November 2022 arXiv papers — page 41

Yue Xin, Kang Zhou, Xuanyao Fong, Yumeng Yang

Spintronic devices have been widely studied for the hardware realization of artificial neurons. The stochastic switching of magnetic tunnel junction driven by the spin torque is commonly used to produce the sigmoid activation function. However, the shape of the activation function in previous studies is fixed during the training of neural network. This restr

M. Ablikim, M. N. Achasov, P. Adlarson, M. Albrecht

Using data taken at 29 center-of-mass energies between 4.16 and 4.70 GeV with the BESIII detector at the Beijing Electron Positron Collider corresponding to a total integrated luminosity of approximately 18.8 $\rm fb^{-1}$, the process $e^+e^- \to p p \bar{p} \bar{n} \pi^{-} + c.c.$ is observed for the first time with a statistical significance of $11.5\sigm

Bosheng Qin, Juncheng Li, Siliang Tang, Yueting Zhuang

Many studies have been conducted to improve the efficiency of Transformer from quadric to linear. Among them, the low-rank-based methods aim to learn the projection matrices to compress the sequence length. However, the projection matrices are fixed once they have been learned, which compress sequence length with dedicated coefficients for tokens in the same

Hanlin Gu, Lixin Fan, Xingxing Tang, Qiang Yang

This paper proposes a general spectral analysis framework that thwarts a security risk in federated Learning caused by groups of malicious Byzantine attackers or colluders, who conspire to upload vicious model updates to severely debase global model performances. The proposed framework delineates the strong consistency and temporal coherence between Byzantin

Yuki Kubota, Yoshihiko Okamoto, Tomoya Kanematsu, Takeshi Yajima

Sintered samples of Cr3Te4 and Cr2Te3 are found to show large strains accompanied by large volume changes under a magnetic field. In Cr3Te4, volume increases of deltaV/V = 500-1170 ppm by applying a magnetic field of 9 T are observed over the entire temperature range below 350 K. At room temperature, the deltaV/V value exceeds 1000 ppm, which is considerably

Al-Akhir Nayan, Boonserm Kijsirikul, Yuji Iwahori

Automatic lymph node (LN) segmentation and detection for cancer staging are critical. In clinical practice, computed tomography (CT) and positron emission tomography (PET) imaging detect abnormal LNs. Despite its low contrast and variety in nodal size and form, LN segmentation remains a challenging task. Deep convolutional neural networks frequently segment

Xianlin Song, Sihang Li, Zhuangzhuang Wang

As an important branch of photoacoustic microscopy, optical-resolution photoacoustic microscopy suffers from limited depth of field due to the strongly focused laser beam. In this work, a 3D information fusion algorithm based on 3D stationary wavelet transform and joint weighted evaluation optimization is proposed to fuse multi-focus photoacoustic data to ac

H. Zhang, S. Liu, S. Ghose, B. Ravel

Nanoscale BaTiO3 particles (approximately 10 nm) prepared by ball-milling a mixture of oleic acid and heptane have been reported to have an electric polarization several times larger than that for bulk BaTiO3. In this work, detailed local, intermediate, and long-range structural studies are combined with spectroscopic measurements to develop a model structur

Bo Jiang, Ya-Feng Liu

Projecting the distance measures onto a low-dimensional space is an efficient way of mitigating the curse of dimensionality in the classical Wasserstein distance using optimal transport. The obtained maximized distance is referred to as projection robust Wasserstein (PRW) distance. In this paper, we equivalently reformulate the computation of the PRW distanc

Irene Amiehe Essomba, Mauro Boero, Kerstin Falk, Guido Ori

Ionic liquids (ILs) feature a tailorable and wide range of structural, chemical and electronic properties that make this class of materials suitable to a broad variety of forefront applications in next-generation electronics. Yet, their intrinsic complexity call for special attention and experimental probes have still limitations in unraveling the interactio

Y. Zhao, C. Huang, J. Luo

The coronavirus disease (COVID-19) has caused enormous disruptions to not only the United States, but also the global economy. Due to the pandemic, issues in the supply chain and concerns about food shortage drove up the food prices. According to the U.S. Bureau of Labor Statistics, the prices for food increased 4.1% and 3.7% over the year ended in August 20

