pwc-archive/papers-with-abstracts huggingface dataset:
Audio-Visual Spatial Integration and Recursive Attention for Robust Sound Source Localization
Authors: Sung Jin Um, DongJin Kim, Jung Uk Kim
Date: 2023-08-11 00:00:00
Tasks: Sound Source Localization

The objective of the sound source localization task is to enable machines to detect the location of sound-making objects within a visual scene. While the audio modality provides spatial cues to locate the sound source, existing approaches only use audio as an auxiliary role to compare spatial regions of the visual modality. Humans, on the other hand, utilize both audio and visual modalities as spatial cues to locate sound sources. In this paper, we propose an audio-visual spatial integration network that integrates spatial cues from both modalities to mimic human behavior when detecting sound-making objects. Additionally, we introduce a recursive attention network to mimic human behavior of iterative focusing on objects, resulting in more accurate attention regions. To effectively encode spatial information from both modalities, we propose audio-visual pair matching loss and spatial region alignment loss. By utilizing the spatial cues of audio-visual modalities and recursively focusing objects, our method can perform more robust sound source localization. Comprehensive experimental results on the Flickr SoundNet and VGG-Sound Source datasets demonstrate the superiority of our proposed method over existing approaches. Our code is available at: https://github.com/VisualAIKHU/SIRA-SSL
SLAM for Multiple Extended Targets using 5G Signal
Authors: Wangjun Jiang, Zhiqing Wei, Zhiyong Feng
Date: 2023-08-04 00:00:00
Tasks: Simultaneous Localization and Mapping

5th Generation (5G) mobile communication systems operating at around 28 GHz have the potential to be applied to simultaneous localization and mapping (SLAM). Most existing 5G SLAM studies estimate environment as many point targets, instead of extended targets. In this paper, we focus on the performance analysis of 5G SLAM for multiple extended targets. To evaluate the mapping performance of multiple extended targets, a new mapping error metric, named extended targets generalized optimal sub-pattern assignment (ET-GOPSA), is proposed in this paper. Compared with the existing metrics, ET-GOPSA not only considers the accuracy error of target estimation, the cost of missing detection, the cost of false detection, but also the cost of matching the estimated point with the extended target. To evaluate the performance of 5G signal in SLAM, we analyze and simulate the mapping error of 5G signal sensing by ET-GOPSA. Simulation results show that, under the condition of SNR = 10 dB, 5G signal sensing can barely meet to meet the requirements of SLAM for multiple extended targets with the carrier frequency of 28 GHz, the bandwidth of 1.23 GHz, and the antenna size of 32.
Covid-19 Public Sentiment Analysis for Indian Tweets Classification
Authors: Mohammad Maksood Akhter, Devpriya Kanojia
Date: 2023-08-01 00:00:00
Tasks: Classification, Sentiment Analysis

When any extraordinary event takes place in the world wide area, it is the social media that acts as the fastest carrier of the news along with the consequences dealt with that event. One can gather much information through social networks regarding the sentiments, behavior, and opinions of the people. In this paper, we focus mainly on sentiment analysis of twitter data of India which comprises of COVID-19 tweets. We show how Twitter data has been extracted and then run sentimental analysis queries on it. This is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and are either positive or negative or neutral in some cases.
A Robust Planning Model for Offshore Microgrid Considering Tidal Power and Desalination
Authors: Zhimeng Wang, Ang Xuan, Xinwei Shen, Yunfei Du, Hongbin Sun
Date: 2023-08-11 00:00:00

Increasing attention has been paid to resources on islands, thus microgrids on islands need to be invested. Different from onshore microgrids, offshore microgrids (OM) are usually abundant in ocean renewable energy (ORE), such as offshore wind, tidal power generation (TPG), etc. Moreover, some special loads such as seawater desalination unit (SDU) should be included. In this sense, this paper proposes a planning method for OM to minimize the investment cost while the ORE's fluctuation could be accommodated with robustness. First, a deterministic planning model (DPM) is formulated for the OM with TPG and SDU. A robust planning model (RPM) is then developed considering the uncertainties from both TPG and load demand. The Column-and-constraint generation (C&CG) algorithm is then employed to solve the RPM, producing planning results for the OM that is robust against the worst scenario. Results of the case studies show that the investment and operation decisions of the proposed model are robust, and TPG shows good complementarity with the other RESs.
Trajectory Generation and Tracking based on Energy Minimization for a Four-Link Brachiation Robot
Authors: Zishang Ji, Xuanyu Zhang, Xuanzhe Wang, Yan Huang
Date: 2023-08-11 00:00:00
Tasks: Model Predictive Control

