pwc-archive/papers-with-abstracts huggingface dataset:
IoT Virtualization with ML-based Information Extraction
Authors: Martin Bauer
Date: 2021-06-10 00:00:00
Tasks: Management

For IoT to reach its full potential, the sharing and reuse of information in different applications and across verticals is of paramount importance. However, there are a plethora of IoT platforms using different representations, protocols and interaction patterns. To address this issue, the Fed4IoT project has developed an IoT virtualization platform that, on the one hand, integrates information from many different source platforms and, on the other hand, makes the information required by the respective users available in the target platform of choice. To enable this, information is translated into a common, neutral exchange format. The format of choice is NGSI-LD, which is being standardized by the ETSI Industry Specification Group on Context Information Management (ETSI ISG CIM). Thing Visors are the components that translate the source information to NGSI-LD, which is then delivered to the target platform and translated into the target format. ThingVisors can be implemented by hand, but this requires significant human effort, especially considering the heterogeneity of low level information produced by a multitude of sensors. Thus, supporting the human developer and, ideally, fully automating the process of extracting and enriching data and translating it to NGSI-LD is a crucial step. Machine learning is a promising approach for this, but it typically requires large amounts of hand-labelled data for training, an effort that makes it unrealistic in many IoT scenarios. A programmatic labelling approach called knowledge infusion that encodes expert knowledge is used for matching a schema or ontology extracted from the data with a target schema or ontology, providing the basis for annotating the data and facilitating the translation to NGSI-LD.
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation
Authors: Xingyi Yang, Muchao Ye, Quanzeng You, Fenglong Ma
Date: 2021-05-25 00:00:00
Proceeding: ACL 2021 5
Tasks: Decoder, Medical Image Analysis, Medical Report Generation, Retrieval, Sentence

Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentence-level templates for clinically accurate report generation. MedWriter first employs the Visual-Language Retrieval~(VLR) module to retrieve the most relevant reports for the given images. To guarantee the logical coherence between sentences, the Language-Language Retrieval~(LLR) module is introduced to retrieve relevant sentences based on the previous generated description. At last, a language decoder fuses image features and features from retrieved reports and sentences to generate meaningful medical reports. We verified the effectiveness of our model by automatic evaluation and human evaluation on two datasets, i.e., Open-I and MIMIC-CXR.
IJB–S: IARPA Janus Surveillance Video Benchmark
Authors: Nathan D. Kalka Noblis, Brianna Maze; James A. Duncan, Kevin O’Connor, Stephen Elliott, Kaleb Hebert, Julia Bryan, Anil K. Jain
Date: 2019-04-25 00:00:00

We present IJB-S dataset, an open-source IARPA Janus Surveillance Video Benchmark and associated protocols. The dataset consists of images and surveillance video collected from 202 subjects at a Department of Defense (DoD) training facility. Surveillance video was captured across multiple vignettes representative of a variety of real-world surveillance use cases that are particularly of interest to law enforcement and national security communities. Each video was annotated by human subject matter experts in order to generate ground truth identity and bounding box face labels. In total, over 10 million annotations were collected for the dataset. We present benchmark results utilizing state of the art deep learning approaches such as FaceNet. Our results illustrate and characterize the difficulty of the dataset.
An Empirical Study of DeFi Liquidations: Incentives, Risks, and Instabilities
Authors: Kaihua Qin, Liyi Zhou, Pablo Gamito, Philipp Jovanovic, Arthur Gervais
Date: 2021-06-11 00:00:00

Financial speculators often seek to increase their potential gains with leverage. Debt is a popular form of leverage, and with over 39.88B USD of total value locked (TVL), the Decentralized Finance (DeFi) lending markets are thriving. Debts, however, entail the risks of liquidation, the process of selling the debt collateral at a discount to liquidators. Nevertheless, few quantitative insights are known about the existing liquidation mechanisms. In this paper, to the best of our knowledge, we are the first to study the breadth of the borrowing and lending markets of the Ethereum DeFi ecosystem. We focus on Aave, Compound, MakerDAO, and dYdX, which collectively represent over 85% of the lending market on Ethereum. Given extensive liquidation data measurements and insights, we systematize the prevalent liquidation mechanisms and are the first to provide a methodology to compare them objectively. We find that the existing liquidation designs well incentivize liquidators but sell excessive amounts of discounted collateral at the borrowers' expenses. We measure various risks that liquidation participants are exposed to and quantify the instabilities of existing lending protocols. Moreover, we propose an optimal strategy that allows liquidators to increase their liquidation profit, which may aggravate the loss of borrowers.
Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI
Authors: Mohit Kumar, Bernhard A. Moser, Lukas Fischer, Bernhard Freudenthaler
Date: 2021-06-06 00:00:00
Tasks: Heart Rate Variability, Privacy Preserving

