pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method
Authors: Bruno Barracosa, Julien Bect, Héloïse Dutrieux Baraffe, Juliette Morin, Josselin Fournel, Emmanuel Vazquez
Date: 2022-07-08 00:00:00
Tasks: Active Learning, Bayesian Optimization

This article focuses on the multi-objective optimization of stochastic simulators with high output variance, where the input space is finite and the objective functions are expensive to evaluate. We rely on Bayesian optimization algorithms, which use probabilistic models to make predictions about the functions to be optimized. The proposed approach is an extension of the Pareto Active Learning (PAL) algorithm for the estimation of Pareto-optimal solutions that makes it suitable for the stochastic setting. We named it Pareto Active Learning for Stochastic Simulators (PALS). The performance of PALS is assessed through numerical experiments over a set of bi-dimensional, bi-objective test problems. PALS exhibits superior performance when compared to other scalarization-based and random-search approaches.
On the Metric Properties of IR Evaluation Measures Based on Ranking Axioms
Authors: Fernando Giner
Date: 2022-07-07 00:00:00
Tasks: Retrieval

The axiomatic analysis of IR evaluation metrics has contributed to a better understanding of their properties. Some works have modelled the effectiveness of retrieval measures with axioms that capture desirable properties on the set of ranked lists of documents. Recently, it has been shown that three of these axioms lead to some orderings. This work formally explores the metric properties of the set of rankings, endowed with these orderings. Based on lattice theory, the possible metrics and pseudo-metrics, defined on these structures, are determined. It is found that, when the relevant documents are prioritized, precision, recall, RBP and DCG are metrics on the set of rankings, however they are pseudo-metrics when the swapping of documents is considered.
A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
Authors: Junchen Jin, Member, IEEE, Dingding Rong, Tong Zhang, Qingyuan Ji, Haifeng Guo, Yisheng Lv, Xiaoliang Ma, and Fei-Yue Wang
Date: 2022-02-14 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2022 2
Tasks: Traffic Prediction

Abstract— Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic
network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential
factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation. The proposed method applies Wasserstein Generative Adversarial Nets (WGAN) for robust data-driven traffic modeling using a combination of generative neural network and discriminative neural network. The generative neural network models the road link features of the adjacent intersections and the control parameters of intersections using a hybrid graph block. In addition, the spatial-temporal relations are captured by stacking a graph convolutional network (GCN), a recurrent neural network (RNN), and an attention mechanism. A comprehensive computational experiment was carried out including comparing model prediction and computational performances with several state-of-the-art deep learning models. The proposed approach has been implemented and applied for predicting short-term link traffic speed in a largescale urban road network in Hangzhou, China. The results suggest that it provides a scalable and effective traffic prediction solution for urban road networks.
Deep Learning to Jointly Schema Match, Impute, and Transform Databases
Authors: Sandhya Tripathi, Bradley A. Fritz, Mohamed Abdelhack, Michael S. Avidan, Yixin Chen, Christopher R. King
Date: 2022-06-22 00:00:00
Tasks: Deep Learning

An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and only partially overlapping features is a prerequisite to developing and testing robust, generalizable algorithms, especially in health care. We approach this issue in the common but difficult case of numeric features such as nearly Gaussian and binary features, where unit changes and variable shift make simple matching of univariate summaries unsuccessful. We develop two novel procedures to address this problem. First, we demonstrate multiple methods of "fingerprinting" a feature based on its associations to other features. In the setting of even modest prior information, this allows most shared features to be accurately identified. Second, we demonstrate a deep learning algorithm for translation between databases. Unlike prior approaches, our algorithm takes advantage of discovered mappings while identifying surrogates for unshared features and learning transformations. In synthetic and real-world experiments using two electronic health record databases, our algorithms outperform existing baselines for matching variable sets, while jointly learning to impute unshared or transformed variables.
Automaton-Guided Control Synthesis for Signal Temporal Logic Specifications
Authors: Qi Heng Ho, Roland B. Ilyes, Zachary N. Sunberg, Morteza Lahijanian
Date: 2022-07-08 00:00:00

This paper presents an algorithmic framework for control synthesis of continuous dynamical systems subject to signal temporal logic (STL) specifications. We propose a novel algorithm to obtain a time-partitioned finite automaton from an STL specification, and introduce a multi-layered framework that utilizes this automaton to guide a sampling-based search tree both spatially and temporally. Our approach is able to synthesize a controller for nonlinear dynamics and polynomial predicate functions. We prove the correctness and probabilistic completeness of our algorithm, and illustrate the efficiency and efficacy of our framework on several case studies. Our results show an order of magnitude speedup over the state of the art.
With a little help from my friends: essentiality vs opportunity in group criticality
Authors: Michele Aleandri, Marco Dall'Aglio
Date: 2022-07-07 00:00:00
Tasks: Blocking

