pwc-archive/papers-with-abstracts huggingface dataset:
FASTdoop: a versatile and efficient library for the input of FASTA and FASTQ files for MapReduce Hadoop bioinformatics applications
Authors: Umberto Ferraro Petrillo, Gianluca Roscigno, Giuseppe Cattaneo, Raffaele Giancarlo
Date: 2017-01-16 00:00:00
Tasks: Diversity, Management

MapReduce Hadoop bioinformatics applications require the availability of special-purpose routines to manage the input of sequence files. Unfortunately, the Hadoop framework does not provide any built-in support for the most popular sequence file formats like FASTA or BAM. Moreover, the development of these routines is not easy, both because of the diversity of these formats and the need for managing efficiently sequence datasets that may count up to billions of characters. We present FASTdoop, a generic Hadoop library for the management of FASTA and FASTQ files. We show that, with respect to analogous input management routines that have appeared in the Literature, it offers versatility and efficiency. That is, it can handle collections of reads, with or without quality scores, as well as long genomic sequences while the existing routines concentrate mainly on NGS sequence data. Moreover, in the domain where a comparison is possible, the routines proposed here are faster than the available ones. In conclusion, FASTdoop is a much needed addition to Hadoop-BAM.
Beyond cost reduction: Improving the value of energy storage in electricity systems
Authors: Maximilian Parzen, Fabian Neumann, Addrian H. Van Der Weijde, Daniel Friedrich, Aristides Kiprakis
Date: 2021-01-15 00:00:00

An energy storage technology is valuable if it makes energy systems cheaper. Traditional ways to improve storage technologies are to reduce their costs; however, the cheapest energy storage is not always the most valuable in energy systems. Modern techno-economical evaluation methods try to address the cost and value situation but do not judge the competitiveness of multiple technologies simultaneously. This paper introduces the market potential method as a new complementary valuation method guiding innovation of multiple energy storage. The market potential method derives the value of technologies by examining common deployment signals from energy system model outputs in a structured way. We apply and compare this method to cost evaluation approaches in a renewables-based European power system model, covering diverse energy storage technologies. We find that characteristics of high-cost hydrogen storage can be more valuable than low-cost hydrogen storage. Additionally, we show that modifying the freedom of storage sizing and component interactions can make the energy system 10% cheaper and impact the value of technologies. The results suggest looking beyond the pure cost reduction paradigm and focus on developing technologies with suitable value approaches that can lead to cheaper electricity systems in future.
Drafting in Collectible Card Games via Reinforcement Learning
Authors: Ronaldo Vieira, Anderson Rocha Tavares, Luiz Chaimowicz
Date: 2020-11-07 00:00:00
Tasks: Card Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Collectible card games are played by tens of millions of players worldwide. Their intricate rules and diverse cards make them much harder than traditional card games. To win, players must be proficient in two interdependent tasks: deck building and battling. In this paper, we present a deep reinforcement learning approach for deck building in arena mode - an understudied game mode present in many collectible card games. In arena, the players build decks immediately before battling by drafting one card at a time from randomly presented candidates. We investigate three variants of the approach and perform experiments on Legends of Code and Magic, a collectible card game designed for AI research. Results show that our learned draft strategies outperform those of the best agents of the game. Moreover, a participant of the Strategy Card Game AI competition improves from tenth to fourth place when coupled with our best draft agent.
Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yoloxóchitl Mixtec
Authors: Jiatong Shi, Jonathan D. Amith, Rey Castillo García, Esteban Guadalupe Sierra, Kevin Duh, Shinji Watanabe
Date: 2021-01-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

"Transcription bottlenecks", created by a shortage of effective human transcribers are one of the main challenges to endangered language (EL) documentation. Automatic speech recognition (ASR) has been suggested as a tool to overcome such bottlenecks. Following this suggestion, we investigated the effectiveness for EL documentation of end-to-end ASR, which unlike Hidden Markov Model ASR systems, eschews linguistic resources but is instead more dependent on large-data settings. We open source a Yolox\'ochitl Mixtec EL corpus. First, we review our method in building an end-to-end ASR system in a way that would be reproducible by the ASR community. We then propose a novice transcription correction task and demonstrate how ASR systems and novice transcribers can work together to improve EL documentation. We believe this combinatory methodology would mitigate the transcription bottleneck and transcriber shortage that hinders EL documentation.
Robust Scheduling of Virtual Power Plant under Exogenous and Endogenous Uncertainties
Authors: Yunfan Zhang, Feng Liu, Zhaojian Wang, Yifan Su, Shengwei Mei
Date: 2021-01-26 00:00:00
Tasks: Scheduling