In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic trustworthy AI framework is introduced. A unified approach to "privacy-preserving interpretable and transferable learning" is considered for studying and optimizing the tradeoffs between privacy, interpretability, and transferability aspects. A variational membership-mapping Bayesian model is used for the analytical approximations of the defined information theoretic measures for privacy-leakage, interpretability, and transferability. The approach consists of approximating the information theoretic measures via maximizing a lower-bound using variational optimization. The study presents a unified information theoretic approach to study different aspects of trustworthy AI in a rigorous analytical manner. The approach is demonstrated through numerous experiments on benchmark datasets and a real-world biomedical application concerned with the detection of mental stress on individuals using heart rate variability analysis.
Dynamic Event-Triggered Consensus of Multi-agent Systems on Matrix-weighted Networks
Authors: Lulu Pan, Haibin Shao, Dewei Li, Lin Liu
Date: 2021-06-11 00:00:00

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks, in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold, which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Distributed event-triggered control protocols are proposed to guarantee leaderless and leader-follower consensus for multi-agent systems on matrix-weighted networks, respectively. Remarkably, the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks, generalizing those results only applicable for scalar-weighted networks. The proposed approach allows each agent to broadcast and receive information only at its triggering instants. Finally, simulation examples are provided to demonstrate the theoretical results.
Safety of Dynamical Systems with Multiple Non-Convex Unsafe Sets Using Control Barrier Functions
Authors: Gennaro Notomista, Matteo Saveriano
Date: 2021-06-11 00:00:00
Tasks: Model Predictive Control, Robot Navigation

This paper presents an approach to deal with safety of dynamical systems in presence of multiple non-convex unsafe sets. While optimal control and model predictive control strategies can be employed in these scenarios, they suffer from high computational complexity in case of general nonlinear systems. Leveraging control barrier functions, on the other hand, results in computationally efficient control algorithms. Nevertheless, when safety guarantees have to be enforced alongside stability objectives, undesired asymptotically stable equilibrium points have been shown to arise. We propose a computationally efficient optimization-based approach which allows us to ensure safety of dynamical systems without introducing undesired equilibria even in presence of multiple non-convex unsafe sets. The developed control algorithm is showcased in simulation and in a real robot navigation application.
Certifying the LTL Formula p Until q in Hybrid Systems
Authors: Hyejin Han, Mohamed Maghenem, Ricardo G. Sanfelice
Date: 2021-06-11 00:00:00

In this paper, we propose sufficient conditions to guarantee that a linear temporal logic (LTL) formula of the form p Until q, denoted by $p \mathcal{U} q$, is satisfied for a hybrid system. Roughly speaking, the formula $p \mathcal{U} q$ is satisfied means that the solutions, initially satisfying proposition p, keep satisfying this proposition until proposition q is satisfied. To certify such a formula, connections to invariance notions such as conditional invariance (CI) and eventual conditional invariance (ECI), as well as finite-time attractivity (FTA) are established. As a result, sufficient conditions involving the data of the hybrid system and an appropriate choice of Lyapunov-like functions, such as barrier functions, are derived. The considered hybrid system is given in terms of differential and difference inclusions, which capture the continuous and the discrete dynamics present in the same system, respectively. Examples illustrate the results throughout the paper.
Distributed Urban Freeway Traffic Optimization Considering Congestion Propagation
Authors: Fengkun Gao, Bo Yang, Cailian Chen, Xinping Guan, Yang Zhang
Date: 2021-06-11 00:00:00

Effective traffic optimization strategies can improve the performance of transportation networks significantly. Most exiting works develop traffic optimization strategies depending on the local traffic states of congested road segments, where the congestion propagation is neglected. This paper proposes a novel distributed traffic optimization method for urban freeways considering the potential congested road segments, which are called potential-homogeneous-area. The proposed approach is based on the intuition that the evolution of congestion may affect the neighbor segments due to the mobility of traffic flow. We identify potential-homogeneous-area by applying our proposed temporal-spatial lambda-connectedness method using historical traffic data. Further, global dynamic capacity constraint of this area is integrated with cell transmission model (CTM) in the traffic optimization problem. To reduce computational complexity and improve scalability, we propose a fully distributed algorithm to solve the problem, which is based on the partial augmented Lagrangian and dual-consensus alternating direction method of multipliers (ADMM). By this means, distributed coordination of ramp metering and variable speed limit control is achieved. We prove that the proposed algorithm converges to the optimal solution so long as the traffic optimization objective is convex. The performance of the proposed method is evaluated by macroscopic simulation using real data of Shanghai, China.
A Simple Approach to Increase the Maximum Allowable Transmission Interval
Authors: Michael Hertneck, Frank Allgöwer
Date: 2020-06-05 00:00:00

