pwc-archive/papers-with-abstracts huggingface dataset:
(Re)Politicizing Digital Well-Being: Beyond User Engagements
Authors: Niall Docherty, Asia J. Biega
Date: 2022-03-15 00:00:00

The psychological costs of the attention economy are often considered through the binary of harmful design and healthy use, with digital well-being chiefly characterised as a matter of personal responsibility. This article adopts an interdisciplinary approach to highlight the empirical, ideological, and political limits of embedding this individualised perspective in computational discourses and designs of digital well-being measurement. We will reveal well-being to be a culturally specific and environmentally conditioned concept and will problematize user engagement as a universal proxy for well-being. Instead, the contributing factors of user well-being will be located in environing social, cultural, and political conditions far beyond the control of individual users alone. In doing so, we hope to reinvigorate the issue of digital well-being measurement as a nexus point of political concern, through which multiple disciplines can study experiences of digital ill as symptomatic of wider social inequalities and (capitalist) relations of power.
A bagging SVM to learn from positive and unlabeled examples
Authors: Fantine Mordelet, Jean-Philippe Vert
Date: 2010-10-05 00:00:00
Tasks: Binary Classification, Information Retrieval, Retrieval

We consider the problem of learning a binary classifier from a training set of positive and unlabeled examples, both in the inductive and in the transductive setting. This problem, often referred to as \emph{PU learning}, differs from the standard supervised classification problem by the lack of negative examples in the training set. It corresponds to an ubiquitous situation in many applications such as information retrieval or gene ranking, when we have identified a set of data of interest sharing a particular property, and we wish to automatically retrieve additional data sharing the same property among a large and easily available pool of unlabeled data. We propose a conceptually simple method, akin to bagging, to approach both inductive and transductive PU learning problems, by converting them into series of supervised binary classification problems discriminating the known positive examples from random subsamples of the unlabeled set. We empirically demonstrate the relevance of the method on simulated and real data, where it performs at least as well as existing methods while being faster.
Semantic StyleGAN
Authors: Researchers at ByteDance Inc, Yichun Shi, Xiao Yang, Yangyue Wan, Xiaohui Shen
Date: 2021-07-01 00:00:00
Proceeding: arXiv:2112.02236v2 [cs.CV] 7 Dec 2021 2021 7
Tasks: Disentanglement

SemanticStyleGAN presents a method where a generator is trained to model local semantic parts separately and synthesizes images in a compositional way.

Experimental results demonstrate that Semantic StyleGAN model provides a strong disentanglement between different spatial areas.

When combined with editing methods designed for StyleGANs, it can achieve a more fine-grained control to edit synthesized or real images.

Key features:

Style mixing between generated images
Texture and structure are locally controlled
Developed by researchers at ByteDance Inc with Yichun Shi, Xiao Yang, Yangyue Wan, and Xiaohui Shen
Incorporating Multi-Agent Systems Technology in Power and Energy Systems of Bangladesh: A Feasibility Study
Authors: Syed Redwan Md Hassan, Nazmul Hasan, Mohammad Ali Siddique, K. M Solaiman Fahim, Rummana Rahman, Lamia Iftekhar
Date: 2022-03-16 00:00:00

The power sector of Bangladesh is presently experiencing essential changes as demand for power services is increasing with rising population and economic development. With a gradual shift from a rigidly centralized structure to a more decentralized and fluid setup, fundamentally because of the enormous advancement of distributed renewable energy sources, the future power system of the nation requires new control strategies to work efficiently and sustainably in the face of evolving conditions and constraints. Multi-Agent Systems (MAS) technology has attributes that meet these prerequisites of modern power systems and has been shown to be effective in dealing with its distributed and complex nature. This is a literature-based feasibility study to explore whether MAS technology is suited to be applied in the context of Bangladesh. For this preliminary paper, we look at the topic from a holistic perspective and conduct a meta-review to curate common applications of Multi-Agent System-based concepts, tools and algorithms on the power and energy sector. We also identify the top challenges of this domain in Bangladesh and connect the potential MAS-based solutions to address each challenge. Our qualitative assessment is motivated to provide a starting point for local researchers eager to experiment with MAS technology for application in Bangladesh.
Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models
Authors: Michael Samet, Christian Bayer, Chiheb Ben Hammouda, Antonis Papapantoleon, Raúl Tempone
Date: 2022-03-15 00:00:00

