pwc-archive/papers-with-abstracts huggingface dataset:
Hybrid Multisource Feature Fusion for the Text Clustering
Authors: Jiaxuan Chen, Shenglin Gui
Date: 2021-08-24 00:00:00
Tasks: Clustering, Text Clustering

The text clustering technique is an unsupervised text mining method which are used to partition a huge amount of text documents into groups. It has been reported that text clustering algorithms are hard to achieve better performance than supervised methods and their clustering performance is highly dependent on the picked text features. Currently, there are many different types of text feature generation algorithms, each of which extracts text features from some specific aspects, such as VSM and distributed word embedding, thus seeking a new way of obtaining features as complete as possible from the corpus is the key to enhance the clustering effects. In this paper, we present a hybrid multisource feature fusion (HMFF) framework comprising three components, feature representation of multimodel, mutual similarity matrices and feature fusion, in which we construct mutual similarity matrices for each feature source and fuse discriminative features from mutual similarity matrices by reducing dimensionality to generate HMFF features, then k-means clustering algorithm could be configured to partition input samples into groups. The experimental tests show our HMFF framework outperforms other recently published algorithms on 7 of 11 public benchmark datasets and has the leading performance on the rest 4 benchmark datasets as well. At last, we compare HMFF framework with those competitors on a COVID-19 dataset from the wild with the unknown cluster count, which shows the clusters generated by HMFF framework partition those similar samples much closer.
Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO
Authors: Xuan Wu, Jizong Han, Di Wang, Pengyue Gao, Quanlong Cui, Liang Chen, Yanchun Liang, Han Huang, Heow Pueh Lee, Chunyan Miao, You Zhou, Chunguo Wu
Date: 2021-08-25 00:00:00
Tasks: Diversity, Single Particle Analysis

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems.
Entropic Gromov-Wasserstein between Gaussian Distributions
Authors: Khang Le, Dung Le, Huy Nguyen, Dat Do, Tung Pham, Nhat Ho
Date: 2021-08-24 00:00:00
Tasks: Form

We study the entropic Gromov-Wasserstein and its unbalanced version between (unbalanced) Gaussian distributions with different dimensions. When the metric is the inner product, which we refer to as inner product Gromov-Wasserstein (IGW), we demonstrate that the optimal transportation plans of entropic IGW and its unbalanced variant are (unbalanced) Gaussian distributions. Via an application of von Neumann's trace inequality, we obtain closed-form expressions for the entropic IGW between these Gaussian distributions. Finally, we consider an entropic inner product Gromov-Wasserstein barycenter of multiple Gaussian distributions. We prove that the barycenter is a Gaussian distribution when the entropic regularization parameter is small. We further derive a closed-form expression for the covariance matrix of the barycenter.
CancerBERT: a BERT model for Extracting Breast Cancer Phenotypes from Electronic Health Records
Authors: Sicheng Zhou, LiWei Wang, Nan Wang, Hongfang Liu, Rui Zhang
Date: 2021-08-25 00:00:00
Tasks: NER

Accurate extraction of breast cancer patients' phenotypes is important for clinical decision support and clinical research. Current models do not take full advantage of cancer domain-specific corpus, whether pre-training Bidirectional Encoder Representations from Transformer model on cancer-specific corpus could improve the performances of extracting breast cancer phenotypes from texts data remains to be explored. The objective of this study is to develop and evaluate the CancerBERT model for extracting breast cancer phenotypes from clinical texts in electronic health records. This data used in the study included 21,291 breast cancer patients diagnosed from 2010 to 2020, patients' clinical notes and pathology reports were collected from the University of Minnesota Clinical Data Repository (UMN). Results: About 3 million clinical notes and pathology reports in electronic health records for 21,291 breast cancer patients were collected to train the CancerBERT model. 200 pathology reports and 50 clinical notes of breast cancer patients that contain 9,685 sentences and 221,356 tokens were manually annotated by two annotators. 20% of the annotated data was used as a test set. Our CancerBERT model achieved the best performance with macro F1 scores equal to 0.876 (95% CI, 0.896-0.902) for exact match and 0.904 (95% CI, 0.896-0.902) for the lenient match. The NER models we developed would facilitate the automated information extraction from clinical texts to further help clinical decision support. Conclusions and Relevance: In this study, we focused on the breast cancer-related concepts extraction from EHR data and obtained a comprehensive annotated dataset that contains 7 types of breast cancer-related concepts. The CancerBERT model with customized vocabulary could significantly improve the performance for extracting breast cancer phenotypes from clinical texts.
AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer
Authors: Jingwei Zhao, Gus Xia
Date: 2021-08-25 00:00:00
Tasks: Music Generation, Style Transfer

