $\alpha _{1} abc+\alpha _{2} a^{*}cb^{*}+\alpha _{3} ba^{*}c +\alpha _{4} cab^{*}+\alpha

Amir Salimi Lafmejani, Spring Berman, Georgios Fainekos

In this paper, we present a decentralized control approach based on a Nonlinear Model Predictive Control (NMPC) method that employs barrier certificates for safe navigation of multiple nonholonomic wheeled mobile robots in unknown environments with static and/or dynamic obstacles. This method incorporates a Learned Barrier Function (LBF) into the NMPC design