pwc-archive/papers-with-abstracts huggingface dataset:
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation
Authors: Zihan Lin, Xuanhua Yang, Xiaoyu Peng, Wayne Xin Zhao, Shaoguo Liu, Liang Wang, Bo Zheng
Date: 2022-08-29 00:00:00
Tasks: Contrastive Learning, Multi-Task Learning, PICO, Recommendation Systems

In modern advertising and recommender systems, multi-task learning (MTL) paradigm has been widely employed to jointly predict diverse user feedbacks (e.g. click and purchase). While, existing MTL approaches are either rigid to adapt to different scenarios, or only capture coarse-grained task relatedness, thus making it difficult to effectively transfer knowledge across tasks. To address these issues, in this paper, we propose an Adaptive Fine-grained Task Relatedness modeling approach, AdaFTR, for joint CTR-CVR estimation. Our approach is developed based on a parameter-sharing MTL architecture, and introduces a novel adaptive inter-task representation alignment method based on contrastive learning.Given an instance, the inter-task representations of the same instance are considered as positive, while the representations of another random instance are considered as negative. Furthermore, we explicitly model fine-grained task relatedness as the contrast strength (i.e. the temperature coefficient in InfoNCE loss) at the instance level. For this purpose, we build a relatedness prediction network, so that it can predict the contrast strength for inter-task representations of an instance. In this way, we can adaptively set the temperature for contrastive learning in a fine-grained way (i.e. instance level), so as to better capture task relatedness. Both offline evaluation with public e-commerce datasets and online test in a real advertising system at Alibaba have demonstrated the effectiveness of our approach.
An event detection technique using social media data
Authors: Muskan Garg
Date: 2022-08-27 00:00:00
Tasks: Event Detection, Management

People post information about different topics which are in their active vocabulary over social media platforms (like Twitter, Facebook, PInterest and Google+). They follow each other and it is more likely that the person who posts information about current happenings will receive better response. Manual analysis of huge amount of data on social media platforms is difficult. This has opened new research directions for automatic analysis of usercontributed social media documents. Automatic social media data analysis is difficult due to abundant information shared by users. Many researchers use Twitter data for Social Media Analysis (SMA) as the Twitter data is freely available in the public domain. One of the most this research work. Event Detection from social media data is used for different applications like traffic congestion detection, disaster and emergency management, and live news detection. Nature of the information which is shared on twitter platform is short-text, noisy, and ambiguous. Thus, event detection and extraction of event phrases from user-generated and illformed data becomes challenging. To address these challenges, events are extracted from streaming social media data in the form of keyphrases using different cognitive properties. The motivation behind this research work is to provide substantial improvements in the lexical variation of event phrases while detecting events and sub-events from twitter data. In this research work, the approach towards event detection from social media data is divided into three phases namely: Identifying sub-graphs in Microblog Word Co-occurrence Network (WCN) which provides important information about keyphrases; Identifying multiple events from social media data; and Ranking contextual information of event phrases.
Multi-level Contrastive Learning Framework for Sequential Recommendation
Authors: Ziyang Wang, Huoyu Liu, Wei Wei, Yue Hu, Xian-Ling Mao, Shaojian He, Rui Fang, Dangyang Chen
Date: 2022-08-27 00:00:00
Tasks: Contrastive Learning, Relation, Sequential Recommendation

Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted to alleviating the data sparsity problem (i.e., limited supervised signals for training), which take account of contrastive learning to incorporate self-supervised signals into SR. Despite their achievements, it is far from enough to learn informative user/item embeddings due to the inadequacy modeling of complex collaborative information and co-action information, such as user-item relation, user-user relation, and item-item relation. In this paper, we study the problem of SR and propose a novel multi-level contrastive learning framework for sequential recommendation, named MCLSR. Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i.e., interest- and feature-level). Specifically, the interest-level contrastive mechanism jointly learns the collaborative information with the sequential transition patterns, and the feature-level contrastive mechanism re-observes the relation between users and items via capturing the co-action information (i.e., co-occurrence). Extensive experiments on four real-world datasets show that the proposed MCLSR outperforms the state-of-the-art methods consistently.
Co-attention network with label embedding for text classification
Authors: Minqian Liu, Lizhao Liu, Junyi Cao, Qing Du
Date: 2021-11-04 00:00:00
Proceeding: Neurocomputing 2021 11
Tasks: Classification, Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

