pwc-archive/papers-with-abstracts huggingface dataset:
Ordering for Communication-Efficient Quickest Change Detection in a Decomposable Graphical Model
Authors: Yicheng Chen, Rick S. Blum, Brian M. Sadler
Date: 2020-08-10 00:00:00
Tasks: Change Detection

A quickest change detection problem is considered in a sensor network with observations whose statistical dependency structure across the sensors before and after the change is described by a decomposable graphical model (DGM). Distributed computation methods for this problem are proposed that are capable of producing the optimum centralized test statistic. The DGM leads to the proper way to collect nodes into local groups equivalent to cliques in the graph, such that a clique statistic which summarizes all the clique sensor data can be computed within each clique. The clique statistics are transmitted to a decision maker to produce the optimum centralized test statistic. In order to further improve communication efficiency, an ordered transmission approach is proposed where transmissions of the clique statistics to the fusion center are ordered and then adaptively halted when sufficient information is accumulated. This procedure is always guaranteed to provide the optimal change detection performance, despite not transmitting all the statistics from all the cliques. A lower bound on the average number of transmissions saved by ordered transmissions is provided and for the case where the change seldom occurs the lower bound approaches approximately half the number of cliques provided a well behaved distance measure between the distributions of the sensor observations before and after the change is sufficiently large. We also extend the approach to the case when the graph structure is different under each hypothesis. Numerical results show significant savings using the ordered transmission approach and validate the theoretical findings.
Adaptive support driven Bayesian reweighted algorithm for sparse signal recovery
Authors: Junlin Li, Wei Zhou, Cheng Cheng
Date: 2020-08-10 00:00:00
Tasks: feature selection, Sparse Learning

Sparse learning has been widely studied to capture critical information from enormous data sources in the filed of system identification. Often, it is essential to understand internal working mechanisms of unknown systems (e.g. biological networks) in addition to input-output relationships. For this purpose, various feature selection techniques have been developed. For example, sparse Bayesian learning (SBL) was proposed to learn major features from a dictionary of basis functions, which makes identified models interpretable. Reweighted L1-regularization algorithms are often applied in SBL to solve optimization problems. However, they are expensive in both computation and memory aspects, thus not suitable for large-scale problems. This paper proposes an adaptive support driven Bayesian reweighted (ASDBR) algorithm for sparse signal recovery. A restart strategy based on shrinkage-thresholding is developed to conduct adaptive support estimate, which can effectively reduce computation burden and memory demands. Moreover, ASDBR accurately extracts major features and excludes redundant information from large datasets. Numerical experiments demonstrate the proposed algorithm outperforms state-of-the-art methods.
Audio-visual Speaker Recognition with a Cross-modal Discriminative Network
Date: 2020-08-10 00:00:00
Tasks: Speaker Recognition

Audio-visual speaker recognition is one of the tasks in the recent 2019 NIST
speaker recognition evaluation (SRE). Studies in neuroscience and computer
science all point to the fact that vision and auditory neural signals interact
in the cognitive process. This motivated us to study a cross-modal network,
namely voice-face discriminative network (VFNet) that establishes the general
relation between human voice and face. Experiments show that VFNet provides
additional speaker discriminative information. With VFNet, we achieve 16.54%
equal error rate relative reduction over the score level fusion audio-visual
baseline on evaluation set of 2019 NIST SRE.
Leverage Point Identification Method for LAV-Based State Estimation
Authors: Mathias Dorier, Guglielmo Frigo, Ali Abur, Mario Paolone
Date: 2020-08-10 00:00:00
Tasks: LEMMA, State Estimation

