Tootfinder

Safe and Robust Reinforcement-Learning: Principles and Practice
Taku Yamagata, Raul Santos-Rodriguez
https://arxiv.org/abs/2403.18539 https://

Safe and Robust Reinforcement-Learning: Principles and Practice
Reinforcement Learning (RL) has shown remarkable success in solving relatively complex tasks, yet the deployment of RL systems in real-world scenarios poses significant challenges related to safety and robustness. This paper aims to identify and further understand those challenges thorough the exploration of the main dimensions of the safe and robust RL landscape, encompassing algorithmic, ethical, and practical considerations. We conduct a comprehensive review of methodologies and open problem…

Distributionally Robust Optimization and Robust Statistics
Jose Blanchet, Jiajin Li, Sirui Lin, Xuhui Zhang
https://arXiv.org/abs/2401.14655 https://

Distributionally Robust Optimization and Robust Statistics
We review distributionally robust optimization (DRO), a principled approach for constructing statistical estimators that hedge against the impact of deviations in the expected loss between the training and deployment environments. Many well-known estimators in statistics and machine learning (e.g. AdaBoost, LASSO, ridge regression, dropout training, etc.) are distributionally robust in a precise sense. We hope that by discussing the DRO interpretation of well-known estimators, statisticians who…

Network Flow Models for Robust Binary Optimization with Selective Adaptability
Merve Bodur, Timothy C. Y. Chan, Ian Yihang Zhu
https://arxiv.org/abs/2403.19471

Network Flow Models for Robust Binary Optimization with Selective Adaptability
Adaptive robust optimization problems have received significant attention in recent years, but remain notoriously difficult to solve when recourse decisions are discrete in nature. In this paper, we propose new reformulation techniques for adaptive robust binary optimization (ARBO) problems with objective uncertainty. Without loss of generality, we focus on ARBO problems with "selective adaptability", a term we coin to describe a common class of linking constraints between first-stage and secon…

Is Watermarking LLM-Generated Code Robust?
Tarun Suresh, Shubham Ugare, Gagandeep Singh, Sasa Misailovic
https://arxiv.org/abs/2403.17983 https://

Is Watermarking LLM-Generated Code Robust?
We present the first study of the robustness of existing watermarking techniques on Python code generated by large language models. Although existing works showed that watermarking can be robust for natural language, we show that it is easy to remove these watermarks on code by semantic-preserving transformations.

Towards Robust Hyperspectral Anomaly Detection: Decomposing Background, Anomaly, and Mixed Noise via Convex Optimization
Koyo Sato, Shunsuke Ono
https://arXiv.org/abs/2401.14814 <…

Towards Robust Hyperspectral Anomaly Detection: Decomposing Background, Anomaly, and Mixed Noise via Convex Optimization
We propose a novel hyperspectral (HS) anomaly detection method that is robust to various types of noise. Most of existing HS anomaly detection methods are designed for cases where a given HS image is noise-free or is contaminated only by small Gaussian noise. However, in real-world situations, observed HS images are often degraded by various types of noise, such as sparse noise and stripe noise, due to sensor failure or calibration errors, significantly affecting the detection performance. To a…

Exploration of Adapter for Noise Robust Automatic Speech Recognition
Hao Shi, Tatsuya Kawahara
https://arxiv.org/abs/2402.18275 https://

Exploration of Adapter for Noise Robust Automatic Speech Recognition
Adapting a robust automatic speech recognition (ASR) system to tackle unseen noise scenarios is crucial. Integrating adapters into neural networks has emerged as a potent technique for transfer learning. This paper thoroughly investigates adapter-based noise-robust ASR adaptation. We conducted the experiments using the CHiME--4 dataset. The results show that inserting the adapter in the shallow layer yields superior effectiveness, and there is no significant difference between adapting solely w…

This https://arxiv.org/abs/2401.11542 has been replaced.
link: https://scholar.google.com/scholar?q=a

Nigel -- Mechatronic Design and Robust Sim2Real Control of an Over-Actuated Autonomous Vehicle
Simulation to reality (sim2real) transfer from a dynamics and controls perspective usually involves re-tuning or adapting the designed algorithms to suit real-world operating conditions, which often violates the performance guarantees established originally. This work presents a generalizable framework for achieving reliable sim2real transfer of autonomy-oriented control systems using multi-model multi-objective robust optimal control synthesis, which lends well to uncertainty handling and dist…

I must say, I haven't really cared about inodes in decades, but I didn't realize their uniqueness was no longer important.
Linus Torvalds flames Google kernel contributor • The Register
https://www.theregister.com/2024/01/29/linux_6_8_rc2/

Linus Torvalds flames Google kernel contributor over filesystem suggestion
Kernel 6.8-rc2 debuts after very robust discussion about 'inodes

Robust Active Speaker Detection in Noisy Environments
Siva Sai Nagender Vasireddy, Chenxu Zhang, Xiaohu Guo, Yapeng Tian
https://arxiv.org/abs/2403.19002 h…

Robust Active Speaker Detection in Noisy Environments
This paper addresses the issue of active speaker detection (ASD) in noisy environments and formulates a robust active speaker detection (rASD) problem. Existing ASD approaches leverage both audio and visual modalities, but non-speech sounds in the surrounding environment can negatively impact performance. To overcome this, we propose a novel framework that utilizes audio-visual speech separation as guidance to learn noise-free audio features. These features are then utilized in an ASD model, an…

Intersection Types In #TypeScript
https://claritydev.net/blog/intersection-types-in-typescript

#Parody #NYTimesPitchbot
Via New York Times Pitchbot
@DougJBalloon
·
25m
The White House physician says: "President Biden is a healthy, active, robust 81-year-old male, who remains fit to successfully execute the duties of the Presidency." But this…

Awoke. Took in some nice robust woodsy flatulence. Determined to consume more horseradish.