Most existing methods for text classification focus on extracting a highly discriminative text representation, which, however, is typically computationally inefficient. To alleviate this issue, label embedding frameworks are proposed to adopt the label-to-text attention that directly uses label information to construct the text representation for more efficient text classification. Although these label embedding methods have achieved promising results, there is still much space for exploring how to se the label information more effectively. In this paper, we seek to exploit the label information by further constructing the text-attended label representation with text-to-label attention. To this end, we propose a Coattention Network with Label Embedding (CNLE) that jointly encodes the text and labels into their mutually attended representations. In this way, the model is able to attend to the relevant parts of both. Experiments show that our approach achieves competitive results compared with previous state-ofthe-art methods on 7 multi-class classification benchmarks and 2 multi-label classification benchmarks.
An Accurate and Hardware-Efficient Dual Spike Detector for Implantable Neural Interfaces
Authors: Xiaorang Guo, MohammadAli Shaeri, Mahsa Shoaran
Date: 2022-08-29 00:00:00

Spike detection plays a central role in neural data processing and brain-machine interfaces (BMIs). A challenge for future-generation implantable BMIs is to build a spike detector that features both low hardware cost and high performance. In this work, we propose a novel hardware-efficient and high-performance spike detector for implantable BMIs. The proposed design is based on a dual-detector architecture with adaptive threshold estimation. The dual-detector comprises two separate TEO-based detectors that distinguish a spike occurrence based on its discriminating features in both high and low noise scenarios. We evaluated the proposed spike detection algorithm on the Wave Clus dataset. It achieved an average detection accuracy of 98.9%, and over 95% in high-noise scenarios, ensuring the reliability of our method. When realized in hardware with a sampling rate of 16kHz and 7-bits resolution, the detection accuracy is 97.4%. Designed in 65nm TSMC process, a 256-channel detector based on this architecture occupies only 682$\mu m^2$ /Channel and consumes 0.07$\mu$W/Channel, improving over the state-of-the-art spike detectors by 39.7% in power consumption and 78.8% in area, while maintaining a high accuracy.
Uniformly Sampled Polar and Cylindrical Grid Approach for 2D, 3D Image Reconstruction using Algebraic Algorithm
Authors: Sudhir Kumar Chaudhary, Pankaj Wahi, Prabhat Munshi
Date: 2022-08-27 00:00:00
Tasks: Image Reconstruction

Image reconstruction by Algebraic Methods (AM) outperforms the transform methods in situations where the data collection procedure is constrained by time, space, and radiation dose. AM algorithms can also be applied for the cases where these constraints are not present but their high computational and storage requirement prohibit their actual breakthrough in such cases. In the present work, we propose a novel Uniformly Sampled Polar/Cylindrical Grid (USPG/USCG) discretization scheme to reduce the computational and storage burden of algebraic methods. The symmetries of USPG/USCG are utilized to speed up the calculations of the projection coefficients. In addition, we also offer an efficient approach for USPG to Cartesian Grid (CG) transformation for the visualization. The Multiplicative Algebraic Reconstruction Technique (MART) has been used to determine the field function of the suggested grids. Experimental projections data of a frog and Cu-Lump have been exercised to validate the proposed approach. A variety of image quality measures have been evaluated to check the accuracy of the reconstruction. Results indicate that the current strategies speed up (when compared to CG-based algorithms) the reconstruction process by a factor of 2.5 and reduce the memory requirement by the factor p, the number of projections used in the reconstruction.
Terahertz Communications Can Work in Rain and Snow: Impact of Adverse Weather Conditions on Channels at 140 GHz
Authors: Priyangshu Sen, Jacob Hall, Michele Polese, Vitaly Petrov, Duschia Bodet, Francesco Restuccia, Tommaso Melodia, Josep M. Jornet
Date: 2022-08-29 00:00:00

