pwc-archive/papers-with-abstracts huggingface dataset:
Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning
Authors: Liqiang Lin, Qingqing Jia, Zheng Cheng, Yanyan Jiang, Yanwen Guo, Jing Ma
Date: 2020-06-30 00:00:00
Tasks: Drug Discovery, Formation Energy, Multi-Task Learning

The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science. However, predicting electronic structure properties like frontier molecular orbital HOMO and LUMO energy levels and their HOMO-LUMO gaps from the small-sized molecule data to larger molecules remains a challenge. Here we develop a multi-level attention strategy that enables chemical interpretable insights to be fused into multi-task learning of up to 110,000 records of data in QM9 for random split evaluation. The good transferability for predicting larger molecules outside the training set is demonstrated in both QM9 and Alchemy datasets. The efficient and accurate prediction of 12 properties including dipole moment, HOMO, and Gibbs free energy within chemical accuracy is achieved by using our specifically designed interpretable multi-level attention neural network, named as DeepMoleNet. Remarkably, the present multi-task deep learning model adopts the atom-centered symmetry functions (ACSFs) descriptor as one of the prediction targets, rather than using ACSFs as input in the conventional way. The proposed multi-level attention neural network is applicable to high-throughput screening of numerous chemical species to accelerate rational designs of drug, material, and chemical reactions.
Air Quality Prediction Using Improved PSO-BP Neural Network
Authors: Yuan Huang, YUXING XIANG, RUIXIAO ZHAO, AND ZHE CHEN
Date: 2020-05-28 00:00:00

Predicting urban air quality is a significant aspect of preventing urban air pollution and improving the living environment of urban residents. The air quality index (AQI) is a dimensionless tool for quantitatively describing air quality. In this paper, a method for optimizing back propagation (BP) neural network based on an improved particle swarm optimization (PSO) algorithm is proposed to predict AQI. The improved PSO algorithm optimizes the variation strategy of the inertia weight as well as the learning factor, guaranteeing its global search ability during the early stage and later enabling its fast convergence to the optimal solution. We introduce an adaptive mutation algorithm during the search process to avoid the particles from falling into the local optimum. Through an analysis and comparison of the experimental results, BP neural network optimized using the improved PSO algorithm achieves a more accurate prediction of AQI.
Cross-lingual Annotation Projection in Legal Texts
Authors: Andrea Galassi, Kasper Drazewski, Marco Lippi, Paolo Torroni
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Cross-Lingual Transfer, Dynamic Time Warping, Sentence, text-classification, Text Classification, Translation, Word Embeddings

We study annotation projection in text classification problems where source documents are published in multiple languages and may not be an exact translation of one another. In particular, we focus on the detection of unfair clauses in privacy policies and terms of service. We present the first English-German parallel asymmetric corpus for the task at hand. We study and compare several language-agnostic sentence-level projection methods. Our results indicate that a combination of word embeddings and dynamic time warping performs best.
Visual relationship detection with deep structural ranking
Authors: Kongming Liang, Yuhong Guo, Hong Chang, Xilin Chen
Date: 2018-04-27 00:00:00
Tasks: Relationship Detection, Visual Relationship Detection

Visual relationship detection aims to describe the interactions between pairs of objects. Different from individual object learning tasks, the number of possible relationships is much larger, which makes it hard to explore only based on the visual appearance of objects. In addition, due to the limited human effort, the annotations for visual relationships are usually incomplete which increases the difficulty of model training and evaluation. In this paper, we propose a novel framework, called Deep Structural Ranking, for visual relationship detection. To complement the representation ability of visual appearance, we integrate multiple cues for predicting the relationships contained in an input image. Moreover, we design a new ranking objective function by enforcing the annotated relationships to have higher relevance scores. Unlike previous works, our proposed method can both facilitate the co-occurrence of relationships and mitigate the incompleteness problem. Experimental results show that our proposed method outperforms the state-of-the-art on the two widely used datasets. We also demonstrate its superiority in detecting zero-shot relationships.
Blind Demixing of Diffused Graph Signals
Authors: Fernando J. Iglesias Garcia, Santiago Segarra, Antonio G. Marques
Date: 2020-12-24 00:00:00
Tasks: blind source separation

