April 2023 arXiv papers — page 81

Lucio Bertoli-Barsotti, Marek Gagolewski, Grzegorz Siudem, Barbara Żogała-Siudem

We introduce an iterative discrete information production process where we can extend ordered normalised vectors by new elements based on a simple affine transformation, while preserving the predefined level of inequality, G, as measured by the Gini index. Then, we derive the family of empirical Lorenz curves of the corresponding vectors and prove that it is

Alina Mreńca-Kolasińska, Szu-Chao Chen, Ming-Hao Liu

The presence of periodic modulation in graphene leads to a reconstruction of the band structure and formation of minibands. In an external uniform magnetic field, a fractal energy spectrum called Hofstadter butterfly is formed. Particularly interesting in this regard are superlattices with tunable modulation strength, such as electrostatically induced ones i

Jane Glanzer, Siddharth Soni, Jaidyn Spoon, Anamaria Effler

Environmental seismic disturbances limit the sensitivity of LIGO gravitational wave detectors. Trains near the LIGO Livingston detector produce low frequency (0.5-10 Hz) ground noise that couples into the gravitational wave sensitive frequency band (10-100 Hz) through light reflected in mirrors and other surfaces. We investigate the effect of trains during t

Hemin Rahimi, Hadi jahanirad

3D FPGAs have recently been produced as the next generation of the FPGA family to continue the integration of more transistors on a single chip seamlessly. In this paper, we propose a complete CAD flow to implement an arbitrary logic circuit on the 3D FPGA. The partitioning and placement stages of the flow are based on the simulated annealing algorithm. Furt

Yuanjiang Tang, Chao Liang, Xin Wen, Weipeng Li

Narrow linewidth is a long-pursuing goal in precision measurement and sensing. We propose a parity-time (PT )-symmetric feedback method to narrow the linewidths of resonance systems. By using a quadrature measurement-feedback loop, we transform a dissipative resonance system into a PT-symmetric system. Unlike the conventional PT-symmetric systems which typic

Gilda Rech Bansimba, Regis Freguin Babindamana, Basile Guy R. Bossoto

From the results in the literature, the algebraic set of the hyperbola with parameter $n$ defined by $\mathcal{B}_{n}(X, Y, Z)_{\mid_{x\geq 4n}}= \displaystyle \lbrace \left(X: Y: Z\right)\in \mathbb{P}^{2}(\mathbb{Q}) \ \vert \ \displaystyle Y^{2}=X^{2}-4nXZ \rbrace$ where $n$ is a semiprime is proved to be in relation with prime factors of $n$. In the affi

Tong Zhang, Wenxue Cui, Chen Hui, Feng Jiang

Deep network-based image and video Compressive Sensing(CS) has attracted increasing attentions in recent years. However, in the existing deep network-based CS methods, a simple stacked convolutional network is usually adopted, which not only weakens the perception of rich contextual prior knowledge, but also limits the exploration of the correlations between

Aleksandr Talitckii, Brendon K. Colbert, Matthew M. Peet

The accuracy and complexity of machine learning algorithms based on kernel optimization are determined by the set of kernels over which they are able to optimize. An ideal set of kernels should: admit a linear parameterization (for tractability); be dense in the set of all kernels (for robustness); be universal (for accuracy). Recently, a framework was propo

Guolei Xiang

The properties of inorganic nanomaterials in adsorption, catalysis, and photoluminescence are commonly affected or dominated by particle size, surface ligand, and ligand coverage; however, it has been long remaining challenging to generally understand underlying physical and chemical principles with a unified model. In this review the electronic-level princi

Shreyan Mitra, Leilani Gilpin

Explanatory systems make machine learning models more transparent. However, they are often inconsistent. In order to quantify and isolate possible scenarios leading to this discrepancy, this paper compares two explanatory systems, SHAP and LIME, based on the correlation of their respective importance scores using 14 machine learning models (7 regression and

Rahul Kale, Vrizlynn L. L. Thing

With increased reliance on Internet based technologies, cyberattacks compromising users' sensitive data are becoming more prevalent. The scale and frequency of these attacks are escalating rapidly, affecting systems and devices connected to the Internet. The traditional defense mechanisms may not be sufficiently equipped to handle the complex and ever-changi

Forrest DiPaola, Anshuman Bhardwaj, Lydia Sam

Contemporary sea level rise (SLR) research seldom considers enabling effective geovisualisation for the communities. This lack of knowledge transfer impedes raising awareness on climate change and its impacts. The goal of this study is to produce an online SLR map accessible to the public that allows them to interact with evolving high-resolution geospatial

