Tootfinder

Machine learning enables cheaper and safer low-power magnetic resonance imaging (MRI) without sacrificing accuracy, according to a new study.
According to the authors, these advances pave the way for affordable, patient-centric, and deep learning-powered ultra-low-field (ULF) MRI scanners,
addressing unmet clinical needs in diverse healthcare settings worldwide.

Machine learning enables cheaper and safer low-power MRI
Machine learning enables cheaper and safer low-power magnetic resonance imaging (MRI) without sacrificing accuracy, according to a new study.

Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning
Aleksandr Petrov, Craig Macdonald
https://arxiv.org/abs/2403.04875 https://

Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning
Adaptations of Transformer models, such as BERT4Rec and SASRec, achieve state-of-the-art performance in the sequential recommendation task according to accuracy-based metrics, such as NDCG. These models treat items as tokens and then utilise a score-and-rank approach (Top-K strategy), where the model first computes item scores and then ranks them according to this score. While this approach works well for accuracy-based metrics, it is hard to use it for optimising more complex beyond-accuracy m…

Self-consistent context aware conformer transducer for speech recognition
Konstantin Kolokolov, Pavel Pekichev, Karthik Raghunathan
https://arxiv.org/abs/2402.06592

Self-consistent context aware conformer transducer for speech recognition
We propose a novel neural network architecture based on conformer transducer that adds contextual information flow to the ASR systems. Our method improves the accuracy of recognizing uncommon words while not harming the word error rate of regular words. We explore the uncommon words accuracy improvement when we use the new model and/or shallow fusion with context language model. We found that combination of both provides cumulative gain in uncommon words recognition accuracy.

Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccination
Soojong Kim, Kwanho Kim, Claire Wonjeong Jo
https://arxiv.org/abs/2404.06731

Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccination
Objective. Vaccination has engendered a spectrum of public opinions, with social media acting as a crucial platform for health-related discussions. The emergence of artificial intelligence technologies, such as large language models (LLMs), offers a novel opportunity to efficiently investigate public discourses. This research assesses the accuracy of ChatGPT, a widely used and freely available service built upon an LLM, for sentiment analysis to discern different stances toward Human Papillomav…

This https://arxiv.org/abs/2404.03778 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Flattening the Parent Bias: Hierarchical Semantic Segmentation in the Poincaré Ball
Hierarchy is a natural representation of semantic taxonomies, including the ones routinely used in image segmentation. Indeed, recent work on semantic segmentation reports improved accuracy from supervised training leveraging hierarchical label structures. Encouraged by these results, we revisit the fundamental assumptions behind that work. We postulate and then empirically verify that the reasons for the observed improvement in segmentation accuracy may be entirely unrelated to the use of the …

EY pushing AI,
" We built a large language model and GenAI chatbot that incorporated regulatory compliance elements with company policies. To date, our payroll Azure Open AI GPT chatbot has improved the employee experience by more than 25%, increased the accuracy of answers by more than 30%, and reduced the business cost of answering employee questions by more than 80%. Pretty extraordinary results all framed around making the working world work better."

Not too bad…

Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning
Aleksandr Petrov, Craig Macdonald
https://arxiv.org/abs/2403.04875 https://

Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning
Adaptations of Transformer models, such as BERT4Rec and SASRec, achieve state-of-the-art performance in the sequential recommendation task according to accuracy-based metrics, such as NDCG. These models treat items as tokens and then utilise a score-and-rank approach (Top-K strategy), where the model first computes item scores and then ranks them according to this score. While this approach works well for accuracy-based metrics, it is hard to use it for optimising more complex beyond-accuracy m…

This https://arxiv.org/abs/2310.04044 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Graph-based 3D Collision-distance Estimation Network with Probabilistic Graph Rewiring
We aim to solve the problem of data-driven collision-distance estimation given 3-dimensional (3D) geometries. Conventional algorithms suffer from low accuracy due to their reliance on limited representations, such as point clouds. In contrast, our previous graph-based model, GraphDistNet, achieves high accuracy using edge information but incurs higher message-passing costs with growing graph size, limiting its applicability to 3D geometries. To overcome these challenges, we propose GDN-R, a nov…

This https://arxiv.org/abs/2304.05946 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Entanglement detection with classical deep neural networks
In this study, we introduce an autonomous method for addressing the detection and classification of quantum entanglement, a core element of quantum mechanics that has yet to be fully understood. We employ a multi-layer perceptron to effectively identify entanglement in both two- and three-qubit systems. Our technique yields impressive detection results, achieving nearly perfect accuracy for two-qubit systems and over $90\%$ accuracy for three-qubit systems. Additionally, our approach successful…

Measuring Fiber Positioning Accuracy and Throughput with Fiber Dithering for the Dark Energy Spectroscopic Instrument
E. F. Schlafly, D. Schlegel, S. BenZvi, A. Raichoor, J. E. Forero-Romero, J. Aguilar, S. Ahlen, S. Bailey, A. Bault, D. Brooks, T. Claybaugh, K. Dawson, A. de la Macorra, Arjun Dey, P. Doel, E. Gazta\~naga, S. Gontcho A Gontcho, J. Guy, C. Hahn, K. Honscheid, J. Jimenez, S. Kent, D. Kirkby, T. Kisner, A. Kremin, A. Lambert, M. Landriau, M. E. Levi, M. Manera, P. Martini…

Measuring Fiber Positioning Accuracy and Throughput with Fiber Dithering for the Dark Energy Spectroscopic Instrument
Highly multiplexed, fiber-fed spectroscopy is enabling surveys of millions of stars and galaxies. The performance of these surveys depends on accurately positioning fibers in the focal plane to capture target light. We describe a technique to measure the positioning accuracy of fibers by dithering fibers slightly around their ideal locations. This approach also enables measurement of the total system throughput and point spread function delivered to the focal plane. We then apply this technique…

Toward ultra-efficient high fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models via LES-trained machine learning
Christian Santoni, Dichang Zhang, Zexia Zhang, Dimitris Samaras, Fotis Sotiropoulos, Ali Khosronejad
https://arxiv.org/abs/2404.07938

Toward ultra-efficient high fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models via LES-trained machine learning
This study proposes a novel machine learning (ML) methodology for the efficient and cost-effective prediction of high-fidelity three-dimensional velocity fields in the wake of utility-scale turbines. The model consists of an auto-encoder convolutional neural network with U-Net skipped connections, fine-tuned using high-fidelity data from large-eddy simulations (LES). The trained model takes the low-fidelity velocity field cost-effectively generated from the analytical engineering wake model as …

LLM4Decompile: Decompiling Binary Code with Large Language Models
Hanzhuo Tan, Qi Luo, Jing Li, Yuqun Zhang
https://arxiv.org/abs/2403.05286 https://

LLM4Decompile: Decompiling Binary Code with Large Language Models
Decompilation aims to restore compiled code to human-readable source code, but struggles with details like names and structure. Large language models (LLMs) show promise for programming tasks, motivating their application to decompilation. However, there does not exist any open-source LLM for decompilation. Moreover, existing decompilation evaluation systems mainly consider token-level accuracy and largely ignore code executability, which is the most important feature of any program. Therefore,…

Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers
Kunal Sawarkar, Abhilasha Mangal, Shivam Raj Solanki
https://arxiv.org/abs/2404.07220

Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers
Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a private knowledge base of documents with Large Language Models (LLM) to build Generative Q\&A (Question-Answering) systems. However, RAG accuracy becomes increasingly challenging as the corpus of documents scales up, with Retrievers playing an outsized role in the overall RAG accuracy by extracting the most relevant document from the corpus to provide context to the LLM. In this paper, we propose the 'Blended RAG' method o…

Fabricating Paper Circuits with Subtractive Processing
Ruhan Yang, Krithik Ranjan, Ellen Yi-Luen Do
https://arxiv.org/abs/2404.07364 https://

Fabricating Paper Circuits with Subtractive Processing
This paper introduces a new method of paper circuit fabrication that overcomes design barriers and increases flexibility in circuit design. Conventional circuit boards rely on thin traces, which limits the complexity and accuracy when applied to paper circuits. To address this issue, we propose a method that uses large conductive zones in paper circuits and performs subtractive processing during their fabrication. This approach eliminates design barriers and allows for more flexibility in circu…

This https://arxiv.org/abs/2312.09062 has been replaced.
initial toot: https://mastoxiv.page/@ar…

W4$Λ$: leveraging $Λ$ coupled cluster for accurate computational thermochemistry approaches
High-accuracy composite wavefunction methods like Weizmann-4 (W4) theory, high-accuracy extrapolated \textit{ab initio} thermochemistry (HEAT), and Feller-Peterson-Dixon (FPD) enable sub-kJ/mol accuracy in gas-phase thermochemical properties. Their biggest computational bottleneck is the evaluation of the valence post-CCSD(T) correction term. We demonstrate here, for the W4-17 thermochemistry benchmark and subsets thereof, that the lambda coupled cluster expansion converges more rapidly and smo…

Distributed Record Linkage in Healthcare Data with Apache Spark
Mohammad Heydari, Reza Sarshar, Mohammad Ali Soltanshahi
https://arxiv.org/abs/2404.07939 h…

Distributed Record Linkage in Healthcare Data with Apache Spark
Healthcare data is a valuable resource for research, analysis, and decision-making in the medical field. However, healthcare data is often fragmented and distributed across various sources, making it challenging to combine and analyze effectively. Record linkage, also known as data matching, is a crucial step in integrating and cleaning healthcare data to ensure data quality and accuracy. Apache Spark, a powerful open-source distributed big data processing framework, provides a robust platform …

Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
Ping Guo, Cheng Gong, Xi Lin, Zhiyuan Yang, Qingfu Zhang
https://arxiv.org/abs/2403.05100

Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has underscored the need for robust deep learning systems. Conventional robustness evaluations have relied on adversarial accuracy, which measures a model's performance under a specific perturbation intensity. However, this singular metric does not fully encapsulate the overall resilience of a model against varying degrees of perturbation. To address this gap, we propose a new metric …

This https://arxiv.org/abs/2204.07682 has been replaced.
link: https://scholar.google.com/scholar?q=a

Reliability Evaluation of Individual Predictions: A Data-centric Approach
Machine learning models only provide probabilistic guarantees on the expected loss of random samples from the distribution represented by their training data. As a result, a model with high accuracy, may or may not be reliable for predicting an individual query point. To address this issue, XAI aims to provide explanations of individual predictions, while approaches such as conformal predictions, probabilistic predictions, and prediction intervals count on the model's certainty in its predictio…

Evaluation and Optimization of Positional Accuracy for Maritime Positioning Systems
Atilla Alpay Nalcaci, Fidan Mehmeti, Wolfgang Kellerer, Florian Schiegg
https://arxiv.org/abs/2404.04593

Evaluation and Optimization of Positional Accuracy for Maritime Positioning Systems
Navigation and trajectorial estimation of maritime vessels are contingent upon the context of positional accuracy. Even the smallest deviations in the estimation of a given vessel may result in detrimental consequences in terms of economic and ecologic quotients. To ensure an agile and precise environment for maritime vessel positional estimation, preexisting marine radar technologies can be utilized in a way that ensures a higher level of precision compared to GNSS-based identification and pos…

The power of binary pulsars in testing Gauss-Bonnet gravity
Petar Y. Yordanov, Kalin V. Staykov, Stoytcho S. Yazadjiev, Daniela D. Doneva
https://arxiv.org/abs/2402.06305

The power of binary pulsars in testing Gauss-Bonnet gravity
Binary pulsars are a powerful tool for probing strong gravity that still outperforms direct gravitational wave observations in a number of directions due to the remarkable accuracy of the pulsar timing. They can constrain very precisely the presence of additional charges of the orbiting neutron stars leading to new channels of energy and angular momentum loss, such as the scalar dipole radiation. In the present paper, we explore in detail the possibility of constraining different classes of sca…

This https://arxiv.org/abs/2311.10568 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Phase Guided Light Field for Spatial-Depth High Resolution 3D Imaging
On 3D imaging, light field cameras typically are of single shot, and however, they heavily suffer from low spatial resolution and depth accuracy. In this paper, by employing an optical projector to project a group of single high-frequency phase-shifted sinusoid patterns, we propose a phase guided light field algorithm to significantly improve both the spatial and depth resolutions for off-the-shelf light field cameras. First, for correcting the axial aberrations caused by the main lens of our l…

Hashing Beam Training for Near-Field Communications
Yuan Xu, Wei Li, Chongwen Huang, Chen Zhu, Zhaohui Yang, Jun Yang, Jiguang He, Zhaoyang Zhang, M\'erouane Debbah
https://arxiv.org/abs/2403.06074

Hashing Beam Training for Near-Field Communications
In this paper, we investigate the millimeter-wave (mmWave) near-field beam training problem to find the correct beam direction. In order to address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme for the near-field scenario. Specifically, we first design a set of sparse bases based on the polar domain sparsity of the near-field channel. Then, the random hash functions are chosen to con…

Energy-superconvergent Runge-Kutta Time Discretizations
Jinjie Liu, Moysey Brio
https://arxiv.org/abs/2405.05448 https://arxiv.org/pd…

Energy-superconvergent Runge-Kutta Time Discretizations
In this paper, we investigate the energy accuracy of explicit Runge-Kutta (RK) time discretization for antisymmetric autonomous linear systems and present a framework for constructing RK methods with an order of energy accuracy much greater than the number of stages. For an $s$-stage, $p$th-order RK method, we show that the energy accuracy can achieve superconvergence with an order up to $2s-p+1$ if $p$ is even. Several energy-superconvergent methods, including five- to seven-stage fourth-order…

This https://arxiv.org/abs/2403.01192 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Composite Decomposition Method for Large-Scale Global Optimization
Cooperative co-evolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of non-additively separable functions, i…

This https://arxiv.org/abs/2308.01270 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