Next-generation wireless networks will leverage the spectrum above 100 GHz to enable ultra-high data rate communications over multi-GHz-wide bandwidths. The propagation environment at such high frequencies, however, introduces challenges throughout the whole protocol stack design, from physical layer signal processing to application design. Therefore, it is fundamental to develop a holistic understanding of the channel propagation and fading characteristics over realistic deployment scenarios and ultra-wide bands. In this paper, we conduct an extensive measurement campaign to evaluate the impact of weather conditions on a wireless link in the 130-150 GHz band through a channel sounding campaign with clear weather, rain, and snow in a typical urban backhaul scenario. We present a novel channel sounder design that captures signals with -82 dBm sensitivity and 20 GHz of bandwidth. We analyze link budget, capacity, as well as channel parameters such as the delay spread and the K-factor. Our experimental results indicate that in the considered context the adverse weather does not interrupt the link, but introduces some additional constraints (e.g., high delay spread and increase in path loss in snow conditions) that need to be accounted for in the design of reliable Sixth Generation (6G) communication links above 100 GHz.
Data-Driven Distributed Voltage Control for Microgrids: A Koopman-based Approach
Authors: Vladimir Toroa, Duvan Tellez-Castro, Eduardo Mojica-Nava, Naly Rakoto-Ravalontsalama
Date: 2022-08-29 00:00:00
Tasks: Model Predictive Control

This paper presents a distributed data-driven control to regulate the voltage in an alternate current microgrid (MG). Following the hierarchical control frame for MGs, a secondary control for voltage is designed with a data-driven strategy using the Koopman operator. The Koopman operator approach represents the nonlinear behavior of voltage as a linear problem in the space of observables or lifted space. The representation in the lifted space is used together with linear consensus to design a model predictive control (MPC). The complete algorithm is proved in an MG model including changes in load, transmission lines, and the communication graph. The data-driven model regulates voltage using a distributed approach based only on local measurements, and includes reactive power constraints and control cost minimization.
Distributed Dynamic Platoons Control and Junction Crossing Optimization for Mixed Traffic Flow in Smart Cities- Part II. Stability, Optimization, and Performance Analysis
Authors: Bohui Wang, Rong Su
Date: 2022-08-29 00:00:00
Tasks: Management

In part II, we present a fully distributed nonlinear variable time headway space strategy to ensure the subsequent safe cruising and junction crossing, where the cooperative perception of multiple neighbors stimuli and the cooperative tracking of the follower connected automated vehicles(CAVs) to the leader CAV are developed, which will result in a heterogeneous traffic flow dynamic. Once the proper length of the platoon determined by the ADSCAS characterized in part I is formed, we propose a cooperative observer design to estimate the leader CAV's acceleration adjustment which is affected by the unknown traffic lights. We shall show that the distributed and resilient nonlinear platoons control and junction crossing problem will be solved by a robust cooperative trajectory tracking optimization algorithm to ensure the fast formation and split of the platoons and safe junction cruising within the finite time horizons, taking into account the social driving behaviors(SDBs) of the surrounding vehicles(SVs), the dynamics of the follower CAVs, and an upcoming traffic signal schedule while minimizing the overall platoons fuel consumption. Performance analysis and case studies are presented to illustrate the effectiveness of the proposed approaches for multiple platoon dynamic management, which also show that the cooperation between CAVs and human-driven vehicles(HDVs) can further smooth out the driving trajectory, reduce the fuel consumption, and enhance the safety of the mixed traffic flow
Minimum Input Design for Direct Data-driven Property Identification of Unknown Linear Systems
Authors: Shubo Kang, Keyou You
Date: 2022-08-29 00:00:00