Accompaniment arrangement is a difficult music generation task involving intertwined constraints of melody, harmony, texture, and music structure. Existing models are not yet able to capture all these constraints effectively, especially for long-term music generation. To address this problem, we propose AccoMontage, an accompaniment arrangement system for whole pieces of music through unifying phrase selection and neural style transfer. We focus on generating piano accompaniments for folk/pop songs based on a lead sheet (i.e., melody with chord progression). Specifically, AccoMontage first retrieves phrase montages from a database while recombining them structurally using dynamic programming. Second, chords of the retrieved phrases are manipulated to match the lead sheet via style transfer. Lastly, the system offers controls over the generation process. In contrast to pure learning-based approaches, AccoMontage introduces a novel hybrid pathway, in which rule-based optimization and deep learning are both leveraged to complement each other for high-quality generation. Experiments show that our model generates well-structured accompaniment with delicate texture, significantly outperforming the baselines.
Automated Environmental Monitoring Intelligent System Based on Compact Autonomous Robots for The Sevastopol Bay
Authors: Y. E. Shishkin, Aleksandr N. Grekov
Date: 2021-08-25 00:00:00

This paper proposes an intelligent system concept for automatic monitoring of aquatic en-vironment main parameters in order to detect their anomalies and assess quantitative and qualitative indicators, including the determination of the field under study spatial and tem-poral characteristics. The system is built on the basis of a small autonomous surface robots network. A conceptual model of a monitoring system for the implementation of environ-mental parameters automated integrated monitoring throughout the entire observation field is proposed. A software model has been developed and simulation experiments have been conducted to calculate the main indicators and assess environment spatial and temporal variability. According to the results of the simulation, control maps of the stations optimal density were formed. The proposed approach to solving the problem of monitoring the aquatic environment in comparison with the traditional has such advantages as scalability, flexibility, speed of deployment and clotting, self-organization, the ability to create a wide field of view by changing the number of robots.
INVIGORATE: Interactive Visual Grounding and Grasping in Clutter
Authors: Hanbo Zhang, Yunfan Lu, Cunjun Yu, David Hsu, Xuguang Lan, Nanning Zheng
Date: 2021-08-25 00:00:00
Tasks: Blocking, Object, object-detection, Object Detection, Question Generation, Question-Generation, Visual Grounding

This paper presents INVIGORATE, a robot system that interacts with human through natural language and grasps a specified object in clutter. The objects may occlude, obstruct, or even stack on top of one another. INVIGORATE embodies several challenges: (i) infer the target object among other occluding objects, from input language expressions and RGB images, (ii) infer object blocking relationships (OBRs) from the images, and (iii) synthesize a multi-step plan to ask questions that disambiguate the target object and to grasp it successfully. We train separate neural networks for object detection, for visual grounding, for question generation, and for OBR detection and grasping. They allow for unrestricted object categories and language expressions, subject to the training datasets. However, errors in visual perception and ambiguity in human languages are inevitable and negatively impact the robot's performance. To overcome these uncertainties, we build a partially observable Markov decision process (POMDP) that integrates the learned neural network modules. Through approximate POMDP planning, the robot tracks the history of observations and asks disambiguation questions in order to achieve a near-optimal sequence of actions that identify and grasp the target object. INVIGORATE combines the benefits of model-based POMDP planning and data-driven deep learning. Preliminary experiments with INVIGORATE on a Fetch robot show significant benefits of this integrated approach to object grasping in clutter with natural language interactions. A demonstration video is available at https://youtu.be/zYakh80SGcU.
Temporal envelope and fine structure cues for dysarthric speech detection using CNNs
Authors: Ina Kodrasi
Date: 2021-08-25 00:00:00

Deep learning-based techniques for automatic dysarthric speech detection have recently attracted interest in the research community. State-of-the-art techniques typically learn neurotypical and dysarthric discriminative representations by processing time-frequency input representations such as the magnitude spectrum of the short-time Fourier transform (STFT). Although these techniques are expected to leverage perceptual dysarthric cues, representations such as the magnitude spectrum of the STFT do not necessarily convey perceptual aspects of complex sounds. Inspired by the temporal processing mechanisms of the human auditory system, in this paper we factor signals into the product of a slowly varying envelope and a rapidly varying fine structure. Separately exploiting the different perceptual cues present in the envelope (i.e., phonetic information, stress, and voicing) and fine structure (i.e., pitch, vowel quality, and breathiness), two discriminative representations are learned through a convolutional neural network and used for automatic dysarthric speech detection. Experimental results show that processing both the envelope and fine structure representations yields a considerably better dysarthric speech detection performance than processing only the envelope, fine structure, or magnitude spectrum of the STFT representation.
Evaluating Efficacy of Indoor Non-Pharmaceutical Interventions against COVID-19 Outbreaks with a Coupled Spatial-SIR Agent-Based Simulation Framework
Authors: Chathika Gunaratne, Rene Reyes, Erik Hemberg, Una-May O'Reilly
Date: 2021-08-25 00:00:00

Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important for organizations to evaluate the efficacy of interventions aiming at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with a SIR epidemiological model to assess the relative risks of different intervention strategies. By applying our model on MIT's STATA building, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that a combination of lowering the number of individuals admitted below the current recommendations and advising individuals to reduce the frequency at which they leave their workstations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the SIR model, we compare relative infection risk of four respiratory diseases, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.
A new state estimation approach-Adaptive Fading Cubature Kalman filter
Authors: Mundla Narasimhappa
Date: 2021-08-25 00:00:00
Tasks: State Estimation

This paper presents a novel adaptive fading cubature Kalman filter (AFCKF) based on double transitive factors. The developed adaptive algorithm is explained in two stages; stage (i) a single transitive factor is used to update the predicted state error covariance, ${\bf \hat P_{k}}^{-}$ based on innovation or residual vector, whereas, in stage (ii), the measurement noise covariance matrix, ${\bf \hat R_{k}^{*}}$ is scaled by another transitive factor. Furthermore, showing the proof concept for estimation of the process noise, ${\bf \hat Q_{k}^{*}}$ and measurement noise covariance matrices by combining the innovation and residual vector in the AFCKF algorithm. It can provide reliable state estimation in the presence of unknown noise statistics. Bench-marking target tracking example is considered to show the performance improvement of the developed algorithms. As compared with existing adaptive approaches, the proposed fading algorithm can provide better estimation results.
Data-driven system analysis of nonlinear systems using polynomial approximation
Authors: Tim Martin, Frank Allgöwer
Date: 2021-08-25 00:00:00

In the context of data-driven control of nonlinear systems, many approaches lack of rigorous guarantees, call for nonconvex optimization, or require knowledge of a function basis containing the system dynamics. To tackle these drawbacks, we establish a polynomial representation of nonlinear functions based on a polynomial sector by Taylor's theorem and a set-membership for Taylor polynomials. The latter is obtained from finite noisy samples. By incorporating the measurement noise, the error of polynomial approximation, and potentially given prior knowledge on the structure of the system dynamics, we achieve computationally tractable conditions by sum of squares relaxation to verify dissipativity and incremental dissipativity of nonlinear dynamical systems with rigorous guarantees. The framework is extended by combining multiple Taylor polynomial approximations which yields a less conservative piecewise polynomial system representation. The proposed approach is applied for numerical and experimental examples. There it is compared to a least-squares-error model including knowledge from first principle.
A Survey on Radio Frequency Identification as a Scalable Technology to Face Pandemics
Authors: Giulio M. Bianco, Cecilia Occhiuzzi, Nicoletta Panunzio, Gaetano Marrocco
Date: 2021-08-25 00:00:00
Tasks: Edge-computing, Management, Survey

The COVID-19 pandemic drastically changed our way of living. To minimize life losses, multi-level strategies requiring collective efforts were adopted while waiting for the vaccines' rollout. The management of such complex processes has taken benefit from the rising framework of the Internet of Things (IoT), and particularly the Radiofrequency Identification (RFID) since it is probably the most suitable approach to both the micro (user) and the macro (processes) scale. Hence, a single infrastructure can support both the logistic and monitoring issues related to the war against a pandemic. Based on the COVID-19 experience, this paper is a survey on how state-of-the-art RFID systems can be employed in facing future pandemic outbreaks. The three pillars of the contrast of the pandemic are addressed: 1) use of Personal Protective Equipment (PPE), 2) access control and social distancing, and 3) early detection of symptoms. For each class, the envisaged RFID devices and procedures are discussed based on the available technology and the current worldwide research. This survey that RFID could generate an extraordinary amount of data so that complementary paradigms of Edge Computing and Artificial intelligence can be tightly integrated to extract profiles and identify anomalous events in compliance with privacy and security.
Hierarchical Optimization-Based Model Predictive Control for a Class of Discrete Fuzzy Large-Scale Systems Considering Time-Varying Delays and Disturbances
Authors: Mohammad Sarbaz, Iman Zamani, Mohammad Manthouri, Asier Ibeas
Date: 2021-08-25 00:00:00
Tasks: Model Predictive Control