We define a notion of the criticality of a player for simple monotone games based on cooperation with other players, either to form a winning coalition or to break a winning one, with an essential role for all the players involved. We compare it with the notion of differential criticality given by Beisbart that measures power as the opportunity left by other players. We prove that our proposal satisfies an extension of the strong monotonicity introduced by Young, assigns no power to null players and does not reward free riders, and can easily be computed from the minimal winning and blocking coalitions. An application to the Italian elections is presented. Our analysis shows that the measures of group criticality defined so far cannot weigh essential players while only remaining an opportunity measure. We propose a group opportunity test to reconcile the two views.
Integrated Sensing and Communication with Delay Alignment Modulation: Performance Analysis and Beamforming Optimization
Authors: Zhiqiang Xiao, Yong Zeng
Date: 2022-07-08 00:00:00
Tasks: Integrated sensing and communication, ISAC

Delay alignment modulation (DAM) has been recently proposed to enable manipulable channel delay spread for efficient single- or multi-carrier communications. In particular, with perfect delay alignment, inter-symbol interference (ISI) can be eliminated even with single-carrier (SC) transmission, without relying on sophisticated channel equalization. The key ideas of DAM are delay pre-compensation and path-based beamforming, so that all multi-path signal components may arrive at the receiver simultaneously and be superimposed constructively, rather than causing the detrimental ISI. Compared to the classic orthogonal frequency division multiplexing (OFDM) transmission, DAM-enabled SC communication has several appealing advantages, including low peak-to-average-power ratio (PAPR) and high tolerance for Doppler frequency shift, which renders DAM also appealing for radar sensing. Therefore, in this paper, DAM is investigated for integrated sensing and communication (ISAC) systems. We first study the output signal-to-noise ratios (SNRs) for ISI-free SC communication and radar sensing, and then derive the closed-form expressions for DAM-based sensing in terms of the ambiguity function (AF) and integrated sidelobe ratio (ISR). Furthermore, we study the beamforming design problem for DAM-based ISAC to maximize the communication SNR while guaranteeing the sensing performance in terms of the sensing SNR and ISR. Finally, we provide performance comparison between DAM and OFDM for ISAC, and it is revealed that DAM signal may achieve better communication and sensing performance, thanks to its low PAPR, reduced guard interval overhead, as well as higher tolerance for Doppler frequency shift. Simulation results are provided to show the great potential of DAM for ISAC.
Rhythm and form in music: a complex systems approach
Authors: Blas Kolic, Mateo Tonatiuh Rodriguez-Cervantes, Pablo Padilla-Longoria, Francis Knights
Date: 2022-07-07 00:00:00
Tasks: Form, Rhythm

There has been an everlasting discussion around the concept of form in music. This work is motivated by such debate by using a complex systems framework in which we study the form as an emergent property of rhythm. Such a framework corresponds with the traditional notion of musical form and allows us to generalize this concept to more general shapes and structures in music. We develop the three following metrics of the rhythmic complexity of a musical piece and its parts: 1) the rhythmic heterogeneity, based on the permutation entropy, where high values indicate a wide variety of rhythmic patterns; 2) the syncopation, based on the distribution of on-beat onsets, where high values indicate a high proportion of off-the-beat notes; and 3) the component extractor, based on the communities of a visibility graph of the rhythmic figures over time, where we identify structural components that constitute the piece at a (to be explained) perceptual level. With the same parameters, our metrics are comparable within a piece or between pieces.
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems
Authors: D'Jeff Kanda Nkashama, Arian Soltani, Jean-Charles Verdier, Marc Frappier, Pierre-Martin Tardif, Froduald Kabanza
Date: 2022-06-25 00:00:00
Tasks: Anomaly Detection, Data Poisoning, Intrusion Detection

Recently, advances in deep learning have been observed in various fields, including computer vision, natural language processing, and cybersecurity. Machine learning (ML) has demonstrated its ability as a potential tool for anomaly detection-based intrusion detection systems to build secure computer networks. Increasingly, ML approaches are widely adopted than heuristic approaches for cybersecurity because they learn directly from data. Data is critical for the development of ML systems, and becomes potential targets for attackers. Basically, data poisoning or contamination is one of the most common techniques used to fool ML models through data. This paper evaluates the robustness of six recent deep learning algorithms for intrusion detection on contaminated data. Our experiments suggest that the state-of-the-art algorithms used in this study are sensitive to data contamination and reveal the importance of self-defense against data perturbation when developing novel models, especially for intrusion detection systems.
Code Translation with Compiler Representations
Authors: Marc Szafraniec, Baptiste Roziere, Hugh Leather, Francois Charton, Patrick Labatut, Gabriel Synnaeve
Date: 2022-06-30 00:00:00
Tasks: Code Translation, Machine Translation, NMT, Translation

