pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating The Impact Of Species Specialisation On Ecological Network Robustness Using Analytic Methods
Authors: Chris Jones, Damaris Zurell, Karoline Wiesner
Date: 2023-06-27 00:00:00

Ecological networks describe the interactions between different species, informing us of how they rely on one another for food, pollination and survival. If a species in an ecosystem is under threat of extinction, it can affect other species in the system and possibly result in their secondary extinction as well. Understanding how (primary) extinctions cause secondary extinctions on ecological networks has been considered previously using computational methods. However, these methods do not provide an explanation for the properties which make ecological networks robust, and can be computationally expensive. We develop a new analytic model for predicting secondary extinctions which requires no non-deterministic computational simulation. Our model can predict secondary extinctions when primary extinctions occur at random or due to some targeting based on the number of links per species or risk of extinction, and can be applied to an ecological network of any number of layers. Using our model, we consider how false positives and negatives in network data affect predictions for network robustness. We have also extended the model to predict scenarios in which secondary extinctions occur once species lose a certain percentage of interaction strength, and to model the loss of interactions as opposed to just species extinction. From our model, it is possible to derive new analytic results such as how ecological networks are most robust when secondary species degree variance is minimised. Additionally, we show that both specialisation and generalisation in distribution of interaction strength can be advantageous for network robustness, depending upon the extinction scenario being considered.
Assessing Heterogeneity of Treatment Effects
Authors: Tetsuya Kaji, JIANFEI CAO
Date: 2023-06-26 00:00:00
Tasks: counterfactual

Treatment effect heterogeneity is of major interest in economics, but its assessment is often hindered by the fundamental lack of identification of the individual treatment effects. For example, we may want to assess the effect of a poverty reduction measure at different levels of poverty, but the causal effects on wealth at different wealth levels are not identified. Or, we may be interested in the proportion of workers who benefit from the minimum wage increase, but the proportion is not identified in the absence of counterfactuals. This paper derives bounds useful in such situations, which only depend on the marginal distributions of the outcomes. The bounds are nonparametrically sharp, making clear the maximum extent to which the data can speak about the heterogeneity of the treatment effects. An application to microfinance shows that the bounds can be informative even when the average treatment effects are not significant. Another application to the welfare reform identifies a nonnegligible portion of workers who increased and decreased working hours due to the reform.
Identifying Socially Disruptive Policies
Authors: Eric Auerbach, Yong Cai
Date: 2023-06-26 00:00:00

Social disruption occurs when a policy creates or destroys many network connections between agents. It is a costly side effect of many interventions and so a growing empirical literature recommends measuring and accounting for social disruption when evaluating the welfare impact of a policy. However, there is currently little work characterizing what can actually be learned about social disruption from data in practice. In this paper, we consider the problem of identifying social disruption in a research design that is popular in the literature. We provide two sets of identification results. First, we show that social disruption is not generally point identified, but informative bounds can be constructed using the eigenvalues of the network adjacency matrices observed by the researcher. Second, we show that point identification follows from a theoretically motivated monotonicity condition, and we derive a closed form representation. We apply our methods in two empirical illustrations and find large policy effects that otherwise might be missed by alternatives in the literature.
Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle
Authors: Thomas Dohmke, Marco Iansiti, Greg Richards
Date: 2023-06-26 00:00:00

This study examines the impact of GitHub Copilot on a large sample of Copilot users (n=934,533). The analysis shows that users on average accept nearly 30% of the suggested code, leading to increased productivity. Furthermore, our research demonstrates that the acceptance rate rises over time and is particularly high among less experienced developers, providing them with substantial benefits. Additionally, our estimations indicate that the adoption of generative AI productivity tools could potentially contribute to a $1.5 trillion increase in global GDP by 2030. Moreover, our investigation sheds light on the diverse contributors in the generative AI landscape, including major technology companies, startups, academia, and individual developers. The findings suggest that the driving force behind generative AI software innovation lies within the open-source ecosystem, particularly in the United States. Remarkably, a majority of repositories on GitHub are led by individual developers. As more developers embrace these tools and acquire proficiency in the art of prompting with generative AI, it becomes evident that this novel approach to software development has forged a unique inextricable link between humans and artificial intelligence. This symbiotic relationship has the potential to shape the construction of the world's software for future generations.
SAR ATR under Limited Training Data Via MobileNetV3
Authors: Chenwei Wang, Siyi Luo, Lin Liu, Yin Zhang, Jifang Pei, Yulin Huang, Jianyu Yang
Date: 2023-06-27 00:00:00