Using graphs to model irregular information domains is an effective approach to deal with some of the intricacies of contemporary (network) data. A key aspect is how the data, represented as graph signals, depend on the topology of the graph. Widely-used approaches assume that the observed signals can be viewed as outputs of graph filters (i.e., polynomials of a matrix representation of the graph) whose inputs have a particular structure. Diffused graph signals, which correspond to an originally sparse (node-localized) signal percolated through the graph via filtering, fall into this class. In that context, this paper deals with the problem of jointly identifying graph filters and separating their (sparse) input signals from a mixture of diffused graph signals, thus generalizing to the graph signal processing framework the classical blind demixing (blind source separation) of temporal and spatial signals. We first consider the scenario where the supporting graphs are different across the signals, providing a theorem for demixing feasibility along with probabilistic bounds on successful recovery. Additionally, an analysis of the degenerate problem of demixing with a single graph is also presented. Numerical experiments with synthetic and real-world graphs empirically illustrating the main theoretical findings close the paper.
Ensemble-CVDNet: A Deep Learning based End-to-End Classification Framework for COVID-19 Detection using Ensembles of Networks
Authors: Coşku Öksüz, Oğuzhan Urhan, Mehmet Kemal Güllü
Date: 2020-12-09 00:00:00
Tasks: Diagnostic, GPU

The new type of coronavirus disease (COVID-19), which started in Wuhan, China in December 2019, continues to spread rapidly affecting the whole world. It is essential to have a highly sensitive diagnostic screening tool to detect the disease as early as possible. Currently, chest CT imaging is preferred as the primary screening tool for evaluating the COVID-19 pneumonia by radiological imaging. However, CT imaging requires larger radiation doses, longer exposure time, higher cost, and may suffer from patient movements. X-Ray imaging is a fast, cheap, more patient-friendly and available in almost every healthcare facility. Therefore, we have focused on X-Ray images and developed an end-to-end deep learning model, i.e. Ensemble-CVDNet, to distinguish COVID-19 pneumonia from non-COVID pneumonia and healthy cases in this work. The proposed model is based on a combination of three lightweight pre-trained models SqueezeNet, ShuffleNet, and EfficientNet-B0 at different depths, and combines feature maps in different abstraction levels. In the proposed end to-end model, networks are used as feature extractors in parallel after fine-tuning, and some additional layers are used at the top of them. The proposed model is evaluated in the COVID-19 Radiography Database, a public data set consisting of 219 COVID-19, 1341 Healthy, and 1345 Viral Pneumonia chest X-Ray images. Experimental results show that our lightweight Ensemble-CVDNet model provides 98.30% accuracy, 97.78% sensitivity, and 97.61% F1 score using only 5.62M parameters. Moreover, it takes about 10ms to process and predict an X-Ray image using the proposed method using a mid level GPU. We believe that the method proposed in this study can be a helpful diagnostic screening tool for radiologists in the early diagnosis of the disease.
Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications
Authors: Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Liang Xiao, Qingqing Wu
Date: 2020-02-27 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its surface reflecting elements to guarantee secure communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated given the different quality of service (QoS) requirements and time-varying channel condition. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning-based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.
Nonequilibrium thermodynamics of input-driven networks
Authors: Kevin S. Chen
Date: 2020-12-24 00:00:00

Neural dynamics of energy-based models are governed by energy minimization and the patterns stored in the network are retrieved when the system reaches equilibrium. However, when the system is driven by time-varying external input, the nonequilibrium process of such physical system has not been well characterized. Here, we study attractor neural networks, specifically the Hopfield network, driven by time-varying external input and measure thermodynamic quantities along trajectories between two collective states. The overlap between distribution of the forward and reversal work along the nonequilibrium trajectories agrees with the equilibrium free energy difference between two states, following the prediction of Crooks fluctuation theorem. We study conditions with different stimulation protocol and neural network constraints. We further discuss how biologically plausible synaptic connections and information processing may play a role in this nonequilibrium framework. These results demonstrate how nonequilibrium thermodynamics can be relevant for neural computation and connect to recent systems neuroscience studies with closed-loop dynamic perturbations.
Learning-based hierarchical control of water reservoir systems
Authors: Pauline Kergus, Simone Formentin, Matteo Giuliani, Andrea Castelletti
Date: 2020-12-24 00:00:00