The state estimation problem can be solved through different methods. In terms of robustness, an effective approach is represented by the Least Absolute Value (LAV) estimator, though vulnerable to leverage points. Based on a previously proposed theorem, in this paper we enunciate, and rigorously demonstrate, a new lemma that proves the identifiability of leverage points in LAV-based state estimation. On the basis of these theoretical foundations, we propose an algorithm for leverage point identification whose performance is validated by means of extensive numerical simulations and compared against more traditional approaches, like Projection Statistics (PS). The obtained results confirm that the proposed method outperforms PS and represents a significant enhancement for LAV-based state estimators as it correctly identifies all the leverage points, independently of the accuracy or the presence of measurement gross errors. A dedicated application example with respect to power system state estimation is finally included and discussed.
Joint Bandwidth Allocation and Path Selection in WANs with Path Cardinality Constraints
Authors: Jinxin Wang, Fan Zhang, Zhonglin Xie, Gong Zhang, Zaiwen Wen
Date: 2020-08-10 00:00:00
Tasks: Fairness

In this paper, we study a joint bandwidth allocation and path selection problem via solving a multi-objective minimization problem under the path cardinality constraints, namely MOPC. Our problem formulation captures various types of objectives including the proportional fairness, the total completion time, as well as the worst-case link utilization ratio. Such an optimization problem is very challenging since it is highly non-convex. Almost all existing works deal with such a problem using relaxation techniques to transform it to be a convex optimization problem. However, we provide a novel solution framework based on the classic alternating direction method of multipliers (ADMM) approach for solving this problem. Our proposed algorithm is simple and easy to be implemented. Each step of our algorithm consists of either finding the maximal root of a single-cubic equation which is guaranteed to have at least one positive solution or solving a one-dimensional convex subproblem in a fixed interval. Under some mild assumptions, we prove that any limiting point of the generated sequence under our proposed algorithm is a stationary point. Extensive numerical simulations are performed to demonstrate the advantages of our algorithm compared with various baselines.
improving partition-block-based acoustic echo canceler in under-modeling scenarios
Authors: Wenzhi Fan, Jing Lu
Date: 2020-08-10 00:00:00
Tasks: Acoustic echo cancellation

Recently, a partitioned-block-based frequency-domain Kalman filter (PFKF) has been proposed for acoustic echo cancellation. Compared with the normal frequency-domain Kalman filter, the PFKF utilizes the partitioned-block structure, resulting in both fast convergence and low time-latency. We present an analysis of the steady-state behavior of the PFKF and found that it suffers from a biased steady-state solution when the filter is of deficient length. Accordingly, we propose an effective modification that has the benefit of the guaranteed optimal steady-state behavior. Simulations are conducted to validate the improved performance of the proposed method.
When words collide: Bayesian meta-analyses of distractor and target properties in the picture-word interference paradigm
Authors: Audrey Bürki, F. -Xavier Alario, Shravan Vasishth
Date: 2020-08-10 00:00:00

In the picture-word interference paradigm, participants name pictures while ignoring a written or spoken distractor word. Naming times to the pictures are slowed down by the presence of the distractor word. Various properties of the distractor modulate this slow down, for example naming times are shorter with frequent vs. infrequent distractors. Building on this line of research, the present study investigates in more detail the impact of distractor and target word properties on picture naming times. We report the results of several Bayesian meta-analyses, based on 35 datasets. The aim of the first analysis was to obtain an estimation of the size of the distractor frequency effect, and of its precision, in typical picture-word interference experiments where this variable is not manipulated. The analysis shows that a one-unit increase in log frequency results in response times to the pictures decreasing by about 4ms (95% Credible Interval: [-6, -2]). With the second and third analyses, we show that after accounting for the effect of frequency, two variables known to influence processing times in visual word processing tasks also influence picture naming times: distractor length and orthographic neighborhood. Finally, we found that distractor word frequency and target word frequency interact; the effect of distractor frequency decreases as the frequency of the target word increases. We discuss the theoretical and methodological implications of these findings, as well as the importance of obtaining high-precision estimates of experimental effects.
Cooperative Communications for Internet of Everything in B5G/6G Hybrid and Ubiquitous Networks: Foundation, Further Optimization and Solutions
Authors: He Huang, Su Hu, Chaowei Yuan
Date: 2020-08-10 00:00:00