Donghyun Kang, Robert S. Danziger, Jalees Rehman, James A. Evans

Market bubbles emerge when asset prices are driven unsustainably higher than asset values and shifts in belief burst them. We demonstrate the same phenomenon for biomedical knowledge when promising research receives inflated attention. We predict deflationary events by developing a diffusion index that captures whether research areas have been amplified with

Yunsu Jang, Youngju Park, Jeil Jung, Hongki Min

We investigate the total energies of spontaneous spin-valley polarized states in bi-, tri-, and tetralayer rhombohedral graphene where the long-range Coulomb correlations are accounted for within the random phase approximation. Our analysis of the phase diagrams for varying carrier doping and perpendicular electric fields shows that the exchange interaction

Subhrajyoty Roy, Abir Sarkar, Abhik Ghosh, Ayanendranath Basu

Robust inference based on the minimization of statistical divergences has proved to be a useful alternative to classical techniques based on maximum likelihood and related methods. Basu et al. (1998) introduced the density power divergence (DPD) family as a measure of discrepancy between two probability density functions and used this family for robust estim

Ruizhi Wang, Xiangtao Wang, Zhenghua Xu, Wenting Xu

In clinical scenarios, multiple medical images with different views are usually generated at the same time, and they have high semantic consistency. However, the existing medical report generation methods cannot exploit the rich multi-view mutual information of medical images. Therefore, in this work, we propose the first multi-view medical report generation

Pooria Mazaheri, Azam Asilian Bidgoli, Shahryar Rahnamayan, H. R. Tizhoosh

Recently, deep learning has started to play an essential role in healthcare applications, including image search in digital pathology. Despite the recent progress in computer vision, significant issues remain for image searching in histopathology archives. A well-known problem is AI bias and lack of generalization. A more particular shortcoming of deep model

Sepideh Azizi, Tahmineh Azizi

During few last years, climate change including global warming which is attributed to human activities and also its long-term adverse effects on the planet's functions have been identified as the most challenging discussion topics which have arisen many concerns and efforts to find the possible solutions. Since the warmth arising from Earth's landscapes affe

Norita Kawanaka, Shin Mineshige

The soft X-ray excess in the spectra of active galactic nuclei is characterized by similar electron temperatures of 0.1 -- 0.3 keV and similar photon indices around 2.2 -- 3, if fitted with inverse Comptonization. It remains a puzzle why both values are not sensitive to the black hole mass nor accretion rate. Supposing that the scattering-dominated surface l

Ki-Young Choi, Jinn-Ouk Gong, Junghoon Joh, Wan-Il Park

We investigate the mass range and the corresponding free-streaming length scale of dark matter produced non-thermally from decay of heavy objects which can be either dominant or sub-dominant at the moment of decay. We show that the resulting dark matter could be very light well below keV scale with a free-streaming length satisfying the Lyman-{\alpha} constr

Morteza Homayounfar, Mohamad Koohi-Moghadam, Reza Rawassizadeh, Varut Vardhanabhuti

As deep neural networks include a high number of parameters and operations, it can be a challenge to implement these models on devices with limited computational resources. Despite the development of novel pruning methods toward resource-efficient models, it has become evident that these models are not capable of handling "imbalanced" and "limited number of

Zhenxiao Zhang, Yuanxiong Guo, Yuguang Fang, Yanmin Gong

Federated Learning (FL) is a collaborative learning framework that enables edge devices to collaboratively learn a global model while keeping raw data locally. Although FL avoids leaking direct information from local datasets, sensitive information can still be inferred from the shared models. To address the privacy issue in FL, differential privacy (DP) mec

Anh-Khoa Nguyen Vu, Thanh-Toan Do, Nhat-Duy Nguyen, Vinh-Tiep Nguyen

Few-shot learning is proposed to tackle the problem of scarce training data in novel classes. However, prior works in instance-level few-shot learning have paid less attention to effectively utilizing the relationship between categories. In this paper, we exploit the hierarchical information to leverage discriminative and relevant features of base classes to

Song Yu, Shufeng Gong, Yanfeng Zhang, Wenyuan Yu

Real-world graphs are constantly evolving, which demands updates of the previous analysis results to accommodate graph changes. By using the memoized previous computation state, incremental graph computation can reduce unnecessary recomputation. However, a small change may propagate over the whole graph and lead to large-scale iterative computations. To addr