M. Afif Ismail, Chrisna Setyo Nugroho, Henry Tsz-King Wong

We propose a novel idea to detect a dark photon in gravitational wave experiments. Our setups are capable of performing the whole process of dark photon production, its decay products, and new physics signal discovery. This mini-LHC is inspired by the recent idea of dark photon detection using laser light in light shining through the wall (LSW) experiments s

Guangyu Wu, Anders Lindquist

In this paper, we aim to propose a consistent non-Gaussian Bayesian filter of which the system state is a continuous function. The distributions of the true system states, and those of the system and observation noises, are only assumed Lebesgue integrable with no prior constraints on what function classes they fall within. This type of filter has significan

Yao Lai, Yao Mu, Ping Luo

Placement is an essential task in modern chip design, aiming at placing millions of circuit modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of intense effort by hardware engineers to produce a layout to minimize delay and energy consumption, deep reinforcement learning has become an emerging autonomous tool. However, the

S. Ohkubo

$\alpha$ cluster structure in nuclei has been long understood based on the geometrical configuration picture. By using the spatially localized Brink $\alpha$ cluster model in the generator coordinate method, it is shown that the $\alpha$ cluster structure has the apparently opposing duality of crystallinity and condensation, a property of supersolids. To stu

Gan Luo, Zhi-Yu Zhang, Thomas G. Bisbas, Di Li

Ions (e.g., H$_3^+$, H$_2$O$^+$) have been used extensively to quantify the cosmic-ray ionization rate (CRIR) in diffuse sightlines. However, measurements of CRIR in low-to-intermediate density gas environments are rare, especially when background stars are absent. In this work, we combine molecular line observations of CO, OH, CH, and HCO$^+$ in the star-fo

Eunmi Chae

A recent progress on laser cooling of molecules is summarized. Since the development during 1980s for atomic species, laser cooling has been the very beginning step to cool and trap atoms for frontier research on quantum simulations, quantum sensing and precision measurements. Despite the complex internal structures of molecules, laser cooling of molecules h

Maohua Le, Reese Scott, Robert Styer

Let $a$, $b$, $c$ be distinct primes with $a<b$. Let $S(a,b,c)$ denote the number of positive integer solutions $(x,y,z)$ of the equation $a^x + b^y = c^z$. In a previous paper \cite{LeSt} it was shown that if $(a,b,c)$ is a triple of distinct primes for which $S(a,b,c)>1$ and $(a,b,c)$ is not one of the six known such triples then $(a,b,c)$ must be one of t

Jiacheng Zhang, Wenyi Yan, Ye Zhang

In this paper, a new speech feature fusion method is proposed for speaker recognition on the basis of the cross gate parallel convolutional neural network (CG-PCNN). The Mel filter bank features (MFBFs) of different frequency resolutions can be extracted from each speech frame of a speaker's speech by several Mel filter banks, where the numbers of the triang

Madhuvanti Muralikrishnan, Amr Hilal, Chreston Miller, Dina Smith-Glaviana

Historic dress artifacts are a valuable source for human studies. In particular, they can provide important insights into the social aspects of their corresponding era. These insights are commonly drawn from garment pictures as well as the accompanying descriptions and are usually stored in a standardized and controlled vocabulary that accurately describes g

Ritesh Dwivedi

In this paper, we discuss about finite groups in which, CGH = NGH, for every abelian subgroup H of non prime power order. Also, we classify all such nilpotent and minimal non nilpotent groups.

Xinying Qiu, Guofeng Shi

We propose InDEX, an Indonesian Idiom and Expression dataset for cloze test. The dataset contains 10438 unique sentences for 289 idioms and expressions for which we generate 15 different types of distractors, resulting in a large cloze-style corpus. Many baseline models of cloze test reading comprehension apply BERT with random initialization to learn embedd

Harit Vishwakarma, Nicholas Roberts, Frederic Sala

Weak supervision (WS) is a rich set of techniques that produce pseudolabels by aggregating easily obtained but potentially noisy label estimates from a variety of sources. WS is theoretically well understood for binary classification, where simple approaches enable consistent estimation of pseudolabel noise rates. Using this result, it has been shown that do

Guangyu Wu, Anders Lindquist

In this paper, we extend our results on the univariate non-Gaussian Bayesian filter using power moments to the multivariate systems, which can be either linear or nonlinear. Doing this introduces several challenging problems, for example a positive parametrization of the density surrogate, which is not only a problem of filter design, but also one of the mul