All three things are true in the US:
1) Driving should be a lot more expensive (fewer/no subsidies).
2) Transit should be a lot cheaper (more subsidies, and ideally free).
3) Transit should be a lot more frequent, clean, reliable, and predictable.
https://mastodon.social/@bryanculber…

This https://arxiv.org/abs/2307.09055 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation
Recently, tensor low-rank representation (TLRR) has become a popular tool for tensor data recovery and clustering, due to its empirical success and theoretical guarantees. However, existing TLRR methods consider Gaussian or gross sparse noise, inevitably leading to performance degradation when the tensor data are contaminated by outliers or sample-specific corruptions. This paper develops an outlier-robust tensor low-rank representation (OR-TLRR) method that provides outlier detection and tenso…

Tommorow, "Regulatory Models for Algorithmic Assessment: Robust Delegation or Kicking The Can?", digital seats available.
https://www.ucl.ac.uk/laws/events/2024/apr/regulatory-models-algorithmic-assessme…

Regulatory Models for Algorithmic Assessment: Robust Delegation or Kicking The Can?
Margot Kaminski, Michael Veale and Jennifer Cobbe will compare and contrast different AI regulatory regimes

Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
Kexin Shi, Jing Zhang, Linjiajie Fang, Wenjia Wang, Bingyi Jing
https://arxiv.org/abs/2403.19276

Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
In implicit collaborative filtering, hard negative mining techniques are developed to accelerate and enhance the recommendation model learning. However, the inadvertent selection of false negatives remains a major concern in hard negative sampling, as these false negatives can provide incorrect information and mislead the model learning. To date, only a small number of studies have been committed to solve the false negative problem, primarily focusing on designing sophisticated sampling algorit…

Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay
Hong Cheng, Huan Luo, Zhi Liu, Wei Sun, Weitao Li, Qiyue Li
https://arxiv.org/abs/2402.17268

Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay
Active distribution networks (ADNs) incorporating massive photovoltaic (PV) devices encounter challenges of rapid voltage fluctuations and potential violations. Due to the fluctuation and intermittency of PV generation, the state gap, arising from time-inconsistent states and exacerbated by imprecisely known system delays, significantly impacts the accuracy of voltage control. This paper addresses this challenge by introducing a framework for delay adaptive Volt/Var control (VVC) in the presenc…

"You copied that function without understanding why it does what it does, and as a result your code IS GARBAGE.
AGAIN."
Linus Torvalds
https://www.theregister.com/2024/01/29/linux_6_8_rc2/

Joint Transmitter Design for Robust Secure Radar-Communication Coexistence Systems
Peng Liu, Zesong Fei, Xinyi Wang, Zhong Zheng, Xiangnan Li, Jie Xu
https://arXiv.org/abs/2401.14666

Joint Transmitter Design for Robust Secure Radar-Communication Coexistence Systems
This paper investigates the spectrum sharing between a multiple-input single-output (MISO) secure communication system and a multiple-input multiple-output (MIMO) radar system in the presence of one suspicious eavesdropper. We jointly design the radar waveform and communication beamforming vector at the two systems, such that the interference between the base station (BS) and radar is reduced, and the detrimental radar interference to the communication system is enhanced to jam the eavesdropper…

Fast and spurious: a robust determination of our peculiar velocity with future galaxy surveys
Fabien Lacasa, Camille Bonvin, Charles Dalang, Ruth Durrer
https://arxiv.org/abs/2402.18438

Fast and spurious: a robust determination of our peculiar velocity with future galaxy surveys
To date, the most precise measurement of the observer's peculiar velocity comes from the dipole in the Cosmic Microwave Background (CMB). This velocity also generates a dipole in the source number counts, whose amplitude is governed not only by the observer velocity, but also by specific properties of the sources, that are difficult to determine precisely. Quantitative studies of the source number counts currently give dipoles which are reasonably well aligned with the CMB dipole, but with a si…

Robust Estimation of Pareto's Scale Parameter from Grouped Data
Chudamani Poudyal
https://arXiv.org/abs/2401.14593 https://arXiv.…

Robust Estimation of Pareto's Scale Parameter from Grouped Data
Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However, the options for robust alternatives to MLE become significantly limited when dealing with grouped loss severity data, with only a handful of methods like least squares, minimum Hellinger distance, and optimal bounded influence function available. This paper introduces a novel robust estimation technique, the Method of…

Computationally Bounded Robust Compilation and Universally Composable Security
Robert Künnemann, Marco Patrignani, Ethan Cecchetti
https://arXiv.org/abs/2401.15041

Computationally Bounded Robust Compilation and Universally Composable Security
Universal Composability (UC) is the gold standard for cryptographic security, but mechanizing proofs of UC is notoriously difficult. A recently-discovered connection between UC and Robust Compilation (RC)$\unicode{x2014}$a novel theory of secure compilation$\unicode{x2014}$provides a means to verify UC proofs using tools that mechanize equality results. Unfortunately, the existing methods apply only to perfect UC security, and real-world protocols relying on cryptography are only computationall…

This https://arxiv.org/abs/2401.11511 has been replaced.
link: https://scholar.google.com/scholar?q=a

MobileARLoc: On-device Robust Absolute Localisation for Pervasive Markerless Mobile AR
Recent years have seen significant improvement in absolute camera pose estimation, paving the way for pervasive markerless Augmented Reality (AR). However, accurate absolute pose estimation techniques are computation- and storage-heavy, requiring computation offloading. As such, AR systems rely on visual-inertial odometry (VIO) to track the device's relative pose between requests to the server. However, VIO suffers from drift, requiring frequent absolute repositioning. This paper introduces Mob…

A good management system doesn’t rely on heroic efforts to save the day. The organization is designed to succeed. It is robust. It will succeed with all the variation thrown at it by the outside world. A good management system takes advantage of the contributions people offer, but it will not perform poorly when others are relied on.
A well run organization has graceful degradation (when one component fails or one person is missing...

Distributionally robust monopoly pricing: Switching from low to high prices in volatile markets
Tim S. G. van Eck, Pieter Kleer, Johan S. H. van Leeuwaarden
https://arxiv.org/abs/2403.19486

Distributionally robust monopoly pricing: Switching from low to high prices in volatile markets
Traditional monopoly pricing assumes sellers have full information about consumer valuations. We consider monopoly pricing under limited information, where a seller only knows the mean, variance and support of the valuation distribution. The objective is to maximize expected revenue by selecting the optimal fixed price. We adopt a distributionally robust framework, where the seller considers all valuation distributions that comply with the limited information. We formulate a maximin problem whi…

Robust integration of fast flavor conversions in classical neutrino transport
Zewei Xiong, Meng-Ru Wu, Manu George, Chun-Yu Lin
https://arxiv.org/abs/2403.17269

Robust integration of fast flavor conversions in classical neutrino transport
The quantum kinetic evolution of neutrinos in dense environments, such as the core-collapse supernovae or the neutron star mergers, can result in fast flavor conversion (FFC), presenting a significant challenge to achieving robust astrophysical modeling of these systems. Recent works that directly simulate the quantum kinetic transport of neutrinos in localized domains have suggested that the asymptotic outcome of FFCs can be modeled by simple analytical prescriptions. In this Letter, we incorp…

Robust Numerical Algebraic Geometry
Emma R. Cobian, Jonathan D. Hauenstein, Charles W. Wampler
https://arxiv.org/abs/2403.18749 https://

Robust Numerical Algebraic Geometry
The field of numerical algebraic geometry consists of algorithms for numerically solving systems of polynomial equations. When the system is exact, such as having rational coefficients, the solution set is well-defined. However, for a member of a parameterized family of polynomial systems where the parameter values may be measured with imprecision or arise from prior numerical computations, uncertainty may arise in the structure of the solution set, including the number of isolated solutions, t…

