pwc-archive/papers-with-abstracts huggingface dataset:
Non-Autoregressive ASR with Self-Conditioned Folded Encoders
Authors: Tatsuya Komatsu
Date: 2022-02-17 00:00:00

This paper proposes CTC-based non-autoregressive ASR with self-conditioned folded encoders. The proposed method realizes non-autoregressive ASR with fewer parameters by folding the conventional stack of encoders into only two blocks; base encoders and folded encoders. The base encoders convert the input audio features into a neural representation suitable for recognition. This is followed by the folded encoders applied repeatedly for further refinement. Applying the CTC loss to the outputs of all encoders enforces the consistency of the input-output relationship. Thus, folded encoders learn to perform the same operations as an encoder with deeper distinct layers. In experiments, we investigate how to set the number of layers and the number of iterations for the base and folded encoders. The results show that the proposed method achieves a performance comparable to that of the conventional method using only 38% as many parameters. Furthermore, it outperforms the conventional method when increasing the number of iterations.
Acoustic Event Detection with Classifier Chains
Authors: Tatsuya Komatsu, Shinji Watanabe, Koichi Miyazaki, Tomoki Hayashi
Date: 2022-02-17 00:00:00
Tasks: Event Detection

This paper proposes acoustic event detection (AED) with classifier chains, a new classifier based on the probabilistic chain rule. The proposed AED with classifier chains consists of a gated recurrent unit and performs iterative binary detection of each event one by one. In each iteration, the event's activity is estimated and used to condition the next output based on the probabilistic chain rule to form classifier chains. Therefore, the proposed method can handle the interdependence among events upon classification, while the conventional AED methods with multiple binary classifiers with a linear layer and sigmoid function have placed an assumption of conditional independence. In the experiments with a real-recording dataset, the proposed method demonstrates its superior AED performance to a relative 14.80% improvement compared to a convolutional recurrent neural network baseline system with the multiple binary classifiers.
Wearable SELD dataset: Dataset for sound event localization and detection using wearable devices around head
Authors: Kento Nagatomo, Masahiro Yasuda, Kohei Yatabe, Shoichiro Saito, Yasuhiro Oikawa
Date: 2022-02-17 00:00:00
Tasks: Sound Event Localization and Detection

Sound event localization and detection (SELD) is a combined task of identifying the sound event and its direction. Deep neural networks (DNNs) are utilized to associate them with the sound signals observed by a microphone array. Although ambisonic microphones are popular in the literature of SELD, they might limit the range of applications due to their predetermined geometry. Some applications (including those for pedestrians that perform SELD while walking) require a wearable microphone array whose geometry can be designed to suit the task. In this paper, for the development of such a wearable SELD, we propose a dataset named Wearable SELD dataset. It consists of data recorded by 24 microphones placed on a head and torso simulators (HATS) with some accessories mimicking wearable devices (glasses, earphones, and headphones). We also provide experimental results of SELD using the proposed dataset and SELDNet to investigate the effect of microphone configuration.
Multi-Fleet Platoon Matching: A Game-Theoretic Approach
Authors: Alexander Johansson, Ehsan Nekouei, Karl Henrik Johansson, Jonas Mårtensson
Date: 2022-02-17 00:00:00

We consider the platoon matching problem for a set of trucks with the same origin, but different destinations. It is assumed that the vehicles benefit from traveling in a platoon for instance through reduced fuel consumption. The vehicles belong to different fleet owners and their strategic interaction is modeled as a non-cooperative game where the vehicle actions are their departure times. Each truck has a preferred departure time and its utility function is defined as the difference between its benefit from platooning and the cost of deviating from its preferred departure time. We show that the platoon matching game is an exact potential game. An algorithm based on best response dynamics is proposed for finding a Nash equilibrium of the game. At a Nash equilibrium, vehicles with the same departure time are matched to form a platoon. Finally, the total fuel reduction at the Nash equilibrium is studied and compared with that of a cooperative matching solution where a common utility function for all vehicles is optimized.
Real-Time Cross-Fleet Pareto-Improving Truck Platoon Coordination
Authors: Alexander Johansson, Jonas Mårtensson, Xiaotong Sun, Yafeng Yin
Date: 2022-02-17 00:00:00