Aiming to mimic the brachiation locomotion of primates, we establish a brachiation robot model capable of swinging between different bars. The robot's design is based on a four-link underactuated structure. We propose an offline trajectory generator with optimization for minimizing energy consumption, which is implemented by direct collocation method to generate joint-space trajectories. We also propose a linear Model Predictive Control (MPC) algorithm as the feedback controller. The proposed MPC concurrently tracks both trajectories in joint space and Cartesian space. In simulation experiments, we analyzed the influence of lower-to-upper arm length ratio and swing time on the motion performance. The simulation results also demonstrate the robot has satisfied ability in trajectory tracking, obstacle avoidance and robustness.
Smart Data Mapping for Connecting Power System Model and Geospatial Data
Authors: Xue Li, Kishan Prudhvi Guddanti, Samrat Acharya, Patrick Royer, Xiaoyuan Fan, Marcelo Elizondo
Date: 2023-08-10 00:00:00

Knowing the geospatial locations of power system model elements and linking load models with end users and their communities are the foundation for analyzing system resilience and vulnerability to natural hazards. However, power system models and geospatial data for power grid assets are often developed asynchronously without close coordination. Creating a direct mapping between the two is a challenging task, mainly due to heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect the two, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.
SNR-based beaconless multi-scan link acquisition model with vibration for LEO-to-ground laser communication
Authors: Sen yang, Xiaofeng Li
Date: 2023-08-06 00:00:00

We propose a link acquisition time model deeply involving the process from the transmitted power to received signal-to-noise ratio (SNR) for LEO-to-ground laser communication for the first time. Compared with the conventional acquisition models founded on geometry analysis with divergence angle threshold, utilizing SNR as the decision criterion is more appropriate for practical engineering requirements. Specially, under the combined effects of platform vibration and turbulence, we decouple the parameters of beam divergence angle, spiral pitch, and coverage factor at a fixed transmitted power for a given average received SNR threshold. Then the single-scan acquisition probability is obtained by integrating the field of uncertainty (FOU), probability distribution of coverage factor, and receiver field angle. Consequently, the closed-form analytical expression of acquisition time expectation adopting multi-scan, which ensures acquisition success, with essential reset time between single-scan is derived. The optimizations concerning the beam divergence angle, spiral pitch, and FOU are presented. Moreover, the influence of platform vibration is investigated. All the analytical derivations are confirmed by Monte Carlo simulations. Notably, we provide a theoretical method for designing the minimum divergence angle modulated by the laser, which not only improves the acquisition performance within a certain vibration range, but also achieves a good trade-off with the system complexity.
Molecular fingerprinting of biological nanoparticles with a label-free optofluidic platform
Authors: Alexia Stollmann, Jose Garcia-Guirado, Jae-Sang Hong, Hyungsoon Im, Hakho Lee, Jaime Ortega Arroyo, Romain Quidant
Date: 2023-08-11 00:00:00
Tasks: Specificity

Label-free detecting multiple analytes in a high-throughput fashion has been one of the long-sought goals in biosensing applications. Yet, for all-optical approaches, interfacing state-of-the-art label-free techniques with microfluidics tools that can process small volumes of sample with high throughput, and with surface chemistry that grants analyte specificity, poses a critical challenge to date. Here, we introduce an optofluidic platform that brings together state-of-the-art digital holography with PDMS microfluidics by using supported lipid bilayers as a surface chemistry building block to integrate both technologies. Specifically, this platform fingerprints heterogeneous biological nanoparticle populations via a multiplexed label-free immunoaffinity assay with single particle sensitivity. Herein, we first thoroughly characterise the robustness and performance of the platform, and then apply it to profile four distinct ovarian cell-derived extracellular vesicle populations over a panel of surface protein biomarkers, thus developing a unique biomarker fingerprint for each cell line. We foresee that our approach will find many applications where routine and multiplexed characterisation of biological nanoparticles is required.
PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences
Authors: Martin Buttenschoen, Garrett M. Morris, Charlotte M. Deane
Date: 2023-08-10 00:00:00
Tasks: Deep Learning, valid