I. Chifan, A. Ioana, D. Osin, B. Sun

Given a countable group $G$, let ${\rm L}(G)$ denote its von Neumann algebra. For a wide class of ICC groups with Kazhdan's property (T), we confirm a conjecture of V.F.R. Jones asserting that $Out(\text{L}(G))\cong Char (G)\rtimes Out(G)$. As an application, we show that, for every countable group $Q$, there exists an ICC group $G$ with property (T) such th

Atefeh Gilani, Gowtham R. Kurri, Oliver Kosut, Lalitha Sankar

We introduce a family of information leakage measures called maximal $(\alpha,\beta)$-leakage (M$\alpha$beL), parameterized by real numbers $\alpha$ and $\beta$ greater than or equal to 1. The measure is formalized via an operational definition involving an adversary guessing an unknown (randomized) function of the data given the released data. We obtain a s

I. Chifan, A. Ioana, D. Osin, B. Sun

We show that every finite group realizes as the outer automorphism group of an ICC hyperbolic group with Kazhdan property (T). This result complements the well-known theorem of Paulin stating that the outer automorphism group of every hyperbolic group with property (T) is finite. We also show that, for every countable group $Q$, there exists an acylindricall

Hao Xiang, Changyan Yi, Kun Wu, Jiayuan Chen

Human digital twin (HDT) is expected to revolutionize the future human lifestyle and prompts the development of advanced human-centric applications (e.g., Metaverse) by bridging physical and virtual spaces. However, the fulfillment of HDT poses stringent demands on the pervasive connectivity, real-time feedback, multi-modal data transmission and ultra-high r

Kwei-Herng Lai, Lan Wang, Huiyuan Chen, Kaixiong Zhou

Time series anomaly detection is a challenging task with a wide range of real-world applications. Due to label sparsity, training a deep anomaly detector often relies on unsupervised approaches. Recent efforts have been devoted to time series domain adaptation to leverage knowledge from similar domains. However, existing solutions may suffer from negative kn

Jun Hong, Chenhui Wang, Yongping Zhang

Bose-Einstein condensates (BECs) in periodic potentials generate interesting physics on the instabilities of Bloch states. The lowest-energy Bloch states of BECs in pure nonlinear lattices are dynamically and Landau unstable, which breaks down BEC superfluidity. In this paper we propose to use an out-of-phase linear lattice to stabilize them. The stabilizati

Shuichi Kawano, Toshikazu Fukushima, Junichi Nakagawa, Mamoru Oshiki

The multivariate regression model basically offers the analysis of a single dataset with multiple responses. However, such a single-dataset analysis often leads to unsatisfactory results. Integrative analysis is an effective method to pool useful information from multiple independent datasets and provides better performance than single-dataset analysis. In t

Jiayu Li, Peijie Sun, Zhefan Wang, Weizhi Ma

Ranking ensemble is a critical component in real recommender systems. When a user visits a platform, the system will prepare several item lists, each of which is generally from a single behavior objective recommendation model. As multiple behavior intents, e.g., both clicking and buying some specific item category, are commonly concurrent in a user visit, it

Taketo Akama, Hiroaki Kitano, Katsuhiro Takematsu, Yasushi Miyajima

In the realm of music information retrieval, similarity-based retrieval and auto-tagging serve as essential components. Given the limitations and non-scalability of human supervision signals, it becomes crucial for models to learn from alternative sources to enhance their performance. Self-supervised learning, which exclusively relies on learning signals der

Hengzhe Lu, Zhibin Qi, Yuqiang Huang, Man Cheng

Spin current generation and manipulation remain the key challenge of spintronics, in which relativistic spinorbit coupling (SOC) play a ubiquitous role. In this letter, we demonstrate that hidden Rashba spins in the non-magnetic, centrosymmetric lattice of multilayer SnSe2 can be efficiently activated by spin-orbit scattering introduced by Se vacancies. Via

Jin-Sheng Wu, Marina Torres Lazaro, Souvik Ghosh, Haridas Mundoor

Chiral nematic or cholesteric liquid crystals (LCs) are mesophases with long-ranged orientational order featuring a quasi-layered periodicity imparted by a helical configuration but lacking positional order. Doping molecular cholesteric LCs with thin colloidal rods with a large length-to-width ratio or disks with a large diameter-to-thickness ratio adds anot

Spencer A. Hurt, Meredith A. MacGregor

We place lower limits on the obliquities between debris disks and their host stars for 31 systems by comparing their disk and stellar inclinations. While previous studies did not find evidence for misalignment, we identify 6 systems with minimum obliquities falling between ~30{\deg}-60{\deg}, indicating that debris disks can be significantly misaligned with