Virtual power plant (VPP) provides a flexible solution to distributed energy resources integration by aggregating renewable generation units, conventional power plants, energy storages, and flexible demands. This paper proposes a novel model for determining the optimal offering strategy in the day-ahead energy-reserve market and the optimal self-scheduling plan. It considers exogenous uncertainties (or called decision-independent uncertainties, DIUs) associated with market clearing prices and available wind power generation, as well as the endogenous uncertainties (or called decision-dependent uncertainties, DDUs) pertaining to real-time reserve deployment requests. A tractable solution method based on strong duality theory, McCormick relaxation, and the Benders' decomposition to solve the proposed stochastic adaptive robust optimization with DDUs formulation is developed. Simulation results demonstrate the applicability of the proposed approach.
Estimates for solutions of homogeneous time-delay systems: Comparison of Lyapunov-Krasovskii and Lyapunov-Razumikhin techniques
Authors: Gerson Portilla, Irina Alexandrova, Sabine Mondié, Alexey Zhabko
Date: 2021-01-25 00:00:00

In this contribution, the estimates for the response of time delay systems with nonlinear homogeneous right-hand side of degree strictly greater than one are constructed. The existing results obtained via the Lyapunov--Razumikhin approach are reminded. Their proofs, revisited in the appendix, lead to explicit expressions of the involved constants. Based on a recently introduced Lyapunov--Krasovskii functional and known estimates of the domain of attraction, we present new estimates of the system response. We compare both approaches and discuss the illustrative examples.
Data-Driven Set-Based Estimation using Matrix Zonotopes with Set Containment Guarantees
Authors: Amr Alanwar, Alexander Berndt, Karl Henrik Johansson, Henrik Sandberg
Date: 2021-01-26 00:00:00
Tasks: State Estimation

We propose a method to perform set-based state estimation of an unknown dynamical linear system using a data-driven set propagation function. Our method comes with set-containment guarantees, making it applicable to safety-critical systems. The method consists of two phases: (1) an offline learning phase where we collect noisy input-output data to determine a function to propagate the state-set ahead in time; and (2) an online estimation phase consisting of a time update and a measurement update. It is assumed that known finite sets bound measurement noise and disturbances, but we assume no knowledge of their statistical properties. These sets are described using zonotopes, allowing efficient propagation and intersection operations. We propose a new approach to compute a set of models consistent with the data and noise-bound, given input-output data in the offline phase. The set of models is utilized in replacing the unknown dynamics in the data-driven set propagation function in the online phase. Then, we propose two approaches to perform the measurement update. Simulations show that the proposed estimator yields state sets comparable in volume to the 3{\sigma} confidence bounds obtained by a Kalman filter approach, but with the addition of state set-containment guarantees. We observe that using constrained zonotopes yields smaller sets but with higher computational costs than unconstrained ones.
Data-Driven Distributionally Robust Optimization for Real-Time Economic Dispatch Considering Secondary Frequency Regulation Cost
Authors: Likai Liu, Zechun Hu, Xiaoyu Duan, Nikhil Pathak
Date: 2021-01-20 00:00:00

With the large-scale integration of renewable power generation, frequency regulation resources (FRRs) are required to have larger capacities and faster ramp rates, which increases the cost of the frequency regulation ancillary service. Therefore, it is necessary to consider the frequency regulation cost and constraint along with real-time economic dispatch (RTED). In this paper, a data-driven distributionally robust optimization (DRO) method for RTED considering automatic generation control (AGC) is proposed. First, a Copula-based AGC signal model is developed to reflect the correlations among the AGC signal, load power and renewable generation variations. Secondly, samples of the AGC signal are taken from its conditional probability distribution under the forecasted load power and renewable generation variations. Thirdly, a distributionally robust RTED model considering the frequency regulation cost and constraint is built and transformed into a linear programming problem by leveraging the Wasserstein metric-based DRO technique. Simulation results show that the proposed method can reduce the total cost of power generation and frequency regulation.
The hybrid number of a ploidy profile
Authors: Katharina T. Huber, Liam J. Maher
Date: 2021-01-26 00:00:00

Polyploidization, whereby an organism inherits multiple copies of the genome of their parents, is an important evolutionary event that has been observed in plants and animals. One way to study such events is in terms of the ploidy number of the species that make up a dataset of interest. It is therefore natural to ask: How much information about the evolutionary past of the set of species that form a dataset can be gleaned from the ploidy numbers of the species? To help answer this question, we introduce and study the novel concept of a ploidy profile which allows us to formalize it in terms of a multiplicity vector indexed by the species the dataset is comprised of. Using the framework of a phylogenetic network, we present a closed formula for computing the hybrid number (i.e. the minimal number of polyploidization events required to explain a ploidy profile) of a large class of ploidy profiles. This formula relies on the construction of a certain phylogenetic network from the simplification sequence of a ploidy profile and the hybrid number of the ploidy profile with which this construction is initialized. Both of them can be computed easily in case the ploidy numbers that make up the ploidy profile are not too large. To help illustrate the applicability of our approach, we apply it to a simplified version of a publicly available Viola dataset.
Quasi Static Atmospheric Model for Aircraft Trajectory Prediction and Flight Simulation
Authors: Eduardo Gallo
Date: 2021-01-26 00:00:00
Tasks: Trajectory Prediction

