pwc-archive/papers-with-abstracts huggingface dataset:
GeoLocator: a location-integrated large multimodal model for inferring geo-privacy
Authors: Yifan Yang, Siqin Wang, Daoyang Li, Yixian Zhang, Shuju Sun, Junzhou He
Date: 2023-11-21 00:00:00
Tasks: Image Comprehension

Geographic privacy or geo-privacy refers to the keeping private of one's geographic location, especially the restriction of geographical data maintained by personal electronic devices. Geo-privacy is a crucial aspect of personal security; however, it often goes unnoticed in daily activities. With the surge in the use of Large Multimodal Models (LMMs), such as GPT-4, for Open Source Intelligence (OSINT), the potential risks associated with geo-privacy breaches have intensified. This study develops a location-integrated GPT-4 based model named GeoLocator and designs four-dimensional experiments to demonstrate its capability in inferring the locational information of input imageries and/or social media contents. Our experiments reveal that GeoLocator generates specific geographic details with high accuracy and consequently embeds the risk of the model users exposing geospatial information to the public unintentionally, highlighting the thread of online data sharing, information gathering technologies and LLMs on geo-privacy. We conclude with the broader implications of GeoLocator and our findings for individuals and the community at large, by emphasizing the urgency for enhanced awareness and protective measures against geo-privacy leakage in the era of advanced AI and widespread social media usage.
On the Limitation of Diffusion Models for Synthesizing Training Datasets
Authors: Shin'ya Yamaguchi, Takuma Fukuda
Date: 2023-11-22 00:00:00

Synthetic samples from diffusion models are promising for leveraging in training discriminative models as replications of real training datasets. However, we found that the synthetic datasets degrade classification performance over real datasets even when using state-of-the-art diffusion models. This means that modern diffusion models do not perfectly represent the data distribution for the purpose of replicating datasets for training discriminative tasks. This paper investigates the gap between synthetic and real samples by analyzing the synthetic samples reconstructed from real samples through the diffusion and reverse process. By varying the time steps starting the reverse process in the reconstruction, we can control the trade-off between the information in the original real data and the information added by diffusion models. Through assessing the reconstructed samples and trained models, we found that the synthetic data are concentrated in modes of the training data distribution as the reverse step increases, and thus, they are difficult to cover the outer edges of the distribution. Our findings imply that modern diffusion models are insufficient to replicate training data distribution perfectly, and there is room for the improvement of generative modeling in the replication of training datasets.
Automated Measurement of Pericoronary Adipose Tissue Attenuation and Volume in CT Angiography
Authors: Andrew M. Nguyen, Tejas Sudharshan Mathai, Liangchen Liu, Jianfei Liu, Ronald M. Summers
Date: 2023-11-22 00:00:00

Pericoronary adipose tissue (PCAT) is the deposition of fat in the vicinity of the coronary arteries. It is an indicator of coronary inflammation and associated with coronary artery disease. Non-invasive coronary CT angiography (CCTA) is presently used to obtain measures of the thickness, volume, and attenuation of fat deposition. However, prior works solely focus on measuring PCAT using semi-automated approaches at the right coronary artery (RCA) over the left coronary artery (LCA). In this pilot work, we developed a fully automated approach for the measurement of PCAT mean attenuation and volume in the region around both coronary arteries. First, we used a large subset of patients from the public ImageCAS dataset (n = 735) to train a 3D full resolution nnUNet to segment LCA and RCA. Then, we automatically measured PCAT in the surrounding arterial regions. We evaluated our method on a held-out test set of patients (n = 183) from the same dataset. A mean Dice score of 83% and PCAT attenuation of -73.81 $\pm$ 12.69 HU was calculated for the RCA, while a mean Dice score of 81% and PCAT attenuation of -77.51 $\pm$ 7.94 HU was computed for the LCA. To the best of our knowledge, we are the first to develop a fully automated method to measure PCAT attenuation and volume at both the RCA and LCA. Our work underscores how automated PCAT measurement holds promise as a biomarker for identification of inflammation and cardiac disease.
Single Image Compressed Sensing MRI via a Self-Supervised Deep Denoising Approach
Authors: Marlon Bran Lorenzana, Feng Liu, Shekhar S. Chandra
Date: 2023-11-22 00:00:00
Tasks: compressed sensing, Denoising, Image Compressed Sensing

