pwc-archive/papers-with-abstracts huggingface dataset:
Content-Preserving Image Stitching with Piecewise Rectangular Boundary Constraints
Authors: Yun Zhang, Yu-Kun Lai, and Fang-Lue Zhang, Member, IEEE
Date: 2021-07-01 00:00:00
Proceeding: IEEE Transactions on Visualization and Computer Graphics 2021 7
Tasks: Image Stitching

Abstract—This paper proposes an approach to content-
preserving image stitching with regular boundary constraints,
which aims to stitch multiple images to generate a panoramic
image with a piecewise rectangular boundary. Existing methods
treat image stitching and rectangling as two separate steps,
which may result in suboptimal results as the stitching process
is not aware of the further warping needs for rectangling. We
address these limitations by formulating image stitching with
regular boundaries in a uniﬁed optimization. Starting from the
initial stitching results produced by the traditional warping-
based optimization, we obtain the irregular boundary from the
warped meshes by polygon Boolean operations which robustly
handle arbitrary mesh compositions. By analyzing the irregular
boundary, we construct a piecewise rectangular boundary. Based
on this, we further incorporate line and regular boundary
preservation constraints into the image stitching framework, and
conduct iterative optimization to obtain an optimal piecewise
rectangular boundary. Thus we can make the boundary of
the stitching results as close as possible to a rectangle, while
reducing unwanted distortions. We further extend our method
to video stitching, by integrating the temporal coherence into
the optimization. Experiments show that our method efﬁciently
produces visually pleasing panoramas with regular boundaries
and unnoticeable distortions.
Tackling multiple object tracking with complicated motions—Re-designing the integration of motion and appearance
Authors: FanYang, ZhengWang, YangWu, SakrianiSakti, SatoshiNakamura
Date: 2022-08-22 00:00:00
Proceeding: Image and Vision Computing 2022 8
Tasks: Multiple Object Tracking, Object, Object Tracking

Although numerous data association methods have been proposed for Multiple Object Tracking (MOT), how to integrate different features in the data association remains an open problem. For instance, over-relying on the motion feature may fail to do the necessary data association when object movements are complicated, while only depending on the appearance feature may lead to incorrect association results when intra-frame objects have similar appearances. To make an improved trade-off between the appearance feature and motion feature, we re-designed the integration of motion and appearance. In our online approach, the location and motion of each object are cast to adaptive searching windows, and within searching windows, matching is only related to the similarity of appearance features. In our offline approach, tracklets generated from our online approach are refined by forming the motion feature as spatiotemporal constraints and utilizing the appearance for clustering. We conduct experiments on multiple MOT datasets from diverse perspectives, including variant motion speed, illumination condition, object categories, etc., and verify that our method can reach robust performance. Moreover, this method also demonstrates its effectiveness by further improving our previous 1st place solutions in two CVPR 2020 MOT challenges.
A deep learning network for cloud-to-ground lightning nowcasting with multisource data
Authors: Kanghui Zhou, Yongguang Zheng, Wansheng Dong, and Tingbo Wang
Date: 2020-05-01 00:00:00
Proceeding: journal 2020 5
Tasks: Semantic Segmentation

Precise and timely lightning nowcasting is still a great challenge for meteorologists. In this study, a new semantic segmentation deep learning network for cloud-to-ground (CG) lightning nowcasting, named LightningNet, has been developed. This network is based on multisource observation data, including data from a geostationary meteorological satellite, Doppler weather radar network, and CG lightning location system. LightningNet, with an encoder–decoder architecture, consists of 20 three-dimensional convolutional layers, pooling and upsampling layers, normalization layers, and a softmax classifier. The central–eastern and southern China was selected as the study area, with considerations given to the topography and spatial coverage of the weather radar and lightning observation networks. Brightness temperatures (TB) of six infrared bands from the Himawari-8 satellite, composite reflectivity mosaic, and CG lightning densities were used as the predictors because of their close relationships with lightning activity. The multisource data were first interpolated into a uniform spatial/temporal resolution of 0.05° × 0.05°/10 min, and then training and test datasets were constructed, respectively. LightningNet was trained to extract the features of lightning initiation, development, and dissipation. The evaluation results demonstrated that LightningNet was able to achieve good performance of 0–1-h lightning nowcasts using the multisource data. The probability of detection, the false alarm ratio, the area under relative operating characteristic curve, and the threat score (TS) of LightningNet with all three types of data reached 0.633, 0.386, 0.931, and 0.453, respectively. Because geostationary meteorological satellite and radar both possess the capability of capturing lightning initiation (LI) features, LightningNet also showed good performance in LI nowcasting. When all three types of data were used, more than 50% LI was predicted accurately and the TS exceeded 0.36. LightningNet’s nowcast performance using triple-source data was clearly superior to that using only single-source or dual-source data, and these findings indicate that LightningNet has good capability of combining multisource data effectively to produce more reliable lightning nowcasts.
Synthesis of Parametric Hybrid Automata from Time Series
Authors: Miriam García Soto, Thomas A. Henzinger, Christian Schilling
Date: 2022-07-13 00:00:00
Tasks: Time Series, Time Series Analysis