BCDDO: Binary Child Drawing Development Optimization
A lately created metaheuristic algorithm called Child Drawing Development Optimization (CDDO) has proven to be effective in a number of benchmark tests. A Binary Child Drawing Development Optimization (BCDDO) is suggested for choosing the wrapper features in this study. To achieve the best classification accuracy, a subset of crucial features is selected using the suggested BCDDO. The proposed feature selection technique's efficiency and effectiveness are assessed using the Harris Hawk, Grey Wo…

Secrecy Enhancement for UAV-enabled Integrated Sensing and Communication System
Chaedam Son, Seongah Jeong
https://arxiv.org/abs/2404.07024 https://…

Secrecy Enhancement for UAV-enabled Integrated Sensing and Communication System
In this correspondence, we propose an unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) system, where a full-duplex UAV equipped with uniform planar array (UPA) is adopted as a base station for the multiuser downlink communications, while sensing and jamming a passive ground eavesdropper. The goal of this work is to maximize the sum secrecy rate of ground users subject to the constraints of sensing accuracy and UAV's operational capability by jointly optimizing t…

Crazy #MechanicalWatch, but I feel like it will find enthusiasts here. You can read time from it with an accuracy of 15 minutes, but it shows the operation of the mechanism as if looking from underneath.
#Xeric Invertor II Automatic

This https://arxiv.org/abs/2403.03442 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

CAMASim: A Comprehensive Simulation Framework for Content-Addressable Memory based Accelerators
Content addressable memory (CAM) stands out as an efficient hardware solution for memory-intensive search operations by supporting parallel computation in memory. However, developing a CAM-based accelerator architecture that achieves acceptable accuracy, while minimizing hardware cost and catering to both exact and approximate search, still presents a significant challenge especially when considering a broader spectrum of applications. This complexity stems from CAM's rapid evolution across mul…

A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics
Bryan Bo Cao, Abhinav Sharma, Lawrence O'Gorman, Michael Coss, Shubham Jain
https://arxiv.org/abs/2404.05981

A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics
Despite accuracy and computation benchmarks being widely available to help choose among neural network models, these are usually trained on datasets with many classes, and do not give a precise idea of performance for applications of few (< 10) classes. The conventional procedure to predict performance is to train and test repeatedly on the different models and dataset variations of interest. However, this is computationally expensive. We propose an efficient classification difficulty measure t…

Surrogate modeling for probability distribution estimation:uniform or adaptive design?
Maijia Su, Ziqi Wang, Oreste Salvatore Bursi, Marco Broccardo
https://arxiv.org/abs/2404.07323

Surrogate modeling for probability distribution estimation:uniform or adaptive design?
The active learning (AL) technique, one of the state-of-the-art methods for constructing surrogate models, has shown high accuracy and efficiency in forward uncertainty quantification (UQ) analysis. This paper provides a comprehensive study on AL-based global surrogates for computing the full distribution function, i.e., the cumulative distribution function (CDF) and the complementary CDF (CCDF). To this end, we investigate the three essential components for building surrogates, i.e., types of …

OpenAI releases a tool to detect DALL-E 3-created images, claiming 98% accuracy for unaltered images, and joins Microsoft and Adobe's content credentials group (Deepa Seetharaman/Wall Street Journal)
https://www.

A preconditioning for the spectral solution of incompressible variable-density flows
L. ReynierLMFA, Bastien Di PierroLMFA, Fr\'ed\'eric AlizardLMFA, Anne CadiouLMFA, Lionel Le PenvenLMFA, Marc BuffatLMFA
https://arxiv.org/abs/2403.06654

A preconditioning for the spectral solution of incompressible variable-density flows
In the present study, the efficiency of preconditioners for solving linear systems associated with the discretized variable-density incompressible Navier-Stokes equations with semiimplicit second-order accuracy in time and spectral accuracy in space is investigated. The method, in which the inverse operator for the constant-density flow system acts as preconditioner, is implemented for three iterative solvers: the General Minimal Residual, the Conjugate Gradient and the Richardson Minimal Resid…

Comparing Compressed and Full-modeling Analyses with FOLPS: Implications for DESI 2024 and beyond
H. E. Noriega, A. Aviles, H. Gil-Mar\'in, S. Ramirez-Solano, S. Fromenteau, M. Vargas-Maga\~na, J. Aguilar, S. Ahlen, O. Alves, S. Brieden, D. Brooks, J. L. Cervantes-Cota, S. Chen, T. Claybaugh, S. Cole, K. Dawson, A. de la Macorra, A. de Mattia, P. Doel, N. Findlay, J. E. Forero-Romero, E. Gazta\~naga, S. Gontcho A Gontcho, K. Honscheid, J. Hou, C. Howlett, M. Ishak, S. Juneau, Y. La…

Comparing Compressed and Full-modeling Analyses with FOLPS: Implications for DESI 2024 and beyond
The Dark Energy Spectroscopic Instrument (DESI) will provide unprecedented information about the large-scale structure of our Universe. In this work, we study the robustness of the theoretical modelling of the power spectrum of FOLPS, a novel effective field theory-based package for evaluating the redshift space power spectrum in the presence of massive neutrinos. We perform this validation by fitting the AbacusSummit high-accuracy $N$-body simulations for Luminous Red Galaxies, Emission Line G…

Improving link prediction accuracy of network embedding algorithms via rich node attribute information
Weiwei Gu, Jinqiang Hou, Weiyi Gu
https://arxiv.org/abs/2403.04282

Improving link prediction accuracy of network embedding algorithms via rich node attribute information
Complex networks are widely used to represent an abundance of real-world relations ranging from social networks to brain networks. Inferring missing links or predicting future ones based on the currently observed network is known as the link prediction task.Recent network embedding based link prediction algorithms have demonstrated ground-breaking performance on link prediction accuracy. Those algorithms usually apply node attributes as the initial feature input to accelerate the convergence sp…

Optical and Transport Properties of Plasma Mixtures from Ab Initio Molecular Dynamics
Alexander J. White, Galen T. Craven, Vidushi Sharma, Lee A. Collins
https://arxiv.org/abs/2404.07800

Optical and Transport Properties of Plasma Mixtures from Ab Initio Molecular Dynamics
Predicting the charged particle transport properties of warm dense matter / hot dense plasma mixtures is a challenge for analytical models. High accuracy ab initio methods are more computationally expensive, but can provide critical insight by explicitly simulating mixtures. In this work, we investigate the transport properties and optical response of warm dense carbon-hydrogen mixtures at varying concentrations under either conserved electronic pressure or mass density at a constant temperatur…

Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardship
Amelia Jim\'enez-S\'anchez, Natalia-Rozalia Avlona, Dovile Juodelyte, Th\'eo Sourget, Caroline Vang-Larsen, Hubert Dariusz Zaj\k{a}c, Veronika Cheplygina
https://arxiv.org/abs/2402…

Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardship
Medical imaging datasets are fundamental to artificial intelligence (AI) in healthcare. The accuracy, robustness and fairness of diagnostic algorithms depend on the data (and its quality) on which the models are trained and evaluated. Medical imaging datasets have become increasingly available to the public, and are often hosted on Community-Contributed Platforms (CCP), including private companies like Kaggle or HuggingFace. While open data is important to enhance the redistribution of data's p…

