May 2022 arXiv papers — page 45

Jinning Li, Chen Tang, Masayoshi Tomizuka, Wei Zhan

Offline Reinforcement learning (RL) has shown potent in many safe-critical tasks in robotics where exploration is risky and expensive. However, it still struggles to acquire skills in temporally extended tasks. In this paper, we study the problem of offline RL for temporally extended tasks. We propose a hierarchical planning framework, consisting of a low-le

Somnath De, Shraddha Gupta, Vishnu R Unni, Rewanth Ravindran

Cyclones are amongst the most hazardous extreme weather events on Earth. When two co-rotating cyclones come in close proximity, a possibility of complete merger (CM) arises due to their interactions. However, identifying the transitions in the interaction of binary cyclones and predicting the merger is challenging for weather forecasters. In the present stud

Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long

Conversational recommender systems (CRS) explicitly solicit users' preferences for improved recommendations on the fly. Most existing CRS solutions count on a single policy trained by reinforcement learning for a population of users. However, for users new to the system, such a global policy becomes ineffective to satisfy them, i.e., the cold-start challenge

Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan, Mikhail Belkin

While neural networks can be approximated by linear models as their width increases, certain properties of wide neural networks cannot be captured by linear models. In this work we show that recently proposed Neural Quadratic Models can exhibit the "catapult phase" [Lewkowycz et al. 2020] that arises when training such models with large learning rates. We th

Libin Zhu, Chaoyue Liu, Mikhail Belkin

In this paper we show that feedforward neural networks corresponding to arbitrary directed acyclic graphs undergo transition to linearity as their "width" approaches infinity. The width of these general networks is characterized by the minimum in-degree of their neurons, except for the input and first layers. Our results identify the mathematical structure u

Mingzhe Sui, Hanting Li, Zhaoqing Zhu, Feng Zhao

2D+3D facial expression recognition (FER) can effectively cope with illumination changes and pose variations by simultaneously merging 2D texture and more robust 3D depth information. Most deep learning-based approaches employ the simple fusion strategy that concatenates the multimodal features directly after fully-connected layers, without considering the d

Andrzej Reinke, Matteo Palieri, Benjamin Morrell, Yun Chang

Lidar odometry has attracted considerable attention as a robust localization method for autonomous robots operating in complex GNSS-denied environments. However, achieving reliable and efficient performance on heterogeneous platforms in large-scale environments remains an open challenge due to the limitations of onboard computation and memory resources neede

Tianming Zheng, Ping Yi, Yue Wu

As a national critical infrastructure, the smart grid has attracted widespread attention for its cybersecurity issues. The development towards an intelligent, digital, and Internet-connected smart grid has attracted external adversaries for malicious activities. It is necessary to enhance its cybersecurity by both improving the existing defense approaches an

Xiaoguang Li, Ninghui Li, Wenhai Sun, Neil Zhenqiang Gong

Although local differential privacy (LDP) protects individual users' data from inference by an untrusted data curator, recent studies show that an attacker can launch a data poisoning attack from the user side to inject carefully-crafted bogus data into the LDP protocols in order to maximally skew the final estimate by the data curator. In this work, we furt

Arya Tafvizi, Besim Avci, Mukund Sundararajan

Area Under the Receiver Operating Characteristic Curve (AUC-ROC) is a popular evaluation metric for binary classifiers. In this paper, we discuss techniques to segment the AUC-ROC along human-interpretable dimensions. AUC-ROC is not an additive/linear function over the data samples, therefore such segmenting the overall AUC-ROC is different from tabulating t

Yoonjae Park, David T. Limmer

We employ quasiparticle path integral molecular dynamics to study how the excitonic properties of model semiconductors are altered by electron-phonon coupling. We describe ways within a path integral representation of the system to evaluate the renormalized mass, binding energy, and radiative recombination rate of excitons in the presence of a fluctuating la

Hideya Ochiai, Yuwei Sun, Qingzhe Jin, Nattanon Wongwiwatchai

Privacy-sensitive data is stored in autonomous vehicles, smart devices, or sensor nodes that can move around with making opportunistic contact with each other. Federation among such nodes was mainly discussed in the context of federated learning with a centralized mechanism in many works. However, because of multi-vendor issues, those nodes do not want to re