Xinying Qiu, Shuxuan Liao, Jiajun Xie, Jian-Yun Nie

In the prompt-specific holistic score prediction task for Automatic Essay Scoring, the general approaches include pre-trained neural model, coherence model, and hybrid model that incorporate syntactic features with neural model. In this paper, we propose a novel approach to extract and represent essay coherence features with prompt-learning NSP that shows to

Ting-Chun Lin, Isaac H. Kim, Min-Hsiu Hsieh

Let $S(\rho)$ be the von Neumann entropy of a density matrix $\rho$. Weak monotonicity asserts that $S(\rho_{AB}) - S(\rho_A) + S(\rho_{BC}) - S(\rho_C)\geq 0$ for any tripartite density matrix $\rho_{ABC}$, a fact that is equivalent to the strong subadditivity of entropy. We prove an operator inequality, which, upon taking an expectation value with respect

Kang Yin, Byeong-Hoo Lee, Byoung-Hee Kwon, Jeong-Hyun Cho

Data augmentation approaches are widely explored for the enhancement of decoding electroencephalogram signals. In subject-independent brain-computer interface system, domain adaption and generalization are utilized to shift source subjects' data distribution to match the target subject as an augmentation. However, previous works either introduce noises (e.g.

J. W. Burby, P. J. Morrison

We present a generalization of Vlasov-Maxwell kinetic theory that accounts for intense electromagnetic fields. A strongly-radiating, possibly optically-thick plasma is decomposed into fragments, each comprising a charged particle together with its self-generated electromagnetic field. Assuming weak inter-fragment correlations, but strong intra-fragment corre

David Yudin, Daniel Floryan, Tyler Van Buren

We use small-amplitude inviscid theory to study the swimming performance of a flexible flapping plate with time-varying flexibility. The stiffness of the plate oscillates at twice the frequency of the kinematics in order to maintain a symmetric motion. Plates with constant and time-periodic stiffness are compared over a range of mean plate stiffness, oscilla

Adrian B. Culver, Pratik Sathe, Rahul Roy

We present a perturbative approach to disordered systems in one spatial dimension that accesses the full range of phase disorder and clarifies the connection between localization and phase information. We consider a long chain of identically disordered scatterers and expand in the reflection strength of any individual scatterer. We apply this expansion to se

J. W. Burby, R. S. MacKay, S. Naik

In a magnetic field, transitions between classes of guiding-centre motion can lead to cross-field diffusion and escape. We say a magnetic field is isodrastic if guiding centres make no transitions between classes of motion. Therefore, this is an important ideal for enhancing confinement. First, we present a weak formulation, based on the longitudinal adiabat

Byoung-Hee Kwon, Byeong-Hoo Lee, Jeong-Hyun Cho

An electroencephalogram is an effective approach that provides a bidirectional pathway between the user and computer in a non-invasive way. In this study, we adopted the visual imagery data for controlling the BCI-based robotic arm. Visual imagery increases the power of the alpha frequency range of the visual cortex over time as the user performs the task. W

Jinshuai Bai, Laith Alzubaidi, Qingxia Wang, Ellen Kuhl

Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challenging or even impossible in many real-world applications, such as structural risk estimation and medical diagnosis. This presents a significant barrier to the practical implementati

Zhanna Alsar, Bayan Kurbanova, Nurzhan Serik, Kurbangali Tnyshtykbayev

Slow sand filtration is most appropriate where there is funding to subsidize the initial cost of the filter, available training for use and maintenance, locally available sand, and a transportation network capable of moving the filter. Since the experimental study of slow filtration is taking a long time, theoretical analysis, modelling and simulation studie

Ryodai Kawaguchi, Shinji Tsujikawa

Primordial black holes (PBHs) can be the source for all or a part of today's dark matter density. Inflation provides a mechanism for generating the seeds of PBHs in the presence of a temporal period where the velocity of an inflaton field $\phi$ rapidly decreases toward 0. We compute the primordial power spectra of curvature perturbations generated during Ga

Pablo Shmerkin, Hong Wang

We show that the Hausdorff dimension of $(s,t)$-Furstenberg sets is at least $s+t/2+\epsilon$, where $\epsilon>0$ depends only on $s$ and $t$. This improves the previously best known bound for $2s<t\le 1+\epsilon(s,t)$, in particular providing the first improvement since 1999 to the dimension of classical $s$-Furstenberg sets for $s<1/2$. We deduce this from