Towards a realistic noise modelling of quantum sensors for future satellite gravity missions
Jo\~ao Encarna\c{c}\~ao, Christian Siemes, Ilias Daras, Olivier Carraz, Aaron Strangfeld, Philipp Zingerle, Roland Pail
https://arxiv.org/abs/2404.07835

Towards a realistic noise modelling of quantum sensors for future satellite gravity missions
Cold Atom Interferometry accelerometers and gradiometers have emerged as promising candidates for future gravimetric satellite missions due to their potential for detecting gravitational forces and gradients with high precision and accuracy. Mapping the Earth's gravity field from space offers valuable insights into climate change, hydro- and biosphere evolution, and seismic activity prediction. Current satellite gravimetry missions have demonstrated the utility of gravity data in understanding …

Helicity-dependent parton distribution functions at next-to-next-to-leading order accuracy from inclusive and semi-inclusive deep-inelastic scattering data
MAP (Multi-dimensional Analyses of Partonic distributions), Collaboration, :, Valerio Bertone, Amedeo Chiefa, Emanuele R. Nocera
https://arxiv.org/abs/2404.04712

Helicity-dependent parton distribution functions at next-to-next-to-leading order accuracy from inclusive and semi-inclusive deep-inelastic scattering data
We present MAPPDFpol1.0, a new determination of the helicity-dependent parton distribution functions (PDFs) of the proton from a set of longitudinally polarised inclusive and semi-inclusive deep-inelastic scattering data. The determination includes, for the first time, next-to-next-to-leading order QCD corrections to both processes, and is carried out in a framework that combines a neural-network parametrisation of PDFs with a Monte Carlo representation of their uncertainties. We discuss the qu…

Deep Prompt Multi-task Network for Abuse Language Detection
Jian Zhu, Yuping Ruan, Jingfei Chang, Cheng Luo
https://arxiv.org/abs/2403.05268 https://

Deep Prompt Multi-task Network for Abuse Language Detection
The detection of abusive language remains a long-standing challenge with the extensive use of social networks. The detection task of abusive language suffers from limited accuracy. We argue that the existing detection methods utilize the fine-tuning technique of the pre-trained language models (PLMs) to handle downstream tasks. Hence, these methods fail to stimulate the general knowledge of the PLMs. To address the problem, we propose a novel Deep Prompt Multi-task Network (DPMN) for abuse lang…

This https://arxiv.org/abs/2312.13473 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Accuracy vs Memory Advantage in the Quantum Simulation of Stochastic Processes
Many inference scenarios rely on extracting relevant information from known data in order to make future predictions. When the underlying stochastic process satisfies certain assumptions, there is a direct mapping between its exact classical and quantum simulators, with the latter asymptotically using less memory. Here we focus on studying whether such quantum advantage persists when those assumptions are not satisfied, and the model is doomed to have imperfect accuracy. By studying the trade-o…

This https://arxiv.org/abs/2402.17569 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Backpropagation-Based Analytical Derivatives of EKF Covariance for Active Sensing
To enhance accuracy of robot state estimation, perception-aware (or active sensing) methods seek trajectories that minimize uncertainty. To this aim, one possibility is to seek trajectories that minimize the final covariance of an extended Kalman filter (EKF), w.r.t. its control inputs over a given horizon. However, this can be computationally demanding. In this article, we derive novel backpropagation analytical formulas for the derivatives of the final covariance of an EKF w.r.t. its inputs. …

HAM-TTS: Hierarchical Acoustic Modeling for Token-Based Zero-Shot Text-to-Speech with Model and Data Scaling
Chunhui Wang, Chang Zeng, Bowen Zhang, Ziyang Ma, Yefan Zhu, Zifeng Cai, Jian Zhao, Zhonglin Jiang, Yong Chen
https://arxiv.org/abs/2403.05989

HAM-TTS: Hierarchical Acoustic Modeling for Token-Based Zero-Shot Text-to-Speech with Model and Data Scaling
Token-based text-to-speech (TTS) models have emerged as a promising avenue for generating natural and realistic speech, yet they grapple with low pronunciation accuracy, speaking style and timbre inconsistency, and a substantial need for diverse training data. In response, we introduce a novel hierarchical acoustic modeling approach complemented by a tailored data augmentation strategy and train it on the combination of real and synthetic data, scaling the data size up to 650k hours, leading to…

"This work shows that a #decentralized approach to #moderation can lead to higher content reliability on #socialmedia. This approach is also more efficient and scalable than centralized moderation…

Empowering social media users to assess content helps fight misinformation
MIT researchers built a prototype social media platform to study the effects of giving users more agency to assess content for accuracy and control the posts they see based on accuracy assessments from others. Users were able to make accurate assessments, despite having no prior training, and they valued and utilized the assessment and filtering tools they designed.

An efficient truncation scheme for Eulerian and total Lagrangian SPH methods
Zhentong Wang, Chi Zhang, Oskar J. Haidn, Xiangyu Hu
https://arxiv.org/abs/2405.05155

An efficient truncation scheme for Eulerian and total Lagrangian SPH methods
In smoothed particle hydrodynamics (SPH) method, the particle-based approximations are implemented via kernel functions, and the evaluation of performance involves two key criteria: numerical accuracy and computational efficiency. In the SPH community, the Wendland kernel reigns as the prevailing choice due to its commendable accuracy and reasonable computational efficiency. Nevertheless, there exists an urgent need to enhance the computational efficiency of numerical methods while upholding ac…

Who's going to be sifting Stack Overflow for relevance and accuracy before OpenAI incorporates it?
Or is this for an entirely new offering "All The Wrong Answers, But Not Necessarily In The Right Order"?
#AI #Stupidity

This https://arxiv.org/abs/2309.08689 has been replaced.
initial toot: https://mastoxiv.page/@a…

A collinear-spin machine learned interatomic potential for Fe$_{7}$Cr$_{2}$Ni alloy
We have developed a new machine learned interatomic potential for the prototypical austenitic steel Fe$_{7}$Cr$_{2}$Ni, using the Gaussian approximation potential (GAP) framework. This new GAP can model the alloy's properties with close to density functional theory (DFT) accuracy, while at the same time allowing us to access larger length and time scales than expensive first-principles methods. We also extended the GAP input descriptors to approximate the effects of collinear spins (Spin GAP), …

This https://arxiv.org/abs/2306.08167 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms
Machine learning (ML) models that achieve high average accuracy can still underperform on semantically coherent subsets ("slices") of data. This behavior can have significant societal consequences for the safety or bias of the model in deployment, but identifying these underperforming slices can be difficult in practice, especially in domains where practitioners lack access to group annotations to define coherent subsets of their data. Motivated by these challenges, ML researchers have develope…

Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-synchronous Federated Learning
Liangkun Yu, Xiang Sun, Rana Albelaihi, Chaeeun Park, Sihua Shao
https://arxiv.org/abs/2403.06900

Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-synchronous Federated Learning
Federated Learning (FL) revolutionizes collaborative machine learning among Internet of Things (IoT) devices by enabling them to train models collectively while preserving data privacy. FL algorithms fall into two primary categories: synchronous and asynchronous. While synchronous FL efficiently handles straggler devices, it can compromise convergence speed and model accuracy. In contrast, asynchronous FL allows all devices to participate but incurs high communication overhead and potential mod…

Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
Ping Guo, Cheng Gong, Xi Lin, Zhiyuan Yang, Qingfu Zhang
https://arxiv.org/abs/2403.05100

Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has underscored the need for robust deep learning systems. Conventional robustness evaluations have relied on adversarial accuracy, which measures a model's performance under a specific perturbation intensity. However, this singular metric does not fully encapsulate the overall resilience of a model against varying degrees of perturbation. To address this gap, we propose a new metric …

New slow-roll approximations for inflation in Einstein-Gauss-Bonnet gravity
E. O. Pozdeeva, M. A. Skugoreva, A. V. Toporensky, S. Yu. Vernov
https://arxiv.org/abs/2403.06147

New slow-roll approximations for inflation in Einstein-Gauss-Bonnet gravity
We propose new slow-roll approximations for inflationary models with the Gauss-Bonnet term. We find more accurate expressions of the standard slow-roll parameters as functions of the scalar field. To check the accuracy of approximations considered we construct inflationary models with quadratic and quartic monomial potentials and the Gauss-Bonnet term. Numerical analysis of these models indicates that the proposed inflationary scenarios do not contradict to the observation data. New slow-roll a…

This https://arxiv.org/abs/2301.00349 has been replaced.
link: https://scholar.google.com/scholar?q=a

Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty
Medical image segmentation is critical for disease diagnosis and treatment assessment. However, concerns regarding the reliability of segmentation regions persist among clinicians, mainly attributed to the absence of confidence assessment, robustness, and calibration to accuracy. To address this, we introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. DEviS not only enhances the calibration and robustness of bas…

Improving 2D-3D Dense Correspondences with Diffusion Models for 6D Object Pose Estimation
Peter H\"onig, Stefan Thalhammer, Markus Vincze
https://arxiv.org/abs/2402.06436

Improving 2D-3D Dense Correspondences with Diffusion Models for 6D Object Pose Estimation
Estimating 2D-3D correspondences between RGB images and 3D space is a fundamental problem in 6D object pose estimation. Recent pose estimators use dense correspondence maps and Point-to-Point algorithms to estimate object poses. The accuracy of pose estimation depends heavily on the quality of the dense correspondence maps and their ability to withstand occlusion, clutter, and challenging material properties. Currently, dense correspondence maps are estimated using image-to-image translation mo…

Deep Prompt Multi-task Network for Abuse Language Detection
Jian Zhu, Yuping Ruan, Jingfei Chang, Cheng Luo
https://arxiv.org/abs/2403.05268 https://

Deep Prompt Multi-task Network for Abuse Language Detection
The detection of abusive language remains a long-standing challenge with the extensive use of social networks. The detection task of abusive language suffers from limited accuracy. We argue that the existing detection methods utilize the fine-tuning technique of the pre-trained language models (PLMs) to handle downstream tasks. Hence, these methods fail to stimulate the general knowledge of the PLMs. To address the problem, we propose a novel Deep Prompt Multi-task Network (DPMN) for abuse lang…

Prediction-Correction Algorithm for Time-Varying Smooth Non-Convex Optimization
Hidenori Iwakiri, Tomoya Kamijima, Shinji Ito, Akiko Takeda
https://arxiv.org/abs/2402.06181

Prediction-Correction Algorithm for Time-Varying Smooth Non-Convex Optimization
Time-varying optimization problems are prevalent in various engineering fields, and the ability to solve them accurately in real-time is becoming increasingly important. The prediction-correction algorithms used in smooth time-varying optimization can achieve better accuracy than that of the time-varying gradient descent (TVGD) algorithm. However, none of the existing prediction-correction algorithms can be applied to general non-strongly-convex functions, and most of them are not computational…

This https://arxiv.org/abs/2312.04049 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

An Actuator with Magnetic Restoration, Part II: Drive Circuit and Control Loops
In part II, an op-amp-based drive is proposed and designed. Subsequently, a very accurate model for the drive circuit and the current loop is developed as a simulation platform, while its simplified version is derived, tailored for efficient design purposes. Through a comprehensive evaluation, the accuracy and efficacy of both the actuator and drive circuit modeling is scrutinized, showcasing their superiorities over existing approaches. The importance of eddy current modeling is underscored. A…

#AlphaFold3 is here.
https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/

AlphaFold 3 predicts the structure and interactions of all of life’s molecules
Our new AI model AlphaFold 3 can predict the structure and interactions of all life’s molecules with unprecedented accuracy.

HDA-LVIO: A High-Precision LiDAR-Visual-Inertial Odometry in Urban Environments with Hybrid Data Association
Jian Shi, Wei Wang, Mingyang Qi, Xin Li, Ye Yan
https://arxiv.org/abs/2403.06590

HDA-LVIO: A High-Precision LiDAR-Visual-Inertial Odometry in Urban Environments with Hybrid Data Association
To enhance localization accuracy in urban environments, an innovative LiDAR-Visual-Inertial odometry, named HDA-LVIO, is proposed by employing hybrid data association. The proposed HDA_LVIO system can be divided into two subsystems: the LiDAR-Inertial subsystem (LIS) and the Visual-Inertial subsystem (VIS). In the LIS, the LiDAR pointcloud is utilized to calculate the Iterative Closest Point (ICP) error, serving as the measurement value of Error State Iterated Kalman Filter (ESIKF) to construct…

MP2-based composite extrapolation schemes can predict core-ionization energies for first-row elements with coupled-cluster level accuracy
Anton Morgunov, Henry K. Tran, Oinam Romesh Meitei, Yu-Che Chien, Troy Van Voorhis
https://arxiv.org/abs/2403.06364

MP2-based composite extrapolation schemes can predict core-ionization energies for first-row elements with coupled-cluster level accuracy
X-ray photoelectron spectroscopy (XPS) measures core-electron binding energies (CEBEs) to reveal element-specific insights into chemical environment and bonding. Accurate theoretical CEBE prediction aids XPS interpretation but requires proper modeling of orbital relaxation and electron correlation upon core-ionization. This work systematically investigates basis set selection for extrapolation to the complete basis set (CBS) limit of CEBEs from $Δ$MP2 and $Δ$CC energies across 94 K-edges in d…

This https://arxiv.org/abs/2311.16594 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

Monitor Placement for Fault Localization in Deep Neural Network Accelerators
Systolic arrays are a prominent choice for deep neural network (DNN) accelerators because they offer parallelism and efficient data reuse. Improving the reliability of DNN accelerators is crucial as hardware faults can degrade the accuracy of DNN inferencing. Systolic arrays make use of a large number of processing elements (PEs) for parallel processing, but when one PE is faulty, the error propagates and affects the outcomes of downstream PEs. Due to the large number of PEs, the cost associate…