Gaurav Sawant

We show an analogue of a theorem of An, Ghosh, Guan, and Ly on weighted badly approximable vectors for totally imaginary number fields. We show that for $G=\mathrm{SL}_2(\mathbb{C})\times\dots\times\mathrm{SL}_2(\mathbb{C})$ and $\Gamma<G$ a lattice subgroup, the points of $G/\Gamma$ with bounded orbits under a one-parameter Ad-semisimple subgroup of $G$ for

Manuel H Muñoz-Arias

In this work we revisit the problem of equilibration in isolated many-body interacting quantum systems. We pay particular attention to quantum chaotic Hamiltonians, and rather than focusing on the properties of the asymptotic states and how they adhere to the predictions of the Eigenstate Thermalization Hypothesis, we focus on the equilibration process itsel

Koki Shimizu, Hiroki Hashiguchi

This paper discusses the computation of exact powers for Roy's test in multivariate analysis of variance~(MANOVA). We derive an exact expression for the largest eigenvalue of a singular noncentral Beta matrix in terms of the product of zonal polynomials. The numerical computation for that distribution is conducted by an algorithm that expands the product of

Davor Runje, Sharath M. Shankaranarayana

Wider adoption of neural networks in many critical domains such as finance and healthcare is being hindered by the need to explain their predictions and to impose additional constraints on them. Monotonicity constraint is one of the most requested properties in real-world scenarios and is the focus of this paper. One of the oldest ways to construct a monoton

Xin Huang, Weike Yu

In this paper, we consider transversally harmonic maps between Riemannian manifolds with Riemannian foliations. In terms of the Bochner techniques and sub-Laplacian comparison theorem, we are able to establish a generalization of the Schwarz lemma for transversally harmonic maps of bounded generalized transversal dilatation. In addition, we also obtain a Sch

Mohammad Rowshan, Jinhong Yuan

Maximum-likelihood (ML) decoding can be used to obtain the optimal performance of error correction codes. However, the size of the search space and consequently the decoding complexity grows exponentially, making it impractical to be employed for long codes. In this paper, we propose an approach to constrain the search space for error patterns under a recent

Van-Nhiem Tran, Chi-En Huang, Shen-Hsuan Liu, Kai-Lin Yang

In recent years, self-supervised learning has been studied to deal with the limitation of available labeled-dataset. Among the major components of self-supervised learning, the data augmentation pipeline is one key factor in enhancing the resulting performance. However, most researchers manually designed the augmentation pipeline, and the limited collections

Xihao Xie, Jia Zhang, Rahul Ramachandran, Tsengdar J. Lee

As increasingly more software services have been published onto the Internet, it remains a significant challenge to recommend suitable services to facilitate scientific workflow composition. This paper proposes a novel NLP-inspired approach to recommending services throughout a workflow development process, based on incrementally learning latent service repr

Seung-Hwan Do, Hao Zhang, David A. Dahlbom, Travis J. Williams

Quantum magnets admit more than one classical limit and $N$-level systems with strong single-ion anisotropy are expected to be described by a classical approximation based on SU($N$) coherent states. Here we test this hypothesis by modeling finite temperature inelastic neutron scattering (INS) data of the effective spin-one antiferromagnet \bfso{}. The measu

F. Suekane, Y. Hino, W. Noguchi, T. Tokuraku

DaRveX stands for "Decay at Rest $\nu_e$-Pb cross (X) section measurement". So far, there has not been good target to detect low energy $\nu_e$. Lead is expected to be an excellent $\nu_e$ target because the cross section is expected to be very large and the delayed coincidence technique can be used using final state neutron(s). However, the cross section ha

Zhangkai Huang

Given an RCD$(K,N)$ space $({X},\mathsf{d},\mathfrak{m})$, one can use its heat kernel $\rho$ to map it into the $L^2$ space by a locally Lipschitz map $\Phi_t(x):=\rho(x,\cdot,t)$. The space $(X,\mathsf{d},\mathfrak{m})$ is said to be an isometrically heat kernel immersing space, if each $\Phi_t$ is an isometric immersion {}{after a normalization}. A main r

Jacob Nibauer, Vasily Belokurov, Miles Cranmer, Jeremy Goodman

We present a data-driven method for reconstructing the galactic acceleration field from phase-space measurements of stellar streams. Our approach is based on a flexible and differentiable fit to the stream in phase-space, enabling a direct estimate of the acceleration vector along the stream. Reconstruction of the local acceleration field can be applied inde