Siddhant Das, Dirk-André Deckert, Leopold Kellers, Simon Krekels

The double-slit experiment is one of the quintessential quantum experiments. However, it tends to be overlooked that a theoretical account of this experiment requires the specification of the joint position and time distribution of detection at the screen, whose position marginal yields the famous interference pattern. The difficulty then arises what this di

Thomas Gilton

In this paper, we investigate the relationship between the Tukey order and PCF theory, as applied to sets of regular cardinals. We show that it is consistent that for all sets $A$ of regular cardinals that the Tukey spectrum of $A$, denoted $\operatorname{spec}(A)$, is equal to the set of possible cofinalities of $A$, denoted $\operatorname{pcf}(A)$; this is

Pedro Lopes, Manpreet Singh

Quandles are self-distributive, right-invertible, idempotent algebras. A group with conjugation for binary operation is an example of a quandle. Given a quandle $(Q, \ast)$ and a positive integer $n$, define $a\ast_n b = (\cdots (a\ast \underbrace{b)\ast \cdots )\ast b}_{n}$, where $a, b \in Q$. Then, $(Q, \ast_n)$ is again a quandle. We set forth the follow

Emile Bouaziz

We show that any continuous $\mathbf{C}$-linear Lie algebra splitting of the symbol map from the Atiyah algebra of a vector bundle on a complex manifold is given by a differential operator of order at most the rank of the bundle plus one. Bundles equipped with such a splitting can be thought of as \emph{infinitesimally equivariant} bundles, and our theorem i

Sérgio Jesus, José Pombal, Duarte Alves, André Cruz

Evaluating new techniques on realistic datasets plays a crucial role in the development of ML research and its broader adoption by practitioners. In recent years, there has been a significant increase of publicly available unstructured data resources for computer vision and NLP tasks. However, tabular data -- which is prevalent in many high-stakes domains --

Kevin Lin, Chung-Ching Lin, Lin Liang, Zicheng Liu

Traditional methods of reconstructing 3D human pose and mesh from single images rely on paired image-mesh datasets, which can be difficult and expensive to obtain. Due to this limitation, model scalability is constrained as well as reconstruction performance. Towards addressing the challenge, we introduce Mesh Pre-Training (MPT), an effective pre-training st

Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft

In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen to apply their methods on real-world problems, implementing such methods in real industry environments often is a frustrating and tedious process. Generally, academic researc

Govind R. Gopal, Bhaskar D. Rao

We examine the problem of uplink cell-free access point (AP) placement in the context of optimal throughput. In this regard, we formulate two main placement problems, namely the sum rate and minimum rate maximization problems, and discuss the challenges associated with solving the underlying optimization problem with the help of some simple scenarios. As a p

Thien Nguyen, Daanish Arya, Marcus Doherty, Nils Herrmann

Quantum computing has the potential to offer substantial computational advantages over conventional computing. Recent advances in quantum computing hardware and algorithms have enabled a class of classically parallel quantum workloads, whereby individual quantum circuits can execute independently on many quantum processing units. Here, we present the full-st

Clayton Sanford, Anna Kwa, Oliver Watt-Meyer, Spencer Clark

While previous works have shown that machine learning (ML) can improve the prediction accuracy of coarse-grid climate models, these ML-augmented methods are more vulnerable to irregular inputs than the traditional physics-based models they rely on. Because ML-predicted corrections feed back into the climate model's base physics, the ML-corrected model regula

Cory Glover, Tyler Jones, Mark Kempton, Alice Oveson

In this paper, we consider three variations on standard PageRank: Non-backtracking PageRank, $\mu$-PageRank, and $\infty$-PageRank, all of which alter the standard formula by adjusting the likelihood of backtracking in the algorithm's random walk. We show that in the case of regular and bipartite biregular graphs, standard PageRank and its variants are equiv

Luke W. Sagers, James A. Diao, Matthew Groh, Pranav Rajpurkar

Dermatological classification algorithms developed without sufficiently diverse training data may generalize poorly across populations. While intentional data collection and annotation offer the best means for improving representation, new computational approaches for generating training data may also aid in mitigating the effects of sampling bias. In this p

Francesco Capuano, Davorin Peceli, Gabriele Tiboni, Alexandr Špaček

In this study we explore the optimization of laser pulse duration to obtain the shortest possible pulse. We do this by employing a feedback loop between a pulse shaper and pulse duration measurements. We apply to this problem several iterative algorithms including gradient descent, Bayesian Optimization and genetic algorithms, using a simulation of the actua

Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Alexandre Lacoste

Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with the ability to control and influence the environment. However, without appropriate knowledge and exploration, skills may provide control only over a restricted area of the environment, limiting their applicability. Furthermo

Pedro Lauand, Davide Poderini, Ranieri Nery, George Moreno

Seen from the modern lens of causal inference, Bell's theorem is nothing else than the proof that a specific classical causal model cannot explain quantum correlations. It is thus natural to move beyond Bell's paradigmatic scenario and consider different causal structures. For the specific case of three observable variables, it is known that there are three

Forrest Valdez, Viphretuo Mere, Shayan Mookherjea

An integrated hybrid thin-film lithium niobate (TFLN) electro-optic Mach-Zehnder modulator (MZM) is shown at near-infrared wavelengths. The design uses TFLN bonded to planarized silicon nitride waveguide circuits, and does not require etching or patterning of TFLN. The push-pull MZM achieves a half-wave voltage length product ($V_\pi L$) of 0.8 V$.$cm at 784

Gerald V. Dunne, Adrien Florio, Dmitri E. Kharzeev

The Schwinger process in strong electric fields creates particles and antiparticles that are entangled. The entropy of entanglement between particles and antiparticles has been found to be equal to the statistical Gibbs entropy of the produced system. Here we study the effect of quantum interference in sequences of electric pulses, and show that quantum inte

Soodeh Atefi, Sakshyam Panda, Emmanouil Panaousis, Aron Laszka

In the wake of a cybersecurity incident, it is crucial to promptly discover how the threat actors breached security in order to assess the impact of the incident and to develop and deploy countermeasures that can protect against further attacks. To this end, defenders can launch a cyber-forensic investigation, which discovers the techniques that the threat a

Paolo A. Sossi, Peter M. E. Tollan, James Badro, Dan J. Bower

Atmospheres are products of time-integrated mass exchange between the surface of a planet and its interior. On Earth and other planetary bodies, magma oceans likely marked significant atmosphere-forming events, during which both steam- and carbon-rich atmospheres may have been generated. However, the nature of Earth's early atmosphere, and those around other

Yanbang Wang, Jon Kleinberg

We study an issue commonly seen with graph data analysis: many real-world complex systems involving high-order interactions are best encoded by hypergraphs; however, their datasets often end up being published or studied only in the form of their projections (with dyadic edges). To understand this issue, we first establish a theoretical framework to characte

K. Trachenko

Fundamental physical constants play a profound role in physics. For example, they govern nuclear reactions, formation of stars, nuclear synthesis and stability of biologically vital elements. These are high-energy processes discussed in particle physics, astronomy and cosmology. More recently, it was realised that fundamental physical constants extend their

Christian Howell, Mark Kempton, Kellon Sandall, John Sinkovic

A result of Bapat and Sivasubramanian gives the inertia of the distance squared matrix of a tree. We develop general tools on how pendant vertices and degree 2 vertices affect the inertia of the distance squared matrix and use these to give an alternative proof of this result. We further use these tools to extend this result to certain families of unicyclic

Sara Chahid, Serafim Teknowijoyo, Armen Gulian

We designed and experimentally demonstrated a four-terminal superconducting device which can function as a non-latching (reversible) superconducting switch from the diode regime to the resistive state by applying a control current much smaller than the main transport current. The device utilizes a vortex-based superconducting diode mechanism which is switche

Daniel Opoku Mensah, Godwin Badu-Marfo, Bilal Farooq

Deep generative models have become useful for synthetic data generation, particularly population synthesis. The models implicitly learn the probability distribution of a dataset and can draw samples from a distribution. Several models have been proposed, but their performance is only tested on a single cross-sectional sample. The implementation of population

Mainak Pal, Andreas Kreisel, W. A. Atkinson, P. J. Hirschfeld

Theoretical studies of disordered $d$-wave superconductors have focused, with a few exceptions, on optimally doped models with strong scatterers. Addressing recent controversies about the nature of the overdoped cuprates, however, requires studies of the weaker scattering associated with dopant atoms. Here we study simple models of such systems in the self-c

David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup

Using massive datasets to train large-scale models has emerged as a dominant approach for broad generalization in natural language and vision applications. In reinforcement learning, however, a key challenge is that available data of sequential decision making is often not annotated with actions - for example, videos of game-play are much more available than

