pwc-archive/papers-with-abstracts huggingface dataset:
Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam
Authors: Ming-Zher Poh, Daniel J. McDuff, Rosalind W. Picard
Date: 2010-10-14 00:00:00
Proceeding: IEEE Transactions on Biomedical Engineering 2010 10
Tasks: Photoplethysmography (PPG) heart rate estimation

We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.
Landslide Monitoring based on Terrestrial Laser Scanning: A Novel Semi-automatic Workflow
Authors: Yue Pan
Date: 2018-09-01 00:00:00

In this paper, we propose a workflow that uses Terrestrial Laser Scanning(TLS) to semi-automatically monitor landslide and then test it in practice. Firstly, several groups of TLS stations are set on different time to collect the raw point cloud of the object mountain. Next, Hierarchical Merging Based Multi-view (HMMR) registration algorithm is adapted to accomplish single-phase multi-view registration.In order to analyze deformation between multiple periods, Iterative Global Similarity Point (IGSP) algorithm is applied to accomplish multiple-phase registration, which outperforms ICP in experiments. Then the cloth simulation filtering (CSF) algorithm was used together with manual post-processing to remove vegetation on the slope. After that, the mountain slope's digital terrain model (DTM) is generated for each period, and the distance between adjacent DTMs are calculated as the landslide deformation mass. Furthermore, average deformation rate of the landslide surface is calculated and analyzed.To validate the effectiveness of proposed workflow, we uses the TLS data of five periods of the landslide in the Shanhou village of northern Changshan Island from 2013 to 2015. The results indicate that the method can obtain centimeter-level deformation monitoring accuracy which can effectively monitor and analyze long-term landslide morphology and trend as well as position the significant deformation area and determine the type of landslide. In addition, the process can be automated to provide end-to-end TLS based long-term landslide monitoring applications, providing reference for monitoring and early warning of potential landslides.
Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification
Authors: Shujun Yang, Yu Zhang, Yuheng Jia, and Weijia Zhang
Date: 2022-08-18 00:00:00
Proceeding: journal 2022 8
Tasks: Hyperspectral Image Classification, image-classification, Image Classification, Superpixels

Given the detrimental effect of spectral variations in a hyperspectral image (HSI), this article investigates to recover its discriminative representation to improve the classification performance. We propose a new method, namely local low-rank approximation with superpixel-guided locality preserving graph (LLRA-SLPG), which can reduce the spectral variations and preserve the local manifold structure of an HSI. Specifically, the LLRA-SLPG method first clusters pixels of an HSI into several groups (i.e., superpixels). By taking advantage of the local manifold structure, a Laplacian graph is constructed from the superpixels to ensure that a typical pixel should be similar to its neighbors within the same superpixel. The LLRA-SLPG model can increase the compactness of pixels belonging to the same class by reducing spectral variations while promoting local consistency via the Laplacian graph. The objective function of the LLRA-SLPG model can be solved efficiently in an iterative manner. Experimental results on four benchmark datasets validate the superiority of the LLRA-SLPG model over state-of-the-art methods, particularly in cases where only extremely few training pixels are available.
A Novel Unsupervised Domain Adaption Method for Depth-Guided Semantic Segmentation Using Coarse-to-Fine Alignment
Authors: Kieu Dang Nam, Nguyen Minh Tu, Trinh Van Dieu, Muriel Visani, Nguyen Thi-Oanh, Dinh Viet Sang
Date: 2022-09-13 00:00:00
Proceeding: IEEE Access 2022 9
Tasks: Depth Estimation, Domain Adaptation, Segmentation, Semantic Segmentation, Style Transfer, Unsupervised Domain Adaptation

Domain adaptation methods in machine learning deal with the domain shift issue by aligning source and target data representation. This paper proposes a novel domain adaptation method for semantic segmentation that exploits the Fourier transform on chromatic space to improve the quality of style transfer, and generates pseudo-labels for self-training by combining the results from different teachers obtained at different rounds of self-training. Our method also applies class-level adversarial learning to achieve a more fine-grained alignment between the two domains, and a late fusion with a depth-estimation model to improve its segmentation outputs. Experiments show that our method yields superior performance in terms of accuracy compared to other existing state-of-the-art methods.
One-shot Detail Retouching with Patch Space Neural Transformation Blending
Authors: Fazilet Gokbudak, Cengiz Oztireli
Date: 2022-10-03 00:00:00
Tasks: One-Shot Learning, Photo Retouching