Efficiently pricing multi-asset options is a challenging problem in quantitative finance. When the characteristic function is available, Fourier-based methods are competitive compared to alternative techniques because the integrand in the frequency space often has a higher regularity than that in the physical space. However, when designing a numerical quadrature method for most Fourier pricing approaches, two key aspects affecting the numerical complexity should be carefully considered: (i) the choice of damping parameters that ensure integrability and control the regularity class of the integrand and (ii) the effective treatment of high dimensionality. We propose an efficient numerical method for pricing European multi-asset options based on two complementary ideas to address these challenges. First, we smooth the Fourier integrand via an optimized choice of the damping parameters based on a proposed optimization rule. Second, we employ sparsification and dimension-adaptivity techniques to accelerate the convergence of the quadrature in high dimensions. The extensive numerical study on basket and rainbow options under the multivariate geometric Brownian motion and some L\'evy models demonstrates the advantages of adaptivity and the damping rule on the numerical complexity of quadrature methods. Moreover, for the tested two-asset examples, the proposed approach outperforms the COS method in terms of computational time. Finally, we show significant speed-up compared to the Monte Carlo method for up to six dimensions.
Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks
Authors: HaoYu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Mark Kilgard, Anima Anandkumar, Brucek Khailany, Vivek Singh, Haoxing Ren
Date: 2022-03-12 00:00:00
Tasks: BIG-bench Machine Learning

Lithography simulation is a critical step in VLSI design and optimization for manufacturability. Existing solutions for highly accurate lithography simulation with rigorous models are computationally expensive and slow, even when equipped with various approximation techniques. Recently, machine learning has provided alternative solutions for lithography simulation tasks such as coarse-grained edge placement error regression and complete contour prediction. However, the impact of these learning-based methods has been limited due to restrictive usage scenarios or low simulation accuracy. To tackle these concerns, we introduce an dual-band optics-inspired neural network design that considers the optical physics underlying lithography. To the best of our knowledge, our approach yields the first published via/metal layer contour simulation at 1nm^2/pixel resolution with any tile size. Compared to previous machine learning based solutions, we demonstrate that our framework can be trained much faster and offers a significant improvement on efficiency and image quality with 20X smaller model size. We also achieve 85X simulation speedup over traditional lithography simulator with 1% accuracy loss.
Tethered Power for a Series of Quadcopters: Analysis and Applications
Authors: Karan P. Jain, Prasanth Kotaru, Massimiliano de Sa, Koushil Sreenath, Mark W. Mueller
Date: 2022-03-15 00:00:00

Tethered quadcopters are used for extended flight operations where the power to the system is provided via a tether connected to an external power source. In this work, we consider a system of multiple quadcopters powered by a single tether. We study the design factors that influence the power requirements, such as the electrical resistance of the tether, input voltage, and quadcopters' positions. We present an analysis to predict the required power to be supplied to a series of N tethered quadcopters, with respect to the thrust of each quadcopter which guarantees electrical safety and helps in design optimization. We find that there is a critical boundary of thrusts that cannot be exceeded due to fundamental electrical limitations. We compare the power consumption for one tethered quadcopter and two tethered quadcopters and show that for large quadcopters far enough from the anchor point, a two-quadcopter system consumes lesser power. We show that, for a representative use case of firefighting, a tethered system with two quadcopters consumes 26% less power than a corresponding system with one quadcopter. Finally, we present experiments demonstrating the use of a two-quadcopter tethered system as compared to a one-quadcopter tethered system in a cluttered environment, such as passing through a window and grasping an object over an obstacle.
A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition
Authors: Jisheng Bai, Jianfeng Chen, Mou Wang, Muhammad Saad Ayub
Date: 2022-03-16 00:00:00
Tasks: Acoustic Scene Classification, Data Augmentation, Scene Classification

Environmental sound recognition (ESR) is an emerging research topic in audio pattern recognition. Many tasks are presented to resort to computational models for ESR in real-life applications. However, current models are usually designed for individual tasks, and are not robust and applicable to other tasks. Cross-task models, which promote unified knowledge modeling across various tasks, have not been thoroughly investigated. In this article, we propose a cross-task model for three different tasks of ESR: 1) acoustic scene classification; 2) urban sound tagging; and 3) anomalous sound detection. An architecture named SE-Trans is presented that uses attention mechanism-based Squeeze-and-Excitation and Transformer encoder modules to learn the channelwise relationship and temporal dependencies of the acoustic features. FMix is employed as the data augmentation method that improves the performance of ESR. Evaluations for the three tasks are conducted on the recent databases of detection and classification of acoustic scenes and event challenges. The experimental results show that the proposed cross-task model achieves state-of-the-art performance on all tasks. Further analysis demonstrates that the proposed cross-task model can effectively utilize acoustic knowledge across different ESR tasks.
Towards a Roadmap on Software Engineering for Responsible AI
Authors: Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Zhenchang Xing
Date: 2022-03-09 00:00:00

