pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Agent Consensus Subject to Communication and Privacy Constraints
Authors: Dipankar Maity, Panagiotis Tsiotras
Date: 2021-02-21 00:00:00
Tasks: Quantization

We consider a multi-agent consensus problem in the presence of adversarial agents. The adversaries are able to listen to the inter-agent communications and try to estimate the state of the agents. The agents have a limited bit-rate for communication and are required to quantize the transmitted signal in order to meet the bit-rate constraint of the communication channel. We propose a consensus protocol that is protected against the adversaries, i.e., the expected mean-square error of the adversary state estimate is lower bounded. In order to deal with the bit-rate constraint, we propose a dynamic quantization scheme that guarantees protected consensus.
Angle-Based Sensor Network Localization
Authors: Gangshan Jing, Changhuang Wan, Ran Dai
Date: 2019-12-03 00:00:00

This paper studies angle-based sensor network localization (ASNL) in a plane, which is to determine locations of all sensors in a sensor network, given locations of partial sensors (called anchors) and angle measurements obtained in the local coordinate frame of each sensor. Firstly it is shown that a framework with a non-degenerate bilateration ordering must be angle fixable, implying that it can be uniquely determined by angles between edges up to translations, rotations, reflections and uniform scaling. Then ASNL is proved to have a unique solution if and only if the grounded framework is angle fixable and anchors are not all collinear. Subsequently, ASNL is solved in centralized and distributed settings, respectively. The centralized ASNL is formulated as a rank-constrained semi-definite program (SDP) in either a noise-free or a noisy scenario, with a decomposition approach proposed to deal with large-scale ASNL. The distributed protocol for ASNL is designed based on inter-sensor communications. Graphical conditions for equivalence of the formulated rank-constrained SDP and a linear SDP, decomposition of the SDP, as well as the effectiveness of the distributed protocol, are proposed, respectively. Finally, simulation examples demonstrate our theoretical results.
Interview Hoarding
Authors: Vikram Manjunath, Thayer Morrill
Date: 2021-02-12 00:00:00

Many centralized matching markets are preceded by interviews between participants. We study the impact on the final match of an increase in the number of interviews for one side of the market. Our motivation is the match between residents and hospitals where, due to the COVID-19 pandemic, interviews for the 2020-21 season of the National Residency Matching Program were switched to a virtual format. This drastically reduced the cost to applicants of accepting interview invitations. However, the reduction in cost was not symmetric since applicants, not programs, previously bore most of the costs of in-person interviews. We show that if doctors can accept more interviews, but the hospitals do not increase the number of interviews they offer, then no previously matched doctor is better off and many are potentially harmed. This adverse consequence is the result of what we call interview hoarding. We prove this analytically and characterize optimal mitigation strategies for special cases. We use simulations to extend these insights to more general settings.
A Decentralized Multi-UAV Spatio-Temporal Multi-Task Allocation Approach for Perimeter Defense
Authors: Shridhar Velhal, Suresh Sundaram, Narasimhan Sundararajan
Date: 2021-02-15 00:00:00

This paper provides a new solution approach to a multi-player perimeter defense game, in which the intruders' team tries to enter the territory, and a team of defenders protects the territory by capturing intruders on the perimeter of the territory. The objective of the defenders is to detect and capture the intruders before the intruders enter the territory. Each defender independently senses the intruder and computes his trajectory to capture the assigned intruders in a cooperative fashion. The intruder is estimated to reach a specific location on the perimeter at a specific time. Each intruder is viewed as a spatio-temporal task, and the defenders are assigned to execute these spatio-temporal tasks. At any given time, the perimeter defense problem is converted into a Decentralized Multi-UAV Spatio-Temporal Multi-Task Allocation (DMUST-MTA) problem. The cost of executing a task for a trajectory is defined by a composite cost function of both the spatial and temporal components. In this paper, a decentralized consensus-based bundle algorithm has been modified to solve the spatio-temporal multi-task allocation problem, and the performance evaluation of the proposed approach is carried out based on Monte-Carlo simulations. The simulation results show the effectiveness of the proposed approach to solve the perimeter defense game under different scenarios. Performance comparison with a state-of-the-art centralized approach with full observability, clearly indicates that DMUST-MTA achieves similar performance in a decentralized way with partial observability conditions with a lesser computational time and easy scaling up.
Automated and Distributed Statistical Analysis of Economic Agent-Based Models
Authors: Andrea Vandin, Daniele Giachini, Francesco Lamperti, Francesca Chiaromonte
Date: 2021-02-10 00:00:00
Tasks: counterfactual

