pwc-archive/papers-with-abstracts huggingface dataset:
Overlapping Community Detection in Weighted Temporal Text Networks
Authors: R. Dong, J. Yang, Y. Chen
Date: 2020-03-17 00:00:00
Tasks: Community Detection

Network is a powerful language to represent relational data. One way to understand network is to analyze groups of nodes which share same properties or functions. The task of discovering such groups is known as community detection. The community detection in real-life networks, the majority of which are weighted temporal text networks, is confronted with two main problems - how to model the weight of edges and how to exploit the temporal information. Existing works either ignore the edge weight or utilize it in graph measures like modularity, which lacks scalability. And currently the common-used method involving temporal information is to discretize the time, which leads to series of problems. We are thus motivated to present a new method to encode the edge weight and temporal information. A probabilistic generative model, named Custom Temporal Community Detection (CTCD) is introduced, which views the link between two nodes as a weighted edge with several time stamps. Our model utilizes network, semantic and temporal information simultaneously to extract temporal community affiliations for individual user, influence strength across communities and temporal interested topic in each community. An efficient inference method, which scales linearly, and corresponding parallel implementation are proposed to adapt to large datasets. Through the knowledge extracted by CTCD, we are able to spot the community shift of the individual user, to which little attention has been given, and employ it to track the development of the communities over time. Moreover, experiments on two large-scale weighted temporal text networks show that CTCD gains significant improvement over state-of-the-art methods on a series of tasks.
Testing Robustness of Camera Fingerprint (PRNU) Detectors
Authors: Fernando Martin-Rodriguez
Date: 2021-02-18 00:00:00

In the field of forensic imaging, it is important to be able to extract a 'camera fingerprint' from one or a small set of images known to have been taken by the same camera. Ideally, that fingerprint would be used to identify an individual source camera. Camera fingerprint is based on certain kind of random noise present in all image sensors that is due to manufacturing imperfections and thus unique and impossible to avoid. PRNU (Photo-Response Non-Uniformity) has become the most widely used method for SCI (Source Camera Identification). In this paper, we design a set of 'attacks' to a PRNU based SCI system and we measure the success of each method. We understand an attack method as any processing that alters minimally image quality and that is designed to fool PRNU detectors (or, generalizing, any camera fingerprint detector). The PRNU based SCI system was taken from an outstanding reference that is publicly available.
Highway Traffic Control via Smart e-Mobility -- Part II: Dutch A13 Case Study
Authors: Carlo Cenedese, Michele Cucuzzella, Jacquelien M. A. Scherpen, Sergio Grammatico, Ming Cao
Date: 2021-02-18 00:00:00

In this paper, we study how to alleviate highway traffic congestions by encouraging plug-in electric and hybrid vehicles to stop at charging stations around peak congestion times. Specifically, we focus on a case study and simulate the adoption of a dynamic charging price depending on the traffic congestion. We use real traffic data of the A13 highway stretch between The Hague and Rotterdam, in The Netherlands, to identify the Cell Transmission Model. Then, we apply the algorithm proposed in (Part I: Theory) to different scenarios, validating the theoretical results and showing the benefits of our strategy in terms of traffic congestion alleviation. Finally, we carry out a sensitivity analysis of the proposed algorithm and discuss how to optimize its performance.
Network Embedding Using Deep Robust Nonnegative Matrix Factorization
Authors: Qiong Zhang
Date: 2020-05-04 00:00:00
Tasks: Network Embedding

As an effective technique to learn low-dimensional node features in complicated network
environment, network embedding has become a promising research direction in the field of network analysis.
Due to the virtues of better interpretability and flexibility, matrix factorization based methods for network
embedding have received increasing attentions. However, most of them are inadequate to learn more
complicated hierarchical features hidden in complex networks because of their mechanisms of single-layer
factorization structure. Besides, their original feature matrices used for factorization and their robustness
against noises also need to be further improved. To solve these problems, we propose a novel network
embedding method named DRNMF (deep robust nonnegative matrix factorization), which is formed by
multi-layer NMF learning structure. Meanwhile, DRNMF employs the combination of high-order proximity
matrices of the network as the original feature matrix for the factorization. To improve the robustness against
noises, we use `2,1 norm to devise the objective function for the DRNMF network embedding model.
Effective iterative update rules are derived to resolve the model, and the convergence of these rules are
strictly proved. Moreover, we introduce a pre-training strategy to improve the efficiency of convergence.
Extensive experiments on several benchmarks of complex networks demonstrate that our proposed method
DRNMF is effective and has better performance than the state-of-the-art matrix factorization based methods
for network embedding.
MDCNN-SID: Multi-scale Dilated Convolution Network for Singer Identification
Authors: xulong Zhang, Jianzong Wang, Ning Cheng, Jing Xiao
Date: 2020-04-09 00:00:00
Tasks: Artist classification, Music Generation, Singer Identification