Tyler H. Chang, Jakob R. Elias, Stefan M. Wild, Santanu Chaudhuri

In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balancing performance trade-offs and constraints. For these reasons, we present an active learning process based on multiobjective black-box optimi

Thanh-Danh Nguyen, Anh-Khoa Nguyen Vu, Nhat-Duy Nguyen, Vinh-Tiep Nguyen

Camouflaged object detection and segmentation is a new and challenging research topic in computer vision. There is a serious issue of lacking data on concealed objects such as camouflaged animals in natural scenes. In this paper, we address the problem of few-shot learning for camouflaged object detection and segmentation. To this end, we first collect a new

Behrooz Mirzaii, Elvis Torres Pérez

Let $A$ be a local domain of characteristic $2$ such that its residue field has more than $64$ elements. Then we find an exact relation between the third integral homology of the group $\mathrm{SL}_2(A)$ and Hutchinson's refined Bloch group $\mathcal{RB}(A)$.

Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Ilya Safro

Quantum Machine Learning is an emerging sub-field in machine learning where one of the goals is to perform pattern recognition tasks by encoding data into quantum states. This extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimiz

Yi-Jun Chang, Da Wei Zheng

We consider the massively parallel computation (MPC) model, which is a theoretical abstraction of large-scale parallel processing models such as MapReduce. In this model, assuming the widely believed 1-vs-2-cycles conjecture, solving many basic graph problems in $O(1)$ rounds with a strongly sublinear memory size per machine is impossible. We improve on the

Xiaojuan Tian

Graphene nanosheets (GNS) paper is widely regarded as a promising candidate for heat dissipation due to its outstanding thermal conductivity. However, the accompanied high electrical conductivity makes it unfavorable for thermal management of high power electronics since it runs a high risk of short circuits. To eliminate the risk from the high electrical co

Bamelak Tadele, Volodymyr Shyianov, Faouzi Bellili, Amine Mezghani

This paper develops a linear minimum mean-square error (LMMSE) channel estimator for single and multicarrier systems that takes advantage of the mutual coupling in antenna arrays. We model the mutual coupling through multiport networks and express the single-user multiple-input multiple-output (MIMO) communication channel in terms of the impedance and scatte

Anguo Jiang, Naihuan Jing, Ning Liu

We study the $Q$-Kostka polynomials $L_{\lambda\mu}(t)$ by the vertex operator realization of the $Q$-Hall-Littlewood functions $G_{\lambda}(x;t)$ and derive new formulae for $L_{\lambda\mu}(t)$. In particular, we have established stability property for the Q-Kostka polynomials. We also introduce spin Green polynomials $Y^{\lambda}_{\mu}(t)$ as both an analo

Honghua Zhang, Meihua Dang, Nanyun Peng, Guy Van den Broeck

Despite the success of autoregressive large language models in text generation, it remains a major challenge to generate text that satisfies complex constraints: sampling from the conditional distribution ${\Pr}(\text{text} | \alpha)$ is intractable for even the simplest lexical constraints $\alpha$. To overcome this challenge, we propose to use tractable pr

Wenpeng Lu, Sibo Wei, Xueping Peng, Yi-fei Wang

By summarizing longer consumer health questions into shorter and essential ones, medical question-answering systems can more accurately understand consumer intentions and retrieve suitable answers. However, medical question summarization is very challenging due to obvious distinctions in health trouble descriptions from patients and doctors. Although deep le

J. Xavier Prochaska, Claudie Beaulieu, Katerina Giamalaki

We introduce a new methodology to study marine heat waves, extreme events in the sea surface temperature (SST) of the global ocean. Motivated by previously large and impactful marine heat waves and by theoretical expectation that the dominant heating processes coherently affect large regions of the ocean, we introduce a methodology from computer vision to co

Numair Khan, Eric Penner, Douglas Lanman, Lei Xiao

Depth estimation is an important step in many computer vision problems such as 3D reconstruction, novel view synthesis, and computational photography. Most existing work focuses on depth estimation from single frames. When applied to videos, the result lacks temporal consistency, showing flickering and swimming artifacts. In this paper we aim to estimate tem

Klaudia Wrzask

One of the most crucial tasks for naval architects is computing the energy required to meet the ship's operational needs. When predicting a ship's energy requirements, a series of resistance tests on a scaled model vessel is carried out in the constant speed stage. Another important component is the ship's hydrodynamic added mass, which should al