We propose an algorithmic approach for synthesizing linear hybrid automata from time-series data. Unlike existing approaches, our approach provides a whole family of models. Each model in the family is guaranteed to capture the input data up to a precision error {\epsilon}, in the following sense: For each time series, the model contains an execution that is {\epsilon}-close to the data points. Our construction allows to effectively choose a model from this family with minimal precision error {\epsilon}. We demonstrate the algorithm's efficiency and its ability to find precise models in two case studies.
Communication network model of the immune system identifies the impact of interactions with SARS-CoV-2 proteins
Authors: Swarnavo Sarkar
Date: 2022-08-12 00:00:00

Interactions between SARS-CoV-2 and human proteins (SARS-CoV-2 PPIs) cause information transfer through biochemical pathways that contribute to the immunopathology of COVID-19. Here, we present a communication network model of the immune system to compute the information transferred by the viral proteins using the available SARS-CoV-2 PPIs data. The amount of transferred information depends on the reference state of the immune system, or the state without SARS-CoV-2 PPIs, and can quantify how many variables of the immune system are controlled by the viral proteins. The information received by the immune system proteins from the viral proteins is useful to identify the biological processes (BPs) susceptible to dysregulation, and also to estimate the duration of viral PPIs necessary for the dysregulation to occur. We found that computing the drop in information from viral PPIs due to drugs provides a direct measure for the efficacy of therapies.
OUTformation: Distributed Data-Gathering with Feedback under Unknown Environment and Communication Delay Constraints
Authors: SooJean Han, Michelle Effros, Richard M. Murray
Date: 2022-08-12 00:00:00
Tasks: State Estimation

Towards the informed design of large-scale distributed data-gathering architectures under real-world assumptions such as nonzero communication delays and unknown environment dynamics, this paper considers the effects of allowing feedback communication from the central processor to external sensors. Using simple but representative state-estimation examples, we investigate fundamental tradeoffs between the mean-squared error (MSE) of the central processor's estimate of the environment state, and the total power expenditure per sensor under more conventional architectures without feedback (INformation) versus those with broadcast feedback (OUTformation). The primary advantage of enabling feedback is that each sensor's understanding of the central processor's estimate improves, which enables each sensor to determine when and what parts of its current observations to transmit. We use theory to demonstrate conditions in which OUTformation maintains the same MSE as INformation with less power expended on average, and conditions in which OUTformation obtains less MSE than INformation at additional power cost. These performance tradeoffs are also considered under settings where environments undergo less variation, and sensors implement random backoff times to prevent transmission collisions. Our results are supported via numerical studies, which show that the properties derived in theory still hold even when some of the simplifying assumptions are removed.
Towards 6G-V2X: Aggregated RF-VLC for Ultra-Reliable and Low-Latency Autonomous Driving
Authors: Gurinder Singh, Anand Srivastava, Vivek Ashok Bohara, Md Noor-A-Rahim, Zilong Liu, Dirk Pesch
Date: 2022-08-12 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

We are witnessing a transition to a new era where driverless cars will be pervasively connected to deliver significantly improved safety, traffic efficiency, and travel experiences. A diverse set of advanced vehicular use cases including connected autonomous vehicles will be made possible by building upon the emerging sixth-generation (6G) wireless networks. Among many 6G wireless technologies, the principal objective of this paper is to introduce the potential benefits of the hybrid integration of Vehicular Visible Light Communication (V VLC) and Vehicular Radio Frequency (V RF) communication systems by studying the impact of interference as well as various meteorological phenomenon viz. rain, fog and dry snow. In particular, we show that regardless of any meteorological impact, a properly configured link-aggregated hybrid V-VLC/V-RF system is capable of meeting stringent ultra high reliability (>99.999%) and ultra-low latency (<3 ms) requirements, making it a promising candidate for 6G Vehicle-to-Everything (V2X) Communications. To stimulate future research in the hybrid RF-VLC V2X space, we also highlight the potential challenges and research directions.
Sim2real for Autonomous Vehicle Control using Executable Digital Twin
Authors: Jean Pierre Allamaa, Panagiotis Patrinos, Herman Van der Auweraer, Tong Duy Son
Date: 2022-08-12 00:00:00