This https://arxiv.org/abs/2404.15828 has been replaced.
link: https://scholar.google.com/scholar?q=a

Robust Quantum Gate Complexity: Foundations
Optimal control of closed quantum systems is a well studied geometrically elegant set of computational theory and techniques that have proven pivotal in the implementation and understanding of quantum computers. The design of a circuit itself corresponds to an optimal control problem of choosing the appropriate set of gates (which appear as control operands) in order to steer a qubit from an initial, easily prepared state, to one that is informative to the user in some sense, for e.g., an oracl…

SciPhi-AI/R2R: A #framework for rapid development and deployment of production-ready #RAG systems
R2R was conceived to bridge the gap between experimental RAG models and robust, production-ready systems.
#ai

Robust Containment Queries over Collections of Rational Parametric Curves via Generalized Winding Numbers
Jacob Spainhour, David Gunderman, Kenneth Weiss
https://arxiv.org/abs/2403.17371

Robust Containment Queries over Collections of Rational Parametric Curves via Generalized Winding Numbers
Point containment queries for regions bound by watertight geometric surfaces, i.e. closed and without self-intersections, can be evaluated straightforwardly with a number of well-studied algorithms. However, when such assumptions on domain geometry are not met, these methods are theoretically unfounded at best and practically unusable at worst. More robust classification schemes utilize generalized winding numbers, a mathematical construction that is indifferent to imperfections in the often hu…

This https://arxiv.org/abs/2402.16107 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

FuseChat: Knowledge Fusion of Chat Models
While training large language models (LLMs) from scratch can indeed lead to models with distinct capabilities and strengths, this approach incurs substantial costs and may lead to potential redundancy in competencies. An alternative strategy is to combine existing LLMs into a more robust LLM, thereby diminishing the necessity for expensive pre-training. However, due to the diverse architectures of LLMs, direct parameter blending proves to be unfeasible. Recently, \textsc{FuseLLM} introduced the…

Distributed Maximum Consensus over Noisy Links
Ehsan Lari, Reza Arablouei, Naveen K. D. Venkategowda, Stefan Werner
https://arxiv.org/abs/2403.18509 https:…

Distributed Maximum Consensus over Noisy Links
We introduce a distributed algorithm, termed noise-robust distributed maximum consensus (RD-MC), for estimating the maximum value within a multi-agent network in the presence of noisy communication links. Our approach entails redefining the maximum consensus problem as a distributed optimization problem, allowing a solution using the alternating direction method of multipliers. Unlike existing algorithms that rely on multiple sets of noise-corrupted estimates, RD-MC employs a single set, enhanc…

A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew, Alexandre Hutton, Roland Wiest, Johannes Kaesmacher, Uta Hanning, Arsany Hakim, Richard Zubal, Waldo Valenzuela, David Robben, Diana M. Sima, Vincenzo Anania, Arne Brys, James A. Meakin, Anne Mickan, Gabriel Broocks, Christian Heitkamp, Shengbo Gao, Kongming Liang, Ziji Zhang, Md Mahfuzur Rahman Siddiqu…

A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES'22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge…

Series D, Episode 07 - Assassin
CAPTAIN: That's not a very healthy place to visit, ma'am.
SERVALAN: I have a robust constitution, Captain.
https://blake.torpidity.net/s/407/26 📺 B7B3

The imitation game reloaded: effective shadows of dynamically robust spinning Proca stars
Ivo Sengo, Pedro V. P. Cunha, Carlos A. R. Herdeiro, Eugen Radu
https://arxiv.org/abs/2402.14919

The imitation game reloaded: effective shadows of dynamically robust spinning Proca stars
We analyse the lensing images by dynamically robust rotating (mini-)Proca stars surrounded by thin accretion disks. Due to their peculiar geodesic structure we show that these images exhibit striking similarities with the ones of BHs, for appropriately chosen disk intensity profile, when imposing a GRMHD-motivated emission cut off. Additionally, and unlike the non-rotating case, these similarities prevail even when considering equatorial observations. This example illustrates how a horizonless …

This https://arxiv.org/abs/2307.09055 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation
Recently, tensor low-rank representation (TLRR) has become a popular tool for tensor data recovery and clustering, due to its empirical success and theoretical guarantees. However, existing TLRR methods consider Gaussian or gross sparse noise, inevitably leading to performance degradation when the tensor data are contaminated by outliers or sample-specific corruptions. This paper develops an outlier-robust tensor low-rank representation (OR-TLRR) method that provides outlier detection and tenso…

I think I also like #SolveSpace because it feels kinda like a unix'y cli program:
· It does one thing only: 2D/3D #CAD
· Its interface is basic and robust. Nothing fancy. It literally uses a monospace font.
· 1 part = 1 file. You can assemble multiple parts in a different file.
·…

Robust Filter Design for Graph Signals
Lucia Testa, Stefania Sardellitti, Sergio Barbarossa
https://arxiv.org/abs/2403.16983 https://…

Robust Filter Design for Graph Signals
Our goal in this paper is the robust design of filters acting on signals observed over graphs subject to small perturbations of their edges. The focus is on developing a method to identify spectral and polynomial graph filters that can adapt to the perturbations in the underlying graph structure while ensuring the filters adhere to the desired spectral mask. To address this, we propose a novel approach that leverages approximate closed-form expressions for the perturbed eigendecomposition of th…

GROS: A General Robust Aggregation Strategy
Alejandro Cholaquidis, Emilien Joly, Leonardo Moreno
https://arxiv.org/abs/2402.15442 https://

GROS: A General Robust Aggregation Strategy
A new, very general, robust procedure for combining estimators in metric spaces is introduced GROS. The method is reminiscent of the well-known median of means, as described in \cite{devroye2016sub}. Initially, the sample is divided into $K$ groups. Subsequently, an estimator is computed for each group. Finally, these $K$ estimators are combined using a robust procedure. We prove that this estimator is sub-Gaussian and we get its break-down point, in the sense of Donoho. The robust procedure in…

An Efficient Risk-aware Branch MPC for Automated Driving that is Robust to Uncertain Vehicle Behaviors
Luyao Zhang, George Pantazis, Shaohang Han, Sergio Grammatico
https://arxiv.org/abs/2403.18695

An Efficient Risk-aware Branch MPC for Automated Driving that is Robust to Uncertain Vehicle Behaviors
One of the critical challenges in automated driving is ensuring safety of automated vehicles despite the unknown behavior of the other vehicles. Although motion prediction modules are able to generate a probability distribution associated with various behavior modes, their probabilistic estimates are often inaccurate, thus leading to a possibly unsafe trajectory. To overcome this challenge, we propose a risk-aware motion planning framework that appropriately accounts for the ambiguity in the es…

This https://arxiv.org/abs/2112.05010 has been replaced.
link: https://scholar.google.com/scholar?q=a