In recent years, deep learning has been widely used to solve the bottleneck problem of synthetic aperture radar (SAR) automatic target recognition (ATR). However, most current methods rely heavily on a large number of training samples and have many parameters which lead to failure under limited training samples. In practical applications, the SAR ATR method needs not only superior performance under limited training data but also real-time performance. Therefore, we try to use a lightweight network for SAR ATR under limited training samples, which has fewer parameters, less computational effort, and shorter inference time than normal networks. At the same time, the lightweight network combines the advantages of existing lightweight networks and uses a combination of MnasNet and NetAdapt algorithms to find the optimal neural network architecture for a given problem. Through experiments and comparisons under the moving and stationary target acquisition and recognition (MSTAR) dataset, the lightweight network is validated to have excellent recognition performance for SAR ATR on limited training samples and be very computationally small, reflecting the great potential of this network structure for practical applications.
Methodology for Jointly Assessing Myocardial Infarct Extent and Regional Contraction in 3-D CMRI
Authors: Y. Chenoune, C. Pellot-Barakat, C. Constantinides, R. El Berbari, M. Lefort, E. Roullot, E. Mousseaux, F. Frouin
Date: 2023-06-27 00:00:00
Tasks: Management

Automated extraction of quantitative parameters from Cardiac Magnetic Resonance Images (CMRI) is crucial for the management of patients with myocardial infarct. This work proposes a post-processing procedure to jointly analyze Cine and Delayed-Enhanced (DE) acquisitions in order to provide an automatic quantification of myocardial contraction and enhancement parameters and a study of their relationship. For that purpose, the following processes are performed: 1) DE/Cine temporal synchronization and 3D scan alignment, 2) 3D DE/Cine rigid registration in a region about the heart, 3) segmentation of the myocardium on Cine MRI and superimposition of the epicardial and endocardial contours on the DE images, 4) quantification of the Myocardial Infarct Extent (MIE), 5) study of the regional contractile function using a new index, the Amplitude to Time Ratio (ATR). The whole procedure was applied to 10 patients with clinically proven myocardial infarction. The comparison between the MIE and the visually assessed regional function scores demonstrated that the MIE is highly related to the severity of the wall motion abnormality. In addition, it was shown that the newly developed regional myocardial contraction parameter (ATR) decreases significantly in delayed enhanced regions. This largely automated approach enables a combined study of regional MIE and left ventricular function.
Cross Spectral Image Reconstruction Using a Deep Guided Neural Network
Authors: Frank Sippel, Jürgen Seiler, André Kaup
Date: 2023-06-27 00:00:00
Tasks: Data Augmentation, Image Reconstruction, SSIM

Cross spectral camera arrays, where each camera records different spectral content, are becoming increasingly popular for RGB, multispectral and hyperspectral imaging, since they are capable of a high resolution in every dimension using off-the-shelf hardware. For these, it is necessary to build an image processing pipeline to calculate a consistent image data cube, i.e., it should look like as if every camera records the scene from the center camera. Since the cameras record the scene from a different angle, this pipeline needs a reconstruction component for pixels that are not visible to peripheral cameras. For that, a novel deep guided neural network (DGNet) is presented. Since only little cross spectral data is available for training, this neural network is highly regularized. Furthermore, a new data augmentation process is introduced to generate the cross spectral content. On synthetic and real multispectral camera array data, the proposed network outperforms the state of the art by up to 2 dB in terms of PSNR on average. Besides, DGNet also tops its best competitor in terms of SSIM as well as in runtime by a factor of nearly 12. Moreover, a qualitative evaluation reveals visually more appealing results for real camera array data.
SCAN: Semantic Communication with Adaptive Channel Feedback
Authors: Guangyi Zhang, Qiyu Hu, Yunlong Cai, Guanding Yu
Date: 2023-06-27 00:00:00
Tasks: Semantic Communication