Most singer identification methods are processed in the frequency domain, which potentially leads to information loss during the spectral transformation. In this paper, instead of the frequency domain, we propose an end-to-end architecture that addresses this problem in the waveform domain. An encoder based on Multi-scale Dilated Convolution Neural Networks (MDCNN) was introduced to generate wave embedding from the raw audio signal. Specifically, dilated convolution layers are used in the proposed method to enlarge the receptive field, aiming to extract song-level features. Furthermore, skip connection in the backbone network integrates the multi-resolution acoustic features learned by the stack of convolution layers. Then, the obtained wave embedding is passed into the following networks for singer identification. In experiments, the proposed method achieves comparable performance on the benchmark dataset of Artist20, which significantly improves related works.
Adjoint Method for Macroscopic Phase-Resetting Curves of Generic Spiking Neural Networks
Authors: Gregory Dumont, Alberto Pérez-Cervera, Boris Gutkin
Date: 2021-02-18 00:00:00

Brain rhythms emerge as a result of synchronization among interconnected spiking neurons. Key properties of such rhythms can be gleaned from the phase-resetting curve (PRC). Inferring the macroscopic PRC and developing a systematic phase reduction theory for emerging rhythms remains an outstanding theoretical challenge. Here we present a practical theoretical framework to compute the PRC of generic spiking networks with emergent collective oscillations. To do so, we adopt a refractory density approach where neurons are described by the time since their last action potential. In the thermodynamic limit, the network dynamics are captured by a continuity equation known as the refractory density equation. We develop an appropriate adjoint method for this equation which in turn gives a semi-analytical expression of the infinitesimal PRC. We confirm the validity of our framework for specific examples of neural networks. Our theoretical findings highlight the relationship between key biological properties at the individual neuron scale and the macroscopic oscillatory properties assessed by the PRC.
Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games
Authors: Jan Zimmermann, Tatiana Tatarenko, Volker Willert, Jürgen Adamy
Date: 2021-02-18 00:00:00
Tasks: Distributed Optimization

We are concerned with finding Nash Equilibria in agent-based multi-cluster games, where agents are separated into distinct clusters. While the agents inside each cluster collaborate to achieve a common goal, the clusters are considered to be virtual players that compete against each other in a non-cooperative game with respect to a coupled cost function. In such scenarios, the inner-cluster problem and the game between the clusters need to be solved simultaneously. Therefore, the resulting inter-cluster Nash Equilibrium should also be a minimizer of the social welfare problem inside the clusters. In this work, this setup is cast as a distributed optimization problem with sparse state information. Hence, critical information, such as the agent's cost functions, remain private. We present a distributed algorithm that converges with a linear rate to the optimal solution. Furthermore, we apply our algorithm to an extended cournot game to verify our theoretical results.
Transient Performance of Tube-based Robust Economic Model Predictive Control
Authors: Christian Klöppelt, Lukas Schwenkel, Frank Allgöwer, Matthias A. Müller
Date: 2021-02-18 00:00:00
Tasks: Model Predictive Control

In this paper, we provide non-averaged and transient performance guarantees for recently developed, tube-based robust economic model predictive control (MPC) schemes. In particular, we consider both tube-based MPC schemes with and without terminal conditions. We show that the closed-loop performance obtained by applying such MPC schemes is approximately optimal when evaluated both on finite and infinite time horizons. These performance bounds are similar to those derived previously for nominal economic MPC. The theoretical results are discussed in a numerical example.
Distributed control of multi-consensus
Authors: Lucia Valentina Gambuzza, Mattia Frasca
Date: 2021-02-18 00:00:00

We consider the problem of steering a multi-agent system to multi-consensus, namely a regime where groups of agents agree on a given value which may be different from group to group. We first address the problem by using distributed proportional controllers that implement additional links in the network modeling the communication protocol among agents and introduce a procedure for the optimal selection of them. Both the cases of single integrators and of second-order dynamics are taken into account and the stability for the multi-consensus state is studied, ultimately providing conditions for the gain of the controllers. We then extend the approach to controllers that either add or remove links in the original structure, by preserving eventually the weak connectedness of the resulting graph.
Highway Traffic Control via Smart e-Mobility -- Part I: Theory
Authors: Carlo Cenedese, Michele Cucuzzella, Jacquelien M. A. Scherpen, Sergio Grammatico, Ming Cao
Date: 2021-02-18 00:00:00