The Value of Robust Assortment Optimization Under Ranking-based Choice Models
We study a class of robust assortment optimization problems that was proposed by Farias, Jagabathula, and Shah (2013). The goal in these problems is to find an assortment that maximizes a firm's worst-case expected revenue under all ranking-based choice models that are consistent with the historical sales data generated by the firm's past assortments. We establish for various settings that these robust optimization problems can either be solved in polynomial-time or can be reformulated as compa…

Joint Transmitter Design for Robust Secure Radar-Communication Coexistence Systems
Peng Liu, Zesong Fei, Xinyi Wang, Zhong Zheng, Xiangnan Li, Jie Xu
https://arXiv.org/abs/2401.14666

Joint Transmitter Design for Robust Secure Radar-Communication Coexistence Systems
This paper investigates the spectrum sharing between a multiple-input single-output (MISO) secure communication system and a multiple-input multiple-output (MIMO) radar system in the presence of one suspicious eavesdropper. We jointly design the radar waveform and communication beamforming vector at the two systems, such that the interference between the base station (BS) and radar is reduced, and the detrimental radar interference to the communication system is enhanced to jam the eavesdropper…

A Multi-hop Secure UWOC assisted Local Area Network for UIoT and Underwater Monitoring
Maaz Salman, Javad Bolboli, Wan-Young Chung
https://arxiv.org/abs/2403.19180

A Multi-hop Secure UWOC assisted Local Area Network for UIoT and Underwater Monitoring
Underwater environment is substantially less explored territory as compared to earth surface due to lack of robust underwater communication infrastructure. For Internet of Underwater things connectivity, underwater wireless optical communication can play a vital role, compared to conventional radio frequency communication, due to longer range, high data rate, low latency, and unregulated bandwidth. This study proposes underwater wireless optical communication driven local area network UWOC LAN,…

This https://arxiv.org/abs/2310.16281 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Improving Robust Decisions with Data
A decision-maker faces uncertainty governed by a data-generating process (DGP), which is only known to belong to a set of sequences of independent but possibly non-identical distributions. A robust decision maximizes the expected payoff against the worst possible DGP in this set. This paper characterizes when and how such robust decisions can be improved with data, measured by the expected payoff under the true DGP, no matter which possible DGP is the truth. It further develops novel and simple…

Robust estimations from distribution structures: V. Non-asymptotic
Tuobang Li
https://arxiv.org/abs/2403.18951 https://arxiv.org/pdf/…

Robust estimations from distribution structures: V. Non-asymptotic
Due to the complexity of order statistics, the finite sample behaviour of robust statistics is generally not analytically solvable. While the Monte Carlo method can provide approximate solutions, its convergence rate is typically very slow, making the computational cost to achieve the desired accuracy unaffordable for ordinary users. In this paper, we propose an approach analogous to the Fourier transformation to decompose the finite sample structure of the uniform distribution. By obtaining se…

This https://arxiv.org/abs/2404.15719 has been replaced.
link: https://scholar.google.com/scholar?q=a

HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action Recognition
Skeleton-based action recognition has gained considerable traction thanks to its utilization of succinct and robust skeletal representations. Nonetheless, current methodologies often lean towards utilizing a solitary backbone to model skeleton modality, which can be limited by inherent flaws in the network backbone. To address this and fully leverage the complementary characteristics of various network architectures, we propose a novel Hybrid Dual-Branch Network (HDBN) for robust skeleton-based…

A robust two-level overlapping preconditioner for Darcy flow in high-contrast media
Changqing Ye, Shubin Fu, Eric T. Chung, Jizu Huang
https://arxiv.org/abs/2403.19356

A robust two-level overlapping preconditioner for Darcy flow in high-contrast media
In this article, a two-level overlapping domain decomposition preconditioner is developed for solving linear algebraic systems obtained from simulating Darcy flow in high-contrast media. Our preconditioner starts at a mixed finite element method for discretizing the partial differential equation by Darcy's law with the no-flux boundary condition and is then followed by a velocity elimination technique to yield a linear algebraic system with only unknowns of pressure. Then, our main objective is…

Quantum Algorithms: A New Frontier in Financial Crime Prevention
Abraham Itzhak Weinberg, Alessio Faccia
https://arxiv.org/abs/2403.18322 https://

Quantum Algorithms: A New Frontier in Financial Crime Prevention
Financial crimes fast proliferation and sophistication require novel approaches that provide robust and effective solutions. This paper explores the potential of quantum algorithms in combating financial crimes. It highlights the advantages of quantum computing by examining traditional and Machine Learning (ML) techniques alongside quantum approaches. The study showcases advanced methodologies such as Quantum Machine Learning (QML) and Quantum Artificial Intelligence (QAI) as powerful solutions…

Solving the waste bin location problem with uncertain waste generation rate: a bi-objective robust optimization approach
Diego Rossit, Jonathan Bard
https://arxiv.org/abs/2403.19476

Solving the waste bin location problem with uncertain waste generation rate: a bi-objective robust optimization approach
An efficient Municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and livability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, e.g., the waste generation rate in an urban area. With this in mind, this paper contributes with a robust optimization model to design the …

This https://arxiv.org/abs/2211.16879 has been replaced.
link: https://scholar.google.com/scholar?q=a

Robust, fast and accurate mapping of diffusional mean kurtosis
Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning and monitoring of many neurological diseases and disorders. However, robust, fast and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical f…

This https://arxiv.org/abs/2311.10600 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Robust universal quantum processors in spin systems via Walsh pulse sequences
We propose a protocol to realize quantum simulation and computation in spin systems with long-range interactions. Our approach relies on the local addressing of single spins with external fields parametrized by Walsh functions. This enables a mapping from a class of target Hamiltonians, defined by the graph structure of their interactions, to pulse sequences. We then obtain a recipe to implement arbitrary two-body Hamiltonians and universal quantum circuits. Performance guarantees are provided …

Enhancing mmWave Radar Point Cloud via Visual-inertial Supervision
Cong Fan, Shengkai Zhang, Kezhong Liu, Shuai Wang, Zheng Yang, Wei Wang
https://arxiv.org/abs/2404.17229

Enhancing mmWave Radar Point Cloud via Visual-inertial Supervision
Complementary to prevalent LiDAR and camera systems, millimeter-wave (mmWave) radar is robust to adverse weather conditions like fog, rainstorms, and blizzards but offers sparse point clouds. Current techniques enhance the point cloud by the supervision of LiDAR's data. However, high-performance LiDAR is notably expensive and is not commonly available on vehicles. This paper presents mmEMP, a supervised learning approach that enhances radar point clouds using a low-cost camera and an inertial m…

Robust estimations from distribution structures: V. Non-asymptotic
Tuobang Li
https://arxiv.org/abs/2403.18951 https://arxiv.org/pdf/…