In this work, we propose a sim2real method to transfer and adapt a nonlinear model predictive controller (NMPC) from simulation to the real target system based on executable digital twin (xDT). The xDT model is a high fidelity vehicle dynamics simulator, executable online in the control parameter randomization and learning process. The parameters are adapted to gradually improve control performance and deal with changing real-world environment. In particular, the performance metric is not required to be differentiable nor analytical with respect to the control parameters and system dynamics are not necessary linearized. Eventually, the proposed sim2real framework leverages altogether online high fidelity simulator, data-driven estimations, and simulation based optimization to transfer and adapt efficiently a controller developed in simulation environment to the real platform. Our experiment demonstrates that a high control performance is achieved without tedious time and labor consuming tuning.
Prevalence and scalable control of localized networks
Authors: Chao Duan, Takashi Nishikawa, Adilson E. Motter
Date: 2022-08-11 00:00:00

The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers while incurring a computational cost orders-of-magnitude lower. We validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks as well as three large empirical networks: synchronization dynamics in the Eastern U.S. power grid, epidemic spreading mediated by the global air transportation network, and Alzheimer's disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.
Enhancing Oceanic Variables Forecast in the Santos Channel by Estimating Model Error with Random Forests
Authors: Felipe M. Moreno, Caio F. D. Netto, Marcel R. de Barros, Jefferson F. Coelho, Lucas P. de Freitas, Marlon S. Mathias, Luiz A. Schiaveto Neto, Marcelo Dottori, Fabio G. Cozman, Anna H. R. Costa, Edson S. Gomi, Eduardo A. Tannuri
Date: 2022-07-22 00:00:00

In this work we improve forecasting of Sea Surface Height (SSH) and current velocity (speed and direction) in oceanic scenarios. We do so by resorting to Random Forests so as to predict the error of a numerical forecasting system developed for the Santos Channel in Brazil. We have used the Santos Operational Forecasting System (SOFS) and data collected in situ between the years of 2019 and 2021. In previous studies we have applied similar methods for current velocity in the channel entrance, in this work we expand the application to improve the SHH forecast and include four other stations in the channel. We have obtained an average reduction of 11.9% in forecasting Root-Mean Square Error (RMSE) and 38.7% in bias with our approach. We also obtained an increase of Agreement (IOA) in 10 of the 14 combinations of forecasted variables and stations.
The effect of ambient air pollution on birth outcomes in Norway
Authors: Xiaoguang Ling
Date: 2022-08-12 00:00:00

Ambient air pollution is harmful to the fetus even in countries with relatively low levels of pollution. In this paper, I examine the effects of ambient air pollution on birth outcomes in Norway. I find that prenatal exposure to ambient nitric oxide in the last trimester causes significant birth weight and birth length loss under the same sub-postcode fixed effects and calendar month fixed effects, whereas other ambient air pollutants such as nitrogen dioxide and sulfur dioxide appear to be at safe levels for the fetus in Norway. In addition, the marginal adverse effect of ambient nitric oxide is larger for newborns with disadvantaged parents. Both average concentrations of nitric oxide and occasional high concentration events can adversely affect birth outcomes. The contributions of my work include: first, my finding that prenatal exposure to environmental nitric oxide has an adverse effect on birth outcomes fills a long-standing knowledge gap. Second, with the large sample size and geographic division of sub-postal codes in Norway, I can control for a rich set of spatio-temporal fixed effects to overcome most of the endogeneity problems caused by the choice of residential area and date of delivery. In addition, I study ambient air pollution in a low-pollution setting, which provides new evidence on the health effects of low ambient air pollution.
Multiple access for near-field communications: SDMA or LDMA?
Authors: Zidong Wu, Linglong Dai
Date: 2022-08-12 00:00:00

