pwc-archive/papers-with-abstracts huggingface dataset:
TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers
Authors: Tai An, Xin Zhang, Chunlei Huo, Bin Xue, Lingfeng Wang, Chunhong Pan
Date: 2022-02-05 00:00:00
Proceeding: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022 2
Tasks: Decoder, Multi-Frame Super-Resolution, Super-Resolution

Multiimage super-resolution (MISR), as one of the most promising directions in remote sensing, has become a needy technique in the satellite market. A sequence of images collected by satellites often has plenty of views and a long time span, so integrating multiple low-resolution views into a high-resolution image with details emerges as a challenging problem. However, most MISR methods based on deep learning cannot make full use of multiple images. Their fusion modules are incapable of adapting to an image sequence with weak temporal correlations well. To cope with these problems, we propose a novel end-to-end framework called TR-MISR. It consists of three parts: An encoder based on residual blocks, a transformer-based fusion module, and a decoder based on subpixel convolution. Specifically, by rearranging multiple feature maps into vectors, the fusion module can assign dynamic attention to the same area of different satellite images simultaneously. In addition, TR-MISR adopts an additional learnable embedding vector that fuses these vectors to restore the details to the greatest extent.TR-MISR has successfully applied the transformer to MISR tasks for the first time, notably reducing the difficulty of training the transformer by ignoring the spatial relations of image patches. Extensive experiments performed on the PROBA-V Kelvin dataset demonstrate the superiority of the proposed model that provides an effective method for transformers in other low-level vision tasks.
Efficient Model-Driven Network for Shadow Removal
Authors: Yurui Zhu, Zeyu Xiao, Yanchi Fang, Xueyang Fu, Zhiwei Xiong, Zheng-Jun Zha
Date: 2022-02-01 00:00:00
Proceeding: AAAI 2022 2
Tasks: Image Shadow Removal, model, Shadow Removal

Abstract: Deep Convolutional Neural Networks (CNNs)-based methods have achieved significant breakthroughs in the task of single image shadow removal. However, the performance of these methods remains limited for several reasons. First, the existing shadow illumination model ignores the spatially-variant property of the shadow images, hindering their further performance. Second, most deep CNNs-based methods directly estimate the shadow-free results from the input shadow images like a black box, thus losing the desired interpretability. To address these issues, we first propose a new shadow illumination model for the shadow removal task. This new shadow illumination model ensures the identity mapping among non-shadow regions, and adaptively performs fine-grained spatial mapping between shadow regions and their references. Then, based on the shadow illumination model, we reformulate the shadow removal task as a variational optimization problem. To effectively solve the variational problem, we design an iterative algorithm and unfold it into a deep network, naturally increasing the interpretability of the deep model. Experiments show that our method could achieve state-of-the-art performance with 46% parameters, 21% floating point of operations (FLOPs) and over 17 times faster than SOTA method DHAN \cite{cun2020towards}.
Burr detection and classification using RUSTICO and image processing
Authors: Virginia Riego, Lidia Sánchez-González, Laura Fernández-Robles, Alexis Gutiérrez-Fernández, Nicola Strisciuglio
Date: 2021-11-17 00:00:00
Proceeding: Journal of Computational Science 2021 11
Tasks: Classification, Vocal Bursts Type Prediction

Machined workpieces must satisfy quality standards such as avoid the presence of burrs in edge finishing to reduce production costs and time. In this work we consider three types of burr that are determined by the distribution of the edge shape on a microscopic scale: knife-type (without imperfections), saw-type (presence of small splinters that could be accepted) and burr-breakage (substantial deformation that produces unusable workpieces). The proposed method includes RUSTICO to classify automatically the edge of each piece according to its burr type. Experimental results validate its effectiveness, yielding a 91.2% F1-Score and identifying completely the burr-breakage type.
OptMatch: Optimized Matchmaking via Modeling the High-Order Interactions on the Arena
Authors: Linxia Gong, Xiaochuan Feng, Dezhi Ye, Hao Li, Runze Wu, Jianrong Tao, Changjie Fan, Peng Cui
Date: 2020-08-20 00:00:00
Proceeding: SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 8