Robust estimations from distribution structures: V. Non-asymptotic
Due to the complexity of order statistics, the finite sample behaviour of robust statistics is generally not analytically solvable. While the Monte Carlo method can provide approximate solutions, its convergence rate is typically very slow, making the computational cost to achieve the desired accuracy unaffordable for ordinary users. In this paper, we propose an approach analogous to the Fourier transformation to decompose the finite sample structure of the uniform distribution. By obtaining se…

Scalable Robust Sparse Principal Component Analysis
Xiao Ling, Paul Brooks
https://arxiv.org/abs/2402.16712 https://arxiv.org/pdf/240…

Scalable Robust Sparse Principal Component Analysis
In this work, we propose an optimization framework for estimating a sparse robust one-dimensional subspace. Our objective is to minimize both the representation error and the penalty, in terms of the l1-norm criterion. Given that the problem is NP-hard, we introduce a linear relaxation-based approach. Additionally, we present a novel fitting procedure, utilizing simple ratios and sorting techniques. The proposed algorithm demonstrates a worst-case time complexity of $O(n^2 m \log n)$ and, in ce…

This https://arxiv.org/abs/2109.02473 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Robust Cybersecurity Topic Classification Tool
In this research, we use user defined labels from three internet text sources (Reddit, Stackexchange, Arxiv) to train 21 different machine learning models for the topic classification task of detecting cybersecurity discussions in natural text. We analyze the false positive and false negative rates of each of the 21 model's in a cross validation experiment. Then we present a Cybersecurity Topic Classification (CTC) tool, which takes the majority vote of the 21 trained machine learning models as…

Numerical parameterization of stationary axisymmetric black holes in a theory agnostic framework
Olzhas Mukazhanov, Rittick Roy, Temur Mirzaev, Cosimo Bambi
https://arxiv.org/abs/2404.17055

Numerical parameterization of stationary axisymmetric black holes in a theory agnostic framework
The pursuit of a comprehensive theory of gravity has led to the exploration of various alternative models, necessitating a model-independent framework. The Konoplya-Rezzolla-Zhidenko (KRZ) parameterization offers a robust method for approximating stationary axisymmetric black hole spacetimes, characterized by a rapidly converging continued-fraction expansion. However, while analytical metrics benefit from this approach, numerical metrics derived from complex gravitational theories remain presen…

This https://arxiv.org/abs/2402.17424 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

ViTaL: An Advanced Framework for Automated Plant Disease Identification in Leaf Images Using Vision Transformers and Linear Projection For Feature Reduction
Our paper introduces a robust framework for the automated identification of diseases in plant leaf images. The framework incorporates several key stages to enhance disease recognition accuracy. In the pre-processing phase, a thumbnail resizing technique is employed to resize images, minimizing the loss of critical image details while ensuring computational efficiency. Normalization procedures are applied to standardize image data before feature extraction. Feature extraction is facilitated thro…

This https://arxiv.org/abs/2308.02396 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar
Detecting human presence indoors with millimeter-wave frequency-modulated continuous-wave (FMCW) radar faces challenges from both moving and stationary clutter. This work proposes a robust and real-time capable human presence and out-of-distribution (OOD) detection method using 60 GHz short-range FMCW radar. HOOD solves the human presence and OOD detection problems simultaneously in a single pipeline. Our solution relies on a reconstruction-based architecture and works with radar macro and micr…

A robust parameterized enhanced shift-splitting preconditioner for three-by-three block saddle point problems
Sk. Safique Ahmad, Pinki Khatun
https://arxiv.org/abs/2402.17357

A robust parameterized enhanced shift-splitting preconditioner for three-by-three block saddle point problems
This paper proposes a new parameterized enhanced shift-splitting {\it (PESS)} preconditioner to solve the three-by-three block saddle point problem ({\it SPP}). In addition, necessary and sufficient criteria are established for the convergence of the proposed {\it PESS} iterative process for any random initial guess. Furthermore, we meticulously investigate the spectral bounds of the {\it PESS} preconditioned matrix. Moreover, empirical investigation has been performed for the sensitivity analy…

This https://arxiv.org/abs/2310.16281 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Improving Robust Decisions with Data
A decision-maker faces uncertainty governed by a data-generating process (DGP), which is only known to belong to a set of sequences of independent but possibly non-identical distributions. A robust decision maximizes the expected payoff against the worst possible DGP in this set. This paper characterizes when and how such robust decisions can be improved with data, measured by the expected payoff under the true DGP, no matter which possible DGP is the truth. It further develops novel and simple…

CLIPPER : A Fast Maximal Clique Algorithm for Robust Global Registration
Kaveh Fathian, Tyler Summers
https://arxiv.org/abs/2402.15464 https://

CLIPPER+: A Fast Maximal Clique Algorithm for Robust Global Registration
We present CLIPPER+, an algorithm for finding maximal cliques in unweighted graphs for outlier-robust global registration. The registration problem can be formulated as a graph and solved by finding its maximum clique. This formulation leads to extreme robustness to outliers; however, finding the maximum clique is an NP-hard problem, and therefore approximation is required in practice for large-size problems. The performance of an approximation algorithm is evaluated by its computational comple…

CPR: Retrieval Augmented Generation for Copyright Protection
Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto
https://arxiv.org/abs/2403.18920

CPR: Retrieval Augmented Generation for Copyright Protection
Retrieval Augmented Generation (RAG) is emerging as a flexible and robust technique to adapt models to private users data without training, to handle credit attribution, and to allow efficient machine unlearning at scale. However, RAG techniques for image generation may lead to parts of the retrieved samples being copied in the model's output. To reduce risks of leaking private information contained in the retrieved set, we introduce Copy-Protected generation with Retrieval (CPR), a new method …

Noise-Tolerant Optimization Methods for the Solution of a Robust Design Problem
Yuchen Lou, Shigeng Sun, Jorge Nocedal
https://arXiv.org/abs/2401.15007 htt…

Noise-Tolerant Optimization Methods for the Solution of a Robust Design Problem
The development of nonlinear optimization algorithms capable of performing reliably in the presence of noise has garnered considerable attention lately. This paper advocates for strategies to create noise-tolerant nonlinear optimization algorithms by adapting classical deterministic methods. These adaptations follow certain design guidelines described here, which make use of estimates of the noise level in the problem. The application of our methodology is illustrated by the development of a li…

Robust Analysis of Full-Duplex Two-Way Space Shift Keying With RIS Systems
Xusheng Zhu, Wen Chen, Qingqing Wu, Wen Fang, Chaoying Huang, Jun Li
https://arxiv.org/abs/2403.17751