This paper studies a multi-fleet platoon coordination system in transport networks that deploy hubs to form trucks into platoons. The trucks belong to different fleets that are interested in increasing their profits by platooning across fleets. The profit of each fleet incorporates platooning rewards and costs for waiting at hubs. Each truck has a fixed route and a waiting time budget to spend at the hubs along its route. To ensure that all fleets are willing to participate in the system, we develop a cross-fleet Pareto-improving coordination strategy that guarantees higher fleet profits than a coordination strategy without cross-fleet platoons. By leveraging multiple hubs for platoon formation, the coordination strategy can be implemented in a real-time and distributed fashion while largely reducing the amount of travel information to be shared for system-wide coordination. We evaluate the proposed strategy in a simulation study over the Swedish transportation network. The cross-fleet platooning strategy significantly improves fleets' profits compared with single-fleet platooning, especially the profits from smaller fleets. The cross-fleet platooning strategy also shows strong competitiveness in terms of the system-wide profit compared to the case when a system planner optimizes all fleets' total profit.
Game Theoretic Models for Profit-Sharing in Multi-fleet Platoons
Authors: Alexander Johansson, Jonas Mårtensson
Date: 2022-02-17 00:00:00

Profit-sharing is needed within platoons in order for competing transportation companies to collaborate in forming platoons. In this paper, we propose distribution models of the profit designed for vehicles that are located at the same origin and are operated by competing transportation companies. The vehicles have default departure times, but can decide to depart at other times in order to benefit from platooning. We model the strategic interaction among vehicles with game theory and consider pure Nash equilibria as the solution concept. In a numerical evaluation we compare the outcomes of the games associated with different distribution models of the profit.
Robust Frequency Constrained UC Using Data Driven Logistic Regression for Island Power Systems
Authors: Mohammad Rajabdorri, Enrique Lobato, Lukas Sigrist
Date: 2022-02-17 00:00:00
Tasks: regression, Scheduling

In the current practice of short-term power scheduling, online power reserves are used to address generation mismatches and contingencies. Neither online inertia nor the speed of the committed units is considered in the scheduling process. With the increasing injection of uncertain renewable energy sources, this practice is starting to fall short especially in island power systems, where the primary frequency response is already scarce, and any contingency leads to potentially poor frequency response. This paper introduces a data driven linear constraint to improve the post-fault frequency quality in island power systems. A coherent initial data-set is obtained by simulating the system frequency response of single outages. Then logistic regression is employed as a predictive analytic procedure to differentiate the acceptable and unacceptable incidents. To compare the conventional methods with the proposed approach and also to handle the uncertain nature of renewable energy generation, an adaptive robust unit commitment formulation is utilized. Results for the island power system of La Palma show that depending on the chosen cut-point on the logistic regression estimation the proposed method can improve the frequency response quality of the system while reducing the operation costs.
Augmentation of Generalized Multivariable Grid-Forming Control for Power Converters with Cascaded Controllers
Authors: Meng Chen, Dao Zhou, Ali Tayyebi, Eduardo Prieto-Araujo, Florian Dörfler, Frede Blaabjerg
Date: 2022-02-17 00:00:00