The last few years have seen the development of numerous deep learning-based protein-ligand docking methods. They offer huge promise in terms of speed and accuracy. However, despite claims of state-of-the-art performance in terms of crystallographic root-mean-square deviation (RMSD), upon closer inspection, it has become apparent that they often produce physically implausible molecular structures. It is therefore not sufficient to evaluate these methods solely by RMSD to a native binding mode. It is vital, particularly for deep learning-based methods, that they are also evaluated on steric and energetic criteria. We present PoseBusters, a Python package that performs a series of standard quality checks using the well-established cheminformatics toolkit RDKit. Only methods that both pass these checks and predict native-like binding modes should be classed as having "state-of-the-art" performance. We use PoseBusters to compare five deep learning-based docking methods (DeepDock, DiffDock, EquiBind, TankBind, and Uni-Mol) and two well-established standard docking methods (AutoDock Vina and CCDC Gold) with and without an additional post-prediction energy minimisation step using a molecular mechanics force field. We show that both in terms of physical plausibility and the ability to generalise to examples that are distinct from the training data, no deep learning-based method yet outperforms classical docking tools. In addition, we find that molecular mechanics force fields contain docking-relevant physics missing from deep-learning methods. PoseBusters allows practitioners to assess docking and molecular generation methods and may inspire new inductive biases still required to improve deep learning-based methods, which will help drive the development of more accurate and more realistic predictions.
Introducing Hybrid Modeling with Time-series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization
Authors: Niranjan Sitapure, Joseph S Kwon
Date: 2023-07-25 00:00:00
Tasks: Time Series

Most existing digital twins rely on data-driven black-box models, predominantly using deep neural recurrent, and convolutional neural networks (DNNs, RNNs, and CNNs) to capture the dynamics of chemical systems. However, these models have not seen the light of day, given the hesitance of directly deploying a black-box tool in practice due to safety and operational issues. To tackle this conundrum, hybrid models combining first-principles physics-based dynamics with machine learning (ML) models have increased in popularity as they are considered a 'best of both worlds' approach. That said, existing simple DNN models are not adept at long-term time-series predictions and utilizing contextual information on the trajectory of the process dynamics. Recently, attention-based time-series transformers (TSTs) that leverage multi-headed attention mechanism and positional encoding to capture long-term and short-term changes in process states have shown high predictive performance. Thus, a first-of-a-kind, TST-based hybrid framework has been developed for batch crystallization, demonstrating improved accuracy and interpretability compared to traditional black-box models. Specifically, two different configurations (i.e., series and parallel) of TST-based hybrid models are constructed and compared, which show a normalized-mean-square-error (NMSE) in the range of $[10, 50]\times10^{-4}$ and an $R^2$ value over 0.99. Given the growing adoption of digital twins, next-generation attention-based hybrid models are expected to play a crucial role in shaping the future of chemical manufacturing.
Unified Statistical Channel Modeling and performance analysis of Vertical Underwater Wireless Optical Communication Links considering Turbulence-Induced Fading
Authors: Dongling Xu, Xiang Yi, Yalçn Ata, Xinyue Tao, YuXuan Li, Peng Yue
Date: 2023-08-08 00:00:00

The reliability of a vertical underwater wireless optical communication (UWOC) network is seriously impacted by turbulence-induced fading due to fluctuations in the water temperature and salinity, which vary with depth. To better assess the vertical UWOC system performances, an accurate probability distribution function (PDF) model that can describe this fading is indispensable. In view of the limitations of theoretical and experimental studies, this paper is the first to establish a more accurate modeling scheme for wave optics simulation (WOS) by fully considering the constraints of sampling conditions on multi-phase screen parameters. On this basis, we complete the modeling of light propagation in a vertical oceanic turbulence channel and subsequently propose a unified statistical model named mixture Weibull-generalized Gamma (WGG) distribution model to characterize turbulence-induced fading in vertical links. Interestingly, the WGG model is shown to provide a perfect fit with the acquired data under all considered channel conditions. We further show that the application of the WGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the average bit-error rate (BER). The presented results give valuable insight into the practical aspects of development of UWOC networks.
Characterizing Correlation Matrices that Admit a Clustered Factor Representation
Authors: Chen Tong, Peter Reinhard Hansen
Date: 2023-08-11 00:00:00

The Clustered Factor (CF) model induces a block structure on the correlation matrix and is commonly used to parameterize correlation matrices. Our results reveal that the CF model imposes superfluous restrictions on the correlation matrix. This can be avoided by a different parametrization, involving the logarithmic transformation of the block correlation matrix.
Amortized neural networks for agent-based model forecasting
Authors: Denis Koshelev, Alexey Ponomarenko, Sergei Seleznev
Date: 2023-08-03 00:00:00
Tasks: model

In this paper, we propose a new procedure for unconditional and conditional forecasting in agent-based models. The proposed algorithm is based on the application of amortized neural networks and consists of two steps. The first step simulates artificial datasets from the model. In the second step, a neural network is trained to predict the future values of the variables using the history of observations. The main advantage of the proposed algorithm is its speed. This is due to the fact that, after the training procedure, it can be used to yield predictions for almost any data without additional simulations or the re-estimation of the neural network
An approach to extend Cross-Impact Balance method in multiple timespans
Authors: Chonghao Zhao
Date: 2023-08-11 00:00:00
Tasks: Management