Yuhan Zhao, Quanyan Zhu

Trajectory guidance requires a leader robotic agent to assist a follower robotic agent to cooperatively reach the target destination. However, planning cooperation becomes difficult when the leader serves a family of different followers and has incomplete information about the followers. There is a need for learning and fast adaptation of different cooperati

Maryam Bagheri, Josephine Lamp, Xugui Zhou, Lu Feng

Machine Learning (ML) technologies have been increasingly adopted in Medical Cyber-Physical Systems (MCPS) to enable smart healthcare. Assuring the safety and effectiveness of learning-enabled MCPS is challenging, as such systems must account for diverse patient profiles and physiological dynamics and handle operational uncertainties. In this paper, we devel

Cyril Grunspan, Ricardo Perez-Marco

We propose Ping-Pong Swaps: A secure pure peer-to-peer crosschain swap mechanism of tokens or cryptocurrencies that does not require escrow nor an intermediate trusted third party. The only technical requirement is to be able to open unidirectional payment channels in both blockchain protocols. This allows anonymous cryptocurrency trading without the need of

Ashish Kumar Meena, Adi Zitrin, Yolanda Jiménez-Teja, Erik Zackrisson

We report the discovery of two extremely magnified lensed star candidates behind the galaxy cluster MACS J0647.7+7015, in recent multi-band James Webb Space Telescope (JWST) NIRCam observations. The candidates are seen in a previously known, $z_{phot}\simeq4.8$ dropout giant arc that straddles the critical curve. The candidates lie near the expected critical

Chenghao Wu, Hamila Mailee, Zahra Montazeri, Tobias Ritschel

Differentiable rasterization changes the standard formulation of primitive rasterization -- by enabling gradient flow from a pixel to its underlying triangles -- using distribution functions in different stages of rendering, creating a "soft" version of the original rasterizer. However, choosing the optimal softening function that ensures the best performanc

Xin Li, Xiangrui Li, Deng Pan, Yao Qiang

Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of the feature extractor (i.e., last hidden layer) and a linear classifier (i.e., output layer) that are trained jointly with stochastic gradient descent (SGD) on the loss function (e.g., cross-entropy). In each epoch, the true gradient of the loss function is estimated using a m

Xiaowu Dai, Wenlu Xu, Yuan Qi, Michael I. Jordan

Online platforms in the Internet Economy commonly incorporate recommender systems that recommend products (or "arms") to users (or "agents"). A key challenge in this domain arises from myopic agents who are naturally incentivized to exploit by choosing the optimal arm based on current information, rather than exploring various alternatives to gather informat

Frano Rajič, Ivan Stresec, Axel Marmet, Tim Poštuvan

There is no such thing as a perfect dataset. In some datasets, deep neural networks discover underlying heuristics that allow them to take shortcuts in the learning process, resulting in poor generalization capability. Instead of using standard cross-entropy, we explore whether a modulated version of cross-entropy called focal loss can constrain the model so

Gaurav Nirala, Siva T. Pradyumna, Ashok Kumar, Alberto M. Marino

The ability to use the temporal and spatial degrees of freedom of quantum states of light to encode and transmit information is crucial for the implementation of a robust and efficient quantum network. In particular, the large dimensionality of the spatial degree of freedom promises to provide significant enhancements; however, such promise has largely been

Margaret Gamalo, Yihua Zhao, Aijun Gao, Jingjing Ye

Pediatric patients should have access to medicines that have been appropriately evaluated for safety and efficacy. Given this goal of revised labelling, the adequacy of the pediatric clinical development plan and resulting safety database must inform a favorable benefit-risk assessment for the intended use of the medicinal product. While extrapolation from a

Yan Han, Edward W Huang, Wenqing Zheng, Nikhil Rao

Although the bipartite shopping graphs are straightforward to model search behavior, they suffer from two challenges: 1) The majority of items are sporadically searched and hence have noisy/sparse query associations, leading to a \textit{long-tail} distribution. 2) Infrequent queries are more likely to link to popular items, leading to another hurdle known a

Patrick Lee, Anna Feldman, Jing Peng

This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022. Participants were invited to investigate the euphemism detection task: given input text, identify whether it contains a euphemism. The input data is a corpus of sentences containing potentiall

Azer Akhmedov, Pratyush Mishra

We prove the Girth Alternative for a sub-class of the HNN extensions of finitely generated groups. We also produce counterexamples to show that beyond our class, the alternative fails in general.