Cooperative Communications (CC) has been one of most critical communication technologies which plays a founding role on Internet of Everything in B5G/6G networks. As 5G communications standard is gradually established recently, core communications technologies with CC are further studied to significantly improve communication quality and develop new communications scenarios for B5G/6G ubiquitous networks. Considering that CC has been regarded as foundation theory which widely exists in future multiple B5G/6G hybrid scenarios, such as, Cognitive Internet of Things (CIOT) networks, UAVs communications, air-space-ground of integrated networks, underwater acoustic communication and so on, besides it is closely combined with other key technologies, for examples, Massive MIMO, NOMA, Full-duplex transmission, Polar code and so on. Hence, in this paper we review foundation of CC for Internet of Everything in B5G/6G multiple heterogeneous CC networks, and compare fundamental CC algorithms to reveal key of performance improvement. Furthermore we propose that collective communications ideology is theory of foundation to realize communications for arbitrary two points as source/destination devices, sensors, relays, IOT nodes and so on in future.
From private to public governance: The case for reconfiguring energy systems as a commons
Date: 2020-08-10 00:00:00

The discussions around the unsustainability of the dominant socio-economic
structures have yet to produce solutions to address the escalating problems we
face as a species. Such discussions, this paper argues, are hindered by the
limited scope of the proposed solutions within a business-as-usual context as
well as by the underlying technological rationale upon which these solutions
are developed. In this paper, we conceptualize a radical sustainable
alternative to the energy conundrum based on an emerging mode of production and
a commons-based political economy. We propose a commons-oriented Energy
Internet as a potential system for energy production and consumption, which may
be better suited to tackle the current issues society faces. We conclude by
referring to some of the challenges that the implementation of such a proposal
would entail.
Subword Regularization: An Analysis of Scalability and Generalization for End-to-End Automatic Speech Recognition
Date: 2020-08-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Diversity, Machine Translation, Segmentation, speech-recognition, Speech Recognition, Translation

Subwords are the most widely used output units in end-to-end speech
recognition. They combine the best of two worlds by modeling the majority of
frequent words directly and at the same time allow open vocabulary speech
recognition by backing off to shorter units or characters to construct words
unseen during training. However, mapping text to subwords is ambiguous and
often multiple segmentation variants are possible. Yet, many systems are
trained using only the most likely segmentation. Recent research suggests that
sampling subword segmentations during training acts as a regularizer for neural
machine translation and speech recognition models, leading to performance
improvements. In this work, we conduct a principled investigation on the
regularizing effect of the subword segmentation sampling method for a streaming
end-to-end speech recognition task. In particular, we evaluate the subword
regularization contribution depending on the size of the training dataset. Our
results suggest that subword regularization provides a consistent improvement
of (2-8%) relative word-error-rate reduction, even in a large-scale setting
with datasets up to a size of 20k hours. Further, we analyze the effect of
subword regularization on recognition of unseen words and its implications on
beam diversity.
Corporate Governance and Firms Financial Performance in the United Kingdom
Authors: Martin Kyere, Marcel Ausloos
Date: 2020-07-22 00:00:00

The objective of this study is to examine empirically the impact of good corporate governance on financial performance of United Kingdom non-financial listed firms. Agency theory and stewardship theory serve as the bases of a conceptual model. Five corporate governance mechanisms are examined on two financial performance indicators, return on assets (ROA) and Tobin's Q, employing cross-sectional regression methodology. The conclusion drawn from empirical test so performed on 252 firms listed on London Stock Exchange for the year 2014 indicates a positive or a negative relationship, but also sometimes no effect, of corporate governance mechanisms impact on financial performance. The implications are discussed. Thereby, so distinguishing effects due to causes, we present a proof that, when the right corporate governance mechanisms are chosen, the finances of a firm can be improved. The results of this research should have some implication on academia and policy makers thoughts.
Insider Ownership and Dividend Payout Policy: The Role of Business Cycle
Authors: Asmar Aliyeva
Date: 2020-07-19 00:00:00