Cross-Impact Balance Analysis (CIB) is a widely used method to build scenarios and help researchers to formulate policies in different fields, such as management sciences and social sciences. During the development of the CIB method over the years, some derivative methods were developed to expand its application scope, including a method called dynamic CIB. However, the workflow of dynamic CIB is relatively complex. In this article, we provide another approach to extend CIB in multiple timespans based on the concept 'scenario weight' and simplify the workflow to bring convenience to the policy makers.
Ideological Ambiguity and Political Spectrum
Authors: Hector Galindo-Silva
Date: 2023-08-11 00:00:00

This study examines the relationship between ambiguity and the ideological positioning of political parties across the political spectrum. We identify a strong non-monotonic (inverted U-shaped) relationship between party ideology and ambiguity within a sample of 202 European political parties. This pattern is observed across all ideological dimensions covered in the data. To explain this pattern, we propose a novel theory that suggests centrist parties are perceived as less risky by voters compared to extremist parties, giving them an advantage in employing ambiguity to attract more voters at a lower cost. We support our explanation with additional evidence from electoral outcomes and economic indicators in the respective party countries.
Purely Speckled Intensity Images Need for SAR Despeckling with SDS-SAR
Authors: Liang Chen, Yifei Yin, Hao Shi, Jingfei He, Wei Li
Date: 2023-08-11 00:00:00
Tasks: Sar Image Despeckling

Speckle noise is generated along with the SAR imaging mechanism and degrades the quality of SAR images, leading to difficult interpretation. Hence, despeckling is an indispensable step in SAR pre-processing. Fortunately, supervised learning (SL) has proven to be a progressive method for SAR image despeckling. SL methods necessitate the availability of both original SAR images and their speckle-free counterparts during training, whilst speckle-free SAR images do not exist in the real world. Even though there are several substitutes for speckle-free images, the domain gap leads to poor performance and adaptability. Self-supervision provides an approach to training without clean reference. However, most self-supervised methods impose high demands on speckle modeling or specific data, limiting their practicality in real-world applications. To address these challenges, we propose a Self-supervised Despeckling Strategy for SAR images (SDS-SAR) that relies solely on speckled intensity data for training. Firstly, the theoretical feasibility of SAR image despeckling without speckle-free images is established. A self-supervised despeckling criteria suitable for all SAR images is proposed. Subsequently, a Random-Aware sub-SAMpler with Projection correLation Estimation (RA-SAMPLE) is put forth. Mutually independent training pairs can be derived from actual SAR intensity images. Furthermore, a multi-feature loss function is introduced, consisting of a despeckling term, a regularization term, and a perception term. The performance of speckle suppression and texture preservation is well-balanced. Experiments reveal that the proposed method performs on par with supervised approaches on synthetic data and outperforms them on actual data.
End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification
Authors: Siyu Qi, Lahiru D. Chamain, Zhi Ding
Date: 2023-08-10 00:00:00
Tasks: Classification, image-classification, Image Classification, Image Compression

Among major deep learning (DL) applications, distributed learning involving image classification require effective image compression codecs deployed on low-cost sensing devices for efficient transmission and storage. Traditional codecs such as JPEG designed for perceptual quality are not configured for DL tasks. This work introduces an integrative end-to-end trainable model for image compression and classification consisting of a JPEG image codec and a DL-based classifier. We demonstrate how this model can optimize the widely deployed JPEG codec settings to improve classification accuracy in consideration of bandwidth constraint. Our tests on CIFAR-100 and ImageNet also demonstrate improved validation accuracy over preset JPEG configuration.
Companding and Predistortion Techniques for Improved Efficiency and Performance in SWIPT
Authors: Santiago Fernández, F. Javier López-Martínez, Fernando H. Gregorio, Juan Cousseau
Date: 2023-08-11 00:00:00

In this work, we analyze how the use of companding techniques, together with digital predistortion (DPD), can be leveraged to improve system efficiency and performance in simultaneous wireless information and power transfer (SWIPT) systems based on power splitting. By taking advantage of the benefits of each of these well-known techniques to mitigate non-linear effects due to power amplifier (PA) and energy harvesting (EH) operation, we illustrate how DPD and companding can be effectively combined to improve the EH efficiency while keeping unalterable the information transfer performance. We establish design criteria that allow the PA to operate in a higher efficiency region so that the reduction in peak-to-average power ratio over the transmitted signal is translated into an increase in the average radiated power and EH efficiency. The performance of DPD and companding techniques is evaluated in a number of scenarios, showing that a combination of both techniques allows to significantly increase the power transfer efficiency in SWIPT systems.