R. Di Vora, A. Lombardi, A. Ortolan, R. Pengo

A traveling wave parametric amplifier has been integrated in the haloscope of the QUAX experiment. A search for dark matter axions has been performed with a high Q dielectric cavity immersed in a 8 T magnetic field and read by a detection chain having a system noise temperature of about 2.1 K at the frequency of 10.353 GHz. Scanning has been conducted by var

Shuai Jiang, Liesbeth Hondelink, Arief A. Suriawinata, Saeed Hassanpour

In digital pathology, whole slide images (WSIs) are widely used for applications such as cancer diagnosis and prognosis prediction. Visual transformer models have recently emerged as a promising method for encoding large regions of WSIs while preserving spatial relationships among patches. However, due to the large number of model parameters and limited labe

Vincent Bouchard, Reinier Kramer, Quinten Weller

Given a spectral curve with exponential singularities (which we call a "transalgebraic spectral curve"), we extend the definition of topological recursion to include contributions from the exponential singularities in a way that is compatible with limits of sequences of spectral curves. This allows us to prove the topological recursion/quantum curve correspo

Chang-geun Oh, Haruki Watanabe

In the standard mean-field treatment of superconductors, the electron-electron interactions are assumed to be written in terms of local density operators. However, more general interactions, such as pair-hopping interactions, may exist or may be generated in a low-energy effective Hamiltonian. In this work, we study the effect of correlated hopping interacti

Sae Hee Ryu, Garett Reichenbach, Chris M. Jozwiak, Aaron Bostwick

Angle-Resolved Photoemission Spectroscopy (ARPES) is a premier technique for understanding the electronic excitations in conductive, crystalline matter, in which the induced photocurrent is collected and dispersed in energy and angle of emission to reveal the energy- and momentum-dependent single particle spectral function $A(\mathbf{k},\omega)$. So far, ARP

James A. Mingo, Mihai Popa

We compute the limit distribution of partial transposes (when both the number and the size of blocks tends to infinity) for a large class of ensembles of unitarily invariant random matrices. Furthermore, it is shown the asymptotic freeness relation between the ensembles of random matrices, their transposes and their left and right partial transposes.

Yu-Chuan Su, Kelvin C. K. Chan, Yandong Li, Yang Zhao

Many applications can benefit from personalized image generation models, including image enhancement, video conferences, just to name a few. Existing works achieved personalization by fine-tuning one model for each person. While being successful, this approach incurs additional computation and storage overhead for each new identity. Furthermore, it usually e

Oliver Schön, Birgit van Huijgevoort, Sofie Haesaert, Sadegh Soudjani

This paper addresses the problem of data-driven computation of controllers that are correct by design for safety-critical systems and can provably satisfy (complex) functional requirements. With a focus on continuous-space stochastic systems with parametric uncertainty, we propose a two-stage approach that decomposes the problem into a learning stage and a r

Kent E. Morrison

We determine the exact probabilities of the different isomorphism classes of tournaments that result from random sets of three and four independent dice drawn from the balanced uniform model of 3-sided dice.

Mubariz Zaffar, Liangliang Nan, Julian Francisco Pieter Kooij

Visual Place Recognition (VPR) is an image-based localization method that estimates the camera location of a query image by retrieving the most similar reference image from a map of geo-tagged reference images. In this work, we look into two fundamental bottlenecks for its localization accuracy: reference map sparseness and viewpoint invariance. Firstly, the

Ryan Wickman, Bibek Poudel, Michael Villarreal, Xiaofei Zhang

A prevalent limitation of optimizing over a single objective is that it can be misguided, becoming trapped in local optimum. This can be rectified by Quality-Diversity (QD) algorithms, where a population of high-quality and diverse solutions to a problem is preferred. Most conventional QD approaches, for example, MAP-Elites, explicitly manage a behavioral ar

Diego Armentano, Jean-Marc Azaïs, José Rafael León

Let $X(\cdot) $ be a random field $\mathbb{R}^D \to \mathbb{R}^d$, $D\geq d$. We first studied the level set $X^{-1}( u) $, $u \in \mathbb{R}^d$. In particular we gave a weak condition for this level set to be rectifiable. Then, we established a Kac-Rice formula to compute the $D-d$ Hausdorff measure. Our results extend known results, particularly in the non

Kaiyue Wang, Feng Xiong, Yun Long, Yun Ma

We place a molecular Bose-Einstein condensate in a 1D shaken lattice with a Floquet-engineered dispersion, and observe the dynamics in both position and momentum space. At the initial condition of zero momentum, our engineered dispersion is inverted, and therefore unstable. We observe that the condensate is destabilized by the lattice shaking as expected, bu