Although AI is transforming the world, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and frameworks for responsible AI have been issued recently. However, they are high level and difficult to put into practice. On the other hand, most AI researchers focus on algorithmic solutions, while the responsible AI challenges actually crosscut the entire engineering lifecycle and components of AI systems. To close the gap in operationalizing responsible AI, this paper aims to develop a roadmap on software engineering for responsible AI. The roadmap focuses on (i) establishing multi-level governance for responsible AI systems, (ii) setting up the development processes incorporating process-oriented practices for responsible AI systems, and (iii) building responsible-AI-by-design into AI systems through system-level architectural style, patterns and techniques.
Motif Mining: Finding and Summarizing Remixed Image Content
Authors: William Theisen, Daniel Gonzalez Cedre, Zachariah Carmichael, Daniel Moreira, Tim Weninger, Walter Scheirer
Date: 2022-03-16 00:00:00

On the internet, images are no longer static; they have become dynamic content. Thanks to the availability of smartphones with cameras and easy-to-use editing software, images can be remixed (i.e., redacted, edited, and recombined with other content) on-the-fly and with a world-wide audience that can repeat the process. From digital art to memes, the evolution of images through time is now an important topic of study for digital humanists, social scientists, and media forensics specialists. However, because typical data sets in computer vision are composed of static content, the development of automated algorithms to analyze remixed content has been limited. In this paper, we introduce the idea of Motif Mining - the process of finding and summarizing remixed image content in large collections of unlabeled and unsorted data. In this paper, this idea is formalized and a reference implementation is introduced. Experiments are conducted on three meme-style data sets, including a newly collected set associated with the information war in the Russo-Ukrainian conflict. The proposed motif mining approach is able to identify related remixed content that, when compared to similar approaches, more closely aligns with the preferences and expectations of human observers.
DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies
Authors: Pok Wah Chan
Date: 2022-03-11 00:00:00
Tasks: Anomaly Detection, Information Retrieval, Language Modelling, Retrieval

Extreme pricing anomalies may occur unexpectedly without a trivial cause, and equity traders typically experience a meticulous process to source disparate information and analyze its reliability before integrating it into the trusted knowledge base. We introduce DeepTrust, a reliable financial knowledge retrieval framework on Twitter to explain extreme price moves at speed, while ensuring data veracity using state-of-the-art NLP techniques. Our proposed framework consists of three modules, specialized for anomaly detection, information retrieval and reliability assessment. The workflow starts with identifying anomalous asset price changes using machine learning models trained with historical pricing data, and retrieving correlated unstructured data from Twitter using enhanced queries with dynamic search conditions. DeepTrust extrapolates information reliability from tweet features, traces of generative language model, argumentation structure, subjectivity and sentiment signals, and refine a concise collection of credible tweets for market insights. The framework is evaluated on two self-annotated financial anomalies, i.e., Twitter and Facebook stock price on 29 and 30 April 2021. The optimal setup outperforms the baseline classifier by 7.75% and 15.77% on F0.5-scores, and 10.55% and 18.88% on precision, respectively, proving its capability in screening unreliable information precisely. At the same time, information retrieval and reliability assessment modules are analyzed individually on their effectiveness and causes of limitations, with identified subjective and objective factors that influence the performance. As a collaborative project with Refinitiv, this framework paves a promising path towards building a scalable commercial solution that assists traders to reach investment decisions on pricing anomalies with authenticated knowledge from social media platforms in real-time.
Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey
Authors: Liz Martinez Marrero, Juan C. Merlano Duncan, Jorge Querol, Sumit Kumar, Jevgenij Krivochiza, Shree Krishna Sharma, Symeon Chatzinotas, Adriano Camps, Bjorn Otterstern
Date: 2022-03-16 00:00:00
Tasks: Survey

Cohesive Distributed Satellite Systems (CDSS) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSS. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSS. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for DSS. First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSS in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on ML. Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.
Construction of time-varying ISS-Lyapunov Functions for Impulsive Systems
Authors: Patrick Bachmann, Saeed Ahmed
Date: 2022-03-16 00:00:00

Time-varying ISS-Lyapunov functions for impulsive systems provide a necessary and sufficient condition for ISS. This property makes them a more powerful tool for stability analysis than classical candidate ISS-Lyapunov functions providing only a sufficient ISS condition. Moreover, time-varying ISS-Lyapunov functions cover systems with simultaneous instability in continuous and discrete dynamics for which candidate ISS-Lyapunov functions remain inconclusive. The present paper links these two concepts by suggesting a method of constructing time-varying ISS-Lyapunov functions from candidate ISS-Lyapunov functions, thereby effectively combining the ease of construction of candidate ISS-Lyapunov functions with the guaranteed existence of time-varying ISS-Lyapunov functions.
DRT-based modelling framework for Li-ion cells
Authors: Pietro Iurilli, Claudio Brivio, Rafael E. Carrillo, Vanessa Wood
Date: 2022-03-16 00:00:00
Tasks: Management