The classic design of grid-forming control strategies for power converters rely on the stringent assumption of the timescale separation between DC and AC states and their corresponding control loops, e.g., AC and DC loops, power and cascaded voltage and current loops, etc. This paper proposes a multi-input multi-output based grid-forming (MIMO-GFM) control for the power converters using a multivariable feedback structure. First, the MIMO-GFM control couples the AC and DC loops by a general multivariable control transfer matrix. Then, the parameters design is transformed into a standard fixed-structure H-infinity synthesis. By this way, all the loops can be tuned simultaneously and optimally without relying on the assumptions of loop decoupling. Therefore, a superior and robust performance can be achieved. Experimental results verify the proposed method.
Resilience-Motivated Distribution System Restoration Considering Electricity-Water-Gas Interdependency
Authors: Jiaxu Li, Yin Xu, Ying Wang, Meng Li, Jinghan He, Chen-Ching Liu, Kevin P. Schneider
Date: 2022-02-17 00:00:00

A major outage in the electricity distribution system may affect the operation of water and natural gas supply systems, leading to an interruption of multiple services to critical customers. Therefore, enhancing resilience of critical infrastructures requires joint efforts of multiple sectors. In this paper, a distribution system service restoration method considering the electricity-water-gas interdependency is proposed. The objective is to provide electricity, water, and natural gas supplies to critical customers in the desired ratio according to their needs after an extreme event. The operational constraints of electricity, water, and natural gas networks are considered. The characteristics of electricity-driven coupling components, including water pumps and gas compressors, are also modeled. Relaxation techniques are applied to nonconvex constraints posed by physical laws of those networks. Consequently, the restoration problem is formulated as a mixed-integer second-order cone program, which can readily be solved by the off-the-shelf solvers. The proposed method is validated by numerical simulations on electricity-water-gas integrated systems, developed based on benchmark models of the subsystems. The results indicate that considering the interdependency refines the allocation of limited generation resources and demonstrate the exactness of the proposed convex relaxation.
Accelerated iterative tomographic reconstruction with x-ray edge illumination
Authors: Peter Modregger, Tomasz Korzec, Jeff Meganck, Lorenzo Massimi, Alessandro Olivo, Marco Endrizzi
Date: 2022-02-17 00:00:00
Tasks: Tomographic Reconstructions

Compared to standard tomographic reconstruction, iterative approaches offer the possibility to account for extraneous experimental influences, which allows for a suppression of related artifacts. However, the inclusion of corresponding parameters in the iterative forward model typically leads to longer computation times. Here, we demonstrate experimentally for phase sensitive X-ray imaging based on the edge illumination principle that inadequately sampled illumination curves result in ring artifacts in tomographic reconstructions. We take advantage of appropriately sampled illumination curves instead, which enables us to eliminate the corresponding parameter from the forward model and substantially increase computational speed. In addition, we demonstrate a 30\% improvement in spatial resolution of the iterative approach compared with the standard non-iterative single shot approach. Further, we report on several significant improvements in our numerical implementation of the iterative approach, which we make available online with this publication. Finally, we show that the combination of both experimental and algorithmic advancement lead to a total speed increase by one order of magnitude and an improved contrast to noise ratio in the reconstructions.
Dynamical phases in growing populations: understanding recovery from bottlenecks
Authors: Emanuele Crosato, Jeffrey N. Philippson, Shashi Thutupalli, Richard G. Morris
Date: 2022-02-17 00:00:00

Since steep declines in a population's size also typically alter its composition, population bottlenecks are considered highly important for evolution. However, despite such significance, the mechanisms governing the impact of a given population bottleneck remain poorly understood. In this context, we show that long-term post-bottleneck outcomes can depend crucially on the rate at which a diminished population grows whilst recovering. That is, two otherwise identical populations, each having undergone the same bottleneck, can fixate on dramatically different demographics due to different rates of post-bottleneck growth. This behaviour is moreover shown to change non-trivially with different levels of mutation. The underlying mechanism can be traced to intrinsic fluctuations whose standard deviation decreases in time with the inverse square root of the growing population size. Crucially, such decreasing fluctuations couple to the underlying dynamics and result in distinct regimes of (non-equilibrium) demographic behaviour, delimited by abrupt transitions at critical population sizes. Seen through this lens, growth and mutation alter evolutionary outcomes by changing the duration and character of dynamical phases; a feature that we speculate may be of generic importance across many systems.
Objectives of platform research: A co-citation and systematic literature review analysis
Authors: Fabian Schueler, Dimitri Petrik
Date: 2022-02-17 00:00:00
Tasks: Management, Systematic Literature Review