Takeo Moroi, Atsuya Niki

It has been recently known that we can use beams of future lepton colliders, the International Linear Collider (ILC), the Compact Linear Collider (CLIC), and the muon collider, for beam dump experiments if a shield and a detector are installed behind the beam dump. We study the prospect of searching for leptophilic gauge bosons (LGBs) in association with $U(

Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang

We propose Byzantine-robust federated learning protocols with nearly optimal statistical rates. In contrast to prior work, our proposed protocols improve the dimension dependence and achieve a tight statistical rate in terms of all the parameters for strongly convex losses. We benchmark against competing protocols and show the empirical superiority of the pr

Binwei Yao, Chao Shi, Likai Zou, Lingfeng Dai

In a depression-diagnosis-directed clinical session, doctors initiate a conversation with ample emotional support that guides the patients to expose their symptoms based on clinical diagnosis criteria. Such a dialogue system is distinguished from existing single-purpose human-machine dialog systems, as it combines task-oriented and chit-chats with uniqueness

W. B. Li, J. R. Stevens, G. M. Huber

The proposed measurement is a dedicated study to investigate the exclusive electroproduction process: 1H(e, e'p)X, in the backward angle above the resonance region. Here, the produced particle X (pi0 or gamma) is emitted 180 degrees opposite to the virtual photon momentum. This study will apply the well-known L/T separation method of the electroproduction pr

Zeqiao Zhou, Yuxuan Du, Xinmei Tian, Dacheng Tao

The design of fast algorithms for combinatorial optimization greatly contributes to a plethora of domains such as logistics, finance, and chemistry. Quantum approximate optimization algorithms (QAOAs), which utilize the power of quantum machines and inherit the spirit of adiabatic evolution, are novel approaches to tackle combinatorial problems with potentia

Feng Tang, Qiang Ling

Current Siamese-based trackers mainly formulate the visual tracking into two independent subtasks, including classification and localization. They learn the classification subnetwork by processing each sample separately and neglect the relationship among positive and negative samples. Moreover, such tracking paradigm takes only the classification confidence

Bakhtiyor Narzilloev, Ibrar Hussain, Ahmadjon Abdujabbarov, Bobomurat Ahmedov

This work is devoted to the study of the optical properties of the charged-rotating-NUT-Kiselev (CRNK) black hole in the Rastall theory of gravity. By investigating the motion of photons in the CRNK black hole spacetime in the Rastall gravity we show that the deflection angle of photons due to the gravitational lensing is mostly influenced by the NUT charge,

Ledan Qian, Xiao Zhou, Yi Li, Zhongyi Hu

As an essential prerequisite for developing a medical intelligent assistant system, medical image segmentation has received extensive research and concentration from the neural network community. A series of UNet-like networks with encoder-decoder architecture has achieved extraordinary success, in which UNet2+ and UNet3+ redesign skip connections, respectiv

Chenqing Hua, Sitao Luan, Qian Zhang, Jie Fu

Graphs are a powerful data structure to represent relational data and are widely used to describe complex real-world data structures. Probabilistic Graphical Models (PGMs) have been well-developed in the past years to mathematically model real-world scenarios in compact graphical representations of distributions of variables. Graph Neural Networks (GNNs) are

Terra Blevins, Hila Gonen, Luke Zettlemoyer

The emergent cross-lingual transfer seen in multilingual pretrained models has sparked significant interest in studying their behavior. However, because these analyses have focused on fully trained multilingual models, little is known about the dynamics of the multilingual pretraining process. We investigate when these models acquire their in-language and cr

Christopher M. Legner, Gregory L. Tylka, Santosh Pandey

Soybeans are an important crop for global food security. Every year, soybean yields are reduced by numerous soybean diseases, particularly the soybean cyst nematode (SCN). It is difficult to visually identify the presence of SCN in the field, let alone its population densities or numbers, as there are no obvious aboveground disease symptoms. The only definit

Jialiang Wang, Haotian Wei, Yi Wang, Shu Yang

Human activity recognition (HAR) based on multimodal sensors has become a rapidly growing branch of biometric recognition and artificial intelligence. However, how to fully mine multimodal time series data and effectively learn accurate behavioral features has always been a hot topic in this field. Practical applications also require a well-generalized frame