In this paper, we leverage low-level compiler intermediate representations (IR) to improve code translation. Traditional transpilers rely on syntactic information and handcrafted rules, which limits their applicability and produces unnatural-looking code. Applying neural machine translation (NMT) approaches to code has successfully broadened the set of programs on which one can get a natural-looking translation. However, they treat the code as sequences of text tokens, and still do not differentiate well enough between similar pieces of code which have different semantics in different languages. The consequence is low quality translation, reducing the practicality of NMT, and stressing the need for an approach significantly increasing its accuracy. Here we propose to augment code translation with IRs, specifically LLVM IR, with results on the C++, Java, Rust, and Go languages. Our method improves upon the state of the art for unsupervised code translation, increasing the number of correct translations by 11% on average, and up to 79% for the Java -> Rust pair with greedy decoding. We extend previous test sets for code translation, by adding hundreds of Go and Rust functions. Additionally, we train models with high performance on the problem of IR decompilation, generating programming source code from IR, and study using IRs as intermediary pivot for translation.
Tandem Multitask Training of Speaker Diarisation and Speech Recognition for Meeting Transcription
Authors: Xianrui Zheng, Chao Zhang, Philip C. Woodland
Date: 2022-07-08 00:00:00
Tasks: Action Detection, Activity Detection, Self-Supervised Learning, speech-recognition, Speech Recognition

Self-supervised-learning-based pre-trained models for speech data, such as Wav2Vec 2.0 (W2V2), have become the backbone of many speech tasks. In this paper, to achieve speaker diarisation and speech recognition using a single model, a tandem multitask training (TMT) method is proposed to fine-tune W2V2. For speaker diarisation, the tasks of voice activity detection (VAD) and speaker classification (SC) are required, and connectionist temporal classification (CTC) is used for ASR. The multitask framework implements VAD, SC, and ASR using an early layer, middle layer, and late layer of W2V2, which coincides with the order of segmenting the audio with VAD, clustering the segments based on speaker embeddings, and transcribing each segment with ASR. Experimental results on the augmented multi-party (AMI) dataset showed that using different W2V2 layers for VAD, SC, and ASR from the earlier to later layers for TMT not only saves computational cost, but also reduces diarisation error rates (DERs). Joint fine-tuning of VAD, SC, and ASR yielded 16%/17% relative reductions of DER with manual/automatic segmentation respectively, and consistent reductions in speaker attributed word error rate, compared to the baseline with separately fine-tuned models.
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection
Authors: Xuanwen Huang, Yang Yang, Yang Wang, Chunping Wang, Zhisheng Zhang, Jiarong Xu, Lei Chen, Michalis Vazirgiannis
Date: 2022-06-30 00:00:00
Tasks: Anomaly Detection, Graph Anomaly Detection

Graph Anomaly Detection (GAD) has recently become a hot research spot due to its practicability and theoretical value. Since GAD emphasizes the application and the rarity of anomalous samples, enriching the varieties of its datasets is fundamental work. Thus, this paper present DGraph, a real-world dynamic graph in the finance domain. DGraph overcomes many limitations of current GAD datasets. It contains about 3M nodes, 4M dynamic edges, and 1M ground-truth nodes. We provide a comprehensive observation of DGraph, revealing that anomalous nodes and normal nodes generally have different structures, neighbor distribution, and temporal dynamics. Moreover, it suggests that unlabeled nodes are also essential for detecting fraudsters. Furthermore, we conduct extensive experiments on DGraph. Observation and experiments demonstrate that DGraph is propulsive to advance GAD research and enable in-depth exploration of anomalous nodes.
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning
Authors: Changwei Gong, Changhong Jing, Yanyan Shen, Shuqiang Wang
Date: 2022-06-29 00:00:00
Tasks: Community Detection, Dynamic Community Detection, Graph Attention, Graph Representation Learning, Representation Learning

Dynamic community detection has been prospered as a powerful tool for quantifying changes in dynamic brain network connectivity patterns by identifying strongly connected sets of nodes. However, as the network science problems and network data to be processed become gradually more sophisticated, it awaits a better method to efficiently learn low dimensional representation from dynamic network data and reveal its latent function that changes over time in the brain network. In this work, an adversarial temporal graph representation learning (ATGRL) framework is proposed to detect dynamic communities from a small sample of brain network data. It adopts a novel temporal graph attention network as an encoder to capture more efficient spatio-temporal features by attention mechanism in both spatial and temporal dimensions. In addition, the framework employs adversarial training to guide the learning of temporal graph representation and optimize the measurable modularity loss to maximize the modularity of community. Experiments on the real-world brain networks datasets are demonstrated to show the effectiveness of this new method.
Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin
Authors: Zipei Fan, Xiaojie Yang, Wei Yuan, Renhe Jiang, Quanjun Chen, Xuan Song, Ryosuke Shibasaki
Date: 2022-06-21 00:00:00
Tasks: Prediction, Retrieval, Trajectory Prediction