In existing semantic communication systems for image transmission, some images are generally reconstructed with considerably low quality. As a result, the reliable transmission of each image cannot be guaranteed, bringing significant uncertainty to semantic communication systems. To address this issue, we propose a novel performance metric to characterize the reliability of semantic communication systems termed semantic distortion outage probability (SDOP), which is defined as the probability of the instantaneous distortion larger than a given target threshold. Then, since the images with lower reconstruction quality are generally less robust and need to be allocated with more communication resources, we propose a novel framework of Semantic Communication with Adaptive chaNnel feedback (SCAN). It can reduce SDOP by adaptively adjusting the overhead of channel feedback for images with different reconstruction qualities, thereby enhancing transmission reliability. To realize SCAN, we first develop a deep learning-enabled semantic communication system for multiple-input multiple-output (MIMO) channels (DeepSC-MIMO) by leveraging the channel state information (CSI) and noise variance in the model design. We then develop a performance evaluator to predict the reconstruction quality of each image at the transmitter by distilling knowledge from DeepSC-MIMO. In this way, images with lower predicted reconstruction quality will be allocated with a longer CSI codeword to guarantee the reconstruction quality. We perform extensive experiments to demonstrate that the proposed scheme can significantly improve the reliability of image transmission while greatly reducing the feedback overhead.
Recursive LMMSE-Based Iterative Soft Interference Cancellation for MIMO Systems to Save Computations and Memories
Authors: Hufei Zhu, Fuqin Deng, Yikui Zhai, Jiaming Zhong, Yanyang Liang
Date: 2023-06-27 00:00:00

Firstly, a reordered description is given for the linear minimum mean square error (LMMSE)-based iterative soft interference cancellation (ISIC) detection process for Mutipleinput multiple-output (MIMO) wireless communication systems, which is based on the equivalent channel matrix. Then the above reordered description is applied to compare the detection process for LMMSE-ISIC with that for the hard decision (HD)-based ordered successive interference cancellation (OSIC) scheme, to draw the conclusion that the former is the extension of the latter. Finally, the recursive scheme for HD-OSIC with reduced complexity and memory saving is extended to propose the recursive scheme for LMMSE-ISIC, where the required computations and memories are reduced by computing the filtering bias and the estimate from the Hermitian inverse matrix and the symbol estimate vector, and updating the Hermitian inverse matrix and the symbol estimate vector efficiently. Assume N transmitters and M (no less than N) receivers in the MIMO system. Compared to the existing low-complexity LMMSE-ISIC scheme, the proposed recursive LMMSE-ISIC scheme requires no more than 1/6 computations and no more than 1/5 memory units.
Proximal Policy Optimization for Integrated Sensing and Communication in mmWave Systems
Authors: Cristian J. Vaca-Rubio, Carles Navarro Manchón, Ramoni Adeogun, Petar Popovski
Date: 2023-06-27 00:00:00
Tasks: Integrated sensing and communication