When designing Networked Control Systems (NCS), the maximum allowable transmission interval (MATI) is an important quantity, as it provides the admissible time between two transmission instants. An efficient procedure to compute a bound on the MATI such that stability can be guaranteed for general nonlinear NCS is the emulation of a continuous-time controller. In this paper, we present a simple but efficient modification to the well-established emulation-based approach from Carnevale et al. (2007) to derive a bound on the MATI. Whilst only minor technical changes are required, the proposed modification can lead to significant improvements for the MATI bound as compared to Carnevale et al. (2007). We revisit two numerical examples from literature and demonstrate that the improvement may amount to more than 100%.
Allee effect in models of interacting species
Authors: Marcelo N Kuperman, Guillermo Abramson
Date: 2021-06-10 00:00:00

The search for more realistic models for interacting species has produced many adaptations of the original Lotka-Volterra equations, such as the inclusion of the Allee effect and the different Holling's types of functional response. In the present work we show that a correct implementation of both ideas together requires a careful formulation. We focus our work in the fact that a density dependent carrying capacity, combined with the Allee effect, can lead to meaningless effects. We illustrate the difficulties in predator-prey and two-species competition models, together with our proposed solution of the careful inclusion of the corresponding cubic terms.
An RF-source-free microwave photonic radar with an optically injected semiconductor laser for high-resolution detection and imaging
Authors: Pei Zhou, Rengheng Zhang, Nianqiang Li, Zhidong Jiang, Shilong Pan
Date: 2021-06-11 00:00:00

This paper presents a novel microwave photonic (MWP) radar scheme that is capable of optically generating and processing broadband linear frequency-modulated (LFM) microwave signals without using any radio-frequency (RF) sources. In the transmitter, a broadband LFM microwave signal is generated by controlling the period-one (P1) oscillation of an optically injected semiconductor laser. After targets reflection, photonic de-chirping is implemented based on a dual-drive Mach-Zehnder modulator (DMZM), which is followed by a low-speed analog-to-digital converter (ADC) and digital signal processer (DSP) to reconstruct target information. Without the limitations of external RF sources, the proposed radar has an ultra-flexible tunability, and the main operating parameters are adjustable, including central frequency, bandwidth, frequency band, and temporal period. In the experiment, a fully photonics-based Ku-band radar with a bandwidth of 4 GHz is established for high-resolution detection and inverse synthetic aperture radar (ISAR) imaging. Results show that a high range resolution reaching ~1.88 cm, and a two-dimensional (2D) imaging resolution as high as ~1.88 cm x ~2.00 cm are achieved with a sampling rate of 100 MSa/s in the receiver. The flexible tunability of the radar is also experimentally investigated. The proposed radar scheme features low cost, simple structure, and high reconfigurability, which, hopefully, is to be used in future multifunction adaptive and miniaturized radars.
An age-structured SEIR model for COVID--19 incidence in Dublin, Ireland with framework for evaluating health intervention cost
Authors: Fatima-Zahra Jaouimaa, Daniel Dempsey, Suzanne van Osch, Stephen Kinsella, Kevin Burke, Jason Wyse, James Sweeney
Date: 2021-06-11 00:00:00
Tasks: counterfactual, Uncertainty Quantification

Strategies adopted globally to mitigate the threat of COVID-19 have primarily involved lockdown measures with substantial economic and social costs with varying degrees of success. Morbidity patterns of COVID-19 variants have a strong association with age, while restrictive lockdown measures have association with negative mental health outcomes in some age groups. Reduced economic prospects may also afflict some age cohorts more than others. Motivated by this, we propose a model to describe COVID-19 community spread incorporating the role of age-specific social interactions. Through a flexible parameterisation of an age-structured deterministic Susceptible Exposed Infectious Removed (SEIR) model, we provide a means for characterising different forms of lockdown which may impact specific age groups differently. Social interactions are represented through age group to age group contact matrices, which can be trained using available data and are thus locally adapted. This framework is easy to interpret and suitable for describing counterfactual scenarios, which could assist policy makers with regard to minimising morbidity balanced with the costs of prospective suppression strategies. Our work originates from an Irish context and we use disease monitoring data from February 29th 2020 to January 31st 2021 gathered by Irish governmental agencies. We demonstrate how Irish lockdown scenarios can be constructed using the proposed model formulation and show results of retrospective fitting to incidence rates and forward planning with relevant ``what if/instead of'' lockdown counterfactuals with uncertainty quantification. Our formulation is agnostic to a specific locale, in that lockdown strategies in other regions can be straightforwardly encoded using this model. The methods we describe are made publicly available online through an accessible and easy to use web interface.
Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption
Authors: Claudia Noack
Date: 2021-06-11 00:00:00
Tasks: Sensitivity, valid