Business economics research on digital platforms often overlooks existing knowledge from other fields of research leading to conceptual ambiguity and inconsistent findings. To reduce these restrictions and foster the utilization of the extensive body of literature, we apply a mixed methods design to summarize the key findings of scientific platform research. Our bibliometric analysis identifies 14 platform-related research fields. Conducting a systematic qualitative content analysis, we identify three primary research objectives related to platform ecosystems: (1) general literature defining and unifying research on platforms; (2) exploitation of platform and ecosystem strategies; (3) improvement of platforms and ecosystems. Finally, we discuss the identified insights from a business economics perspective and present promising future research directions that could enhance business economics and management research on digital platforms and platform ecosystems.
A 3D index for measuring economic resilience with application to the modern international and global financial crises
Authors: Dimitrios Tsiotas
Date: 2022-02-17 00:00:00
Tasks: Attribute

The study and measurement of economic resilience is ruled by high level of complexity related to the diverse structure, functionality, spatiality, and dynamics describing economic systems. Towards serving the demand of integration, this paper develops a three-dimensional index, capturing engineering, ecological, and evolutionary aspects of economic resilience that are considered separately in the current literature. The proposed index is computed on GDP data of worldwide countries, for the period 1960-2020, concerning 14 crises considered as shocks, and was found well defined in a conceptual context of its components. Its application on real-world data allows introducing a novel classification of countries in terms of economic resilience, and reveals geographical patterns and structural determinants of this attribute. Impressively enough, economic resilience appears positively related to major productivity coefficients, gravitationally driven, and depended on agricultural specialization, with high structural heterogeneity in the low class. Also, the analysis fills the literature gap by shaping the worldwide map of economic resilience, revealing geographical duality and centrifugal patterns in its geographical distribution, a relationship between diachronically good performance in economic resilience and geographical distance from the shocks origin, and a continent differentiation expressed by the specialization of America in engineering resilience, Africa and Asia in ecological and evolutionary resilience, and a relative lag of Europe and Oceania. Finally, the analysis provides insights into the effect of the 2008 on the globe and supports a further research hypothesis that political instability is a main determinant of low economic resilience, addressing avenues of further research.
Continuity Postulates and Solvability Axioms in Economic Theory and in Mathematical Psychology: A Consolidation of the Theory of Individual Choice
Authors: Aniruddha Ghosh, M. Ali Khan, Metin Uyanik
Date: 2022-02-17 00:00:00
Tasks: Relation

This paper presents four theorems that connect continuity postulates in mathematical economics to solvability axioms in mathematical psychology, and ranks them under alternative supplementary assumptions. Theorem 1 connects notions of continuity (full, separate, Wold, weak Wold, Archimedean, mixture) with those of solvability (restricted, unrestricted) under the completeness and transitivity of a binary relation. Theorem 2 uses the primitive notion of a separately-continuous function to answer the question when an analogous property on a relation is fully continuous. Theorem 3 provides a portmanteau theorem on the equivalence between restricted solvability and various notions of continuity under weak monotonicity. Finally, Theorem 4 presents a variant of Theorem 3 that follows Theorem 1 in dispensing with the dimensionality requirement and in providing partial equivalences between solvability and continuity notions. These theorems are motivated for their potential use in representation theorems.
Preference Learning in School Choice Problems
Authors: SangMok Lee
Date: 2022-02-16 00:00:00