Popular methods in compressed sensing (CS) are dependent on deep learning (DL), where large amounts of data are used to train non-linear reconstruction models. However, ensuring generalisability over and access to multiple datasets is challenging to realise for real-world applications. To address these concerns, this paper proposes a single image, self-supervised (SS) CS-MRI framework that enables a joint deep and sparse regularisation of CS artefacts. The approach effectively dampens structured CS artefacts, which can be difficult to remove assuming sparse reconstruction, or relying solely on the inductive biases of CNN to produce noise-free images. Image quality is thereby improved compared to either approach alone. Metrics are evaluated using Cartesian 1D masks on a brain and knee dataset, with PSNR improving by 2-4dB on average.
Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI
Authors: Annesha Ghosh, Gordon Wetzstein, Mert Pilanci, Sara Fridovich-Keil
Date: 2023-11-22 00:00:00
Tasks: Diagnostic, GPU, MRI Reconstruction

Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. These discrepancies can be caused by a variety of factors, including magnetic field inhomogeneities, chemical shifts, or susceptibility differences within the tissues. Such artifacts can manifest as blurring, ghosting, or misregistration of the reconstructed image, and they often compromise its diagnostic quality. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance. Our approach is inspired by recent progress in modeling radiance fields, and is capable of reconstructing both static and dynamic MR images as well as separating fat and water, which is of independent clinical interest. We demonstrate our approach in the context of PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) MRI acquisitions, which are popular for their robustness to motion artifacts. Our method operates in a few minutes on a single GPU, and to our knowledge is the first to correct for chemical shift in gradient echo PROPELLER MRI reconstruction without additional measurements or pretraining data.
Quantum learning and essential cognition under the traction of meta-characteristics in an open world
Authors: Jin Wang, Changlin Song
Date: 2023-11-22 00:00:00

Artificial intelligence has made significant progress in the Close World problem, being able to accurately recognize old knowledge through training and classification. However, AI faces significant challenges in the Open World problem, as it involves a new and unknown exploration journey. AI is not inherently proactive in exploration, and its challenge lies in not knowing how to approach and adapt to the unknown world. How do humans acquire knowledge of the unknown world. Humans identify new knowledge through intrinsic cognition. In the process of recognizing new colors, the cognitive cues are different from known color features and involve hue, saturation, brightness, and other characteristics. When AI encounters objects with different features in the new world, it faces another challenge: where are the distinguishing features between influential features of new and old objects? AI often mistakes a new world's brown bear for a known dog because it has not learned the differences in feature distributions between knowledge systems. This is because things in the new and old worlds have different units and dimensions for their features. This paper proposes an open-world model and elemental feature system that focuses on fundamentally recognizing the distribution differences in objective features between the new and old worlds. The quantum tunneling effect of learning ability in the new and old worlds is realized through the tractive force of meta-characteristic. The outstanding performance of the model system in learning new knowledge (using pedestrian re-identification datasets as an example) demonstrates that AI has acquired the ability to recognize the new world with an accuracy of $96.71\%$ at most and has gained the capability to explore new knowledge, similar to humans.
Point Projection Mapping System for Tracking, Registering, Labeling and Validating Optical Tissue Measurements
Authors: Lianne Feenstra, Stefan D. van der Stel, Marcos Da Silva Guimaraes, Theo J. M Ruers, Behdad Dashtbozorg
Date: 2023-11-22 00:00:00

Validation of newly developed optical tissue sensing techniques for tumor detection during cancer surgery requires an accurate correlation with histological results. Additionally, such accurate correlation facilitates precise data labeling for developing high-performance machine-learning tissue classification models. In this paper, a newly developed Point Projection Mapping system will be introduced, which allows non-destructive tracking of the measurement locations on tissue specimens. Additionally, a framework for accurate registration, validation, and labeling with histopathology results is proposed and validated on a case study. The proposed framework provides a more robust and accurate method for tracking and validation of optical tissue sensing techniques, which saves time and resources compared to conventional techniques available.
A general Framework for Utilizing Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A concise overview
Authors: Absalom E. Ezugwu
Date: 2023-09-14 00:00:00
Tasks: Metaheuristic Optimization, Scheduling

Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem. The algorithms are evaluated based on their ability to improve resource utilization, minimize energy consumption, reduce environmental impact, and promote socially responsible production practices. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives.
Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab-Column Connections
Authors: Sarmed Wahab, Nasim Shakouri Mahmoudabadi, Sarmad Waqas, Nouman Herl, Muhammad Iqbal, Khurshid Alam, Afaq Ahmad
Date: 2023-09-29 00:00:00