We propose a novel approach to the statistical analysis of stochastic simulation models and, especially, agent-based models (ABMs). Our main goal is to provide fully automated, model-independent and tool-supported techniques and algorithms to inspect simulations and perform counterfactual analysis. Our approach: (i) is easy-to-use by the modeller, (ii) improves reproducibility of results, (iii) optimizes running time given the modeller's machine, (iv) automatically chooses the number of required simulations and simulation steps to reach user-specified statistical confidence, and (v) automates a variety of statistical tests. In particular, our techniques are designed to distinguish the transient dynamics of the model from its steady-state behaviour (if any), estimate properties in both 'phases', and provide indications on the (non-)ergodic nature of the simulated processes - which, in turn, allows one to gauge the reliability of a steady-state analysis. Estimates are equipped with statistical guarantees, allowing for robust comparisons across computational experiments. To demonstrate the effectiveness of our approach, we apply it to two models from the literature: a large-scale macro-financial ABM and a small scale prediction market model. Compared to prior analyses of these models, we obtain new insights and we are able to identify and fix some erroneous conclusions.
An Optimized H.266/VVC Software Decoder On Mobile Platform
Authors: Yiming Li, Shan Liu, Yu Chen, Yushan Zheng, Sijia Chen, Bin Zhu, Jian Lou
Date: 2021-03-05 00:00:00
Tasks: 4k, Decoder

As the successor of H.265/HEVC, the new versatile video coding standard (H.266/VVC) can provide up to 50% bitrate saving with the same subjective quality, at the cost of increased decoding complexity. To accelerate the application of the new coding standard, a real-time H.266/VVC software decoder that can support various platforms is implemented, where SIMD technologies, parallelism optimization, and the acceleration strategies based on the characteristics of each coding tool are applied. As the mobile devices have become an essential carrier for video services nowadays, the mentioned optimization efforts are not only implemented for the x86 platform, but more importantly utilized to highly optimize the decoding performance on the ARM platform in this work. The experimental results show that when running on the Apple A14 SoC (iPhone 12pro), the average single-thread decoding speed of the present implementation can achieve 53fps (RA and LB) for full HD (1080p) bitstreams generated by VTM-11.0 reference software using 8bit Common Test Conditions (CTC). When multi-threading is enabled, an average of 32 fps (RA) can be achieved when decoding the 4K bitstreams.
Adaptive Video Configuration and Bitrate Allocation for Teleoperated Vehicles
Authors: Andreas Schimpe, Simon Hoffmann, Frank Diermeyer
Date: 2021-02-22 00:00:00
Tasks: Autonomous Driving

Vehicles with autonomous driving capabilities are present on public streets. However, edge cases remain that still require a human in-vehicle driver. Assuming the vehicle manages to come to a safe state in an automated fashion, teleoperated driving technology enables a human to resolve the situation remotely by a control interface connected via a mobile network. While this is a promising solution, it also introduces technical challenges, one of them being the necessity to transmit video data of multiple cameras from the vehicle to the human operator. In this paper, an adaptive video streaming framework specifically designed for teleoperated vehicles is proposed and demonstrated. The framework enables automatic reconfiguration of the video streams of the multi-camera system at runtime. Predictions of variable transmission service quality are taken into account. With the objective to improve visual quality, the framework uses so-called rate-quality models to dynamically allocate bitrates and select resolution scaling factors. Results from deploying the proposed framework on an actual teleoperated driving system are presented.
MusiCoder: A Universal Music-Acoustic Encoder Based on Transformers
Authors: Yilun Zhao, Jia Guo
Date: 2020-08-03 00:00:00
Tasks: Genre classification, Information Retrieval, Music Genre Classification, Music Information Retrieval, Representation Learning, Retrieval, Self-Supervised Learning