Photo retouching is a difficult task for novice users as it requires expert knowledge and advanced tools. Photographers often spend a great deal of time generating high-quality retouched photos with intricate details. In this paper, we introduce a one-shot learning based technique to automatically retouch details of an input image based on just a single pair of before and after example images. Our approach provides accurate and generalizable detail edit transfer to new images. We achieve these by proposing a new representation for image to image maps. Specifically, we propose neural field based transformation blending in the patch space for defining patch to patch transformations for each frequency band. This parametrization of the map with anchor transformations and associated weights, and spatio-spectral localized patches, allows us to capture details well while staying generalizable. We evaluate our technique both on known ground truth filters and artist retouching edits. Our method accurately transfers complex detail retouching edits.
Modeling of Whole Genomic Sequencing Implementation using System Dynamics and Game Theory
Authors: Marzieh Soltanolkottabi, Hadi A. Khorshidi, Maarten J. IJzerman
Date: 2022-10-02 00:00:00
Tasks: Decision Making, Diagnostic

Biomarker testing is a laboratory test in oncology that is used in the selection of targeted cancer treatments and helping to avoid ineffective treatments. There exist several types of biomarker tests that can be used to detect the presence of particular mutations or variation in gene expression. Whole Genome Sequencing (WGS) is a biomarker test for analyzing the entire genome. WGS can provide more comprehensive diagnostic information, but it is also more expensive than other tests. In this study, System Dynamics and Game Theoretic models are employed to evaluate scenarios, and facilitate organizational decision making regarding WGS implementation. These models evaluate the clinical and economic value of WGS as well as its affordability and accessibility. The evaluated scenarios have covered the timing of implementing WGS using time to diagnosis and total cost.
Joint Optimization of Active and Passive Beamforming in Multi-IRS Aided mmWave Communications
Authors: Renlong Wei, Qing Xue, Shaodan Ma, Yongjun Xu, Li Yan, Xuming Fang
Date: 2022-10-04 00:00:00

Intelligent reflecting surface (IRS) has been considered as a promising technology to alleviate the blockage effect and enhance coverage in millimeter wave (mmWave) communication. To explore the impact of IRS on the performance of mmWave communication, we investigate a multi-IRS assisted mmWave communication network and formulate a sum rate maximization problem by jointly optimizing the active and passive beamforming and the set of IRSs for assistance. The optimization problem is intractable due to the lack of convexity of the objective function and the binary nature of the IRS selection variables. To tackle the complex non-convex problem, an alternating iterative approach is proposed. In particular, utilizing the fractional programming method to optimize the active and passive beamforming and the optimization of IRS selection is solved by enumerating. Simulation results demonstrate the performance gain of our proposed approach.
NARF22: Neural Articulated Radiance Fields for Configuration-Aware Rendering
Authors: Stanley Lewis, Jana Pavlasek, Odest Chadwicke Jenkins
Date: 2022-10-03 00:00:00
Tasks: NeRF

Articulated objects pose a unique challenge for robotic perception and manipulation. Their increased number of degrees-of-freedom makes tasks such as localization computationally difficult, while also making the process of real-world dataset collection unscalable. With the aim of addressing these scalability issues, we propose Neural Articulated Radiance Fields (NARF22), a pipeline which uses a fully-differentiable, configuration-parameterized Neural Radiance Field (NeRF) as a means of providing high quality renderings of articulated objects. NARF22 requires no explicit knowledge of the object structure at inference time. We propose a two-stage parts-based training mechanism which allows the object rendering models to generalize well across the configuration space even if the underlying training data has as few as one configuration represented. We demonstrate the efficacy of NARF22 by training configurable renderers on a real-world articulated tool dataset collected via a Fetch mobile manipulation robot. We show the applicability of the model to gradient-based inference methods through a configuration estimation and 6 degree-of-freedom pose refinement task. The project webpage is available at: https://progress.eecs.umich.edu/projects/narf/.
Learning from Viral Content
Authors: Krishna Dasaratha, Kevin He
Date: 2022-10-03 00:00:00

We study learning on social media with an equilibrium model of users interacting with shared news stories. Rational users arrive sequentially, observe an original story (i.e., a private signal) and a sample of predecessors' stories in a news feed, and then decide which stories to share. The observed sample of stories depends on what predecessors share as well as the sampling algorithm generating news feeds. We focus on how often this algorithm selects more viral (i.e., widely shared) stories. Showing users viral stories can increase information aggregation, but it can also generate steady states where most shared stories are wrong. These misleading steady states self-perpetuate, as users who observe wrong stories develop wrong beliefs, and thus rationally continue to share them. Finally, we describe several consequences for platform design and robustness.
Distributed Differentially Private Control Synthesis for Multi-Agent Systems with Metric Temporal Logic Specifications
Authors: Nasim Baharisangari, Zhe Xu
Date: 2022-10-04 00:00:00