Sourav Chatterjee, Rohan Bopardikar, Marius Guerard, Uttam Thakore

Organizations leverage anomaly and changepoint detection algorithms to detect changes in user behavior or service availability and performance. Many off-the-shelf detection algorithms, though effective, cannot readily be used in large organizations where thousands of users monitor millions of use cases and metrics with varied time series characteristics and

Paul Martens, Shinji Mukohyama, Ryo Namba

We present a cosmological model of an early-time scenario that incorporates a relaxation process of the would-be large vacuum energy, followed by a reheating era connecting to the standard hot big bang universe. Avoiding fine-tuning the cosmological constant is achieved by the dynamics of a scalar field whose kinetic term is modulated by an inverse power of

Jinhong Li, Qiuping Wang, Patrick P. C. Lee

Zoned storage devices, such as zoned namespace (ZNS) solid-state drives (SSDs) and host-managed shingled magnetic recording (HM-SMR) hard-disk drives (HDDs), expose interfaces for host-level applications to support fine-grained, high-performance storage management. Combining ZNS SSDs and HM-SMR HDDs into a unified hybrid storage system is a natural direction

Ebner Pineda, Luz Rodriguez, Wilfredo O. Urbina

In this paper we study the regularity properties of the Gaussian Bessel potentials and Gaussian Bessel fractional derivatives on variable Gaussian Besov-Lipschitz spaces $B_{p(\cdot),q(\cdot)}^{\alpha}(\gamma_{d}),$ that were defined in a previous paper \cite{Pinrodurb}, under certain conditions on $p(\cdot)$ and $q(\cdot)$.

Dylan Miley, Leonardo Bertoncello Machado, Calvin Condo, Albert E. Jergens

Real-time monitoring of the gastrointestinal tract in a safe and comfortable manner is valuable for the diagnosis and therapy of many diseases. Within this realm, our review captures the trends in ingestible capsule systems with a focus on hardware and software technologies used for capsule endoscopy and remote patient monitoring. We introduce the structure

Daye Lim, Valery M. Nakariakov, Yong-Jae Moon

Slow magnetoacoustic oscillations in stellar coronal loops with gravitational stratification are analyzed with a numerical solution of the boundary-value problem for eigenvalues and eigen functions. In this study, we only focus on the resonant periods. The effects of the gravitational stratification, star mass, loop temperature, and loop length on the proper

Xiao Meng, Fan Liu, Christos Masouros, Weijie Yuan

In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling.Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries. To improve the tracking accuracy and communication

Yao-Ming Kuo, Shanq-Jang Ruan, Yu-Chin Chen, Ya-Wen Tu

This article describes a system for analyzing acoustic data to assist in the diagnosis and classification of children's speech sound disorders (SSDs) using a computer. The analysis concentrated on identifying and categorizing four distinct types of Chinese SSDs. The study collected and generated a speech corpus containing 2540 stopping, backing, final conson

Leila Khalili, Yao You, John Bohannon

Transformer language models provide superior accuracy over previous models but they are computationally and environmentally expensive. Borrowing the concept of model cascading from computer vision, we introduce BabyBear, a framework for cascading models for natural language processing (NLP) tasks to minimize cost. The core strategy is inference triage, exiti

Paul Martens, Hiroki Matsui, Shinji Mukohyama

We present a well-tempered DeWitt wave function, which vanishes at the classical big-bang singularity, in Ho\v{r}ava-Lifshitz (HL) cosmology with tensor perturbation, both analytically and numerically. In general relativity, the DeWitt wave function is ill-behaved once the tensor perturbation is taken into account. This is essentially because the amplitude o

Arash Dehzangi

Strained layer superlattice (SLS) material system is a dynamic and relatively new material for infrared detection. Large format, small-pitch and low-cost focal plane arrays (FPAs) with more pixels are in demand for different applications. For the current SLS based FPAs mesa etching are used to define the pixels. For those SLS based FPAs with scaled pixel siz

Shudong Zhang, Haichang Gao, Tianwei Zhang, Yunyi Zhou

Adversarial training (AT) has proven to be one of the most effective ways to defend Deep Neural Networks (DNNs) against adversarial attacks. However, the phenomenon of robust overfitting, i.e., the robustness will drop sharply at a certain stage, always exists during AT. It is of great importance to decrease this robust generalization gap in order to obtain

Jia Cui, Mingze Gao, Xiaoming Zhou, Yang Li

With the rapid development of the energy internet, the proportion of flexible loads in smart grid is getting much higher than before. It is highly important to model flexible loads based on demand response. Therefore, a new demand response method considering multiple flexible loads is proposed in this paper to character the integrated demand response (IDR) r