Robust Analysis of Full-Duplex Two-Way Space Shift Keying With RIS Systems
Reconfigurable intelligent surface (RIS)-assisted index modulation system schemes are considered a promising technology for sixth-generation (6G) wireless communication systems, which can enhance various system capabilities such as coverage and reliability. However, obtaining perfect channel state information (CSI) is challenging due to the lack of a radio frequency chain in RIS. In this paper, we investigate the RIS-assisted full-duplex (FD) two-way space shift keying (SSK) system under imperf…

This https://arxiv.org/abs/2402.01216 has been replaced.
link: https://scholar.google.com/scholar?q=a

Robust Commutation Design: Applied to Switched Reluctance Motors
Switched Reluctance Motors (SRMs) are cost-effective electric actuators that utilize magnetic reluctance to generate torque, with torque ripple arising from unaccounted manufacturing defects in the rotor tooth geometry. This paper aims to design a versatile, resource-efficient commutation function for accurate control of a range of SRMs, mitigating torque ripple despite manufacturing variations across SRMs and individual rotor teeth. The developed commutation function optimally distributes curr…

Robust Evaluation of Longitudinal Surrogate Markers with Censored Data
Denis Agniel, Layla Parast
https://arxiv.org/abs/2402.16969 https://

Robust Evaluation of Longitudinal Surrogate Markers with Censored Data
The development of statistical methods to evaluate surrogate markers is an active area of research. In many clinical settings, the surrogate marker is not simply a single measurement but is instead a longitudinal trajectory of measurements over time, e.g., fasting plasma glucose measured every 6 months for 3 years. In general, available methods developed for the single-surrogate setting cannot accommodate a longitudinal surrogate marker. Furthermore, many of the methods have not been developed …

This https://arxiv.org/abs/2107.06714 has been replaced.
link: https://scholar.google.com/scholar?q=a

Robust Data-Driven CARA Optimization
We focus on data-driven, risk-averse optimization problems where decision-makers exhibit constant absolute risk aversion (CARA), represented by an exponential utility function. We consider payoff functions expressible as conic optimization problems, highlighting their expansive use in prescriptive analytics. Aiming to mitigate the overfitting issues inherent in empirical distribution-based optimization, we employ a robust satisficing strategy, which combines a target parameter with a Wasserstei…

This https://arxiv.org/abs/2311.11016 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SNI-SLAM: Semantic Neural Implicit SLAM
We propose SNI-SLAM, a semantic SLAM system utilizing neural implicit representation, that simultaneously performs accurate semantic mapping, high-quality surface reconstruction, and robust camera tracking. In this system, we introduce hierarchical semantic representation to allow multi-level semantic comprehension for top-down structured semantic mapping of the scene. In addition, to fully utilize the correlation between multiple attributes of the environment, we integrate appearance, geometry…

Distributionally Robust Safe Screening
Hiroyuki Hanada, Satoshi Akahane, Tatsuya Aoyama, Tomonari Tanaka, Yoshito Okura, Yu Inatsu, Noriaki Hashimoto, Taro Murayama, Lee Hanju, Shinya Kojima, Ichiro Takeuchi
https://arxiv.org/abs/2404.16328

Distributionally Robust Safe Screening
In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model robustness against variations in data distribution, with safe screening (SS), a sparse optimization technique designed to identify irrelevant samples and features prior to model training. The core concept of the DRSS method involves reformulating th…

Robust Evaluation of Longitudinal Surrogate Markers with Censored Data
Denis Agniel, Layla Parast
https://arxiv.org/abs/2402.16969 https://

Robust Evaluation of Longitudinal Surrogate Markers with Censored Data
The development of statistical methods to evaluate surrogate markers is an active area of research. In many clinical settings, the surrogate marker is not simply a single measurement but is instead a longitudinal trajectory of measurements over time, e.g., fasting plasma glucose measured every 6 months for 3 years. In general, available methods developed for the single-surrogate setting cannot accommodate a longitudinal surrogate marker. Furthermore, many of the methods have not been developed …

Bayesian Learned Models Can Detect Adversarial Malware For Free
Bao Gia Doan, Dang Quang Nguyen, Paul Montague, Tamas Abraham, Olivier De Vel, Seyit Camtepe, Salil S. Kanhere, Ehsan Abbasnejad, Damith C. Ranasinghe
https://arxiv.org/abs/2403.18309

Bayesian Learned Models Can Detect Adversarial Malware For Free
The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is computationally expensive to scale up to large datasets and comes at the cost of sacrificing model performance for robustness. We hypothesize that adversarial malware exploits the low-confidence regions of models and can be identified using epistemic uncertainty of ML approaches -- epistemic uncertainty in a machine l…

Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction
Xingyu Xu, Yuejie Chi
https://arxiv.org/abs/2403.17042 http…

Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction
In a great number of tasks in science and engineering, the goal is to infer an unknown image from a small number of measurements collected from a known forward model describing certain sensing or imaging modality. Due to resource constraints, this task is often extremely ill-posed, which necessitates the adoption of expressive prior information to regularize the solution space. Score-based diffusion models, due to its impressive empirical success, have emerged as an appealing candidate of an ex…

This https://arxiv.org/abs/2306.02742 has been replaced.
link: https://scholar.google.com/scholar?q=a

Enhanced Robust Motion Control based on Unknown System Dynamics Estimator for Robot Manipulators
To achieve high-accuracy manipulation in the presence of unknown disturbances, we propose two novel efficient and robust motion control schemes for high-dimensional robot manipulators. Both controllers incorporate an unknown system dynamics estimator (USDE) to estimate disturbances without requiring acceleration signals and the inverse of inertia matrix. Then, based on the USDE framework, an adaptive-gain controller and a super-twisting sliding mode controller are designed to speed up the conve…

Precoding for Multi-Cell ISAC: from Coordinated Beamforming to Coordinated Multipoint and Bi-Static Sensing
Nithin Babu, Christos Masouros, Constantinos B. Papadias, Yonina C. Eldar
https://arxiv.org/abs/2402.18387

Precoding for Multi-Cell ISAC: from Coordinated Beamforming to Coordinated Multipoint and Bi-Static Sensing
This paper proposes a framework for designing robust precoders for a multi-input single-output (MISO) system that performs integrated sensing and communication (ISAC) across multiple cells and users. We use Cramer-Rao-Bound (CRB) to measure the sensing performance and derive its expressions for two multi-cell scenarios, namely coordinated beamforming (CBF) and coordinated multi-point (CoMP). In the CBF scheme, a BS shares channel state information (CSI) and estimates target parameters using mon…

Doubly robust causal inference through penalized bias-reduced estimation: combining non-probability samples with designed surveys
Jiacong Du, Xu Shi, Donglin Zeng, Bhramar Mukherjee
https://arxiv.org/abs/2403.18039