In a direct data-driven approach, this paper studies the {\em property identification(ID)} problem to analyze whether an unknown linear system has a property of interest, e.g., stabilizability and structural properties. In sharp contrast to the model-based analysis, we approach it by directly using the input and state feedback data of the unknown system. Via a new concept of sufficient richness of input sectional data, we first establish the necessary and sufficient condition for the minimum input design to excite the system for property ID. Specifically, the input sectional data is sufficiently rich for property ID {\em if and only if} it spans a linear subspace that contains a property dependent minimum linear subspace, any basis of which can also be easily used to form the minimum excitation input. Interestingly, we show that many structural properties can be identified with the minimum input that is however unable to identify the explicit system model. Overall, our results rigorously quantify the advantages of the direct data-driven analysis over the model-based analysis for linear systems in terms of data efficiency.
Distributed Dynamic Platoons Control and Junction Crossing Optimization for Mixed Traffic Flows in Smart Cities- Part I. Fundamentals, Theoretical and Automatic Decision Framework
Authors: Bohui Wang, Rong Su
Date: 2022-08-29 00:00:00
Tasks: Management, Trajectory Planning

This article studies the problems of distributed dynamic platoons control and smart junction crossing optimization for a mixed traffic flow with connected automated vehicles(CAVs) and social human-driven vehicles(HDVs) in a smart city. The goal of this two-part article is to provide an automatic decision framework to ensure the safe and efficient cruising and crossing junctions with multiple dynamic and resilient platoons CAVs, while against the social driving behaviors(SDBs) of the HDVs considered as surrounding vehicles(SVs) and unknown traffic lights connected by Cellular-Vehicle-to-X (C-V2X) infrastructure. We shall show that despite the nonlinearity, non-smoothness, and uncertainty of mixed traffic flows in smart cities, the solutions of safe, efficient, and fuel economic platoon CAVs control for the above problems can be solved by our proposed automatic decision and smart crossing assistant system(ADSCAS) that includes four decision stages and six cruising states characterized within this article. More precisely, in part I, we provide a dynamic platoon management strategy to determine the platoon size with respect to the SDBs of the preceding HDVs and upcoming traffic lights, where the dynamic platoon size is composed of minimizing safe distances and nonlinear functions of the platoon's cruising velocity. We also present the decision and switching scheme for a finite state machine of platoons in the ADSCAS, the design of the reference trajectory planning, and the solution of the fuel economic optimization problem.
Distributed Observers-based Cooperative Platooning Tracking Control and Intermittent Optimization for Connected Automated Vehicles with Unknown Jerk Dynamics
Authors: Bohui Wang, Rong Su, Yun Lu, Lingyin Huang, Nanbin Zhao, Zhijian Hu
Date: 2022-08-29 00:00:00

The unknown sharp changes of vehicle acceleration rates, also called the unknown jerk dynamics, may significantly affect the driving performance of the leader vehicle in a platoon, resulting in more drastic car-following movements in platooning tracking control, which could cause safety and traffic capacity concerns. To address these issues, in this paper, we investigate cooperative platooning tracking control and intermittent optimization problems for connected automated vehicles (CAVs) with a nonlinear car-following model. We assume that the external inputs of the leader CAV contain unknown but bounded jerk parameters, and the acceleration signals of the leader CAV are known only to a few neighboring follower CAVs in a free-design but directed communication network. To solve these problems, a distributed observer law is developed to provide a reference signal expressed as an estimated unknown jerk dynamic of the leader CAV and implemented by each follower CAV. Then, a novel distributed platooning tracking control protocol is proposed to construct the cooperative tracking controllers under identical inter-vehicle constraints, which can ensure a desired safety distance among the CAVs and allow each follower CAV to track their leader CAV by using only local information interaction. We also present a novel intermittent sampling condition and a robust intermittent optimization design that can ensure optimally scheduled feedback gains for the cooperative platooning tracking controllers to minimize the control cost under nonidentical inter-vehicle constraints and unknown jerk dynamics. Simulation case studies are carried out to illustrate the effectiveness of the proposed approaches
An Adaptive Pilot Model with Reaction Time-Delay
Authors: Abdullah Habboush, Yildiray Yildiz
Date: 2022-08-28 00:00:00
Tasks: model