Fine-Tuning Surrogate Gradient Learning for Optimal Hardware Performance in Spiking Neural Networks
Ilkin Aliyev, Tosiron Adegbija
https://arxiv.org/abs/2402.06211

Fine-Tuning Surrogate Gradient Learning for Optimal Hardware Performance in Spiking Neural Networks
The highly sparse activations in Spiking Neural Networks (SNNs) can provide tremendous energy efficiency benefits when carefully exploited in hardware. The behavior of sparsity in SNNs is uniquely shaped by the dataset and training hyperparameters. This work reveals novel insights into the impacts of training on hardware performance. Specifically, we explore the trade-offs between model accuracy and hardware efficiency. We focus on three key hyperparameters: surrogate gradient functions, beta, …

Surrogate modeling for probability distribution estimation:uniform or adaptive design?
Maijia Su, Ziqi Wang, Oreste Salvatore Bursi, Marco Broccardo
https://arxiv.org/abs/2404.07323

Surrogate modeling for probability distribution estimation:uniform or adaptive design?
The active learning (AL) technique, one of the state-of-the-art methods for constructing surrogate models, has shown high accuracy and efficiency in forward uncertainty quantification (UQ) analysis. This paper provides a comprehensive study on AL-based global surrogates for computing the full distribution function, i.e., the cumulative distribution function (CDF) and the complementary CDF (CCDF). To this end, we investigate the three essential components for building surrogates, i.e., types of …

The impact of data set similarity and diversity on transfer learning success in time series forecasting
Claudia Ehrig, Catherine Cleophas, Germain Forestier
https://arxiv.org/abs/2404.06198

The impact of data set similarity and diversity on transfer learning success in time series forecasting
Models, pre-trained on a similar or diverse source data set, have become pivotal in enhancing the efficiency and accuracy of time series forecasting on target data sets by leveraging transfer learning. While benchmarks validate the performance of model generalization on various target data sets, there is no structured research providing similarity and diversity measures explaining which characteristics of source and target data lead to transfer learning success. Our study pioneers in systematic…

Precision premium transformation -- a high-precision astrometric solution based on the precision premium curve
Z. J. Zheng, Q. Y. Peng, F. R. Lin, D. Li, Y. Zheng
https://arxiv.org/abs/2403.05883

Precision premium transformation -- a high-precision astrometric solution based on the precision premium curve
Context. In Gaia era, atmospheric turbulence, which causes stochastic wander of a star image, is a fundamental limitation to the astrometric accuracy of ground-based optical imaging. However, the positional bias caused by turbulence (called turbulence error here) can be effectively reduced by measuring a target relative to another reference (a star or a fast-moving target) which locates in the range of only several tens of arcsec, since they suffer from similar turbulence errors. This phenomeno…

This https://arxiv.org/abs/2402.04798 has been replaced.
link: https://scholar.google.com/scholar?q=a

Spiking-PhysFormer: Camera-Based Remote Photoplethysmography with Parallel Spike-driven Transformer
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning …

This https://arxiv.org/abs/2312.12610 has been replaced.
initial toot: https://mastoxiv.page/@a…

Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers
This work presents the PORTALS framework, which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel (electron temperature, ion temperature, electron density, impurity density and angular rota…

HDA-LVIO: A High-Precision LiDAR-Visual-Inertial Odometry in Urban Environments with Hybrid Data Association
Jian Shi, Wei Wang, Mingyang Qi, Xin Li, Ye Yan
https://arxiv.org/abs/2403.06590

HDA-LVIO: A High-Precision LiDAR-Visual-Inertial Odometry in Urban Environments with Hybrid Data Association
To enhance localization accuracy in urban environments, an innovative LiDAR-Visual-Inertial odometry, named HDA-LVIO, is proposed by employing hybrid data association. The proposed HDA_LVIO system can be divided into two subsystems: the LiDAR-Inertial subsystem (LIS) and the Visual-Inertial subsystem (VIS). In the LIS, the LiDAR pointcloud is utilized to calculate the Iterative Closest Point (ICP) error, serving as the measurement value of Error State Iterated Kalman Filter (ESIKF) to construct…

This https://arxiv.org/abs/2402.13709 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

SaGE: Evaluating Moral Consistency in Large Language Models
Despite recent advancements showcasing the impressive capabilities of Large Language Models (LLMs) in conversational systems, we show that even state-of-the-art LLMs are morally inconsistent in their generations, questioning their reliability (and trustworthiness in general). Prior works in LLM evaluation focus on developing ground-truth data to measure accuracy on specific tasks. However, for moral scenarios that often lack universally agreed-upon answers, consistency in model responses become…

DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency
Wenfang Ya, Kejing Yin, William K. Cheung, Jia Liu, Jing Qin
https://arxiv.org/abs/2403.06197

DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency
The combination of electronic health records (EHR) and medical images is crucial for clinicians in making diagnoses and forecasting prognosis. Strategically fusing these two data modalities has great potential to improve the accuracy of machine learning models in clinical prediction tasks. However, the asynchronous and complementary nature of EHR and medical images presents unique challenges. Missing modalities due to clinical and administrative factors are inevitable in practice, and the signi…

#AlphaFold3 is here.
https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/

AlphaFold 3 predicts the structure and interactions of all of life’s molecules
Our new AI model AlphaFold 3 can predict the structure and interactions of all life’s molecules with unprecedented accuracy.

This https://arxiv.org/abs/2312.04046 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

An Actuator with Magnetic Restoration, Part I: Electromechanical Model and Identification
Electromechanical models are crucial in the design and control of motors and actuators. Modeling, identification, drive, and current control loop of a limited-rotation actuator with magnetic restoration is presented. New nonlinear and linearized electromechanical models are developed for the design of the drive as well as small and large signal controls of the actuator. To attain a higher accuracy and an efficient design, and the eddy-currents in the laminations and magnet are modeled. This inv…

Gradient Descent is Pareto-Optimal in the Oracle Complexity and Memory Tradeoff for Feasibility Problems
Moise Blanchard
https://arxiv.org/abs/2404.06720 h…

Gradient Descent is Pareto-Optimal in the Oracle Complexity and Memory Tradeoff for Feasibility Problems
In this paper we provide oracle complexity lower bounds for finding a point in a given set using a memory-constrained algorithm that has access to a separation oracle. We assume that the set is contained within the unit $d$-dimensional ball and contains a ball of known radius $ε>0$. This setup is commonly referred to as the feasibility problem. We show that to solve feasibility problems with accuracy $ε\geq e^{-d^{o(1)}}$, any deterministic algorithm either uses $d^{1+δ}$ bits of memory or m…

MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models
Olaf Wysocki, Ludwig Hoegner, Uwe Stilla
https://arxiv.org/abs/2402.06288

MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models
Although highly-detailed LoD3 building models reveal great potential in various applications, they have yet to be available. The primary challenges in creating such models concern not only automatic detection and reconstruction but also standard-consistent modeling. In this paper, we introduce a novel refinement strategy enabling LoD3 reconstruction by leveraging the ubiquity of lower LoD building models and the accuracy of MLS point clouds. Such a strategy promises at-scale LoD3 reconstruction…