This research aims at comparative analysis of shear strength prediction at slab-column connection, unifying machine learning, design codes and Finite Element Analysis. Current design codes (CDCs) of ACI 318-19 (ACI), Eurocode 2 (EC2), Compressive Force Path (CFP) method, Feed Forward Neural Network (FNN) based Artificial Neural Network (ANN), PSO-based FNN (PSOFNN), and BAT algorithm-based BATFNN are used. The study is complemented with FEA of slab for validating the experimental results and machine learning predictions.In the case of hybrid models of PSOFNN and BATFNN, mean square error is used as an objective function to obtain the optimized values of the weights, that are used by Feed Forward Neural Network to perform predictions on the slab data. Seven different models of PSOFNN, BATFNN, and FNN are trained on this data and the results exhibited that PSOFNN is the best model overall. PSOFNN has the best results for SCS=1 with highest value of R as 99.37% and lowest of MSE, and MAE values of 0.0275%, and 1.214% respectively which are better than the best FNN model for SCS=4 having the values of R, MSE, and MAE as 97.464%, 0.0492%, and 1.43%, respectively.
Nature Inspired Evolutionary Swarm Optimizers for Biomedical Image and Signal Processing -- A Systematic Review
Authors: Subhrangshu Adhikary
Date: 2023-10-02 00:00:00
Tasks: Articles, Denoising

The challenge of finding a global optimum in a solution search space with limited resources and higher accuracy has given rise to several optimization algorithms. Generally, the gradient-based optimizers converge to the global solution very accurately, but they often require a large number of iterations to find the solution. Researchers took inspiration from different natural phenomena and behaviours of many living organisms to develop algorithms that can solve optimization problems much quicker with high accuracy. These algorithms are called nature-inspired meta-heuristic optimization algorithms. These can be used for denoising signals, updating weights in a deep neural network, and many other cases. In the state-of-the-art, there are no systematic reviews available that have discussed the applications of nature-inspired algorithms on biomedical signal processing. The paper solves that gap by discussing the applications of such algorithms in biomedical signal processing and also provides an updated survey of the application of these algorithms in biomedical image processing. The paper reviews 28 latest peer-reviewed relevant articles and 26 nature-inspired algorithms and segregates them into thoroughly explored, lesser explored and unexplored categories intending to help readers understand the reliability and exploration stage of each of these algorithms.
A versatile circuit for emulating active biological dendrites applied to sound localisation and neuron imitation
Authors: Daniel John Mannion
Date: 2023-10-25 00:00:00

Sophisticated machine learning struggles to transition onto battery-operated devices due to the high-power consumption of neural networks. Researchers have turned to neuromorphic engineering, inspired by biological neural networks, for more efficient solutions. While previous research focused on artificial neurons and synapses, an essential component has been overlooked: dendrites. Dendrites transmit inputs from synapses to the neuron's soma, applying both passive and active transformations. However, neuromorphic circuits replace these sophisticated computational channels with metallic interconnects. In this study, we introduce a versatile circuit that emulates a segment of a dendrite which exhibits gain, introduces delays, and performs integration. We show how sound localisation - a biological example of dendritic computation - is not possible with the existing passive dendrite circuits but can be achieved using this proposed circuit. We also find that dendrites can form bursting neurons. This significant discovery suggests the potential to fabricate neural networks solely comprised of dendrite circuits.
Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm for Solving 0/1 Knapsack Problems
Authors: Kuo-Chun Tseng, Wei-Chieh Lai, I-Chia Chen, Yun-Hsiang Hsiao, Jr-Yu Chiue, Wei-Chun Huang
Date: 2023-11-08 00:00:00
Tasks: Combinatorial Optimization

In this paper, an improved version of QTS (Quantum-inspired Tabu Search) has been proposed, which enhances the utilization of population information, called "amplitude-ensemble" QTS (AE-QTS). This makes AE-QTS more similar to the real quantum search algorithm, Grover Search Algorithm, in abstract concept, while keeping the simplicity of the algorithm. Later, we demonstrate the AE-QTS on the classical combinatorial optimization 0/1 knapsack problem. Experimental results show that the AE-QTS outperforms other algorithms, including the QTS, by at least an average of 20% in all cases and even by 30% in some cases. Even as the problem complexity increases, the quality of the solutions found by our method remains superior to that of the QTS. These results prove that our method has better search performance.
W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators
Authors: Yukito Iba
Date: 2023-11-21 00:00:00