Matchmaking is a core problem for the e-sports and online games, which determines the player satisfaction and further influences the life cycle of the gaming products. Most of matchmaking systems take the form of grouping the queuing players into two opposing teams by following certain rules. The design and implementation of matchmaking systems are usually product-specific and labor-intensive. This paper proposes a two-stage data-driven matchmaking frame-
work (namely OptMatch), which is applicable to most of gaming products and has the minimal product knowledge required. OptMatch contains an offline learning stage and an online planning stage. The offline learning stage includes (1) relationship mining modules to learn the low-dimensional representations of individuals by capturing the high-order inter-personal interactions, and (2) a neural network to incorporate the team-up effect and predict the match outcomes. The online planning stage optimizes the gross player utilities (i.e., satisfaction) during the matchmaking process, by leveraging the learned representations and predictive model. Quantitative evaluations on four real-world datasets and an online experiment on Fever Basketball game are conducted to empirically demonstrate the effectiveness of OptMatch.
Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment
Authors: Linhui Xiao, Jinge Wang, Xiaosong Qiu, Zheng Rong, Xudong Zou
Date: 2019-07-06 00:00:00
Proceeding: Robotics and Autonomous Systems 2019 7
Tasks: object-detection, Object Detection, Pose Estimation, Semantic SLAM, Simultaneous Localization and Mapping, Visual Localization

When working in dynamic environment, traditional SLAM framework performs poorly due to interference from dynamic objects. By taking advantages of deep learning in object detection, a semantic simultaneous localization and mapping framework named Dynamic-SLAM is proposed, in order to solve the problem of SLAM in dynamic environment. First, based on the convolutional neural network, an SSD object detector which combines prior knowledge is constructed to detect dynamic objects in the newly detection thread at semantic level. Then, in view of low recall rate of the existing SSD object detection network, a missed detection compensation algorithm based on the speed invariance in adjacent frames is proposed, which greatly improves the recall rate of detection. Finally, a feature-based visual SLAM system is constructed, which processes the feature points of dynamic objects through a selective tracking algorithm in the tracking thread, to significantly reduce the error of pose estimation caused by incorrect matching. The recall rate of the system is increased from 82.3% to 99.8% compared with the original SSD network. Several experiments show that the localization accuracy of Dynamic-SLAM is higher than the state-of-the-art systems. The system successfully localizes and constructs an accurate environmental map in real-world dynamic environment by using a mobile robot. In sum, our experimental demonstrations verify that Dynamic-SLAM shows improved accuracy and robustness in robot localization and mapping comparing to the state-of-the-art SLAM system in dynamic environment.
Technological evaluation of two AFIS systems
Authors: Marcos Faundez-Zanuy
Date: 2022-02-23 00:00:00
Tasks: Vocal Bursts Valence Prediction

This paper provides a technological evaluation of two Automatic Fingerprint Identification Systems (AFIS) used in forensic applications. Both of them are installed and working in Spanish police premises. The first one is a Printrak AFIS 2000 system with a database of more than 450,000 fingerprints, while the second one is a NEC AFIS 21 SAID NT-LEXS Release 2.4.4 with a database of more than 15 million fingerprints. Our experiments reveal that although both systems can manage inkless fingerprints, the latest one offers better experimental results
Multi-Modal Recurrent Fusion for Indoor Localization
Authors: Jianyuan Yu, Pu, Wang, Toshiaki Koike-Akino, Philip V. Orlik
Date: 2022-02-19 00:00:00
Tasks: Indoor Localization, regression

This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating the localization as a multi-modal sequence regression problem, a multi-stream recurrent fusion method is proposed to combine the current hidden state of each modality in the context of recurrent neural networks while accounting for the modality uncertainty which is directly learned from its own immediate past states. The proposed method was evaluated on the large-scale SPAWC2021 multi-modal localization dataset and compared with a wide range of baseline methods including the trilateration method, traditional fingerprinting methods, and convolution network-based methods.
Memory Planning for Deep Neural Networks
Authors: Maksim Levental
Date: 2022-02-23 00:00:00
Tasks: Memorization

We study memory allocation patterns in DNNs during inference, in the context of large-scale systems. We observe that such memory allocation patterns, in the context of multi-threading, are subject to high latencies, due to \texttt{mutex} contention in the system memory allocator. Latencies incurred due to such \texttt{mutex} contention produce undesirable bottlenecks in user-facing services. Thus, we propose a "memorization" based technique, \texttt{MemoMalloc}, for optimizing overall latency, with only moderate increases in peak memory usage. Specifically, our technique consists of a runtime component, which captures all allocations and uniquely associates them with their high-level source operation, and a static analysis component, which constructs an efficient allocation "plan". We present an implementation of \texttt{MemoMalloc} in the PyTorch deep learning framework and evaluate memory consumption and execution performance on a wide range of DNN architectures. We find that \texttt{MemoMalloc} outperforms state-of-the-art general purpose memory allocators, with respect to DNN inference latency, by as much as 40\%.
Elliptical Slice Sampling for Probabilistic Verification of Stochastic Systems with Signal Temporal Logic Specifications
Authors: Guy Scher, Sadra Sadraddini, Russ Tedrake, Hadas Kress-Gazit
Date: 2022-02-28 00:00:00
Tasks: Decision Making, Motion Planning