Music annotation has always been one of the critical topics in the field of Music Information Retrieval (MIR). Traditional models use supervised learning for music annotation tasks. However, as supervised machine learning approaches increase in complexity, the increasing need for more annotated training data can often not be matched with available data. In this paper, a new self-supervised music acoustic representation learning approach named MusiCoder is proposed. Inspired by the success of BERT, MusiCoder builds upon the architecture of self-attention bidirectional transformers. Two pre-training objectives, including Contiguous Frames Masking (CFM) and Contiguous Channels Masking (CCM), are designed to adapt BERT-like masked reconstruction pre-training to continuous acoustic frame domain. The performance of MusiCoder is evaluated in two downstream music annotation tasks. The results show that MusiCoder outperforms the state-of-the-art models in both music genre classification and auto-tagging tasks. The effectiveness of MusiCoder indicates a great potential of a new self-supervised learning approach to understand music: first apply masked reconstruction tasks to pre-train a transformer-based model with massive unlabeled music acoustic data, and then finetune the model on specific downstream tasks with labeled data.
Deciphering Bitcoin Blockchain Data by Cohort Analysis
Authors: Yulin Liu, Luyao Zhang, Yinhong Zhao
Date: 2021-02-27 00:00:00

Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in the social sciences. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. This enables us to create datasets and visualizations for some key Bitcoin transaction indicators, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the cumulative unspent transaction output (UTXO). We provide a computationally feasible approach for characterizing Bitcoin transactions that paves the way for future economic studies of Bitcoin.
New Computational Techniques for a Faster Variation of BM3D Image Denoising
Authors: Toby Sanders, Sean Larkin
Date: 2021-03-17 00:00:00
Tasks: Denoising, Image Denoising, Image Reconstruction

BM3D has been considered the standard for comparison in the image denoising literature for the last decade. Though it has been shown to be surpassed numerous times by alternative algorithms in terms of PSNR, the margins are very thin, and denoising is approaching a limiting point. The reason for the continued use of BM3D within the literature is due to its off-the-shelf ease-of-use in any application, which alternative improved denoising algorithms sometimes fail to match. This article proposes a new variation of BM3D, which maintains its ease of use but is notably faster. This development brings us closer to real-time ease-of-use application of new state-of-the-art image reconstruction algorithms such as plug-and-play priors. We refer to our variation of BM3D as G-BM3D. In terms of image quality, our algorithm attains very similar denoising performance to the original algorithm. Though our algorithm is written completely in MATLAB software, it is already between 5-20 times faster than the original algorithm, and the modifications to the algorithm are such that it is expected to be significantly faster when ported to CUDA language and with more powerful GPUs. The improved processing time is achieved by two main components. The first component is a new computational strategy that achieves faster block matching, and the second is a new global approach to the 3D wavelet filtering step that allows for significantly improved processing times on GPUs. The fast block matching strategy could also be applied to any of the vast number of nonlocal self-similarity (NSS) denoisers to improve processing times.
A Mathematical Dashboard for the Analysis of Italian COVID-19 Epidemic Data
Authors: Nicola Parolini, Giovanni Ardenghi, Luca Dede', Alfio Quarteroni
Date: 2020-11-22 00:00:00