Diana Chamaki, Mekena Metcalf, Wibe A. de Jong

Compact representations of fermionic Hamiltonians are necessary to perform calculations on quantum computers that lack error-correction. A fermionic system is typically defined within a subspace of fixed particle number and spin while unnecessary states are projected out of the Hilbert space. We provide a bijective mapping using combinatoric ranking to bijec

Mingzhi Tang, Wenfeng Zhou, Yanchao Hu, Gang Wang

Since it is difficult to apply the existing method of friction and heat flux decomposition on the complex surface, a combined decomposition method of friction and heat flux with clear physical interpretation is proposed, which is based on FIK and RD decomposition method and can be applied to arbitrary surface. Based on this method, the aerothermodynamic char

Vishal Patel, Austin Chesmore, Christopher M. Legner, Santosh Pandey

The workplace influences the safety, health, and productivity of workers at multiple levels. To protect and promote total worker health, smart hardware, and software tools have emerged for the identification, elimination, substitution, and control of occupational hazards. Wearable devices enable constant monitoring of individual workers and the environment,

Changan Niu, Chuanyi Li, Bin Luo, Vincent Ng

Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has enabled state-of-the-art results to be achieved on a wide variety of SE tasks. This paper provides an overview of this rapidly advancing field of research and reflects on future resea

Chendong Zhao, Jianzong Wang, Leilai Li, Xiaoyang Qu

Sound event detection is to infer the event by understanding the surrounding environmental sounds. Due to the scarcity of rare sound events, it becomes challenging for the well-trained detectors which have learned too much prior knowledge. Meanwhile, few-shot learning methods promise a good generalization ability when facing a new limited-data task. Recent a

Jinghui Xiao, Qun Liu, Xin Jiang, Yuanfeng Xiong

Pinyin to Character conversion (P2C) task is the key task of Input Method Engine (IME) in commercial input software for Asian languages, such as Chinese, Japanese, Thai language and so on. It's usually treated as sequence labelling task and resolved by language model, i.e. n-gram or RNN. However, the low capacity of the n-gram or RNN limits its performance.

Shuaiqi Wang, Jonathan Hayase, Giulia Fanti, Sewoong Oh

Backdoor attacks are dangerous and difficult to prevent in federated learning (FL), where training data is sourced from untrusted clients over long periods of time. These difficulties arise because: (a) defenders in FL do not have access to raw training data, and (b) a new phenomenon we identify called backdoor leakage causes models trained continuously to e

Man Huang, Luis Carvalho

The binomial deviance and the SVM hinge loss functions are two of the most widely used loss functions in machine learning. While there are many similarities between them, they also have their own strengths when dealing with different types of data. In this work, we introduce a new exponential family based on a convex relaxation of the hinge loss function usi

Jiangdan Li, Jiao Li, Chao Liu, Chunqian Li

Binary evolution leads to the formation of important objects crucial to the development of astrophysics, but the statistical properties of binary populations are still poorly understood. The LAMOST-MRS has provided a large sample of stars to study the properties of binary populations, especially for the mass ratio distributions and the binary fractions. We h

Yuxuan Han, Ruicheng Wang, Jiaolong Yang

This paper deals with the challenging task of synthesizing novel views for in-the-wild photographs. Existing methods have shown promising results leveraging monocular depth estimation and color inpainting with layered depth representations. However, these methods still have limited capability to handle scenes with complex 3D geometry. We propose a new method

Cai-Xia Zhang, Shi-Guo Peng

Scattering phase shift, as a key parameter in scattering theory, plays an important role in characterizing low-energy collisions between ultracold atoms. In this work, we theoretically investigate the universal low-energy behavior of the scattering phase shifts for cold atoms in the presence of spin-orbit coupling. We first construct the asymptotic form of t

Shi-Guo Peng

We show that a variety of nonequilibrium dynamics of interacting many-body systems are universally characterized by an elegant relation, which we call the dynamic virial theorem. The out-of-equilibrium dynamics of quantum correlations is entirely governed by Tan\textquoteright s contact. It gives rise to a series of observable consequences and is closely rel

Tao Han, Shuailong Li, Shufang Su, Wei Su

The potential of the non-Standard Model heavy Higgs bosons in 2HDM at a muon collider is studied. The pair production of the non-SM Higgs bosons via the universal gauge interactions is the dominant mechanism once above the kinematic threshold. On the other hand, single Higgs boson production associated with a pair of heavy fermions is also important in the p