Spatial division multiple access (SDMA) is essential to improve the spectrum efficiency for multi-user multiple-input multiple-output (MIMO) communications. The classical SDMA for massive MIMO with hybrid precoding heavily relies on the angular orthogonality in the far field to distinguish multiple users at different angles, which fails to fully exploit spatial resources in the distance domain. With the dramatically increasing number of antennas, the extremely large-scale antenna array (ELAA) introduces additional resolution in the distance domain in the near field. In this paper, we propose the concept of location division multiple access (LDMA) to provide a new possibility to enhance spectrum efficiency compared with classical SDMA. The key idea is to exploit extra spatial resources in the distance domain to serve different users at different locations (determined by angles and distances) in the near field. Specifically, the asymptotic orthogonality of near-field beam focusing vectors in the distance domain is proved, which reveals that near-field beam focusing is able to focus signals on specific locations with limited leakage energy at other locations. This special property could be leveraged in hybrid precoding to mitigate inter-user interferences for spectrum efficiency enhancement. Moreover, we provide the spherical-domain codebook design method for LDMA communications with the uniform planar array, which provides the sampling method in the distance domain. Additionally, performance analysis of LDMA is provided to reveal that the asymptotic optimal spectrum efficiency could be achieved with the increasing number of antennas. Finally, simulation results verify the superiority of the proposed LDMA over SDMA in different scenarios.
Engineering Semantic Communication: A Survey
Authors: Dylan Wheeler, Balasubramaniam Natarajan
Date: 2022-08-12 00:00:00
Tasks: Knowledge Graphs, Semantic Communication, Survey

As the global demand for data has continued to rise exponentially, some have begun turning to the idea of semantic communication as a means of efficiently meeting this demand. Pushing beyond the boundaries of conventional communication systems, semantic communication focuses on the accurate recovery of the meaning conveyed from source to receiver, as opposed to the accurate recovery of transmitted symbols. In this survey, we aim to provide a comprehensive view of the history and current state of semantic communication and the techniques for engineering this higher level of communication. A survey of the current literature reveals four broad approaches to engineering semantic communication. We term the earliest of these approaches classical semantic information, which seeks to extend information-theoretic results to include semantic information. A second approach makes use of knowledge graphs to achieve semantic communication, and a third utilizes the power of modern deep learning techniques to facilitate this communication. The fourth approach focuses on the significance of information, rather than its meaning, to achieve efficient, goal-oriented communication. We discuss each of these four approaches and their corresponding studies in detail, and provide some challenges and opportunities that pertain to each approach. Finally, we introduce a novel approach to semantic communication, which we term context-based semantic communication. Inspired by the way in which humans naturally communicate with one another, this context-based approach provides a general, optimization-based design framework for semantic communication systems. Together, this survey provides a useful guide for the design and implementation of semantic communication systems.
Asynchronous Multiuser Detection for SCMA with Unknown Delays: A Compressed Sensing Approach
Authors: Dylan Wheeler, Erin E. Tripp, Balasubramaniam Natarajan
Date: 2022-08-12 00:00:00
Tasks: compressed sensing

Despite being the subject of a growing body of research, non-orthogonal multiple access has failed to garner sufficient support to be included in modern standards. One of the more promising approaches to non-orthogonal multiple access is sparse code multiple access, which seeks to utilize non-orthogonal, sparse spreading codes to share bandwidth among users more efficiently than traditional orthogonal methods. Nearly all of the studies regarding sparse code multiple access assume synchronization at the receiver, which may not always be a practical assumption. In this work, we aim to bring this promising technology closer to a practical realization by dropping the assumption of synchronization. We therefore propose a compressed sensing-based delay estimation technique developed specifically for an uplink sparse code multiple access system. The proposed technique can be used with nearly all of the numerous decoding algorithms proposed in the existing literature, including the popular message passing approach. Furthermore, we derive a theoretical bound regarding the recovery performance of the proposed technique, and use simulations to demonstrate its viability in a practical uplink system.
Approximate Extraction of Late-Time Returns via Morphological Component Analysis
Authors: Geoff Goehle, Benjamin Cowen, Thomas E. Blanford, J. Daniel Park, Daniel C. Brown
Date: 2022-08-11 00:00:00
Tasks: Image Reconstruction, Time Series, Time Series Analysis

A fundamental challenge in acoustic data processing is to separate a measured time series into relevant phenomenological components. A given measurement is typically assumed to be an additive mixture of myriad signals plus noise whose separation forms an ill-posed inverse problem. In the setting of sensing elastic objects using active sonar, we wish to separate the early-time returns (e.g., returns from the object's exterior geometry) from late-time returns caused by elastic or compressional wave coupling. Under the framework of Morphological Component Analysis (MCA), we compare two separation models using the short-duration and long-duration responses as a proxy for early-time and late-time returns. Results are computed for Stanton's elastic cylinder model as well as on experimental data taken from an in-Air circular Synthetic Aperture Sonar (AirSAS) system, whose separated time series are formed into imagery. We find that MCA can be used to separate early and late-time responses in both cases without the use of time-gating. The separation process is demonstrated to be robust to noise and compatible with AirSAS image reconstruction. The best separation results are obtained with a flexible, but computationally intensive, frame based signal model, while a faster Fourier Transform based method is shown to have competitive performance.