In this paper, we study how to alleviate highway traffic congestion by encouraging plug-in hybrid and electric vehicles to stop at a charging station around peak congestion times. Specifically, we design a pricing policy to make the charging price dynamic and dependent on the traffic congestion, predicted via the cell transmission model, and the availability of charging spots. Furthermore, we develop a novel framework to model how this policy affects the drivers' decisions by formulating a mixed-integer potential game. Technically, we introduce the concept of "road-to-station" (r2s) and "station-to-road" (s2r) flows, and show that the selfish actions of the drivers converge to charging schedules that are individually optimal in the sense of Nash. In the second part of this work, submitted as a separate paper (Part II: Case Study), we validate the proposed strategy on a simulated highway stretch between The Hague and Rotterdam, in The Netherlands.
Spatial Correlation Robust Inference
Authors: Ulrich K. Müller, Mark W. Watson
Date: 2021-02-18 00:00:00

We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar `estimator plus and minus a standard error times a critical value' form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given `worst-case' spatial covariance model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in large samples whenever the spatial correlation is weak, i.e., with average pairwise correlations that vanish as the sample size gets large. We also provide results on correct coverage in a restricted but nonparametric class of strong spatial correlations, as well as on the efficiency of the method. In a design calibrated to match economic activity in U.S. states the method outperforms previous suggestions for spatially robust inference about the population mean.
Metabolic alterations caused by smoking: the use of 1H-NMR in blood plasma analysis to unravel underlying mechanisms of lung cancers leading risk factor
Authors: Juul Goossens, Jente Gieghase, Elien Derveaux, Peter Adriaensens
Date: 2021-02-18 00:00:00
Tasks: Sensitivity, Specificity

Nowadays, the screening methods for the early detection of lung cancer struggle with several limitations such as many false positive results and low sensitivity. The detection of specific biomarkers is of high interest to complement these conventional screening methods. The objective of this study is to prove the power of 1H-NMR in metabolomics for the detection of smoking behavior, which is the leading risk factor for lung cancer, and as such gain more insights in the metabolic alterations that are caused by smoking. In this research, 1H-NMR spectra of human blood plasma samples were divided in 110 integration regions, from which the integration values were used to train an OPLS-DA classification model that underwent further data reduction. Results show that a classification model could discriminate between individuals based on their smoking status with a sensitivity of 96 percent and a specificity of 94 percent. This study also demonstrates that by performing a pathway-specific variable reduction of almost 50 percent, the sensitivity and specificity of the model almost remains the same. To conclude, 1H-NMR analyses show that the bloods metabolic profile of a smoker is altered compared to that of a non-smoker. Also, pathway-specific variable reduction shows great potential to perform overall data reduction. This workflow could be interesting to apply in the identification of lung cancer, to potentially detect specific biomarkers.
Mobility-Enhanced Simultaneous Lightwave Information and Power Transfer
Authors: Mingqing Liu, Mingliang Xiong, Qingwen Liu
Date: 2021-02-18 00:00:00

Simultaneous lightwave information and power transfer (SLIPT) has been regarded as a promising technology to deal with the ever-growing energy consumption and data-rate demands in the Internet of Things (IoT). We propose a resonant beam based SLIPT system (RB-SLIPT), which deals with the conflict of high deliverable power and mobile receiver positioning with the existing SLIPT schemes. At first, we establish a mobile transmission channel model and depict the energy distribution in the channel. Then, we present a practical design and evaluate the energy/data transfer performance within the moving range of the RB-SLIPT. Numerical evaluation demonstrates that the RB-SLIPT can deliver 5 W charging power and enable 1.5 Gbit/s achievable data rate with the moving range of 20-degree field of view (FOV) over 3 m distance. Thus, RB-SLIPT can simultaneously provide high-power energy and high-rate data transfer, and mobile receiver positioning capability.
Supportive 5G Infrastructure Policies are Essential for Universal 6G: Assessment using an Open-source Techno-economic Simulation Model utilizing Remote Sensing
Authors: Edward J. Oughton, Ashutosh Jha
Date: 2021-02-16 00:00:00