Yiqin Deng, Haixia Zhang, Xianhao Chen, Yuguang Fang

Multi-access edge computing (MEC) is a promising technology to enhance the quality of service, particularly for low-latency services, by enabling computing offloading to edge servers (ESs) in close proximity. To avoid network congestion, collaborative edge computing has become an emerging paradigm to enable different ESs to collaboratively share their data a

Liangqi Yuan, Yunsheng Ma, Lu Su, Ziran Wang

Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the risk of traffic accidents. However, the intrusive design of in-cabin cameras raises concerns about driver privacy. To address this issue, we propose a novel peer-to-peer (P2P) federated learning (FL) framework with continual l

Qianqian Sun, Aref Darzi, Yixuan Pan

In recent years, passively collected GPS data have been popularly applied in various transportation studies, such as highway performance monitoring, travel behavior analysis, and travel demand estimation. Despite multiple advantages, one of the issues is data oscillations (aka outliers or data jumps), which are unneglectable since they may distort mobility p

Navnath Daundkar

In this paper, we obtain an upper bound on the higher topological complexity of the total spaces of fibrations. As an application, we improve the usual dimensional upper bound on higher topological complexity of total spaces of some sphere bundles. We show that this upper bound on the higher topological complexity of the total spaces of fibrations can be imp

C. Estelle Smith, Hannah Miller Hillberg, Zachary Levonian

Following Facebook's introduction of the "Like" in 2009, CaringBridge (a nonprofit health journaling platform) implemented a "Heart" symbol as a single-click reaction affordance in 2012. In 2016, Facebook expanded its Like into a set of emotion-based reactions. In 2021, CaringBridge likewise added three new reactions: "Prayer", "Happy", and "Sad." Through us

Viet Cuong Nguyen, Michael Birnbaum, Munmun De Choudhury

Despite the ever-strong demand for mental health care globally, access to traditional mental health services remains severely limited expensive, and stifled by stigma and systemic barriers. Thus, over the last few years, young people are increasingly turning to content on video-sharing platforms (VSPs) like TikTok and YouTube to help them navigate their ment

Geraldo Botelho, José Lucas P. Luiz, Vinicius C. C. Miranda

We prove the following results: (i) Every absolutely weakly compact set in a Banach lattice is absolutely weakly sequentially compact. (ii) The converse of (i) holds if $E$ is separable or $B_{E^{**}}$ is absolutely weak$^*$ compact. (iii) Every absolutely weakly compact subset of a Banach lattice is contained in the closed convex hull of an absolutely weakl

Zhengang Zhong, Jia-Jie Zhu

This paper presents a novel approach to addressing the distributionally robust nonlinear model predictive control (DRNMPC) problem. Current literature primarily focuses on the static Wasserstein distributionally robust optimal control problem with a prespecified ambiguity set of uncertain system states. Although a few studies have tackled the dynamic setting

G. C. Fritis, P. S. Paz, L. F. G. Lozano, G. Chapiro

Motivated by the foam displacement in porous media with linear adsorption, we extended the existing framework for two-phase flow containing an active tracer described by a non-strictly hyperbolic system of conservation laws. We solved the global Riemann problem by presenting possible wave sequences that composed this solution. Although the problems are well-

Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Fred Zhang

We study dynamic algorithms robust to adaptive input generated from sources with bounded capabilities, such as sparsity or limited interaction. For example, we consider robust linear algebraic algorithms when the updates to the input are sparse but given by an adversary with access to a query oracle. We also study robust algorithms in the standard centralize

Ziyu Zhao, Dae-Sung Hwangbo, Sumit Saurabh, Clark Rosensweig

The circadian clock can adapt itself to external cues, but the molecular mechanisms and regulatory networks governing circadian oscillations' transient adjustments are still largely unknown. Here we consider the specific case of circadian oscillations transiently responding to a temperature change. Using a framework motivated by Floquet theory, we model the

Shanto Roy, Emmanouil Panaousis, Cameron Noakes, Aron Laszka

The MITRE ATT&CK framework, a comprehensive knowledge base of adversary tactics and techniques, has been widely adopted by the cybersecurity industry as well as by academic researchers. Its broad range of industry applications include threat intelligence, threat detection, and incident response, some of which go beyond what it was originally designed for. De

Samaneh Azadi, Thomas Hayes, Akbar Shah, Guan Pang

Recent large-scale text-to-image generation models have made significant improvements in the quality, realism, and diversity of the synthesized images and enable users to control the created content through language. However, the personalization aspect of these generative models is still challenging and under-explored. In this work, we propose a pipeline tha