The optimal control of a water reservoir systems represents a challenging problem, due to uncertain hydrologic inputs and the need to adapt to changing environment and varying control objectives. In this work, we propose a real-time learning-based control strategy based on a hierarchical predictive control architecture. Two control loops are implemented: the inner loop is aimed to make the overall dynamics similar to an assigned linear through data-driven control design, then the outer economic model-predictive controller compensates for model mismatches, enforces suitable constraints, and boosts the tracking performance. The effectiveness of the proposed approach as compared to traditional dynamic programming strategies is illustrated on an accurate simulator of the Hoa Binh reservoir in Vietnam. Results show that the proposed approach performs better than the one based on stochastic dynamic programming.
LTE Cell Load Estimation Based on DCI Message Decoding
Authors: Daniil Chirkov, Artur Gaysin, Ivan Ashaev
Date: 2020-12-24 00:00:00

The modern mobile communication systems, the task of analyzing the parameters of quality of service and network information load remains one of the most popular. There are several ways getting this information in LTE networks. For example, by evaluation of test signals or estimation special reference signals in LTE frame. In this paper, it is proposed to obtain information about the cell load by decoding service messages of the DCI control information block. The main idea of this method is that the information load of LTE cell is estimated by decoding DCI messages in the LTE physical control channel and establishing the number of unique identifiers. For decoding the LTE signal, was used MATLAB LTE Toolbox and software-defined radio platform USRP 2920. In the work, the UE detection algorithm was tested on the signal generated using MATLAB software, and evaluated the information load of the network of a real LTE base station.
Resilient Self/Event-Triggered Consensus Based on Ternary Control
Authors: Hiroki Matsume, YuAn Wang, Hideaki Ishii
Date: 2020-12-24 00:00:00

The paper considers the problem of multi-agent consensus in the presence of adversarial agents which may try to prevent and introduce undesired influence on the coordination among the regular agents. To our setting, we extend the so-called mean subsequence reduced algorithms with the aim to reduce the amount of communication via two measures: The agents exchange information in the form of ternary data at each transmission and moreover keep the frequency of data exchange low by employing self- and event-triggered communication. We will observe that in hostile environments with adversaries, the self-triggered approach can bring certain advantages over the event-triggered counterpart.
Distributed Fusion Estimation for Stochastic Uncertain Systems with Network-Induced Complexity and Multiple Noise
Authors: Li Liu, Wenju Zhou, Minrui Fei, Zhile Yang, Hongyong Yang, Huiyu Zhou
Date: 2020-12-24 00:00:00

This paper investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties. First, a novel signal selection method based on event-trigger is developed to handle network-induced packet dropouts as well as packet disorders resulting from random transmission delays, where the ${H_2}/{H_\infty }$ performance of the system is analyzed in different noise environments. In addition, a linear delay compensation strategy is further employed for solving the complexity network-induced problem, which may deteriorate the system performance. Moreover, a weighted fusion scheme is used to integrate multiple resources through an error cross-covariance matrix. Several case studies validate the proposed algorithm and demonstrate satisfactory system performance in target tracking.
Convergence rates of large-time sensitivities with the Hansen--Scheinkman decomposition
Authors: Hyungbin Park
Date: 2019-12-07 00:00:00

This paper investigates the large-time asymptotic behavior of the sensitivities of cash flows. In quantitative finance, the price of a cash flow is expressed in terms of a pricing operator of a Markov diffusion process. We study the extent to which the pricing operator is affected by small changes of the underlying Markov diffusion. The main idea is a partial differential equation (PDE) representation of the pricing operator by incorporating the Hansen--Scheinkman decomposition method. The sensitivities of the cash flows and their large-time convergence rates can be represented via simple expressions in terms of eigenvalues and eigenfunctions of the pricing operator. Furthermore, compared to the work of Park (Finance Stoch. 4:773-825, 2018), more detailed convergence rates are provided. In addition, we discuss the application of our results to three practical problems: utility maximization, entropic risk measures, and bond prices. Finally, as examples, explicit results for several market models such as the Cox--Ingersoll--Ross (CIR) model, 3/2 model and constant elasticity of variance (CEV) model are presented.
DeepSIM: GPS Spoofing Detection on UAVs using Satellite Imagery Matching
Authors: Nian Xue, Liang Niu, Xianbin Hong, Zhen Li, Larissa Hoffaeller, Christina Pöpper
Date: 2020-12-01 00:00:00

Unmanned Aerial Vehicles (UAVs), better known as drones, have significantly advanced fields such as aerial surveillance, military reconnaissance, cadastral surveying, disaster monitoring, and delivery services. However, UAVs rely on civilian (unauthenticated) GPS for navigation which can be trivially spoofed.