Aircraft trajectory prediction requires the determination of the atmospheric properties (pressure, temperature, and density) encountered by the aircraft during its flight. This is accomplished by employing a tabulated prediction published by a meteorological service, a static atmosphere model that does not consider the atmosphere variation with time or horizontal position, such as the International Civil Aviation Organization (ICAO) Standard Atmosphere (ISA), or a variation to the later so it better conforms with the expected flight conditions. This article proposes an easy-to-use quasi static model that introduces temperature and pressure variations while respecting all the hypotheses of the ISA model, resulting in more realistic trajectory predictions as the obtained atmospheric properties bear a higher resemblance with the local conditions encountered during the flight. The proposed model relies on two parameters, the temperature and pressure offsets, and converges to the ISA model when both are zero. Expressions to obtain the atmospheric properties are established, and their dependencies with both parameters explained. The author calls this model INSA (ICAO Non Standard Atmosphere) and releases its C++ implementation as open-source software.
Real-time Video Streaming and Control of Cellular-Connected UAV System: Prototype and Performance Evaluation
Authors: Hui Zhou, Fenghe Hu, Michal Juras, Asish B Mehta, Yansha Deng
Date: 2021-01-26 00:00:00

Unmanned aerial vehicles (UAVs) play an increasingly important role in military, public, and civilian applications, where providing connectivity to UAVs is crucial for its real-time control, video streaming, and data collection. Considering that cellular networks offer wide area, high speed, and secure wireless connectivity, cellular-connected UAVs have been considered as an appealing solution to provide UAV connectivity with enhanced reliability, coverage, throughput, and security. Due to the nature of UAVs mobility, the throughput, reliability and End-to-End (E2E) delay of UAVs communication under various flight heights, video resolutions, and transmission frequencies remain unknown. To evaluate these parameters, we develop a cellular-connected UAV testbed based on the Long Term Evolution (LTE) network with its uplink video transmission and downlink control\&command (CC) transmission. We also design algorithms for sending control signal and controlling UAV. The indoor experimental results provide fundamental insights for the cellular-connected UAV system design from the perspective of transmission frequency, adaptability, and link outage, respectively.
Matching visual induction effects on screens of different size
Authors: Trevor D. Canham, Javier Vazquez-Corral, Elise Mathieu, Marcelo Bertalmío
Date: 2020-05-06 00:00:00

In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This presents a practical challenge for the preservation of the artistic intentions of filmmakers, as it can lead to shifts in image appearance between viewing destinations. In this work we show that a neural field model based on the efficient representation principle is able to predict induction effects, and how by regularizing its associated energy functional the model is still able to represent induction but is now invertible. From this we propose a method to pre-process an image in a screen-size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images.
No-harm principle, rationality, and Pareto optimality in games
Authors: Shaun Hargreaves Heap, Mehmet S. Ismail
Date: 2021-01-26 00:00:00

Mill's classic argument for liberty requires that people's exercise of freedom should be governed by a no-harm principle (NHP). In this paper, we develop the concept of a no-harm equilibrium in $n$-person games where players maximize utility subject to the constraint of the NHP. Our main result is in the spirit of the fundamental theorems of welfare economics. We show that for every initial `reference point' in a game the associated no-harm equilibrium is Pareto efficient and, conversely, every Pareto efficient point can be supported as a no-harm equilibrium for some initial reference point.
Channel Estimation for Practical IRS-Assisted OFDM Systems
Authors: Wanning Yang, Hongyu Li, Ming Li, Yang Liu, Qian Liu
Date: 2020-12-25 00:00:00

Intelligent reflecting surface (IRS), composed of a large number of hardware-efficient passive elements, is deemed as a potential technique for future wireless communications since it can adaptively enhance the propagation environment. In order to effectively utilize IRS to achieve promising beamforming gains, the problem of channel state information (CSI) acquisition needs to be carefully considered. However, most recent works assume to employ an ideal IRS, i.e., each reflecting element has constant amplitude, variable phase shifts, as well as the same response for the signals with different frequencies, which will cause severe estimation error due to the mismatch between the ideal IRS and the practical one. In this paper, we study channel estimation in practical IRS-aided orthogonal frequency division multiplexing (OFDM) systems with discrete phase shifts. Different from the prior works which assume that IRS has an ideal reflection model, we perform channel estimation by considering amplitude-phase shift-frequency relationship for the response of practical IRS. Aiming at minimizing normalized-mean-square-error (NMSE) of the estimated channel, a novel IRS time-varying reflection pattern is designed by leveraging the alternating optimization (AO) algorithm for the case of using low-resolution phase shifters. Moreover, for the high-resolution IRS cases, we provide another practical reflection pattern scheme to further reduce the complexity. Simulation results demonstrate the necessity of considering practical IRS model for channel estimation and the effectiveness of our proposed channel estimation methods.
Robust Online Time Series Prediction with Recurrent Neural Networks
Authors: Tian Guo, Zhao Xu, Xin Yao, Haifeng Chen, Karl Aberer, Koichi Funaya
Date: 2016-12-26 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction

Time series forecasting for streaming data plays an
important role in many real applications, ranging from IoT
systems, cyber-networks, to industrial systems and healthcare.
However the real data is often complicated with anomalies and
change points, which can lead the learned models deviating from
the underlying patterns of the time series, especially in the context
of online learning mode. In this paper we present an adaptive
gradient learning method for recurrent neural networks (RNN)
to forecast streaming time series in the presence of anomalies
and change points. We explore the local features of time series
to automatically weight the gradients of the loss of the newly
available observations with distributional properties of the data
in real time. We perform extensive experimental analysis on both
synthetic and real datasets to evaluate the performance of the
proposed method.
Location Privacy Protection in Online Query based on Privacy Region Replacement
Authors: Yali JI, Ruowei GUI, Xiaolin Gui, Dong LIAO, Xinyang Lin
Date: 2020-01-06 00:00:00
Tasks: ARC

In order to realize online location query based on Privacy in the location-based service (LBS), a location privacy   protection method based on privacy region replacement (PRR) was proposed to improve the query response time without reducing the degree of privacy protection in this paper. Firstly, a privacy region was generated according to the density of people and privacy requirements. Secondly, the privacy region was replaced into an anonymous region based on the personnel distribution in the privacy region. And then the coverage degree between the anonymous region and the user query region is calculated. Finally, a new query region was used to carry on the online query. Comparing with other methods such as Casper, Fragment, and ARC, the query response time was reduced at least 17.33 in PRR with the same degree of privacy protection. The results show that the Quantity of services was improved effectively using the PRR in Online query.
Understanding the Tradeoffs in Client-side Privacy for Downstream Speech Tasks
Authors: Peter Wu, Paul Pu Liang, Jiatong Shi, Ruslan Salakhutdinov, Shinji Watanabe, Louis-Philippe Morency
Date: 2021-01-22 00:00:00
Tasks: Representation Learning, speech-recognition, Speech Recognition

As users increasingly rely on cloud-based computing services, it is important to ensure that uploaded speech data remains private. Existing solutions rely either on server-side methods or focus on hiding speaker identity. While these approaches reduce certain security concerns, they do not give users client-side control over whether their biometric information is sent to the server. In this paper, we formally define client-side privacy and discuss its three unique technical challenges: (1) direct manipulation of raw data on client devices, (2) adaptability with a broad range of server-side processing models, and (3) low time and space complexity for compatibility with limited-bandwidth devices. Solving these challenges requires new models that achieve high-fidelity reconstruction, privacy preservation of sensitive personal attributes, and efficiency during training and inference. As a step towards client-side privacy for speech recognition, we investigate three techniques spanning signal processing, disentangled representation learning, and adversarial training. Through a series of gender and accent masking tasks, we observe that each method has its unique strengths, but none manage to effectively balance the trade-offs between performance, privacy, and complexity. These insights call for more research in client-side privacy to ensure a safer deployment of cloud-based speech processing services.
Query processing for location services without compromising privacy
Authors: Chi-Yin Cho, Mohamed F. Mokbel, Walid G. Aref
Date: 2009-12-05 00:00:00

In this article, we present a new privacy-aware query processing framework, Capser*, in which mobile and stationary users can obtain snapshot and/or continuous location-based services without revealing their private location information. In particular, we propose a privacy-aware query processor embedded inside a location-based database server to deal with snapshot and continuous queries based on the knowledge of the user's cloaked location rather than the exact location. Our proposed privacy-aware query processor is completely independent of how we compute the user's cloaked location. In other words, any existing location anonymization algorithms that blur the user's private location into cloaked rectilinear areas can be employed to protect the user's location privacy. We first propose a privacy-aware query processor that not only supports three new privacy-aware query types, but also achieves a trade-off between query processing cost and answer optimality. Then, to improve system scalability of processing continuous privacy-aware queries, we propose a shared execution paradigm that shares query processing among a large number of continuous queries. The proposed scalable paradigm can be tuned through two parameters to trade off between system scalability and answer optimality. Experimental results show that our query processor achieves high quality snapshot and continuous location-based services while supporting queries and/or data with cloaked locations.