This https://arxiv.org/abs/2311.16594 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

Monitor Placement for Fault Localization in Deep Neural Network Accelerators
Systolic arrays are a prominent choice for deep neural network (DNN) accelerators because they offer parallelism and efficient data reuse. Improving the reliability of DNN accelerators is crucial as hardware faults can degrade the accuracy of DNN inferencing. Systolic arrays make use of a large number of processing elements (PEs) for parallel processing, but when one PE is faulty, the error propagates and affects the outcomes of downstream PEs. Due to the large number of PEs, the cost associate…

Multi-Robot Target Tracking with Sensing and Communication Danger Zones
Jiazhen Li, Peihan Li, Yuwei Wu, Gaurav S. Sukhatme, Vijay Kumar, Lifeng Zhou
https://arxiv.org/abs/2404.07880

Multi-Robot Target Tracking with Sensing and Communication Danger Zones
Multi-robot target tracking finds extensive applications in different scenarios, such as environmental surveillance and wildfire management, which require the robustness of the practical deployment of multi-robot systems in uncertain and dangerous environments. Traditional approaches often focus on the performance of tracking accuracy with no modeling and assumption of the environments, neglecting potential environmental hazards which result in system failures in real-world deployments. To addr…

This https://arxiv.org/abs/2309.08968 has been replaced.
link: https://scholar.google.com/scholar?q=a

Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference
Large language models (LLMs) have revolutionized natural language processing (NLP) by excelling at understanding and generating human-like text. However, their widespread deployment can be prohibitively expensive. SortedNet is a recent training technique for enabling dynamic inference by leveraging the modularity in networks and sorting sub-models based on computation/accuracy in a nested manner. We extend SortedNet to generative NLP tasks, making large language models dynamic without any Pre-T…

This https://arxiv.org/abs/2305.01147 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy. This paper introduces an end-to-end deep learning model, named RKGCN, which dynamically analyses each user's preferences and makes a recommendation of suitable items. It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information…

This https://arxiv.org/abs/2309.11772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Active Learning for a Recursive Non-Additive Emulator for Multi-Fidelity Computer Experiments
Computer simulations have become essential for analyzing complex systems, but high-fidelity simulations often come with significant computational costs. To tackle this challenge, multi-fidelity computer experiments have emerged as a promising approach that leverages both low-fidelity and high-fidelity simulations, enhancing both the accuracy and efficiency of the analysis. In this paper, we introduce a new and flexible statistical model, the Recursive Non-Additive (RNA) emulator, that integrate…

Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
Fatima Ezzeddine, Mirna Saad, Omran Ayoub, Davide Andreoletti, Martin Gjoreski, Ihab Sbeity, Marc Langheinrich, Silvia Giordano
https://arxiv.org/abs/2404.06144

Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
Anomaly detection (AD), also referred to as outlier detection, is a statistical process aimed at identifying observations within a dataset that significantly deviate from the expected pattern of the majority of the data. Such a process finds wide application in various fields, such as finance and healthcare. While the primary objective of AD is to yield high detection accuracy, the requirements of explainability and privacy are also paramount. The first ensures the transparency of the AD proces…

DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency
Wenfang Ya, Kejing Yin, William K. Cheung, Jia Liu, Jing Qin
https://arxiv.org/abs/2403.06197

DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency
The combination of electronic health records (EHR) and medical images is crucial for clinicians in making diagnoses and forecasting prognosis. Strategically fusing these two data modalities has great potential to improve the accuracy of machine learning models in clinical prediction tasks. However, the asynchronous and complementary nature of EHR and medical images presents unique challenges. Missing modalities due to clinical and administrative factors are inevitable in practice, and the signi…

Efficient Estimation of the Convective Cooling Rate of Photovoltaic Arrays with Various Geometric Configurations: a Physics-Informed Machine Learning Approach
Dapeng Wang, Zhaojian Liang, Ziqi Zhang, Mengying Li
https://arxiv.org/abs/2403.06418

Efficient Estimation of the Convective Cooling Rate of Photovoltaic Arrays with Various Geometric Configurations: a Physics-Informed Machine Learning Approach
Convective heat transfer is crucial for photovoltaic (PV) systems, as the power generation of PV is sensitive to temperature. The configuration of PV arrays have a significant impact on convective heat transfer by influencing turbulent characteristics. Conventional methods of quantifying the configuration effects are either through Computational Fluid Dynamics (CFD) simulations or empirical methods, which face the challenge of either high computational demand or low accuracy, especially when co…

BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learning
Haoyue Sheng, Linrui Ma, Jean-Francois Samson, Dianbo Liu
https://arxiv.org/abs/2402.06499

BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learning
Background: Chest X-ray imaging-based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to complex interpretations and the growing workload of radiologists. While recent advances in deep learning offer promising solutions, there is still a critical issue of domain inconsistency in cross-domain transfer learning, which hampers the efficiency and accuracy of diagnostic processes. This study aims to address the domain inconsistency proble…

This https://arxiv.org/abs/2311.14020 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Simulation of Bound-State-Enhanced Quantum Metrology
Quantum metrology explores quantum effects to improve the measurement accuracy of some physical quantities beyond the classical limit. However, due to the interaction between the system and the environment, the decoherence can significantly reduce the accuracy of the measurement. Many methods have been proposed to restore the accuracy of the measurement in the long-time limit. Recently, it has been found that the bound state can assist the error-free measurement and recover the $t^{-1}$ scaling…

This https://arxiv.org/abs/2312.08488 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

A PnP Algorithm for Two-Dimensional Pose Estimation
We propose a PnP algorithm for a camera constrained to two-dimensional motion (applicable, for instance, to many wheeled robotics platforms). Leveraging this assumption allows accuracy and performance improvements over 3D PnP algorithms due to the reduction in search space dimensionality. It also reduces the incidence of ambiguous pose estimates (as, in most cases, the spurious solutions fall outside the plane of movement). Our algorithm finds an approximate solution by solving a polynomial sys…

This https://arxiv.org/abs/2309.08968 has been replaced.
link: https://scholar.google.com/scholar?q=a

Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference
Large language models (LLMs) have revolutionized natural language processing (NLP) by excelling at understanding and generating human-like text. However, their widespread deployment can be prohibitively expensive. SortedNet is a recent training technique for enabling dynamic inference by leveraging the modularity in networks and sorting sub-models based on computation/accuracy in a nested manner. We extend SortedNet to generative NLP tasks, making large language models dynamic without any Pre-T…

This https://arxiv.org/abs/2402.07354 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models
Identification of tumor margins is essential for surgical decision-making for glioblastoma patients and provides reliable assistance for neurosurgeons. Despite improvements in deep learning architectures for tumor segmentation over the years, creating a fully autonomous system suitable for clinical floors remains a formidable challenge because the model predictions have not yet reached the desired level of accuracy and generalizability for clinical applications. Generative modeling techniques h…