Eduarda Fonseca da Nova Cruz, David Möckli

Bell non-locality is a term that applies to specific modifications and interpretations of quantum mechanics. Yet, Bell's original 1964 theorem is often used to assert that unmodified quantum mechanics itself is non-local and that local realist interpretations are untenable. Motivated by Bell's original inequality, we identify four viable categories of quantu

Jianqiao Mo, Jayanth Gopinath, Brandon Reagen

Privacy and security have rapidly emerged as priorities in system design. One powerful solution for providing both is privacy-preserving computation, where functions are computed directly on encrypted data and control can be provided over how data is used. Garbled circuits (GCs) are a PPC technology that provide both confidential computing and control over h

Austin Cheng, Andy Cai, Santiago Miret, Gustavo Malkomes

We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustness guarantees. Molecular string representations, such as SMILES and SELFIES, serve as the basis for molecular generation and optimization in chemical language models, deep generati

Yang Zhang, Ling-Fang Lin, Adriana Moreo, Elbio Dagotto

The layered material Mn$_3$Si$_2$Te$_6$, with alternating stacking honeycomb and triangular layers, is attracting considerable attention due to its rich physical properties. Here, using density functional theory and classical Monte Carlo (MC) methods, we systematically study this system. Near the Fermi level, the states are mainly contributed by Te $5p$ orbi

Francesco Paolo Rizzuto, Thorsten Naab, Antti Rantala, Peter H. Johansson

We present $N\mathrm{-body} $ simulations, including post-Newtonian dynamics, of dense clusters of low-mass stars harbouring central black holes (BHs) with initial masses of 50, 300, and 2000 $\mathrm{M_{\odot}}$. The models are evolved with the $N\mathrm{-body} $ code \textsc{bifrost} to investigate the possible formation and growth of massive BHs by the ti

Gaoyuan Wu

Shape optimization is of great significance in structural engineering, as an efficient geometry leads to better performance of structures. However, the application of gradient-based shape optimization for structural and architectural design is limited, which is partly due to the difficulty and the complexity in gradient evaluation. In this work, an efficient

Tanzila Rahman, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov

There has been a recent explosion of impressive generative models that can produce high quality images (or videos) conditioned on text descriptions. However, all such approaches rely on conditional sentences that contain unambiguous descriptions of scenes and main actors in them. Therefore employing such models for more complex task of story visualization, w

Brooke Polak, Hubert Klahr

Planetary embryos are built through the collisional growth of 10-100 km sized objects called planetesimals, a formerly large population of objects, of which asteroids, comets and Kuiper-Belt objects represent the leftovers from planet formation in our solar system. Here, we follow the paradigm that turbulence created over-dense pebble clouds, which then coll

Vikas Raunak, Arul Menezes

Large sequence to sequence models for tasks such as Neural Machine Translation (NMT) are usually trained over hundreds of millions of samples. However, training is just the origin of a model's life-cycle. Real-world deployments of models require further behavioral adaptations as new requirements emerge or shortcomings become known. Typically, in the space of

R. V. Bettker, P. P. Minini, A. G. Pereira, M. Ritt

We study the problem of learning good heuristic functions for classical planning tasks with neural networks based on samples represented by states with their cost-to-goal estimates. The heuristic function is learned for a state space and goal condition with the number of samples limited to a fraction of the size of the state space, and must generalize well f

Kumar Sankar Ray

We mimic the cognitive ability of Human perception, based on Bayesian hypothesis, to recognize view-based 3D objects. We consider approximate Bayesian (Empirical Bayesian) for perceptual inference for recognition. We essentially handle computation with perception.