In this paper, we propose a distributed differentially private receding horizon control (RHC) approach for multi-agent systems (MAS) with metric temporal logic (MTL) specifications. In the MAS considered in this paper, each agent privatizes its sensitive information from other agents using a differential privacy mechanism. In other words, each agent adds privacy noise (e.g., Gaussian noise) to its output to maintain its privacy and communicates its noisy output with its neighboring agents. We define two types of MTL specifications for the MAS: agent-level specifications and system-level specifications. Agents should collaborate to satisfy the system-level MTL specifications with a minimum probability while each agent must satisfy its own agent-level MTL specifications at the same time. In the proposed distributed RHC approach, each agent communicates with its neighboring agents to acquire their noisy outputs and calculates an estimate of the system-level trajectory. Then each agent synthesizes its own control inputs such that the system-level specifications are satisfied with a minimum probability while the agent-level specifications are also satisfied. In the proposed optimization formulation of RHC, we directly incorporate Kalman filter equations to calculate the estimates of the system-level trajectory, and we use mixed-integer linear programming (MILP) to encode the MTL specifications as optimization constraints. Finally, we implement the proposed distributed RHC approach in a case study.
Robust Multi-Agent Coordination from CaTL+ Specifications
Authors: Wenliang Liu, Kevin Leahy, Zachary Serlin, Calin Belta
Date: 2022-10-04 00:00:00

We consider the problem of controlling a heterogeneous multi-agent system required to satisfy temporal logic requirements. Capability Temporal Logic (CaTL) was recently proposed to formalize such specifications for deploying a team of autonomous agents with different capabilities and cooperation requirements. In this paper, we extend CaTL to a new logic CaTL+, which is more expressive than CaTL and has semantics over a continuous workspace shared by all agents. We define two novel robustness metrics for CaTL+: the traditional robustness and the exponential robustness. The latter is sound, differentiable almost everywhere and eliminates masking, which is one of the main limitations of the traditional robustness metric. We formulate a control synthesis problem to maximize CaTL+ robustness and propose a two-step optimization method to solve this problem. Simulation results are included to illustrate the increased expressivity of CaTL+ and the efficacy of the proposed control synthesis approach.
Enhanced CNN with Global Features for Fault Diagnosis of Complex Chemical Processes
Authors: Qiugang Lu, Saif S. S. Al-Wahaibi
Date: 2022-10-04 00:00:00
Tasks: Dimensionality Reduction, Fault Diagnosis

Convolutional neural network (CNN) models have been widely used for fault diagnosis of complex systems. However, traditional CNN models rely on small kernel filters to obtain local features from images. Thus, an excessively deep CNN is required to capture global features, which are critical for fault diagnosis of dynamical systems. In this work, we present an improved CNN that embeds global features (GF-CNN). Our method uses a multi-layer perceptron (MLP) for dimension reduction to directly extract global features and integrate them into the CNN. The advantage of this method is that both local and global patterns in images can be captured by a simple model architecture instead of establishing deep CNN models. The proposed method is applied to the fault diagnosis of the Tennessee Eastman process. Simulation results show that the GF-CNN can significantly improve the fault diagnosis performance compared to traditional CNN. The proposed method can also be applied to other areas such as computer vision and image processing.
Inclusion of Frequency Nadir constraint in the Unit Commitment Problem of Small Power Systems Using Machine Learning
Authors: Mohammad Rajabdorri, Behzad Kazemtabrizi, Matthias Troffaes, Lukas Sigrist, Enrique Lobato
Date: 2022-10-04 00:00:00
Tasks: Scheduling

As the intention is to reduce the amount of thermal generation and to increase the share of clean energy, power systems are increasingly becoming susceptible to frequency instability after outages due to reduced levels of inertia. To address this issue frequency constraints are being included in the scheduling process, which ensure a tolerable frequency deviation in case of any contingencies. In this paper, a method is proposed to integrate the non-linear frequency nadir constraint into the unit commitment problem, using machine learning. First a synthetic training dataset is generated. Then two of the available classic machine learning methods, namely logistic regression and support vector machine, are proposed to predict the frequency nadir. To be able to compare the machine learning methods to traditional frequency constrained unit commitment approaches, simulations on the power system of La Palma island are carried out for both proposed methods as well as an analytical linearized formulation of the frequency nadir. Our results show that the unit commitment problem with a machine learning based frequency nadir constraint is solved considerably faster than with the analytical formulation, while still achieving an acceptable frequency response quality after outages.
Generalized Homogeneous Rigid-BodyAttitude Control
Authors: Yu Zhou, Andrey Polyakov, Gang Zheng
Date: 2022-10-04 00:00:00

The attitude tracking problem for a full-actuated rigid body in 3D is studied using a impulsive system model based on Lie algebra so(3). A nonlinear homogeneous controller is designed to globally track a smooth attitude trajectory in a finite or a (nearly) fixed time. A global settling time estimate is obtained, which is easily adjustable by tuning the homogeneity degree. The local input-to-state stability is proven. Simulations illustrating the performance of the proposed algorithm are presented.
Direct identification of continuous-time linear switched state-space models
Authors: Manas Mejari, Dario Piga
Date: 2022-10-04 00:00:00
Tasks: State Space Models