Stephanie M. Bohaichuk, Fabian Ripka, Vijin Venu, Florian Christaller

We demonstrate Rydberg atom-based radio frequency sensing with a colinear three-photon scheme in a room temperature cesium vapor cell that minimizes residual Doppler broadening of the probe laser absorption feature. A sub-200 kHz spectral linewidth is observed and extends the self-calibrated Autler-Townes sensing regime to weaker fields by a factor of ~18 co

Xuan-Bac Nguyen, Chi Nhan Duong, Marios Savvides, Kaushik Roy

Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal. This is because of the limitation of large-scale balanced data with well-annotated labels for sensitive or protected attributes. In this paper, we first evaluate demographic bias in deep clustering models from the perspective of

Ilgin Dogan, Zuo-Jun Max Shen, Anil Aswani

Motivated by a number of real-world applications from domains like healthcare and sustainable transportation, in this paper we study a scenario of repeated principal-agent games within a multi-armed bandit (MAB) framework, where: the principal gives a different incentive for each bandit arm, the agent picks a bandit arm to maximize its own expected reward pl

Yithsbey Giraldo, R. Martínez, Eduardo Rojas, Juan C. Salazar

A model with fermion and scalar fields charged under a Peccei-Queen~(PQ) symmetry is proposed. The PQ charges are chosen in such a way that they can reproduce mass matrices with five texture zeros, {which can generate} the fermion masses, the CKM matrix, and the PMNS matrix of the Standard Model~(SM). To obtain this result, at least 4~Higgs doublets are need

Karl Christ, Qixiao Ma

Let $G$ be a finite graph of genus $g$. Let $d$ and $r$ be non-negative integers such that the Brill-Noether number is non-negative. It is known that for some $k$ sufficiently large, the $k$-th homothetic refinement $G^{(k)}$ of $G$ admits a divisor of degree $d$ and rank at least $r$. We use results from algebraic geometry to give an upper bound for $k$ in

Christian Schubert

The worldline formalism provides an alternative to Feynman diagrams that has been found particularly useful for external-field calculations in quantum electrodynamics. Here I summarize its present range of applications, which includes Schwinger pair creation, photon splitting in constant fields and plane-wave backgrounds, as well as nonlinear Compton scatter

Anastasiia Alokhina, Jan van den Brand

Algebraic data structures are the main subroutine for maintaining distances in fully dynamic graphs in subquadratic time. However, these dynamic algebraic algorithms generally cannot maintain the shortest paths, especially against adaptive adversaries. We present the first fully dynamic algorithm that maintains the shortest paths against an adaptive adversar

Julia Wiktor, Erik Fransson, Dominik Kubicki, Paul Erhart

Halide perovskites have emerged as one of the most interesting materials for optoelectronic applications due to their favorable properties, such as defect-tolerance and long charge carrier lifetimes, which are attributed to their dynamic softness. However, this softness has led to apparent disagreements between the local instantaneous and global average stru

Bangyao Zhao, Jane E. Huggins, Jian Kang

Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of

Reilly Derrick, Howard Isaacson

After having left the heliosphere, Voyager 1 and Voyager 2 continue to travel through interstellar space. The Pioneer 10, Pioneer 11, and New Horizons spacecraft are also on paths to pass the heliopause. These spacecraft have communicated with the Deep Station Network (DSN) radio antennas in order to download scientific data and telemetry data. Outward trans

Pete L. Clark, Paul Pollack, Jeremy Rouse, Katherine Thompson

Let $f(t_1,\ldots,t_n)$ be a nondegenerate integral quadratic form. We analyze the asymptotic behavior of the function $D_f(X)$, the number of integers of absolute value up to $X$ represented by $f$. When $f$ is isotropic or $n$ is at least $3$, we show that there is a $\delta(f) \in \mathbb{Q} \cap (0,1)$ such that $D_f(X) \sim \delta(f) X$ and call $\delta

Hank Chen, Florian Girelli

Following the theory of principal $\infty$-bundles of Niklaus-Schreiber-Steveson, we develop a homotopy categorification of Hopf algebras, which model quantum groups. We study their higher-representation theory in the setting of $\mathsf{2Vect}^{hBC}$, which is a homotopy refinement of the notion of 2-vector spaces due to Baez-Crans that allows for higher co

Rex Lam, Eric L. Sandquist, Gail H. Schaefer, Christopher D. Farrington

We present measurements of the interferometrically-resolved binary star system 12 Com and the single giant star 31 Com in the cluster Coma Berenices. 12 Com is a double-lined spectroscopic binary system consisting of a G7 giant and an A3 dwarf at the cluster turnoff. Using an extensive radial velocity dataset and interferometric measurements from PTI and the