Practical adaptive control implementations where human pilots coexist in the loop are still uncommon, despite their success in handling uncertain dynamical systems. This is owing to their special nonlinear characteristics which lead to unfavorable interactions between pilots and adaptive controllers. To pave the way for the implementation of adaptive controllers in piloted applications, we propose an adaptive human pilot model that takes into account the time delay in the pilot's response while operating on an adaptive control system. The model can be utilized in the evaluation of adaptive controllers through the simulation environment and guide in their design.
Structural Adaptivity of Directed Networks
Authors: Lulu Pan, Haibin Shao, Mehran Mesbahi, Dewei Li, Yugeng Xi
Date: 2022-08-28 00:00:00

Network structure plays a critical role in functionality and performance of network systems. This paper examines structural adaptivity of diffusively coupled, directed multi-agent networks that are subject to diffusion performance. Inspired by the observation that the link redundancy in a network may degrade its diffusion performance, a distributed data-driven neighbor selection framework is proposed to adaptively adjust the network structure for improving the diffusion performance of exogenous influence over the network. Specifically, each agent is allowed to interact with only a specific subset of neighbors while global reachability from exogenous influence to all agents of the network is maintained. Both continuous-time and discrete-time directed networks are examined. For each of the two cases, we first examine the reachability properties encoded in the eigenvectors of perturbed variants of graph Laplacian or SIA matrix associated with directed networks, respectively. Then, an eigenvector-based rule for neighbor selection is proposed to derive a reduced network, on which the diffusion performance is enhanced. Finally, motivated by the necessity of distributed and data-driven implementation of the neighbor selection rule, quantitative connections between eigenvectors of the perturbed graph Laplacian and SIA matrix and relative rate of change in agent state are established, respectively. These connections immediately enable a data-driven inference of the reduced neighbor set for each agent using only locally accessible data. As an immediate extension, we further discuss the distributed data-driven construction of directed spanning trees of directed networks using the proposed neighbor selection framework. Numerical simulations are provided to demonstrate the theoretical results.
An Unsupervised Learning-based Framework for Effective Representation Extraction of Reactor Accidents
Authors: Chengyuan Li, Meifu Li, Zhifang Qiu
Date: 2022-08-28 00:00:00
Tasks: Diagnostic

With the increasing use of high-precision system analysis programs in nuclear engineering, the number of high-fidelity computational data for accident simulation is exploding. Therefore, an algorithm that can achieve both automatic extraction of low-dimensional features from the data and guarantee the validity of the features is needed to improve the performance and confidence of the accident diagnosis system. This study proposes an autoencoder-based autonomous learning framework, namely Padded Auto-Encoder (PAE), which is able to automatically encode accident monitoring data that has been noise-added and with partially missing data into low-dimensional feature vectors via a Vision Transformer-based encoder, and to decode the feature vectors into noise-free and complete reconstructed monitoring data. Thus, the encoder part of the framework is able to automatically infer valid representations from partially missing and noisy monitoring data that reflect the complete and noise-free original data, and the representation vectors can be used for downstream tasks for accident diagnosis or else. In this paper, LOCA of HPR1000 was used as the study object, and the PAE was trained by an unsupervised method using cases with different break locations and sizes as the dataset. The encoder part of the pre-trained PAE was subsequently used as the feature extractor for the monitoring data, and several basic statistical learning algorithms for predicting the break locations and sizes. The results of the study show that the pre-trained diagnostic model with two stages has a better performance in break location and size diagnostic capability with an improvement of 41.62% and 80.86% in the metrics respectively, compared to the diagnostic model with end-to-end model structure.