An analysis of the COVID-19 epidemic is proposed on the basis of the epiMOX dashboard (publicly accessible at https://www.epimox.polimi.it) that deals with data of the epidemic trends and outbreaks in Italy from late February 2020. Our analysis provides an immediate appreciation of the past epidemic development, together with its current trends by fostering a deeper interpretation of available data through several critical epidemic indicators. In addition, we complement the epiMOX dashboard with a predictive tool based on an epidemiological compartmental model, named SUIHTER, for the forecast on the near future epidemic evolution.
Joint Satellite Gateway Deployment & Controller Placement in Software-Defined 5G-Satellite Integrated Networks
Authors: Nariman Torkzaban, John S. Baras
Date: 2021-03-15 00:00:00

Several challenging optimization problems arise while considering the deployment of the space-air-ground integrated networks (SAGINs), among which the optimal satellite gateway deployment problem is of significant importance. Moreover, with the increasing interest in the software-defined integration of 5G networks and satellites, the existence of an effective scheme for optimal placement of SDN controllers is essential. In this paper, we discuss the interrelation between the two problems above and propose suitable methods to solve them under various network design criteria. We first provide a MILP model for solving the joint problem, and then motivate the decomposition of the model into two disjoint MILPs. We then show that the resulting problems can be modeled as the optimization of submodular set functions and can be solved efficiently with provable optimality gaps.
Transmitting FRET signals to nerve cells
Authors: Jakub Kmiecik, Pawel Kulakowski, Krzysztof Wojcik, Andrzej Jajszczyk
Date: 2021-03-15 00:00:00

This paper is concerned with a novel method allowing communication between FRET nanonetworks and nerve cells. It is focused on two system components: fluorophores and channelrhodopsins which serve as transmitters and receivers, respectively. Channelrhodopsins are used here also as a FRET signal-to-voltage converter. The trade-off between throughput and bit error rate is also investigated.
Scaling Laws for Unamplified Coherent Transmission in Next-generation Short-Reach and Access Networks
Authors: Giuseppe Rizzelli, Antonino Nespola, Stefano Straullu, Roberto Gaudino
Date: 2021-03-03 00:00:00

International standardization bodies (IEEE and ITU-T) working on the evolution of transmission technologies are still considering traditional direct detection solutions for the most relevant short reach optical link applications, that are Passive Optical Networks (PON) and intra-data center interconnects. Anyway, future jumps towards even higher bit rates per wavelength will require a complete paradigm shift, moving towards coherent technologies. In this paper, we thus study both analytically and experimentally the scaling laws of unamplified coherent transmission in the short-reach communications ecosystems. We believe that, given the extremely tight techno-economic constraints, such a revolutionary transition towards coherent in short-reach first requires a very detailed study of its intrinsic capabilities in largely extending the limitation currently imposed by direct detection systems. To this end, this paper focuses on the ultimate physical layer limitations of unamplified coherent systems in terms of bit rate and power budget. The main parameters of our performance estimation model are extracted through fitting with a set of experimental characterizations and later used as the starting point of a scaling laws study regarding local oscillator power, modulator-induced attenuation, bit rate, and maximum achievable power budget. The analytically predicted performance is then verified through transmission experiments, including a demonstration on a 37-km installed metropolitan dark fiber in the city of Turin.
Overview of Fault Tolerant Techniques in Underwater Sensor Networks
Authors: Lauri Vihman, Maarja Kruusmaa, Jaan Raik
Date: 2019-10-02 00:00:00
Tasks: Weather Forecasting

Sensor networks provide services to a broad range of applications ranging from intelligence service surveillance to weather forecasting. Most of the sensor networks are terrestrial, however much of our planet is covered by water and Underwater Sensor Networks (USN) are an emerging research area. One of the unavoidable increasing challenge for modern technology is tolerating faults - accepting that hardware is imperfect and cope with it. Fault tolerance may have more impact underwater than in terrestrial environment as terrestrial environment is more forgiving, reaching the malfunctioning devices for replacement underwater is harder and may be more costly. Current paper is the first to investigate fault tolerance, particularly cross layer fault tolerance, in USN-s.
Dynamic Clock Reconfiguration for the Constrained IoT and its Application to Energy-efficient Networking
Authors: Michel Rottleuthner, Thomas C. Schmidt, Matthias Wählisch
Date: 2021-02-20 00:00:00