Danny M. den Hamer, Perry Schoor, Tobias B. Polak, Daniel Kapitan

Physicians considering clinical trials for their patients are met with the laborious process of checking many text based eligibility criteria. Large Language Models (LLMs) have shown to perform well for clinical information extraction and clinical reasoning, including medical tests, but not yet in real-world scenarios. This paper investigates the use of Inst

Nikolaos Giatsoglou, Symeon Papadopoulos, Ioannis Kompatsiaris

The recent wave of AI research has enabled a new brand of synthetic media, called deepfakes. Deepfakes have impressive photorealism, which has generated exciting new use cases but also raised serious threats to our increasingly digital world. To mitigate these threats, researchers have tried to come up with new methods for deepfake detection that are more ef

Sai Ho Pun, Aidan Delgado, Christina Dadich, Adam Cronin

Exercising direct control over the unusual electronic structures arising from quantum confinement effects in graphene nanoribbons (GNRs) - atomically defined quasi one-dimensional (1D) strips of graphene - is intimately linked to geometric boundary conditions imposed by the bonding within the ribbon. Besides composition and position of substitutional dopant

A. Torcato, A. Arriaga, G. Eichmann, M. T. Peña

We report progress on calculations of the heavy-light baryons $\Sigma_c$ and $\Lambda_c$ and their excitations with $J^P = 1/2^+$ using functional methods. We employ a covariant quark-diquark approach, where the interaction amounts to a quark exchange between quarks and diquarks and the ingredients are determined from the quark level. A partial-wave analysis

Zvi Bern, John Joseph M. Carrasco, Marco Chiodaroli, Henrik Johansson

In this chapter, we present a scattering-amplitudes perspective on supergravity, and describe its application to the study of ultraviolet properties of supergravity theories at high loop orders. The basic on-shell tools that make such calculations feasible are reviewed, including generalized unitarity, color-kinematics duality, and the double-copy constructi

Ezgi C. Eren, Zhaoyang Zhang, Jonas Rauch, Ravi Kumar

Traditional revenue management relies on long and stable historical data and predictable demand patterns. However, meeting those requirements is not always possible. Many industries face demand volatility on an ongoing basis, an example would be air cargo which has much shorter booking horizon with highly variable batch arrivals. Even for passenger airlines

Pablo V. Negrón-Marrero, Jeyabal Sivaloganathan

For problems in the calculus of cariations that exhibit the Lavrentiev phenomenon, it is known that the \textit{repulsion property} holds, that is, if one approximates the global minimizer in these problems by smooth functions, then the approximate energies will blow up. Thus standard numerical schemes, like the finite element method, may fail when applied d

Rohan Sarkar, Achal Dave, Gerard Medioni, Benjamin Biggs

This paper presents Shape of You (SoY), an approach to improve the accuracy of 3D body shape estimation for vision-based clothing recommendation systems. While existing methods have successfully estimated 3D poses, there remains a lack of work in precise shape estimation, particularly for diverse human bodies. To address this gap, we propose two loss functio

Sarah Bahanshal, Qurrat-Ul-Ain Nadeem, Md. Jahangir Hossain

Holographic multiple-input multiple-output (HMIMO) communication systems utilize spatially-constrained massive MIMO arrays containing large numbers of antennas with sub-wavelength spacing, and have emerged as a promising candidate technology for Sixth Generation (6G) networks. In this paper, we consider the downlink of a multi-user HMIMO communication system

Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Wing-Kwong Chan

Food image-to-recipe aims to learn an embedded space linking the rich semantics in recipes with the visual content in food image for cross-modal retrieval. The existing research works carry out the learning of such space by assuming that all the image-recipe training example pairs belong to the same cuisine. As a result, despite the excellent performance rep

Samuel Olivier, Terry S. Haut

We present high-order, finite element-based Second Moment Methods (SMMs) for solving radiation transport problems in two spatial dimensions. We leverage the close connection between the Variable Eddington Factor (VEF) method and SMM to convert existing discretizations of the VEF moment system to discretizations of the SMM moment system. The moment discretiza

Elena Kulinskaya, David C. Hoaglin

When the individual studies assembled for a meta-analysis report means ($\mu_C$, $\mu_T$) for their treatment (T) and control (C) arms, but those data are on different scales or come from different instruments, the customary measure of effect is the standardized mean difference (SMD). The SMD is defined as the difference between the means in the treatment an