This https://arxiv.org/abs/2310.20091 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t. accuracy, diversity, computational cost, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel model that leverages Gaussian process regression for effective multi-interest recommendati…

Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood
Taojun Hu, Yi Zhou, Sattoshi Hattori
https://arxiv.org/abs/2404.06837

Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood
Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used accounting for between-study heterogeneity. However, meta-analysis of sparse data, which may arise when the event rate is low for binary or count outcomes, poses a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study likelihood may not be good. To reduce bias arising from …

Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning
Emre Ozfatura, Kerem Ozfatura, Alptekin Kupcu, Deniz Gunduz
https://arxiv.org/abs/2404.06230

Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning
Federated learning (FL) has been introduced to enable a large number of clients, possibly mobile devices, to collaborate on generating a generalized machine learning model thanks to utilizing a larger number of local samples without sharing to offer certain privacy to collaborating clients. However, due to the participation of a large number of clients, it is often difficult to profile and verify each client, which leads to a security threat that malicious participants may hamper the accuracy o…

PAS-SLAM: A Visual SLAM System for Planar Ambiguous Scenes
Xinggang Hu, Yanmin Wu, Mingyuan Zhao, Linghao Yang, Xiangkui Zhang, Xiangyang Ji
https://arxiv.org/abs/2402.06131

PAS-SLAM: A Visual SLAM System for Planar Ambiguous Scenes
Visual SLAM (Simultaneous Localization and Mapping) based on planar features has found widespread applications in fields such as environmental structure perception and augmented reality. However, current research faces challenges in accurately localizing and mapping in planar ambiguous scenes, primarily due to the poor accuracy of the employed planar features and data association methods. In this paper, we propose a visual SLAM system based on planar features designed for planar ambiguous scene…

This https://arxiv.org/abs/2311.14049 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Assessment of Deep Learning Segmentation for Real-Time Free-Breathing Cardiac Magnetic Resonance Imaging at Rest and Under Exercise Stress
In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n=15) for cine and real-time free-breathing CMR at rest and under exercise stress were…

This https://arxiv.org/abs/2310.20091 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t. accuracy, diversity, computational cost, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel model that leverages Gaussian process regression for effective multi-interest recommendati…

Incorporating Improved Sinusoidal Threshold-based Semi-supervised Method and Diffusion Models for Osteoporosis Diagnosis
Wenchi Ke
https://arxiv.org/abs/2403.06498

Incorporating Improved Sinusoidal Threshold-based Semi-supervised Method and Diffusion Models for Osteoporosis Diagnosis
Osteoporosis is a common skeletal disease that seriously affects patients' quality of life. Traditional osteoporosis diagnosis methods are expensive and complex. The semi-supervised model based on diffusion model and class threshold sinusoidal decay proposed in this paper can automatically diagnose osteoporosis based on patient's imaging data, which has the advantages of convenience, accuracy, and low cost. Unlike previous semi-supervised models, all the unlabeled data used in this paper are ge…

An efficient domain-independent approach for supervised keyphrase extraction and ranking
Sriraghavendra Ramaswamy
https://arxiv.org/abs/2404.07954 https://…

An efficient domain-independent approach for supervised keyphrase extraction and ranking
We present a supervised learning approach for automatic extraction of keyphrases from single documents. Our solution uses simple to compute statistical and positional features of candidate phrases and does not rely on any external knowledge base or on pre-trained language models or word embeddings. The ranking component of our proposed solution is a fairly lightweight ensemble model. Evaluation on benchmark datasets shows that our approach achieves significantly higher accuracy than several sta…

Improving Factual Accuracy of Neural Table-to-Text Output by Addressing Input Problems in ToTTo
Barkavi Sundararajan, Somayajulu Sripada, Ehud Reiter
https://arxiv.org/abs/2404.04103

Improving Factual Accuracy of Neural Table-to-Text Output by Addressing Input Problems in ToTTo
Neural Table-to-Text models tend to hallucinate, producing texts that contain factual errors. We investigate whether such errors in the output can be traced back to problems with the input. We manually annotated 1,837 texts generated by multiple models in the politics domain of the ToTTo dataset. We identify the input problems that are responsible for many output errors and show that fixing these inputs reduces factual errors by between 52% and 76% (depending on the model). In addition, we obse…

Incorporating Improved Sinusoidal Threshold-based Semi-supervised Method and Diffusion Models for Osteoporosis Diagnosis
Wenchi Ke
https://arxiv.org/abs/2403.06498

Incorporating Improved Sinusoidal Threshold-based Semi-supervised Method and Diffusion Models for Osteoporosis Diagnosis
Osteoporosis is a common skeletal disease that seriously affects patients' quality of life. Traditional osteoporosis diagnosis methods are expensive and complex. The semi-supervised model based on diffusion model and class threshold sinusoidal decay proposed in this paper can automatically diagnose osteoporosis based on patient's imaging data, which has the advantages of convenience, accuracy, and low cost. Unlike previous semi-supervised models, all the unlabeled data used in this paper are ge…

Aligning Large Language Models for Controllable Recommendations
Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, Xing Xie
https://arxiv.org/abs/2403.05063

Aligning Large Language Models for Controllable Recommendations
Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommender systems - systems that are conversational, explainable, and controllable. However, existing literature primarily concentrates on integrating domain-specific knowledge into LLMs to enhance accuracy, often neglecting the ability to follow instructions. To address this gap, we initially introduce a collection of super…

Dynamic Resolution Guidance for Facial Expression Recognition
Jie Ou, Xu Li, Tianxiang Jiang, Yuanlun Xie
https://arxiv.org/abs/2404.06365 https://<…

Dynamic Resolution Guidance for Facial Expression Recognition
Facial expression recognition (FER) is vital for human-computer interaction and emotion analysis, yet recognizing expressions in low-resolution images remains challenging. This paper introduces a practical method called Dynamic Resolution Guidance for Facial Expression Recognition (DRGFER) to effectively recognize facial expressions in images with varying resolutions without compromising FER model accuracy. Our framework comprises two main components: the Resolution Recognition Network (RRN) an…

Aligning Large Language Models for Controllable Recommendations
Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, Xing Xie
https://arxiv.org/abs/2403.05063

Aligning Large Language Models for Controllable Recommendations
Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommender systems - systems that are conversational, explainable, and controllable. However, existing literature primarily concentrates on integrating domain-specific knowledge into LLMs to enhance accuracy, often neglecting the ability to follow instructions. To address this gap, we initially introduce a collection of super…

This https://arxiv.org/abs/2210.07315 has been replaced.
link: https://scholar.google.com/scholar?q=a

Design and Evaluation of a Generic Visual SLAM Framework for Multi-Camera Systems
Multi-camera systems have been shown to improve the accuracy and robustness of SLAM estimates, yet state-of-the-art SLAM systems predominantly support monocular or stereo setups. This paper presents a generic sparse visual SLAM framework capable of running on any number of cameras and in any arrangement. Our SLAM system uses the generalized camera model, which allows us to represent an arbitrary multi-camera system as a single imaging device. Additionally, it takes advantage of the overlapping …