We investigate how the relationship between managerial stock incentives and the dividend payout policy is impacted by the business cycle by using the data of S&P 1500 companies during 2000-2018. We find a strong negative relationship between managerial stock options and annual dividend payouts of companies for the full sample. Although the direction of the relationship is also negative for the recession period, the coefficient is found to be insignificant. We also find that the mentioned relationship may vary during the recession depending on the size of the company. The impact of stock options on the dividend payout is negative for medium-sized companies and the coefficient is both economically and statistically significant. The direction of impact changes for large-cap companies indicating to deterioration of the CEO voting power in those companies and less agency problem. We also determine that the percentage of shares held by the CEO has a positive impact on annual dividends distributed for large-cap companies, whereas this relationship changes in times of recession.
Approximate Optimal Control for Safety-Critical Systems with Control Barrier Functions
Date: 2020-08-10 00:00:00

Control Barrier Functions (CBFs) have become a popular tool for enforcing set
invariance in safety-critical control systems. While guaranteeing safety, most
CBF approaches are myopic in the sense that they solve an optimization problem
at each time step rather than over a long time horizon. This approach may allow
a system to get too close to the unsafe set where the optimization problem can
become infeasible. Some of these issues can be mitigated by introducing
relaxation variables into the optimization problem; however, this compromises
convergence to the desired equilibrium point. To address these challenges, we
develop an approximate optimal approach to the safety-critical control problem
in which the cost of violating safety constraints is directly embedded within
the value function. We show that our method is capable of guaranteeing both
safety and convergence to a desired equilibrium. Finally, we compare the
performance of our method with that of the traditional quadratic programming
approach through numerical examples.
Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback
Authors: Kyeongbo Kong, Woo-Jin Song, Moonsik Min
Date: 2020-08-10 00:00:00
Tasks: Binarization, Knowledge Distillation, Quantization

We propose a deep learning-based channel estimation, quantization, feedback, and precoding method for downlink multiuser multiple-input and multiple-output systems. In the proposed system, channel estimation and quantization for limited feedback are handled by a receiver deep neural network (DNN). Precoder selection is handled by a transmitter DNN. To emulate the traditional channel quantization, a binarization layer is adopted at each receiver DNN, and the binarization layer is also used to enable end-to-end learning. However, this can lead to inaccurate gradients, which can trap the receiver DNNs at a poor local minimum during training. To address this, we consider knowledge distillation, in which the existing DNNs are jointly trained with an auxiliary transmitter DNN. The use of an auxiliary DNN as a teacher network allows the receiver DNNs to additionally exploit lossless gradients, which is useful in avoiding a poor local minimum. For the same number of feedback bits, our DNN-based precoding scheme can achieve a higher downlink rate compared to conventional linear precoding with codebook-based limited feedback.
A single-cell mathematical model of SARS-CoV-2 induced pyroptosis and the effects of anti-inflammatory intervention
Authors: Sara J Hamis, Fiona R Macfarlane
Date: 2020-08-10 00:00:00