Doubly robust causal inference through penalized bias-reduced estimation: combining non-probability samples with designed surveys
Causal inference on the average treatment effect (ATE) using non-probability samples, such as electronic health records (EHR), faces challenges from sample selection bias and high-dimensional covariates. This requires considering a selection model alongside treatment and outcome models that are typical ingredients in causal inference. This paper considers integrating large non-probability samples with external probability samples from a design survey, addressing moderately high-dimensional conf…

This https://arxiv.org/abs/2211.08360 has been replaced.
link: https://scholar.google.com/scholar?q=a

An environmental disturbance observer framework for autonomous surface vessels
This paper proposes a robust disturbance observer framework for maritime autonomous surface vessels considering model and measurement uncertainties. The core contribution lies in a nonlinear disturbance observer, reconstructing the forces on a vessel impacted by the environment. For this purpose, mappings are found leading to synchronized global exponentially stable error dynamics. With the stability theory of Lyapunov, it is proven that the error converges exponentially into a ball, even if th…

Securing OPEN-RAN Equipment Using Blockchain-Based Supply Chain Verification
Ali Mehrban, Mostafa Jani
https://arxiv.org/abs/2402.17632 https://

Securing OPEN-RAN Equipment Using Blockchain-Based Supply Chain Verification
The disaggregated and multi-vendor nature of OPEN-RAN networks introduces new supply chain security risks, making equipment authenticity and integrity crucial challenges. Robust solutions are needed to mitigate vulnerabilities in manufacturing and integration. This paper puts forth a novel blockchain-based approach to secure OPEN-RAN equipment through its lifecycle. By combining firmware authentication codes, a permissioned blockchain ledger, and equipment node validators, we architect a tamper…

This https://arxiv.org/abs/2302.08247 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Towards Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain Regularization
Hyperspectral (HS) unmixing is the process of decomposing an HS image into material-specific spectra (endmembers) and their spatial distributions (abundance maps). Existing unmixing methods have two limitations with respect to noise robustness. First, if the input HS image is highly noisy, even if the balance between sparse and piecewise-smooth regularizations for abundance maps is carefully adjusted, noise may remain in the estimated abundance maps or undesirable artifacts may appear. Second, …

Generation and analysis of synthetic data via Bayesian networks: a robust approach for uncertainty quantification via Bayesian paradigm
Larissa N. A. Martins, Fl\'avio B. Gon\c{c}alves, Thais P. Galletti
https://arxiv.org/abs/2402.17915

Generation and analysis of synthetic data via Bayesian networks: a robust approach for uncertainty quantification via Bayesian paradigm
Safe and reliable disclosure of information from confidential data is a challenging statistical problem. A common approach considers the generation of synthetic data, to be disclosed instead of the original data. Efficient approaches ought to deal with the trade-off between reliability and confidentiality of the released data. Ultimately, the aim is to be able to reproduce as accurately as possible statistical analysis of the original data using the synthetic one. Bayesian networks is a model-b…

This https://arxiv.org/abs/2111.10275 has been replaced.
link: https://scholar.google.com/scholar?q=a

Composite Goodness-of-fit Tests with Kernels
Model misspecification can create significant challenges for the implementation of probabilistic models, and this has led to development of a range of robust methods which directly account for this issue. However, whether these more involved methods are required will depend on whether the model is really misspecified, and there is a lack of generally applicable methods to answer this question. In this paper, we propose one such method. More precisely, we propose kernel-based hypothesis tests fo…

This https://arxiv.org/abs/2209.01183 has been replaced.
link: https://scholar.google.com/scholar?q=a

Indoor Positioning in 5G-Advanced: Challenges and Solution towards Centimeter-level Accuracy with Carrier Phase Enhancements
After robust connectivity, precise positioning is evolving into an innovative component of 5G service offerings for industrial use-cases and verticals with challenging indoor radio environments. In this direction, the 3GPP Rel-16 standard has been a tipping point in specifying critical innovations, followed by enhancements in Rel-17 and Rel-18. In this article, we elaborate on the 5G positioning framework, measurements, and procedures before shifting the focus mainly to recently identified carr…

Finite Sample Analysis for a Class of Subspace Identification Methods
Jiabao He, Ingvar Ziemann, Cristian R. Rojas, H\r{a}kan Hjalmarsson
https://arxiv.org/abs/2404.17331

Finite Sample Analysis for a Class of Subspace Identification Methods
While subspace identification methods (SIMs) are appealing due to their simple parameterization for MIMO systems and robust numerical realizations, a comprehensive statistical analysis of SIMs remains an open problem, especially in the non-asymptotic regime. In this work, we provide a finite sample analysis for a class of SIMs, which reveals that the convergence rates for estimating Markov parameters and system matrices are $\mathcal{O}(1/\sqrt{N})$, in line with classical asymptotic results. B…

This https://arxiv.org/abs/2312.11804 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Gravity-aware Grasp Generation with Implicit Grasp Mode Selection for Underactuated Hands
Learning-based grasp detectors typically assume a precision grasp, where each finger only has one contact point, and estimate the grasp probability. In this work, we propose a data generation and learning pipeline that can leverage power grasping, which has more contact points with an enveloping configuration and is robust against both positioning error and force disturbance. To train a grasp detector to prioritize power grasping while still keeping precision grasping as the secondary choice, w…

The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision
Andreas M\"uller, Erwin Quiring
https://arxiv.org/abs/2403.18587

The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision
Resource efficiency plays an important role for machine learning nowadays. The energy and decision latency are two critical aspects to ensure a sustainable and practical application. Unfortunately, the energy consumption and decision latency are not robust against adversaries. Researchers have recently demonstrated that attackers can compute and submit so-called sponge examples at inference time to increase the energy consumption and decision latency of neural networks. In computer vision, the …

This https://arxiv.org/abs/2306.15173 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Robust propensity score weighting estimation under missing at random
Missing data is frequently encountered in many areas of statistics. Propensity score weighting is a popular method for handling missing data. The propensity score method employs a response propensity model, but correct specification of the statistical model can be challenging in the presence of missing data. Doubly robust estimation is attractive, as the consistency of the estimator is guaranteed when either the outcome regression model or the propensity score model is correctly specified. In t…

This https://arxiv.org/abs/2111.10275 has been replaced.
link: https://scholar.google.com/scholar?q=a

Composite Goodness-of-fit Tests with Kernels
Model misspecification can create significant challenges for the implementation of probabilistic models, and this has led to development of a range of robust methods which directly account for this issue. However, whether these more involved methods are required will depend on whether the model is really misspecified, and there is a lack of generally applicable methods to answer this question. In this paper, we propose one such method. More precisely, we propose kernel-based hypothesis tests fo…

This https://arxiv.org/abs/2210.15658 has been replaced.
link: https://scholar.google.com/scholar?q=a