In school choice, students make decisions based on their expectations of particular schools' suitability, and the decision to gather information about schools is influenced by the acceptance odds determined by the mechanism in place. We study a school choice model where students can obtain information about their preferences by incurring a cost. We demonstrate greater homogeneity in rank-order reports and reduced information acquisition under the Deferred-Acceptance (DA) mechanism, resulting in an increased reliance on random tie-breaking and ultimately inefficient outcomes. Thus, it is critical for the DA mechanism to have easy access to school information in order to maintain its efficiency.
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training
Authors: Chen Feng, Duolikun Danier, Fan Zhang, David Bull
Date: 2022-02-17 00:00:00
Tasks: Video Quality Assessment, Visual Question Answering (VQA)

In recent years, deep learning techniques have shown significant potential for improving video quality assessment (VQA), achieving higher correlation with subjective opinions compared to conventional approaches. However, the development of deep VQA methods has been constrained by the limited availability of large-scale training databases and ineffective training methodologies. As a result, it is difficult for deep VQA approaches to achieve consistently superior performance and model generalization. In this context, this paper proposes new VQA methods based on a two-stage training methodology which motivates us to develop a large-scale VQA training database without employing human subjects to provide ground truth labels. This method was used to train a new transformer-based network architecture, exploiting quality ranking of different distorted sequences rather than minimizing the difference from the ground-truth quality labels. The resulting deep VQA methods (for both full reference and no reference scenarios), FR- and NR-RankDVQA, exhibit consistently higher correlation with perceptual quality compared to the state-of-the-art conventional and deep VQA methods, with average SROCC values of 0.8972 (FR) and 0.7791 (NR) over eight test sets without performing cross-validation. The source code of the proposed quality metrics and the large training database are available at https://chenfeng-bristol.github.io/RankDVQA.
Designing a reliable and low-latency LoRaWAN solution for environmental monitoring in factories at major accident risk
Authors: Dinesh Tamang, Alessandro Pozzebon, Lorenzo Parri, Ada Fort, Andrea Abrardo
Date: 2022-02-17 00:00:00

In this article, we propose a reliable and low-latency Long Range Wide Area Network (LoRaWAN) solution for environmental monitoring in factories at major accident risk (FMAR). In particular, a low power wearable device for sensing the toxic inflammable gases inside an industrial plant is designed with the purpose of avoiding peculiar risks and unwanted accidents to occur. Moreover, the detected data have to be urgently and reliably delivered to remote server to trigger preventive immediate actions so as to improve the machine operation. In these settings, LoRaWAN has been identified as the most proper communications technology to the needs owing to the availability of off the shelf devices and software. Hence, we assess the technological limits of LoRaWAN in terms of latency and reliability and we propose a fully LoRaWAN compliant solution to overcome these limits. The proposed solution envisages coordinated end device (ED) transmissions through the use of Downlink Control Packets (DCPs). Experimental results validate the proposed method in terms of service requirements for the considered FMAR scenario.
Sampling and Reconstructing Angular Domains with Uniform Arrays
Authors: Silvio Mandelli, Marcus Henninger, Jinfeng Du
Date: 2022-02-17 00:00:00
Tasks: ISAC

The surge of massive antenna arrays in wireless networks calls for the adoption of analog/hybrid array solutions, where multiple antenna elements are driven by a common radio front end to form a beam along a specific angle in order to maximize the beamforming gain. Many heuristics have been proposed to sample the angular domain by trading off between sampling step size and overhead, where arbitrarily small angular step size is only attainable with infinite sampling overhead. We show that, for uniform linear and rectangular arrays, lossless reconstruction of the array's angular responses at arbitrary angular precision is possible using a finite number of samples without resorting to assumptions of angular sparsity. The proposed method, sampling and reconstructing angular domain (SARA), defines how many and which angles to be sampled and the corresponding reconstruction. This general solution to scan the angular domain can therefore be applied not only to beam acquisition and channel estimation, but also to radio imaging techniques, making it a candidate for future integrated sensing and communications (ISAC). Extensive simulation results for target detection and radio imaging have demonstrated clear advantages of SARA versus other considered baselines, both in terms of angular reconstruction performance and computational complexity.