In wireless communication systems, mmWave beam tracking is a critical task that affects both sensing and communications, as it is related to the knowledge of the wireless channel. We consider a setup in which a Base Station (BS) needs to dynamically choose whether the resource will be allocated for one of the three operations: sensing (beam tracking), downlink, or uplink transmission. We devise an approach based on the Proximal Policy Optimization (PPO) algorithm for choosing the resource allocation and beam tracking at a given time slot. The proposed framework takes into account the variable quality of the wireless channel and optimizes the decisions in a coordinated manner. Simulation results demonstrate that the proposed method achieves significant performance improvements in terms of average packet error rate (PER) compared to the baseline methods while providing a significant reduction in beam tracking overhead. We also show that our proposed PPO-based framework provides an effective solution to the resource allocation problem in beam tracking and communication, exhibiting a great generalization performance regardless of the stochastic behavior of the system.
Pulse Shape-Aided Multipath Delay Estimation for Fine-Grained WiFi Sensing
Authors: Ke Xu, He Chen, Chenshu Wu
Date: 2023-06-27 00:00:00
Tasks: Benchmarking

Due to the finite bandwidth of practical wireless systems, one multipath component can manifest itself as a discrete pulse consisting of multiple taps in the digital delay domain. This effect is called channel leakage, which complicates the multipath delay estimation problem. In this paper, we develop a new algorithm to estimate multipath delays of leaked channels by leveraging the knowledge of pulse-shaping functions, which can be used to support fine-grained WiFi sensing applications. Specifically, we express the channel impulse response (CIR) as a linear combination of overcomplete basis vectors corresponding to different delays. Considering the limited number of paths in physical environments, we formulate the multipath delay estimation as a sparse recovery problem. We then propose a sparse Bayesian learning (SBL) method to estimate the sparse vector and determine the number of physical paths and their associated delay parameters from the positions of the nonzero entries in the sparse vector. Simulation results show that our algorithm can accurately determine the number of paths, and achieve superior accuracy in path delay estimation and channel reconstruction compared to two benchmarking schemes.
RF-Based Simultaneous Localization and Source Seeking for Multi-Robot Systems
Authors: Ke Xu, Rui Zhang, He Chen
Date: 2023-06-27 00:00:00
Tasks: Position

This paper considers a radio-frequency (RF)-based simultaneous localization and source-seeking (SLASS) problem in multi-robot systems, where multiple robots jointly localize themselves and an RF source using distance-only measurements extracted from RF signals and then control themselves to approach the source. We design a Rao-Blackwellized particle filter-based algorithm to realize the joint localization of the robots and the source. We also devise an information-theoretic control policy for the robots to approach the source. In our control policy, we maximize the predicted mutual information between the source position and the distance measurements, conditioned on the robot positions, to incorporate the robot localization uncertainties. A projected gradient ascent method is adopted to solve the mutual information maximization problem. Simulation results show that the proposed SLASS framework outperforms two benchmarks in terms of the root mean square error (RMSE) of the estimated source position and the decline of the distances between the robots and the source, indicating more effective approaching of the robots to the source.
Beampattern Design for Transmit Architectures Based on Reconfigurable Intelligent Surfaces
Authors: Emanuele Grossi, Luca Venturino
Date: 2023-06-27 00:00:00

In this work, we tackle the problem of beampattern design for a transmit architecture wherein a large reconfigurable intelligent surface (RIS) redirects the radio frequency signals emitted by a few active antennas (sources). After introducing a convenient signal model and discussing the impact of the signal bandwidth, the source-RIS channel, and the system geometry on the involved derivations, we propose to optimize the waveform emitted by each source and the phase shifts introduced by the RIS to match a desired space-frequency distribution of the far-field radiation pattern, which may be relevant in both radar and communication applications. We provide a sub-optimum solution to the considered problem under a constraint on the total power radiated by the sources and, possibly, on the constant modulus of the involved waveforms. The reported example demonstrates that the beampattern of an RIS-based transmit architecture can be well shaped; in particular, for the same array size and the same desired radiation pattern, the resulting approximation error remains comparable to that obtained with a fully-digital MIMO array, especially when constant-modulus waveforms are required, and smaller than that obtained with a phased array.
Sparse wavelet-based solutions for the M/EEG inverse problem
Authors: Samy Mokhtari, Jean-Michel Badier, Christian G. Bénar, Bruno Torrésani
Date: 2023-06-27 00:00:00
Tasks: Data Compression, EEG, regression