Xin-Yi Li, Wei-Jun Lei, Yu-Bin Yang

Multi-hop question answering (QA) is a challenging task requiring QA systems to perform complex reasoning over multiple documents and provide supporting facts together with the exact answer. Existing works tend to utilize graph-based reasoning and question decomposition to obtain the reasoning chain, which inevitably introduces additional complexity and cumu

Aman Kumar, Akshay G Bharadwaj, Binil Starly, Collin Lynch

As the demands for large-scale information processing have grown, knowledge graph-based approaches have gained prominence for representing general and domain knowledge. The development of such general representations is essential, particularly in domains such as manufacturing which intelligent processes and adaptive education can enhance. Despite the continu

Paul Baltescu, Haoyu Chen, Nikil Pancha, Andrew Zhai

Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems. At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases including user, image and search based recommendations. This approach has led to significant improvements in en

Farrux Abdulxamidov, Carlos A. Benavides-Gallego, Wen-Biao Han, Javlon Rayimbaev

In this work, we investigated the motion of spinning test particles around a rotating wormhole, extending, in this way, the previous work of Benavides-Gallego et al. in [Phys. Rev. D 101, no.12, 124024] to the general case. Using the Mathisson-Papapetrous-Dixon equations, we study the effective potential, circular orbits, and the innermost stable circular or

Mikel Artetxe, Jingfei Du, Naman Goyal, Luke Zettlemoyer

Prior work on language model pre-training has explored different architectures and learning objectives, but differences in data, hyperparameters and evaluation make a principled comparison difficult. In this work, we focus on bidirectionality as a key factor that differentiates existing approaches, and present a comprehensive study of its role in next token

Shaowen Zhou, Bowen Yu, Aixin Sun, Cheng Long

Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora. The technique well suits many open-world natural language understanding scenarios, such as automatic knowledge base construction, open-domain question answering, and explicit reasoning. Thanks to the rapid development in deep learning technol

Xiaolei Zhang

In this paper, we say a ring $R$ is Nil$_{\ast}$-Noetherian provided that any nil ideal is finitely generated. First, we show that the Hilbert basis theorem holds for Nil$_{\ast}$-Noetherian rings, that is, $R$ is Nil$_{\ast}$-Noetherian if and only if $R[x]$ is Nil$_{\ast}$-Noetherian, if and only if $R[[x]]$ is Nil$_{\ast}$-Noetherian. Then we discuss some

Kevin Leung, Laura C. Merrill, Katharine L. Harrison

Battery interfaces help govern rate capability, safety/stability, cycle life, and self-discharge, but significant gaps remain in our understanding at atomic length scales that can be exploited to improve interfacial properties. In particular, Li partially plated on copper current collectors, relevant to the anodeless, lithium metal cell which is a holy grail

Rajhans Singh, Ankita Shukla, Pavan Turaga

Deep networks for image classification often rely more on texture information than object shape. While efforts have been made to make deep-models shape-aware, it is often difficult to make such models simple, interpretable, or rooted in known mathematical definitions of shape. This paper presents a deep-learning model inspired by geometric moments, a classic

Ryo Shibata, Hiroyuki Yashima

We propose a new coding scheme, called the delayed coding (DC) scheme, for channels with insertion, deletion, and substitution (IDS) errors. The proposed scheme employs delayed encoding and non-iterative detection and decoding strategies to manage the transmission of multiple codewords in a linear code. In the DC scheme, a channel input sequence consists of

Jacob Miller, Vahan Huroyan, Raymundo Navarrete, Md Iqbal Hossain

When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2-dimensional view of the data. We describe ENS-t-SNE: an algorithm for Embedding Neighborhoods Simultaneously that generalizes the t-Stochastic Neighborhood Embedding approach. By using different viewpoints in ENS-t-SNE's 3D embedding, on

Yinglong Miao, Rui Wang, Kostas Bekris

Recent work in robotic manipulation focuses on object retrieval in cluttered spaces under occlusion. Nevertheless, the majority of efforts lack an analysis of conditions for the completeness of the approaches or the methods apply only when objects can be removed from the workspace. This work formulates the general, occlusion-aware manipulation task, and focu