Autonomous robots typically incorporate complex sensors in their decision-making and control loops. These sensors, such as cameras and Lidars, have imperfections in their sensing and are influenced by environmental conditions. In this paper, we present a method for probabilistic verification of linearizable systems with Gaussian and Gaussian mixture noise models (e.g. from perception modules, machine learning components). We compute the probabilities of task satisfaction under Signal Temporal Logic (STL) specifications, using its robustness semantics, with a Markov Chain Monte-Carlo slice sampler. As opposed to other techniques, our method avoids over-approximations and double-counting of failure events. Central to our approach is a method for efficient and rejection-free sampling of signals from a Gaussian distribution such that satisfy or violate a given STL formula. We show illustrative examples from applications in robot motion planning.
A comparative study of several parameterizations for speaker recognition
Authors: Marcos Faundez-Zanuy
Date: 2022-02-24 00:00:00
Tasks: Quantization, Speaker Recognition, Speaker Verification

This paper presents an exhaustive study about the robustness of several parameterizations, in speaker verification and identification tasks. We have studied several mismatch conditions: different recording sessions, microphones, and different languages (it has been obtained from a bilingual set of speakers). This study reveals that the combination of several parameterizations can improve the robustness in all the scenarios for both tasks, identification and verification. In addition, two different methods have been evaluated: vector quantization, and covariance matrices with an arithmetic-harmonic sphericity measure.
Sensor technologies in cancer research for new directions in diagnosis and treatment: and exploratory analysis
Authors: Mario Coccia, Saeed Roshani, Melika Mosleh
Date: 2022-02-04 00:00:00
Tasks: Articles

The goal of this study is an exploratory analysis concerning main sensor technologies applied in cancer research to detect new directions in diagnosis and treatments. The study focused on types of cancer having a high incidence and mortality worldwide: breast, lung, colorectal and prostate. Data of the Web of Science (WOS) core collection database are used to retrieve articles related to sensor technologies and cancer research over 1991-2021 period. We utilized Gephi software version 0.9.2 to visualize the co-word networks of the interaction between sensor technologies and cancers under study. Results show main clusters of interaction per typology of cancer. Biosensor is the only type of sensor that plays an essential role in all types of cancer: breast cancer, lung cancer, prostate cancer, and colorectal cancer. Electrochemical sensor is applied in all types of cancer under study except lung cancer. Electrochemical biosensor is used in breast cancer, lung cancer, and prostate cancer research but not colorectal cancer. Optical sensor can also be considered one of the sensor technologies that significantly is used in breast cancer, prostate cancer, and colorectal cancer. This study shows that this type of sensor is applied in more diversified approaches. Moreover, the oxygen sensor is mostly studied in lung cancer and breast cancer due to the usage in breath analysis for the treatment process. Finally, Cmos sensor is a technology used mainly in lung cancer and colorectal cancer. Results here suggest new directions for the evolution of science and technology of sensors in cancer research to support innovation and research policy directed to new technological trajectories having a potential of accelerated growth and positive social impact for diagnosis and treatments of cancer.
Safe Control with Minimal Regret
Authors: Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari-Trecate
Date: 2022-03-01 00:00:00

As we move towards safety-critical cyber-physical systems that operate in non-stationary and uncertain environments, it becomes crucial to close the gap between classical optimal control algorithms and adaptive learning-based methods. In this paper, we present an efficient optimization-based approach for computing a finite-horizon robustly safe control policy that minimizes dynamic regret, in the sense of the loss relative to the optimal sequence of control actions selected in hindsight by a clairvoyant controller. By leveraging the system level synthesis framework (SLS), our method extends recent results on regret minimization for the linear quadratic regulator to optimal control subject to hard safety constraints, and allows competing against a safety-aware clairvoyant policy with minor modifications. Numerical experiments confirm superior performance with respect to finite-horizon constrained $\mathcal{H}_2$ and $\mathcal{H}_\infty$ control laws when the disturbance realizations poorly fit classical assumptions.
Online Gradient Descent for Flexible Power Point Tracking Under a Highly Fluctuating Weather and Load
Authors: Muhy Eddin Za'ter, Sandy Yacoub Miguel, Majd Ghazi Batarseh
Date: 2022-03-01 00:00:00
Tasks: Point Tracking