Clock configuration takes a key role in tuning constrained general-purpose microcontrollers for performance, timing accuracy, and energy efficiency. Configuring the underlying clock tree, however, involves a large parameter space with complex dependencies and dynamic constraints. We argue for clock configuration as a generic operating system module that bridges the gap between highly configurable but complex embedded hardware and easy application development. In this paper, we propose a method and a runtime subsystem for dynamic clock reconfiguration on constrained Internet of Things (IoT) devices named ScaleClock. ScaleClock derives measures to dynamically optimize clock configurations by abstracting the hardware-specific clock trees. The ScaleClock system service grants portable access to the optimization potential of dynamic clock scaling for applications. We implement the approach on the popular IoT operating system RIOT for two target platforms of different manufacturers and evaluate its performance in static and dynamic scenarios on real devices. We demonstrate the potential of ScaleClock by designing a platform-independent dynamic voltage and frequency scaling (DVFS) mechanism that enables RIOT to autonomously adapt the hardware performance to requirements of the software currently executed. In a use case study, we manage to boost energy efficiency of constrained network communication by reducing the MCU consumption by 40 % at negligible performance impact.
Dynamic Performance Management: An Approach for Managing the Common Goods
Authors: A. Sardi, E. Sorano
Date: 2021-02-08 00:00:00
Tasks: Management, Systematic Literature Review

Public organizations need innovative approaches for managing common goods and to explain the dynamics linking the (re)generation of common goods and organizational performance. Although system dynamics is recognised as a useful approach for managing common goods, public organizations rarely adopt the system dynamics for this goal. The paper aims to review the literature on the system dynamics and its recent application, known as dynamic performance management, to highlight the state of the art and future opportunities on the management of common goods. The authors analyzed 144 documents using a systematic literature review. The results obtained outline a fair number of documents, countries and journals involving the study of system dynamics, but do not cover sufficient research on the linking between the (re)generation of common goods and organizational performance. This paper outlines academic and practical contributions. Firstly, it contributes to the theory of common goods. It provides insight for linking the management of common goods and organizational performance through the use of dynamic performance management approach. Furthermore, it shows scholars the main research opportunities. Secondly, it indicates to practitioners the documents providing useful ideas on the adoption of system dynamics for managing common goods.
Exact maximal reduction of stochastic reaction networks by species lumping
Authors: Luca Cardelli, Isabel Cristina Perez-Verona, Mirco Tribastone, Max Tschaikowski, Andrea Vandin, Tabea Waizmann
Date: 2021-01-09 00:00:00

Motivation: Stochastic reaction networks are a widespread model to describe biological systems where the presence of noise is relevant, such as in cell regulatory processes. Unfortu-nately, in all but simplest models the resulting discrete state-space representation hinders analytical tractability and makes numerical simulations expensive. Reduction methods can lower complexity by computing model projections that preserve dynamics of interest to the user. Results: We present an exact lumping method for stochastic reaction networks with mass-action kinetics. It hinges on an equivalence relation between the species, resulting in a reduced network where the dynamics of each macro-species is stochastically equivalent to the sum of the original species in each equivalence class, for any choice of the initial state of the system. Furthermore, by an appropriate encoding of kinetic parameters as additional species, the method can establish equivalences that do not depend on specific values of the parameters. The method is supported by an efficient algorithm to compute the largest species equivalence, thus the maximal lumping. The effectiveness and scalability of our lumping technique, as well as the physical interpretability of resulting reductions, is demonstrated in several models of signaling pathways and epidemic processes on complex networks. Availability: The algorithms for species equivalence have been implemented in the software tool ERODE, freely available for download from https://www.erode.eu.