This paper is concerned with variational and Bayesian approaches to neuro-electromagnetic inverse problems (EEG and MEG). The strong indeterminacy of these problems is tackled by introducing sparsity inducing regularization/priors in a transformed domain, namely a spatial wavelet domain. Sparsity in the wavelet domain allows to reach ''data compression'' in the cortical sources domain. Spatial wavelets defined on the mesh graph of the triangulated cortical surface are used, in combination with sparse regression techniques, namely LASSO regression or sparse Bayesian learning, to provide localized and compressed estimates for brain activity from sensor data. Numerical results on simulated and real MEG data are provided, which outline the performances of the proposed approach in terms of localization.
SNR-Independent Joint Source-Channel Coding for wireless image transmission
Authors: Hongjie Yuan, Weizhang Xu, Yuhuan Wang, Xingxing Wang
Date: 2023-06-27 00:00:00

Significant progress has been made in wireless Joint Source-Channel Coding (JSCC) using deep learning techniques. The latest DL-based image JSCC methods have demonstrated exceptional performance during transmission, while also avoiding cliff effects. However, current channel adaptive JSCC methods rely on channel SNR information, which can lead to performance degradation in practical applications due to channel mismatch effects. This paper proposes a novel approach for image transmission, called SNR Independent Joint Source-Channel Coding (SIJSCC), which utilizes Deep Learning techniques to achieve exceptional performance across various signal-to-noise ratio (SNR) levels without SNR estimating. We have designed an Inverted Residual Attention Bottleneck (IRAB) module for the model, which can effectively reduce the number of parameters while expanding the receptive field. In addition, we have incorporated a convolution and self-attention mixed encoding module to establish long-range dependency relationships between channel symbols. Our experiments have shown that SIJSCC outperforms existing channel adaptive DL-based JSCC methods that rely on SNR information. Furthermore, we found that SNR estimation does not significantly benefit SIJSCC, which provides insights for the future design of DL-based JSCC methods. The reliability of the proposed method is further demonstrated through an analysis of the model bottleneck and its adaptability to different domains, as shown by our experiments.
Automated Identication of Atrial Fibrillation from Single-lead ECGs Using Multi-branching ResNet
Authors: Jianxin Xie, Stavros Stavrakis, Bing Yao
Date: 2023-06-26 00:00:00
Tasks: Electrocardiography (ECG), Rhythm

Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. It is of critical importance to develop an advanced analytical model that can effectively interpret the electrocardiography (ECG) signals and provide decision support for accurate AF diagnostics. In this paper, we propose an innovative deep-learning method for automated AF identification from single-lead ECGs. We first engage the continuous wavelet transform (CWT) to extract time-frequency features from ECG signals. Then, we develop a convolutional neural network (CNN) structure that incorporates ResNet for effective network training and multi-branching architectures for addressing the imbalanced data issue to process the 2D time-frequency features for AF classification. We evaluate the proposed methodology using two real-world ECG databases. The experimental results show a superior performance of our method compared with traditional deep learning models.
Geometry of saccades and saccadic cycles
Authors: D. V. Alekseevsky, I. M. Shirokov
Date: 2023-06-26 00:00:00

The paper is devoted to the development of the differential geometry of saccades and saccadic cycles. We recall an interpretation of Donder's and Listing's law in terms of the Hopf fibration of the $3$-sphere over the $2$-sphere. In particular, the configuration space of the eye ball (when the head is fixed) is the 2-dimensional hemisphere $S^+_L$, which is called Listing's hemisphere. We give three characterizations of saccades: as geodesic segment $ab$ in the Listing's hemisphere, as the gaze curve and as a piecewise geodesic curve of the orthogonal group. We study the geometry of saccadic cycle, which is represented by a geodesic polygon in the Listing hemisphere, and give necessary and sufficient conditions, when a system of lines through the center of eye ball is the system of axes of rotation for saccades of the saccadic cycle, described in terms of world coordinates and retinotopic coordinates. This gives an approach to the study the visual stability problem.