The increasing electricity demand and the need for clean and renewable energy resources to satisfy this demand in a cost-effective manner, imposes new challenges on researchers and developers to maximize the output of these renewable resources at all times. However, the increasing penetration of renewable energy into the grid imposes new challenges on the grid operators. All of these challenges and issues gave rise to the need of Maximum Power Point Tracker (MPPT) and Flexible Power Point Trackers (FPPT) in order to maximize the power extracted from Photovoltaic (PV) systems and meet the grid operation constraints. Existing solutions for these algorithms do not take into consideration the very high dynamical nature of weather conditions that affects the output power that can be extracted from the PV modules, whereas in practice, the weather changes dynamically faster than what the algorithms time needed to converge. The work in this document is an attempt to address this shortcoming address shortcoming by utilizing online optimization algorithms for this purpose. Numerical analysis and verification are presented in the document. Code for the algorithms can be found at this link
Dynamic Control of Service Systems with Returns: Application to Design of Post-Discharge Hospital Readmission Prevention Programs
Authors: Timothy C. Y. Chan, Simon Y. Huang, Vahid Sarhangian
Date: 2022-02-28 00:00:00

We study a control problem for queueing systems where customers may return for additional episodes of service after their initial service completion. At each service completion epoch, the decision maker can choose to reduce the probability of return for the departing customer but at a cost that is convex increasing in the amount of reduction in the return probability. Other costs are incurred as customers wait in the queue and every time they return for service. Our primary motivation comes from post-discharge Quality Improvement (QI) interventions (e.g., follow up phone-calls, appointments) frequently used in a variety of healthcare settings to reduce unplanned hospital readmissions. Our objective is to understand how the cost of interventions should be balanced with the reductions in congestion and service costs. To this end, we consider a fluid approximation of the queueing system and characterize the structure of optimal long-run average and bias-optimal transient control policies for the fluid model. Our structural results motivate the design of intuitive surge protocols whereby different intensities of interventions (corresponding to different levels of reduction in the return probability) are provided based on the congestion in the system. Through extensive simulation experiments, we study the performance of the fluid policy for the stochastic system and identify parameter regimes where it leads to significant cost savings compared to a fixed long-run average optimal policy that ignores holding costs and a simple policy that uses the highest level of intervention whenever the queue is non-empty. In particular, we find that in a parameter regime relevant to our motivating application, dynamically adjusting the intensity of interventions could result in up to 25.4% reduction in long-run average cost and 33.7% in finite-horizon costs compared to the simple aggressive policy.
Virtual Reference Feedback Tuning for linear discrete-time systems with robust stability guarantees based on Set Membership
Authors: William D'Amico, Marcello Farina
Date: 2022-02-28 00:00:00

In this paper we propose a novel methodology that allows to design, in a purely data-based fashion and for linear single-input and single-output systems, both robustly stable and performing control systems for tracking piecewise constant reference signals. The approach uses both (i) Virtual Reference Feedback Tuning for enforcing suitable performances and (ii) the Set Membership framework for providing a-priori robust stability guarantees. Indeed, an uncertainty set for the system parameters is obtained through Set Membership identification, where an algorithm based on the scenario approach is proposed to estimate the inflation parameter in a probabilistic way. Based on this set, robust stability conditions are enforced as Linear Matrix Inequality constraints within an optimization problem whose linear cost function relies on Virtual Reference Feedback Tuning. To show the generality and effectiveness of our approach, we apply it to two of the most widely used yet simple control schemes, i.e., where tracking is achieved thanks to (i) a static feedforward action and (ii) an integrator in closed-loop. The proposed method is not fully direct due to the Set Membership identification. However, the uncertainty set is used with the only objective of providing robust stability guarantees for the closed-loop system and it is not directly used for the performances optimization, which instead is totally based on data. The effectiveness of the developed method is demonstrated with reference to two simulation examples. A comparison with other data-driven methods is also carried out.
Control-aware Probabilistic Load Flow for Transmission Systems: An Analytical Method
Authors: Mengshuo Jia, Qianni Cao, Chen Shen, Gabriela Hug
Date: 2022-02-28 00:00:00

Probabilistic load flow (PLF) calculation, as a fundamental tool to analyze transmission system behavior, has been studied for decades. Despite a variety of available methods, existing PLF approaches rarely take system control into account. However, system control, as an automatic buffer between the fluctuations in random variables and the variations in system states, has a significant impact on the final PLF result. To consider control actions' influence, this paper proposes the first analytical PLF method for the transmission grid that takes into account primary and secondary frequency controls. This method is based on a high-precision linear power flow model, whose precision is even further improved in this paper by an original correction approach. This paper also proves that if the joint probability distribution (JPD) of random variables is expressed by a Gaussian mixture model (GMM), then the JPD of system states (e.g., nodal voltages) is an infinite GMM. By leveraging this proposition, the proposed method can generate the joint PLF of the whole system, is applicable to random variables obeying any distributions, and is capable of capturing their correlation. The high accuracy and satisfactory efficiency of this method are verified on test cases scaling from 14 to 1354 buses.