Evaluating the variability of posterior estimates is a key aspect of Bayesian model assessment. In this study, we focus on the posterior covariance matrix W, defined using the log-likelihood of each observation. Previous studies have examined the role of the principal space of W in Bayesian sensitivity analysis, notably MacEachern and Peruggia (2002) and Thomas et al. (2018). In this work, we show that the principal space of W is also relevant for frequentist evaluation, using the recently proposed Bayesian infinitesimal jackknife (IJ) approximation Giordano and Broderick (2023) as a key tool. We next consider the relationship between the matrix W and the Fisher kernel. We show that the Fisher kernel can be regarded as an approximation to W; the matrix W, in itself, can be interpreted as a reproducing kernel, which we refer to as the W-kernel. Based on this connection, we examine the dual relationship between the W-kernel formulation in the data space and the classical asymptotic formulation in the parameter space. These ideas suggest a form of Bayesian-frequentist duality that emerges through the dual structure of kernel PCA, where posterior and frequentist covariances serve as inner products in their respective spaces. As an application, we consider an approximate bootstrap of posterior means based on posterior samples generated by MCMC. We show that the projection onto the principal space of W facilitates frequentist evaluation, particularly of the higher-order term in this procedure. In one of the appendices, we introduce incomplete Cholesky decomposition as an efficient method for computing the principal space of W and discuss the related concept of representative subsets of the observations.
A model-free approach to fingertip slip and disturbance detection for grasp stability inference
Authors: Dounia Kitouni, Mahdi Khoramshahi, Veronique Perdereau
Date: 2023-11-22 00:00:00
Tasks: Time Series

Robotic capacities in object manipulation are incomparable to those of humans. Besides years of learning, humans rely heavily on the richness of information from physical interaction with the environment. In particular, tactile sensing is crucial in providing such rich feedback. Despite its potential contributions to robotic manipulation, tactile sensing is less exploited; mainly due to the complexity of the time series provided by tactile sensors. In this work, we propose a method for assessing grasp stability using tactile sensing. More specifically, we propose a methodology to extract task-relevant features and design efficient classifiers to detect object slippage with respect to individual fingertips. We compare two classification models: support vector machine and logistic regression. We use highly sensitive Uskin tactile sensors mounted on an Allegro hand to test and validate our method. Our results demonstrate that the proposed method is effective in slippage detection in an online fashion.
A projected nonlinear state-space model for forecasting time series signals
Authors: Christian Donner, Anuj Mishra, Hideaki Shimazaki
Date: 2023-11-22 00:00:00
Tasks: Benchmarking, Computational Efficiency, Time Series, Time Series Analysis

Learning and forecasting stochastic time series is essential in various scientific fields. However, despite the proposals of nonlinear filters and deep-learning methods, it remains challenging to capture nonlinear dynamics from a few noisy samples and predict future trajectories with uncertainty estimates while maintaining computational efficiency. Here, we propose a fast algorithm to learn and forecast nonlinear dynamics from noisy time series data. A key feature of the proposed model is kernel functions applied to projected lines, enabling fast and efficient capture of nonlinearities in the latent dynamics. Through empirical case studies and benchmarking, the model demonstrates its effectiveness in learning and forecasting complex nonlinear dynamics, offering a valuable tool for researchers and practitioners in time series analysis.
Enhancing Robotic Manipulation: Harnessing the Power of Multi-Task Reinforcement Learning and Single Life Reinforcement Learning in Meta-World
Authors: Ghadi Nehme, Ishan Sabane, Tejas Y. Deo
Date: 2023-10-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

At present, robots typically require extensive training to successfully accomplish a single task. However, to truly enhance their usefulness in real-world scenarios, robots should possess the capability to perform multiple tasks effectively. To address this need, various multi-task reinforcement learning (RL) algorithms have been developed, including multi-task proximal policy optimization (PPO), multi-task trust region policy optimization (TRPO), and multi-task soft-actor critic (SAC). Nevertheless, these algorithms demonstrate optimal performance only when operating within an environment or observation space that exhibits a similar distribution. In reality, such conditions are often not the norm, as robots may encounter scenarios or observations that differ from those on which they were trained. Addressing this challenge, algorithms like Q-Weighted Adversarial Learning (QWALE) attempt to tackle the issue by training the base algorithm (generating prior data) solely for a particular task, rendering it unsuitable for generalization across tasks. So, the aim of this research project is to enable a robotic arm to successfully execute seven distinct tasks within the Meta World environment. To achieve this, a multi-task soft actor-critic (MT-SAC) is employed to train the robotic arm. Subsequently, the trained model will serve as a source of prior data for the single-life RL algorithm. The effectiveness of this MT-QWALE algorithm will be assessed by conducting tests on various target positions (novel positions). In the end, a comparison is provided between the trained MT-SAC and the MT-QWALE algorithm where the MT-QWALE performs better. An ablation study demonstrates that MT-QWALE successfully completes tasks with a slightly larger number of steps even after hiding the final goal position.