Richa Rastogi, Yair Schiff, Alon Hacohen, Zhaozhi Li

We introduce semi-parametric inducing point networks (SPIN), a general-purpose architecture that can query the training set at inference time in a compute-efficient manner. Semi-parametric architectures are typically more compact than parametric models, but their computational complexity is often quadratic. In contrast, SPIN attains linear complexity via a c

X. H. Li, P. Bai, S. H. Huang, X. Q. Bai

Ultra-broadband imaging devices with high performance are in great demand for a variety of technological applications, including imaging, remote sensing, and communications. An ultra-broadband up-converter is realized based on a p-GaAs homojunction interfacial workfunction internal photoemission (HIWIP) detector-light emitting diode (LED) device. The device

Promit Ghosal, Srinath Mahankali, Yihang Sun

Recently, neural networks have demonstrated remarkable capabilities in mapping two arbitrary sets to two linearly separable sets. The prospect of achieving this with randomly initialized neural networks is particularly appealing due to the computational efficiency compared to fully trained networks. This paper contributes by establishing that, given sufficie

Yakov Shlapentokh-Rothman

In the previous works [I. Rodnianski and Y. Shlapentokh-Rothman, Naked Singularities for the Einstein Vacuum Equations: The Exterior Solution, arXiv:1912.08478 and Y. Shlapentokh-Rothman, Naked Singularities for the Einstein Vacuum Equations: The Interior Solution, arXiv:2204.09891] we have introduced a new type of self-similarity for the Einstein vacuum equ

Taejoon Kong, Nicholas Backes, Gregory Phillips, Santosh Pandey

New combinations of existing antibiotics are being investigated to combat bacterial resilience. This requires detection technologies with reasonable cost, accuracy, resolution, and throughput. Here, we present a multi -drug screening platform for bacterial cultures by combining droplet microfluidics, search algorithms, and imaging with a wide field of view.

Ali Hummos

Animals thrive in a constantly changing environment and leverage the temporal structure to learn well-factorized causal representations. In contrast, traditional neural networks suffer from forgetting in changing environments and many methods have been proposed to limit forgetting with different trade-offs. Inspired by the brain thalamocortical circuit, we i

Yubing Dong, Pengnian Shen

In order to confirm the existence of the dibaryon state $d^*(2380)$ observed at WASA@COSY, we estimate the production of the possible dibaryon and anti-dibaryon pair $d^*\bar{d}^*$ at the energy region of the upcoming experiments at $\bar{\rm{P}}$anda. Based on some qualitative properties of $d^*$ extracted from the analysez in the non-relativistic quark mod

Bongjae Kim, Minjae Kim, Chang-Jong Kang, Jae-Ho Han

In this study, we report the magnetic energy landscape of Sr2RuO4 employing the generalized Bloch approach within density functional theory. We identify the two dominant magnetic instabilities, ferromagnetic and spin-density-wave, together with other predominant instabilities. We show that epitaxial strain can change the overall magnetic tendency of the syst

Michael Dorkenwald, Fanyi Xiao, Biagio Brattoli, Joseph Tighe

We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learning visual semantics (e.g., CVRL), SCVRL is capable of learning both semantic and motion patterns. For that, we reformulate the popular shuffling pretext task within a modern contra

David Hardin

The Rust programming language has garnered significant interest and use as a modern, type-safe, memory-safe, and potentially formally analyzable programming language. Our interest in Rust stems from its potential as a hardware/software co-assurance language, with application to critical systems such as autonomous vehicles. We report on the first known use of

Alessandro Coglio

This paper describes a C code generator for ACL2 that recognizes ACL2 representations of C constructs, according to a shallow embedding of C in ACL2, and translates those representations to the represented C constructs. The code generator also generates ACL2 theorems asserting the correctness of the C code with respect to the ACL2 code. The code generator cu

Alessandro Coglio

This paper describes a code generator that translates ACL2 constructs to corresponding Java constructs, according to a shallow embedding of ACL2 in Java. Starting from purely functional ACL2 code, the generated Java code exhibits imperative and object-oriented features like destructive updates, loops, and overloading. The overall translation from ACL2 to Jav

Alessandro Coglio, Eric McCarthy, Stephen Westfold, Daniel Balasubramanian

Syntheto is a surface language for carrying out formally verified program synthesis by transformational refinement in ACL2 using the APT toolkit. Syntheto aims at providing more familiarity and automation, in order to make this technology more widely usable. Syntheto is a strongly statically typed functional language that includes both executable and non-exe