All the Feels: A dexterous hand with large-area tactile sensing
High cost and lack of reliability has precluded the widespread adoption of dexterous hands in robotics. Furthermore, the lack of a viable tactile sensor capable of sensing over the entire area of the hand impedes the rich, low-level feedback that would improve learning of dexterous manipulation skills. This paper introduces an inexpensive, modular, robust, and scalable platform -- the DManus -- aimed at resolving these challenges while satisfying the large-scale data collection capabilities dem…

This https://arxiv.org/abs/2403.16335 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

MEDDAP: Medical Dataset Enhancement via Diversified Augmentation Pipeline
The effectiveness of Deep Neural Networks (DNNs) heavily relies on the abundance and accuracy of available training data. However, collecting and annotating data on a large scale is often both costly and time-intensive, particularly in medical cases where practitioners are already occupied with their duties. Moreover, ensuring that the model remains robust across various scenarios of image capture is crucial in medical domains, especially when dealing with ultrasound images that vary based on t…

A Robust Bayesian Method for Building Polygenic Risk Scores using Projected Summary Statistics and Bridge Prior
Yuzheng Dun, Nilanjan Chatterjee, Jin Jin, Akihiko Nishimura
https://arXiv.org/abs/2401.15014

A Robust Bayesian Method for Building Polygenic Risk Scores using Projected Summary Statistics and Bridge Prior
Polygenic Risk Scores (PRS) developed from genome-wide association studies (GWAS) are of increasing interest for various clinical and research applications. Bayesian methods have been particularly popular for building PRS in genome-wide scale because of their natural ability to regularize model and borrow information in high-dimension. In this article, we present new theoretical results, methods, and extensive numerical studies to advance Bayesian methods for PRS applications. We conduct theore…

This https://arxiv.org/abs/2312.03573 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On data-driven Wasserstein distributionally robust Nash equilibrium problems with heterogeneous uncertainty
We study stochastic Nash equilibrium problems subject to heterogeneous uncertainty on the cost functions of the individual agents. In our setting, we assume no prior knowledge of the underlying probability distributions of the uncertain variables. To account for this lack of knowledge, we consider an ambiguity set around the empirical probability distribution under the Wasserstein metric. We then show that, under mild assumptions, finite-sample guarantees on the probability that any resulting d…

This https://arxiv.org/abs/2312.02003 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly
Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text generation capabilities, contextual awareness, and robust problem-solving skills, making them invaluable in various domains (e.g., search engines, customer support, translation). In the meantime, LLMs have also gained traction in the security community, revealing security vulnerabilities and showcasing their potentia…

Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets
Jie Wang, Rui Gao, Yao Xie
https://arxiv.org/abs/2403.14822 https://

Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets
We present a new framework to address the non-convex robust hypothesis testing problem, wherein the goal is to seek the optimal detector that minimizes the maximum of worst-case type-I and type-II risk functions. The distributional uncertainty sets are constructed to center around the empirical distribution derived from samples based on Sinkhorn discrepancy. Given that the objective involves non-convex, non-smooth probabilistic functions that are often intractable to optimize, existing methods …

Double Robust Variance Estimation
Bonnie E. Shook-Sa, Paul N. Zivich, Chanhwa Lee, Keyi Xue, Rachael K. Ross, Jessie K. Edwards, Jeffrey S. A. Stringer, Stephen R. Cole
https://arxiv.org/abs/2404.16166

Double Robust Variance Estimation
Doubly robust estimators have gained popularity in the field of causal inference due to their ability to provide consistent point estimates when either an outcome or exposure model is correctly specified. However, the influence function based variance estimator frequently used with doubly robust estimators is only consistent when both the outcome and exposure models are correctly specified. Here, use of M-estimation and the empirical sandwich variance estimator for doubly robust point and varia…

Coca: Improving and Explaining Graph Neural Network-Based Vulnerability Detection Systems
Sicong Cao, Xiaobing Sun, Xiaoxue Wu, David Lo, Lili Bo, Bin Li, Wei Liu
https://arXiv.org/abs/2401.14886

Coca: Improving and Explaining Graph Neural Network-Based Vulnerability Detection Systems
Recently, Graph Neural Network (GNN)-based vulnerability detection systems have achieved remarkable success. However, the lack of explainability poses a critical challenge to deploy black-box models in security-related domains. For this reason, several approaches have been proposed to explain the decision logic of the detection model by providing a set of crucial statements positively contributing to its predictions. Unfortunately, due to the weakly-robust detection models and suboptimal explan…

This https://arxiv.org/abs/2404.13532 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SpringGrasp: An optimization pipeline for robust and compliant dexterous pre-grasp synthesis
Generating stable and robust grasps on arbitrary objects is critical for dexterous robotic hands, marking a significant step towards advanced dexterous manipulation. Previous studies have mostly focused on improving differentiable grasping metrics with the assumption of precisely known object geometry. However, shape uncertainty is ubiquitous due to noisy and partial shape observations, which introduce challenges in grasp planning. We propose, SpringGrasp planner, a planner that considers uncer…

Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets
Jie Wang, Rui Gao, Yao Xie
https://arxiv.org/abs/2403.14822 https://

Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets
We present a new framework to address the non-convex robust hypothesis testing problem, wherein the goal is to seek the optimal detector that minimizes the maximum of worst-case type-I and type-II risk functions. The distributional uncertainty sets are constructed to center around the empirical distribution derived from samples based on Sinkhorn discrepancy. Given that the objective involves non-convex, non-smooth probabilistic functions that are often intractable to optimize, existing methods …

This https://arxiv.org/abs/2404.13532 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SpringGrasp: An optimization pipeline for robust and compliant dexterous pre-grasp synthesis
Generating stable and robust grasps on arbitrary objects is critical for dexterous robotic hands, marking a significant step towards advanced dexterous manipulation. Previous studies have mostly focused on improving differentiable grasping metrics with the assumption of precisely known object geometry. However, shape uncertainty is ubiquitous due to noisy and partial shape observations, which introduce challenges in grasp planning. We propose, SpringGrasp planner, a planner that considers uncer…

This https://arxiv.org/abs/2304.10025 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Identification and multiply robust estimation in causal mediation analysis across principal strata
We consider assessing causal mediation in the presence of a post-treatment event (examples include noncompliance, a clinical event, or a terminal event). We identify natural mediation effects for the entire study population and for each principal stratum characterized by the joint potential values of the post-treatment event. We derive efficient influence functions for each mediation estimand, which motivate a set of multiply robust estimators for inference. The multiply robust estimators are c…

This https://arxiv.org/abs/2010.02848 has been replaced.
link: https://scholar.google.com/scholar?q=a

Unified Robust Estimation
Robust estimation is primarily concerned with providing reliable parameter estimates in the presence of outliers. Numerous robust loss functions have been proposed in regression and classification, along with various computing algorithms. In modern penalised generalised linear models (GLM), however, there is limited research on robust estimation that can provide weights to determine the outlier status of the observations. This article proposes a unified framework based on a large family of loss…