Pyroptosis is an inflammatory mode of cell death that can contribute to the cytokine storm associated with severe cases of coronavirus disease 2019 (COVID-19). The formation of the NLRP3 inflammasome is central to pyroptosis, which may be induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Inflammasome formation, and by extension pyroptosis, may be inhibited by certain anti-inflammatory drugs. In this study, we present a single-cell mathematical model that captures the formation of the NLRP3 inflammasome, pyroptotic cell death and responses to anti-inflammatory intervention that hinder the formation of the NLRP3 inflammasome. The model is formulated in terms of a system of ordinary differential equations (ODEs) that describe the dynamics of the biological components involved in pyroptosis. Our results demonstrate that an anti-inflammatory drug can delay the formation of the NLRP3 inflammasome, and thus may alter the mode of cell death from inflammatory (pyroptosis) to non-inflammatory e.g., apoptosis). The single-cell model is being implemented in a SARS-CoV-2 Tissue Simulator, in collaboration with a multidisciplinary coalition investigating within host-dynamics of COVID-19. In this paper, we provide an overview of the SARS-CoV-2 Tissue Simulator and highlight the effects of pyroptosis on a cellular level.
Viral Aerosol Concentration Characterization and Detection in Bounded Environments
Authors: Osama Amin, Hayssam Dahrouj, Nojood Almayouf, Tareq Y. Al-Naffouri, Basem Shihada, Mohamed-Slim Alouini
Date: 2020-08-10 00:00:00

Viral spread has been intermittently threatening human life over time. Characterizing the viral concentration and modelling the viral transmission are, therefore, considered major milestones for enhancing viral detection capabilities. This paper addresses the problem of viral aerosol detection based on the exhaled breath in a bounded environment, e.g., a bounded room. The paper models the exhaled breath as a cloud which is emitted through the room continuously, and analyzes the temporal-spatial virus concentration by accounting for partial absorption and reflection at each side of the room. The paper first derives a closed form expression of the temporal-spatial virus concentration. It then considers the deployment of a receiver composed of an air sampler and a bio-sensor to detect the viral existence of a specific virus. We, therefore, assess the detection capabilities of the proposed system via evaluating the viral miss-detection probability as a function of the sampling volume and the detection time-instance at the receiver side. Our numerical simulations verify the validity of the analytical results, and illustrate the ability of the proposed system to detect viruses in indoor environments. The results further characterize the impacts of several system parameters on the miss-detection probability.
Decision Conflict, Logit, and the Outside Option
Authors: Georgios Gerasimou
Date: 2020-08-10 00:00:00

Decision makers often opt for the deferral outside option when they find it difficult to make an active choice. Contrary to existing logit models with an outside option where the latter is assigned a fixed value exogenously, this paper introduces and analyzes a class of logit models where that option's value is menu-dependent, may be determined endogenously, and could be interpreted as proxying the varying degree of decision difficulty at different menus. We focus on the *power logit* special class of these models. We show that these predict some observed choice-deferral effects that are caused by hard decisions, including non-monotonic "roller-coaster" choice-overload phenomena that are regulated by the presence or absence of a clearly dominant feasible alternative. We illustrate the usability, novel insights and explanatory gains of the proposed framework for empirical discrete choice analysis and theoretical modelling of imperfectly competitive markets in the presence of potentially indecisive consumers.
Robust and Scalable Techniques for TWR and TDoA based localization using Ultra Wide Band Radios
Authors: Rakshit Ramesh, Aaron John-Sabu, Harshitha S, Siddarth Ramesh, Vishwas Navada B, Mukunth Arunachalam, Bharadwaj Amrutur
Date: 2020-08-10 00:00:00
Tasks: Autonomous Vehicles, Position

Current trends in autonomous vehicles and their applications indicates an increasing need in positioning at low battery and compute cost. Lidars provide accurate localization at the cost of high compute and power consumption which could be detrimental for drones. Modern requirements for autonomous drones such as No-Permit-No-Takeoff (NPNT) and applications restricting drones to a corridor require the infrastructure to constantly determine the location of the drone. Ultra Wide Band Radios (UWB) fulfill such requirements and offer high precision localization and fast position update rates at a fraction of the cost and battery consumption as compared to lidars and also have greater network availability than GPS in a dense forested campus or an indoor setting. We present in this paper a novel protocol and technique to localize a drone for such applications using a Time Difference of Arrival (TDoA) approach. This further increases the position update rates without sacrificing on accuracy and compare it to traditional methods