Zhikang Li, Huiling Zhou, Shuai Bai, Peike Li

The fashion industry has diverse applications in multi-modal image generation and editing. It aims to create a desired high-fidelity image with the multi-modal conditional signal as guidance. Most existing methods learn different condition guidance controls by introducing extra models or ignoring the style prior knowledge, which is difficult to handle multip

Andrew T. Walter, Panagiotis Manolios

ACL2 provides a systems programming capability that allows one to write code that uses and extends ACL2 inside of ACL2. However, for soundness reasons, ACL2 bars the unrestricted use of certain kinds of programming constructs, like destructive updates, higher-order functions, eval, and arbitrary macros. We devised a methodology for writing code in Common Lis

Mertcan Temel

Automatic and efficient verification of multiplier designs, especially through a provably correct method, is a difficult problem. We show how to utilize a theorem prover, ACL2, to implement an efficient rewriting algorithm for multiplier design verification. Through a basic understanding of the features and data structures of ACL2, we created a verified prog

Ben Zhang, Zhetong Dong, Junsong Zhang, Hongwei Lin

The layered structure of deep neural networks hinders the use of numerous analysis tools and thus the development of its interpretability. Inspired by the success of functional brain networks, we propose a novel framework for interpretability of deep neural networks, that is, the functional network. We construct the functional network of fully connected netw

David M. Russinoff

Previous formulations of group theory in ACL2 and Nqthm, based on either "encapsulate" or "defn-sk", have been limited by their failure to provide a path to proof by induction on the order of a group, which is required for most interesting results in this domain beyond Lagrange's Theorem (asserting the divisibility of the order of a group by that of a subgro

Gabriel Menezes, Matteo Sergola

We employ the "KMOC" formalism of [1] to compute classical momentum deflections of spinning bodies with arbitrary spin orientations up to next-to-leading order (one loop). We do this in electrodynamics and gravity. The final result, valid for generic masses, is true for all spins at tree level and up to second (fourth) spin order for the electromagnetic (gra

William D. Young

The theory of asymptotic complexity provides an approach to characterizing the behavior of programs in terms of bounds on the number of computational steps executed or use of computational resources. We describe work using ACL2 to prove complexity properties of programs implemented in a simple imperative programming language embedding via an operational sema

Jagadish Bapanapally, Ruben Gamboa

One of the key steps in the proof of the Banach-Tarski Theorem is the introduction of a free group of rotations. First, a free group of reduced words is generated where each element of the set is represented as an ACL2 list. Then we demonstrate that there is a one-to-one relation between the set of reduced words and a set of 3D rotations. In this paper we pr

Warren A. Hunt, Vivek Ramanathan, J Strother Moore

VWSIM is a circuit simulator for rapid, single-flux, quantum (RSFQ) circuits. The simulator is designed to model and simulate primitive-circuit devices such as capacitors, inductors, Josephson Junctions, and can be extended to simulate other circuit families, such as CMOS. Circuit models can be provided in the native VWSIM netlist format or as SPICE-compatib

David M. Russinoff

We describe an ACL2 program that implements the Hebrew calendar and the formal verification of several of its properties, including the critical result that the algorithm that determines the placement of the new year ensures that the length of every year belongs to a small set of admissible values. These properties have been accepted for many centuries witho

David Greve, Jennifer Davis, Laura Humphrey

The decentralized perimeter surveillance system (DPSS) seeks to provide a decentralized protocol for evenly distributing surveillance of a perimeter over time across an ensemble of unmanned aerial vehicles (UAVs) whose members may communicate only when in close proximity to each other. The protocol must also converge to an even distribution of the perimeter

F. Blanchard, J. E. Nkeck, L. Guiramand, S. Zibod

Ferroelectric materials offer unprecedented ultrafast responses and are of great interest for the development of new polarizable media under the influence of an electromagnetic field. Recent research efforts have demonstrated the role of optical excitation and intense terahertz (THz) pulses in inducing a polar order and revealing a hidden phase transition in

Ruben Gamboa, Alicia Thoney

We report on our experience using ACL2 in the classroom to teach students about software testing. The course COSC2300 at the University of Wyoming is a mostly traditional Discrete Mathematics course, but with a clear focus on computer science applications. For instance, the section on logic and proofs is motivated by the desire to write proofs about computer