In this paper, we present DeepSIM, a satellite imagery matching approach to detect GPS spoofing attacks against UAVs based on deep learning. We make use of the camera(s) a typical UAV is equipped with, and present a system that compares historical satellite images of its GPS-based position (spaceborne photography) with real-time aerial images from its cameras (airborne imagery). Historical images are taken from, e. g., Google Earth or NASA WorldWind. To detect GPS spoofing attacks, we investigate different deep neural network models that compare the real-time camera images with the historical satellite images. To train and test the models, we have constructed the SatUAV dataset (consisting of 967 image pairs), partially by using real UAVs such as the DJI Phantom 4 Advanced. Real-world experimental results show that our best model has a success rate of about 95% in detecting GPS spoofing attacks within less than 100 milliseconds. Our approach does not require any modification of the existing GPS infrastructures and relies only on public satellite imagery, making it a practical solution for many everyday scenarios.
JSSS: free Japanese speech corpus for summarization and simplification
Date: 2020-10-05 00:00:00
Tasks: Form, Speech Synthesis, text-to-speech, Text to Speech

In this paper, we construct a new Japanese speech corpus for speech-based summarization and simplification, "JSSS" (pronounced "j-triple-s"). Given the success of reading-style speech synthesis from short-form sentences, we aim to design more difficult tasks for delivering information to humans. Our corpus contains voices recorded for two tasks that have a role in providing information under constraints: duration-constrained text-to-speech summarization and speaking-style simplification. It also contains utterances of long-form sentences as an optional task. This paper describes how we designed the corpus, which is available on our project page.
generateData—A 2D data generator
Authors: Nuno Fachada, Agostinho C. Rosa
Date: 2020-05-23 00:00:00

generateData is a MATLAB/Octave function for generating 2D data clusters. Data is created along straight lines, which can be more or less parallel depending on the selected input parameters. The function also allows to fine-tune the generated data with respect to number of clusters, total data points, average cluster separation and several other distributional properties.
Generative Art with Swarm Landscapes
Authors: Diogo de Andrade, Nuno Fachada, Carlos M. Fernandes, Agostinho C. Rosa
Date: 2020-11-12 00:00:00

We present a generative swarm art project that creates 3D animations by running a Particle Swarm Optimization algorithm over synthetic landscapes produced by an objective function. Different kinds of functions are explored, including mathematical expressions, Perlin noise-based terrain, and several image-based procedures. A method for displaying the particle swarm exploring the search space in aesthetically pleasing ways is described. Several experiments are detailed and analyzed and a number of interesting visual artifacts are highlighted.
Steady state particle swarm
Authors: Carlos M. Fernandes​​, Nuno Fachada, Juan-Julián Merelo, Agostinho C. Rosa
Date: 2019-08-26 00:00:00

This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak–Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion. The steady state PSO was tested on a set of unimodal, multimodal, noisy and rotated benchmark functions, significantly improving the quality of results and convergence speed of the standard PSOs and more sophisticated PSOs with dynamic parameters and neighborhood. A sensitivity analysis of the parameters confirms the performance enhancement with different parameter settings and scalability tests show that the algorithm behavior is consistent throughout a substantial range of solution vector dimensions.
VOXLINGUA107: A DATASET FOR SPOKEN LANGUAGE RECOGNITION
Authors: Jorgen Valk, Tanel Alumae
Date: 2020-11-25 00:00:00
Tasks: Action Detection, Activity Detection, Language Identification, speaker-diarization, Speaker Diarization, Spoken language identification

This paper investigates the use of automatically collected web audio data for the task of spoken language recognition. We generate semi-random search phrases from language-specific Wikipedia data that are then used to retrieve videos from YouTube for 107 languages. Speech activity detection and speaker diarization are used to extract segments from the videos that contain speech. Post-filtering is used to remove segments from the database that are likely not in the given language, increasing the proportion of correctly labeled segments to 98%, based on crowd-sourced verification. The size of the resulting training set (VoxLingua107) is 6628 hours (62 hours per language on the average) and it is accompanied by an evaluation set of 1609 verified utterances. We use the data to build language recognition models for several spoken language identification tasks. Experiments show that using the automatically retrieved training data gives competitive results to using hand-labeled proprietary datasets. The dataset is publicly available.