In this paper, we develop a method to assess the sensitivity of local average treatment effect estimates to potential violations of the monotonicity assumption of Imbens and Angrist (1994). We parameterize the degree to which monotonicity is violated using two sensitivity parameters: the first one determines the share of defiers in the population, and the second one measures differences in the distributions of outcomes between compliers and defiers. For each pair of values of these sensitivity parameters, we derive sharp bounds on the outcome distributions of compliers in the first-order stochastic dominance sense. We identify the robust region that is the set of all values of sensitivity parameters for which a given empirical conclusion, e.g. that the local average treatment effect is positive, is valid. Researchers can assess the credibility of their conclusion by evaluating whether all the plausible sensitivity parameters lie in the robust region. We obtain confidence sets for the robust region through a bootstrap procedure and illustrate the sensitivity analysis in an empirical application. We also extend this framework to analyze treatment effects of the entire population.
Finding the Contextual Gap Towards Employee Engagement in Financial Sector: A Review Study
Authors: Habiba Akter, Ilham Sentosa, Sheikh Muhamad Hizam, Waqas Ahmed, Arifa Akter
Date: 2021-06-11 00:00:00
Tasks: Articles, Management

This review paper identifies the core evidence of research on employee engagement , considering a stern challenge facing the financial sector nowadays. The study highlights the noteworthy knowledge gaps that will support human resource management practitioners to embed in the research towards sectoral context. Pertinent articles were selected through key search points and excerpt-related literature. The key search points covered the topic related to different terms of engagement for example "employee engagement" OR "work engagement" OR "job engagement" OR "organization engagement" OR "staff engagement" OR "personnel engagement" which were steered in diverse context particularly financial sector. Through critically reviewing the literature for the last 11 years i.e., 2009-2019, we discovered 91 empirical studies in financial sector. From these studies, we found the overall concept of engagement and its different determinants (e.g., organizational factors, individual factors, job factors) as well as its various outcomes (e.g., employee outcomes, organizational outcomes). We also formulated a conceptual model to expand the body of knowledge in the area of employee engagement for a better understanding of its predictors and outcomes. Besides, limitations of the study and future recommendations are also contemplated.
Applying endogenous learning models in energy system optimization
Authors: Jabir Ali Ouassou, Julian Straus, Marte Fodstad, Gunhild Reigstad, Ove Wolfgang
Date: 2021-06-11 00:00:00

Conventional energy production based on fossil fuels causes emissions which contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, an endeavor that requires an accurate modeling of cost reductions due to technological learning effects. In this review, we summarize common methodologies for modeling technological learning and associated cost reductions. The focus is on learning effects in hydrogen production technologies due to their importance in a low-carbon energy system, as well as the application of endogenous learning in energy system models. Finally, we present an overview of the learning rates of relevant low-carbon technologies required to model future energy systems.
The separation of market and price in some free competitions and its related solution to the over-application problem in the job market
Authors: Vincent Zha
Date: 2021-06-10 00:00:00

According to common understanding, in free completion of a private product, market and price, the two main factors in the competition that leads to economic efficiency, always exist together. This paper, however, points out the phenomenon that in some free competitions the two factors are separated hence causing inefficiency. For one type, the market exists whereas the price is absent, i.e. free, for a product. An example of this type is the job application market where the problem of over-application commonly exists, costing recruiters much time in finding desired candidates from massive applicants, resulting in inefficiency. To solve the problem, this paper proposes a solution that the recruiters charge submission fees to the applications to make the competition complete with both factors, hence enhancing the efficiency. For the other type, the price exists whereas the market is absent for a product. An example of this type is the real estate agent market, where the price of the agents exists but the market, i.e. the facility allowing the sellers' information to be efficiently discovered, is largely absent, also causing inefficiency. In summary, the contribution of this paper consists of two aspects: one is the discovery of the possible separation of the two factors in free competitions; the other is, thanks to the discovery, a solution to the over-application problem in the job market.