Andreas Lohrer, Daniyal Kazempour, Maximilian Hünemörder, Peer Kröger

Unsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier datasets. Outliers play a significant role, since they bear the potential to distort the predictions of a machine learning algorithm on a given dataset. Especially among PCA-based methods, outliers have an additional destru

Claire David, Victor Marsault, Nadime Francis

The formalism of RPQs (regular path queries) is an important building block of most query languages for graph databases. RPQs are generally evaluated under homomorphism semantics; in particular only the endpoints of the matched walks are returned. Practical applications often need the full matched walks to compute aggregate values. In those cases, homomorphi

Aravind Gollakota, Adam R. Klivans, Pravesh K. Kothari

A remarkable recent paper by Rubinfeld and Vasilyan (2022) initiated the study of \emph{testable learning}, where the goal is to replace hard-to-verify distributional assumptions (such as Gaussianity) with efficiently testable ones and to require that the learner succeed whenever the unknown distribution passes the corresponding test. In this model, they gav

C. J. Nelson, L. Kleint

Approximately 0.01 % of all Si IV 1394 A spectra sampled in 2013 and 2014 by the Interface Region Imaging Spectrograph (IRIS) have IRIS burst profiles. However, these events are not evenly distributed across datasets with 19.31 % of these spectra being identified in only six rasters. Here, we investigate five of these six datasets, to understand why they con

Balint Varga, Selina Meier, Soeren Hohmann

This paper presents the development of a real-time simulator for the validation of controlling a large vehicle manipulator. The need for this development can be justified by the lack of such a simulator: There are neither open source projects nor commercial products, which would be suitable for testing cooperative control concepts. First, we present the nonl

Thomas M. Hehn, Julian F. P. Kooij, Dariu M. Gavrila

Various state-of-the-art self-supervised visual representation learning approaches take advantage of data from multiple sensors by aligning the feature representations across views and/or modalities. In this work, we investigate how aligning representations affects the visual features obtained from cross-view and cross-modal contrastive learning on images an

Amanpreet Singh, Mike D'Arcy, Arman Cohan, Doug Downey

Learned representations of scientific documents can serve as valuable input features for downstream tasks without further fine-tuning. However, existing benchmarks for evaluating these representations fail to capture the diversity of relevant tasks. In response, we introduce SciRepEval, the first comprehensive benchmark for training and evaluating scientific

Valentin D. Kunz, Raphael C. Assier

We study the problem of diffraction by a right-angled no-contrast penetrable wedge by means of a two-complex-variable Wiener-Hopf approach. Specifically, the analyticity properties of the unknown (spectral) functions of the two-complex-variable Wiener-Hopf equation are studied. We show that these spectral functions can be analytically continued onto a two-co

Rupesh Baniya, Rocky Talchabhadel, Jeeban Panthi, Ganesh R Ghimire

There is a pressing need for a transition from fossil fuel to renewable energy to meet the increasing energy demands and reduce greenhouse gas (GHG) emissions. The Himalayas possess substantial renewable energy potential that can be harnessed through hydropower projects due to its peculiar topographic characteristics and abundant water resources. However, th

Huma Jamil, Yajing Liu, Christina M. Cole, Nathaniel Blanchard

Previous work has shown that a neural network with the rectified linear unit (ReLU) activation function leads to a convex polyhedral decomposition of the input space. These decompositions can be represented by a dual graph with vertices corresponding to polyhedra and edges corresponding to polyhedra sharing a facet, which is a subgraph of a Hamming graph. Th

Vladimir Shein

We prove that if $X$ is a smooth projective variety of dimension greater than 1 over a field $K$ of characteristic zero such that $\operatorname{Pic}(X_{\bar{K}}) = \mathbb{Z}$ and $X_{\bar{K}}$ is simply connected, then the natural map $\rho: \operatorname{Aut}(X) \to \operatorname{Aut}(\operatorname{Sym}^d(X))$ is an isomorphism for every $d > 0$. We also

Kaiyan Li, Hua Li, Mark A. Anastasio

A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. These methods are typically trained by minimizing loss functions that quantify a distance between the denoised image, or a transformed version of it, and the defined target image (e.g., a noise-free or low-noise image). They have demonstrated

Giacomo Bighin, Tilman Enss, Nicolò Defenu

The concept of universality has shaped our understanding of many-body physics, but is mostly limited to homogenous systems. Here, we present a study of universality on a non-homogeneous graph, the long-range diluted graph (LRDG). Its scaling theory is controlled by a single parameter, the spectral dimension $d_{s}$, which plays the role of the relevant param

R. S. Beckmann, Y. Dubois, M. Volonteri, C. A. Dong-Páez

While it is well established that supermassive black holes (SMBHs) co-evolve with their host galaxy, it is currently less clear how lower mass black holes, so-called intermediate mass black holes (IMBHs), evolve within their dwarf galaxy hosts. In this paper, we present results on the evolution of a large sample of IMBHs from the NewHorizon simulation. We sh