Abstract-In this manuscript, model predictive control for class of discrete fuzzy large-scale systems subjected to bounded time-varying delay and disturbances is studied. The considered method is Razumikhin for time-varying delay large-scale systems, in which it includes a Lyapunov function associated with the original non-augmented state space of system dynamics in comparison with the Krasovskii method. As a rule, the Razumikhin method has a perfect potential to avoid the inherent complexity of the Krasovskii method especially in the presence of large delays and disturbances. The considered large-scale system in this manuscript is decomposed into several subsystems, each of which is represented by a fuzzy Takagi-Sugeno (T-S) model and the interconnection between any two subsystems is considered. Because the main section of the model predictive control is optimization, the hierarchical scheme is performed for the optimization problem. Furthermore, persistent disturbances are considered that robust positive invariance and input-to-state stability under such circumstances are studied. The linear matrix inequalities (LMIs) method is performed for our computations. So the closed-loop large-scale system is asymptotically stable. Ultimately, by two examples, the effectiveness of the proposed method is illustrated, and a comparison with other papers is made by remarks.
Multi-lane Unsignalized Intersection Cooperation with Flexible Lane Direction based on Multi-vehicle Formation Control
Authors: Mengchi Cai, Qing Xu, Chaoyi Chen, Jiawei Wang, Keqiang Li, Jianqiang Wang, Xiangbin Wu
Date: 2021-08-25 00:00:00

Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency. Most of the existing research on unsignalized intersection cooperation considers fixed lane direction, where only specific turning behavior of vehicles is allowed on each lane. Given that traffic volume and the proportion of vehicles with different turning expectation may change with time, fixed lane direction may lead to inefficiency at intersections. This paper proposes a multi-lane unsignalized intersection cooperation method that considers flexible lane direction. The two-dimensional distribution of vehicles is calculated and vehicles that are not in conflict are scheduled to pass the intersection simultaneously. The formation reconfiguration method is utilized to achieve collision-free longitudinal and lateral position adjustment of vehicles. Simulations are conducted at different input traffic volumes and turning proportion of incoming vehicles, and the results indicate that our method outperformances the fixed-lane-direction unsignalized cooperation method and the signalized method.
Robust AC Transmission Expansion Plans from A Novel Dual Based Bi-level Approach
Authors: P. Naga Yasasvi, Abheejeet Mohapatra, Suresh Chandra Srivastava
Date: 2021-08-25 00:00:00

The rapid integration of Renewable Energy Sources (RESs) strengthens the need for a power network that can robustly handle the system's uncertain scenarios. Thus, this paper proposes the first nonlinear novel dual based bi-level approach for robust AC Transmission Expansion Planning (TEP) plans with uncertainties in RES generations and loads. It utilizes a convex relaxation and is solved by Benders Decomposition (BD), where the master determines the robust AC TEP plan. The novel dual slave model for the second level of BD circumvents the issues in using the conventional conic dual theory and aids in the worst-case realization of uncertainties using additional novel constraints. The novel dual slave is solved using Interior Point Method (IPM), as it is not a mixed-integer problem. The proposed work also includes additional linear constraints to reduce the BD's slow convergence and direct the master towards optimality. The robustness of the AC TEP plans is verified by Monte-Carlo Simulation (MCS) of the actual nonlinear and non-convex AC Optimal Power Flow (OPF). The effect of the budget of uncertainty on the AC TEP plans is also investigated. A comparison with the results of a previous work reveals the superiority of the proposed work.
Relaxed bearing rigidity and bearing formation control under persistence of excitation
Authors: Zhiqi Tang, Rita Cunha, Tarek Hamel, Carlos Silvestre
Date: 2021-03-10 00:00:00

This paper addresses the problem of time-varying bearing formation control in $d$ $(d\ge 2)$-dimensional Euclidean space by exploring Persistence of Excitation (PE) of the desired bearing reference. A general concept of Bearing Persistently Exciting (BPE) formation defined in $d$-dimensional space is here fully developed. By providing a desired formation that is BPE, distributed control laws for multi-agent systems under both single- and double-integrator dynamics are proposed using bearing measurements (along with velocity measurements when the agents are described by double-integrator dynamics), which guarantee uniform exponential stabilization of the desired formation in terms of shape and scale. A key contribution of this work is to show that the classical bearing rigidity condition on the graph topology, required for achieving the stabilization of a formation up to a scaling factor, is relaxed and extended in a natural manner by exploring PE conditions imposed either on a specific set of desired bearing vectors or on the whole desired formation. Simulation results are provided to illustrate the performance of the proposed control method.