Work has now begun on the sixth generation of cellular technologies (`6G`) and cost-efficient global broadband coverage is already becoming a key pillar. Indeed, we are still far from providing universal and affordable broadband connectivity, despite this being a key part of the Sustainable Development Goals (Target 9.c). Currently, both Mobile Network Operators and governments still lack independent analysis of the strategies that can help achieve this target with the cellular technologies available (4G and 5G). Therefore, this paper undertakes quantitative assessment demonstrating how current 5G policies affect universal broadband, as well as drawing conclusions over how decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies. As an example, the assessment approach is applied to India, the world`s second-largest mobile market and a country with very high spectrum prices. The results demonstrate the trade-offs between technological decisions. This includes demonstrating how important current infrastructure policy is, particularly given fiber backhaul will be essential for delivering 6G quality of service. We find that by eliminating the spectrum licensing costs, 100% 5G population coverage can viably be achieved using fiber backhaul. Therefore, supportive infrastructure policies are essential in providing a superior foundation for evolution to future cellular generation, such as 6G.
On MPC without terminal conditions for dynamic non-holonomic robots
Authors: Franz Rußwurm, Willem Esterhuizen, Karl Worthmann, Stefan Streif
Date: 2021-02-18 00:00:00
Tasks: Model Predictive Control

We consider an input-constrained differential-drive robot with actuator dynamics. For this system, we establish asymptotic stability of the origin on arbitrary compact, convex sets using Model Predictive Control (MPC) without stabilizing terminal conditions despite the presence of state constraints and actuator dynamics. We note that the problem without those two additional ingredients was essentially solved beforehand, despite the fact that the linearization is not stabilizable. We propose an approach successfully solving the task at hand by combining the theory of barriers to characterize the viability kernel and an MPC framework based on so-called cost controllability. Moreover, we present a numerical case study to derive quantitative bounds on the required length of the prediction horizon. To this end, we investigate the boundary of the viability kernel and a neighbourhood of the origin, i.e. the most interesting areas.
Integrating prediction in mean-variance portfolio optimization
Authors: Andrew Butler, Roy H. Kwon
Date: 2021-02-18 00:00:00
Tasks: Decision Making, Portfolio Optimization, Prediction, regression, Stochastic Optimization

Prediction models are traditionally optimized independently from their use in the asset allocation decision-making process. We address this shortcoming and present a framework for integrating regression prediction models in a mean-variance optimization (MVO) setting. Closed-form analytical solutions are provided for the unconstrained and equality constrained MVO case. For the general inequality constrained case, we make use of recent advances in neural-network architecture for efficient optimization of batch quadratic-programs. To our knowledge, this is the first rigorous study of integrating prediction in a mean-variance portfolio optimization setting. We present several historical simulations using both synthetic and global futures data to demonstrate the benefits of the integrated approach.
The logic of quantum mechanics - Take II
Authors: Bob Coecke
Date: 2012-04-16 00:00:00

We put forward a new take on the logic of quantum mechanics, following Schroedinger's point of view that it is composition which makes quantum theory what it is, rather than its particular propositional structure due to the existence of superpositions, as proposed by Birkhoff and von Neumann. This gives rise to an intrinsically quantitative kind of logic, which truly deserves the name `logic' in that it also models meaning in natural language, the latter being the origin of logic, that it supports automation, the most prominent practical use of logic, and that it supports probabilistic inference.
COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm
Authors: Celestine Iwendi 1 *, Ali Kashif Bashir 2, Atharva Peshkar 3, R. Sujatha4, Jyotir Moy Chatterjee5, Swetha Pasupuleti 6, Rishita Mishra7, Sofia Pillai 8 and Ohyun Jo9 *
Date: 2020-07-03 00:00:00

Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time
collection, and processing of end-user devices is now in high demand. It is now
superlative to use AI to detect and predict pandemics of a colossal nature. The
Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China,
has had disastrous effects on the global community and has overburdened advanced
healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases
and 282,244 deaths have been recorded as of 11th May 2020, according to the
European Centre for Disease Prevention and Control agency. However, the current rapid
and exponential rise in the number of patients has necessitated efficient and quick
prediction of the possible outcome of an infected patient for appropriate treatment using
AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the
AdaBoost algorithm. The model uses the COVID-19 patient’s geographical, travel, health,
and demographic data to predict the severity of the case and the possible outcome,
recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the
dataset used. The data analysis reveals a positive correlation between patients’ gender
and deaths, and also indicates that the majority of patients are aged between 20 and
70 years.
mRNA Codon Optimization on Quantum Comptuers
Authors: Dillion M. Fox, Kim M. Branson, Ross C. Walker
Date: 2021-01-20 00:00:00
Tasks: Combinatorial Optimization, Translation

Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. An adiabatic quantum computer (AQC) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The AQC is found to be competitive in identifying optimal solutions and future generations of AQCs may be able to outperform classical GAs. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.