This paper presents an algorithm for direct continuous-time (CT) identification of linear switched state-space (LSS) models. The key idea for direct CT identification is based on an integral architecture consisting of an LSS model followed by an integral block. This architecture is used to approximate the continuous-time state map of a switched system. A properly constructed objective criterion is proposed based on the integral architecture in order to estimate the unknown parameters and signals of the LSS model. A coordinate descent algorithm is employed to optimize this objective, which alternates between computing the unknown model matrices, switching sequence and estimating the state variables. The effectiveness of the proposed algorithm is shown via a simulation case study.
Learning of Dynamical Systems under Adversarial Attacks -- Null Space Property Perspective
Authors: Han Feng, Baturalp Yalcin, Javad Lavaei
Date: 2022-10-04 00:00:00

We study the identification of a linear time-invariant dynamical system affected by large-and-sparse disturbances modeling adversarial attacks or faults. Under the assumption that the states are measurable, we develop necessary and sufficient conditions for the recovery of the system matrices by solving a constrained lasso-type optimization problem. In addition, we provide an upper bound on the estimation error whenever the disturbance sequence is a combination of small noise values and large adversarial values. Our results depend on the null space property that has been widely used in the lasso literature, and we investigate under what conditions this property holds for linear time-invariant dynamical systems. Lastly, we further study the conditions for a specific probabilistic model and support the results with numerical experiments.
Speed-Adaptive Model-Free Lateral Control for Automated Cars
Authors: Marcos Moreno-Gonzalez, Antonio Artuñedo, Jorge Villagra, Cédric Join, Michel Fliess
Date: 2022-10-04 00:00:00

In order to increase the number of situations in which an intelligent vehicle can operate without human intervention, lateral control is required to accurately guide it in a reference trajectory regardless of the shape of the road or the longitudinal speed. Some studies address this problem by tuning a controller for low and high speeds and including an output adaptation law. In this paper, a strategy framed in the Model-Free Control paradigm is presented to laterally control the vehicle over a wide speed range. Tracking quality, system stability and passenger comfort are thoroughly analyzed and compared to similar control structures. The results obtained both in simulation and with a real vehicle show that the developed strategy tracks a large number of trajectories with high degree of accuracy, safety and comfort.
Safety-Aware Learning-Based Control of Systems with Uncertainty Dependent Constraints (extended version)
Authors: Jafar Abbaszadeh Chekan, Cedric Langbort
Date: 2022-10-04 00:00:00
Tasks: Gaussian Processes

The problem of safely learning and controlling a dynamical system - i.e., of stabilizing an originally (partially) unknown system while ensuring that it does not leave a prescribed 'safe set' - has recently received tremendous attention in the controls community. Further complexities arise, however, when the structure of the safe set itself depends on the unknown part of the system's dynamics. In particular, a popular approach based on control Lyapunov functions (CLF), control barrier functions (CBF) and Gaussian processes (to build confidence set around the unknown term), which has proved successful in the known-safe set setting, becomes inefficient as-is, due to the introduction of higher-order terms to be estimated and bounded with high probability using only system state measurements. In this paper, we build on the recent literature on GPs and reproducing kernels to perform this latter task, and show how to correspondingly modify the CLF-CBF-based approach to obtain safety guarantees. Namely, we derive exponential CLF and second relative order exponential CBF constraints whose satisfaction guarantees stability and forward in-variance of the partially unknown safe set with high probability. To overcome the intractability of verification of these conditions on the continuous domain, we apply discretization of the state space and use Lipschitz continuity properties of dynamics to derive equivalent CLF and CBF certificates in discrete state space. Finally, we present an algorithm for the control design aim using the derived certificates.
Two-Player Reconnaissance Game with Half-Planar Target and Retreat Regions
Authors: Yoonjae Lee, Efstathios Bakolas
Date: 2022-10-04 00:00:00
Tasks: Vocal Bursts Valence Prediction

This paper is concerned with the reconnaissance game that involves two mobile agents: the Intruder and the Defender. The Intruder is tasked to reconnoiter a territory of interest (target region) and then return to a safe zone (retreat region), where the two regions are disjoint half-planes, while being chased by the faster Defender. This paper focuses on the scenario where the Defender is not guaranteed to capture the Intruder before the latter agent reaches the retreat region. The goal of the Intruder is to minimize its distance to the target region, whereas the Defender's goal is to maximize the same distance. The game is decomposed into two phases based on the Intruder's myopic goal. The complete solution of the game corresponding to each phase, namely the Value function and state-feedback equilibrium strategies, is developed in closed-form using differential game methods. Numerical simulation results are presented to showcase the efficacy of our solutions.