Knowing "what is happening" and "what will happen" of the mobility in a city is the building block of a data-driven smart city system. In recent years, mobility digital twin that makes a virtual replication of human mobility and predicting or simulating the fine-grained movements of the subjects in a virtual space at a metropolitan scale in near real-time has shown its great potential in modern urban intelligent systems. However, few studies have provided practical solutions. The main difficulties are four-folds. 1) The daily variation of human mobility is hard to model and predict; 2) the transportation network enforces a complex constraints on human mobility; 3) generating a rational fine-grained human trajectory is challenging for existing machine learning models; and 4) making a fine-grained prediction incurs high computational costs, which is challenging for an online system. Bearing these difficulties in mind, in this paper we propose a two-stage human mobility predictor that stratifies the coarse and fine-grained level predictions. In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level. In the second stage, the coarse predictions are resolved to a fine-grained level via a probabilistic trajectory retrieval method, which offloads most of the heavy computations to the offline phase. We tested our method using a real-world mobile phone GPS dataset in the Kanto area in Japan, and achieved good prediction accuracy and a time efficiency of about 2 min in predicting future 1h movements of about 220K mobile phone users on a single machine to support more higher-level analysis of mobility prediction.
Approximation by Simple Poles -- Part I: Density and Geometric Convergence Rate in Hardy Space
Authors: Michael W. Fisher, Gabriela Hug, Florian Dörfler
Date: 2022-07-08 00:00:00

Optimal linear feedback control design is a valuable but challenging problem due to nonconvexity of the underlying optimization and infinite dimensionality of the Hardy space of stabilizing controllers. A powerful class of techniques for solving optimal control problems involves using reparameterization to transform the control design to a convex but infinite dimensional optimization. To make the problem tractable, historical work focuses on Galerkin-type finite dimensional approximations to Hardy space, especially those involving Lorentz series approximations such as the finite impulse response appproximation. However, Lorentz series approximations can lead to infeasibility, difficulty incorporating prior knowledge, deadbeat control in the case of finite impulse response, and increased suboptimality, especially for systems with large separation of time scales. The goal of this two-part article is to introduce a new Galerkin-type method based on approximation by transfer functions with a selection of simple poles, and to apply this simple pole approximation for optimal control design. In Part I, error bounds for approximating arbitrary transfer functions in Hardy space are provided based on the geometry of the pole selection. It is shown that the space of transfer functions with these simple poles converges to the full Hardy space, and a uniform convergence rate is provided based purely on the geometry of the pole selection. This is then specialized to derive a convergence rate for a particularly interesting pole selection based on an Archimedes spiral. In Part II, the simple pole approximation is combined with system level synthesis, a recent reparameterization approach, to develop a new control design method. This technique is convex and tractable, always feasible, can include prior knowledge, does not result in deadbeat control, and works well for systems with large separation of tim
Power Response and Modelling Aspects of Power Electronic Loads in Case of Voltage Drops
Authors: Sebastian Liemann, Christian Rehtanz
Date: 2022-07-08 00:00:00

In this paper, the power response of power electronic loads in case of voltage drops are measured and their dynamics are analysed. Based on this, dynamic simulation models are derived which can be used for voltage stability investigations. For this, four loads with different power factor techniques are considered. In addition, the impact of the grid impedance and input filter on the power response are measured in the laboratory. Based on the measurements, the simulation models are described. It is also outlined under which aspects the components of the loads are normally dimensioned if no detailed information is available. A comparison with the measurements demonstrates that the simulation models capture the main dynamics. At the end of the paper, the load models are compared to a constant power load in a short-term voltage stability use case. The results indicate that the power electronic loads have a more positive influence on short-term voltage stability in case of voltage drops. Overall, the contributions of the paper are the identification of the basic power dynamics of power electronic loads for different voltage drops and a subsequent derivation of suitable simulation load models for voltage stability investigations.
Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF
Authors: Ricarda-Samantha Götte, Julia Timmermann
Date: 2022-07-08 00:00:00
Tasks: State Estimation

State estimation when only a partial model of a considered system is available remains a major challenge in many engineering fields. This work proposes a joint, square-root unscented Kalman filter to estimate states and model uncertainties simultaneously by linear combinations of physics-motivated library functions. Using a sparsity promoting approach, a selection of those linear combinations is chosen and thus an interpretable model can be extracted. Results indicate a small estimation error compared to a traditional square-root unscented Kalman filter and exhibit the enhancement of physically meaningful models.