The correct assessment of battery states is essential to maximize battery pack performances while ensuring reliable and safe operation. This work introduces EIS2MOD, a novel modelling framework for Li-ion cells based on Distribution of Relaxation Time (DRT). A physically based Electric Circuit Model (ECM) is developed starting from Electrochemical Impedance Spectroscopy (EIS) and Open Circuit Voltage (OCV) measurements. DRT is applied to deconvolve the electrochemical phenomena from the EIS. The presented methodology is based on: i) DRT calculation from EIS, ii) DRT analysis for ECM configuration and iii) Model parameters extraction and fitting. The proposed framework is applied to large format Li-ion pouch cells, which are tested over the whole State of Charge (SoC) range and a wide temperature range (-10{\deg}C to 35{\deg}C). Different current profiles have been tested to validate the model, showing its high accuracy in reproducing the battery cell behavior (e.g. RMSE on the battery terminals voltage lower than 1.50% for driving cycle simulations at variable temperature and SoC). An additional advantage of EIS2MOD is its light computational load thus offering an attractive framework for battery management system implementation.
A distributionally robust optimization approach to two-sided chance constrained stochastic model predictive control with unknown noise distribution
Authors: Yuan Tan, Jun Yang, Wen-Hua Chen, Shihua Li
Date: 2022-03-16 00:00:00
Tasks: Model Predictive Control

In this work, we propose a distributionally robust stochastic model predictive control (DR-SMPC) algorithm to address the problem of two-sided chance constrained discrete-time linear system corrupted by additive noise. The prevalent mechanism to cope with two-sided chance constraints is the so-called risk allocation approach, which conservatively approximates the two-sided chance constraints with two single chance constraints by applying the Boole's inequality. In this proposed DR-SMPC framework, an exact tractable second-order cone (SOC) approach is adopted to abstract the two-sided chance constraints by considering the first and second moments of the noise. The proposed DR-SMPC algorithm is able to guarantee that the worst-case probability of violating both the upper and lower limits of safety constraints is within the pre-specified maximum probability (PsMP). By flexibly adjusting this PsMP, the feasible region of the initial states can be increased for the SMPC problem. The recursive feasibility and convergence of the proposed DR-SMPC are established rigorously by introducing binary initialization strategy of nominal state. Simulation studies of two practical cases are conducted to demonstrate the effectiveness of the proposed DR-SMPC algorithm.
Synthesis of the Supremal Covert Attacker Against Unknown Supervisors by Using Observations
Authors: Ruochen Tai, Liyong Lin, Yuting Zhu, Rong Su
Date: 2022-03-16 00:00:00

In this paper, we consider the problem of synthesizing the supremal covert damage-reachable attacker, in the setup where the model of the supervisor is unknown to the adversary but the adversary has recorded a (prefix-closed) finite set of observations of the runs of the closed-loop system. The synthesized attacker needs to ensure both the damage-reachability and the covertness against all the supervisors which are consistent with the given set of observations. There is a gap between the de facto supremality, assuming the model of the supervisor is known, and the supremality that can be attained with a limited knowledge of the model of the supervisor, from the adversary's point of view. We consider the setup where the attacker can exercise sensor replacement/deletion attacks and actuator enablement/disablement attacks. The solution methodology proposed in this work is to reduce the synthesis of the supremal covert damage-reachable attacker, given the model of the plant and the finite set of observations, to the synthesis of the supremal safe supervisor for certain transformed plant, which shows the decidability of the observation-assisted covert attacker synthesis problem. The effectiveness of our approach is illustrated on a water tank example adapted from the literature.
LPV sequential loop closing for high-precision motion systems
Authors: Yorick Broens, Hans Butler, Roland Tóth
Date: 2022-03-15 00:00:00
Tasks: Position, Vocal Bursts Intensity Prediction

Increasingly stringent throughput requirements in the industry necessitate the need for lightweight design of high-precision motion systems to allow for high accelerations, while still achieving accurate positioning of the moving-body. The presence of position dependent dynamics in such motion systems severely limits achievable position tracking performance using conventional sequential loop closing (SLC) control design strategies. This paper presents a novel extension of the conventional SLC design framework towards linear-parameter-varying systems, which allows to circumvent limitations that are introduced by position dependent effects in high-precision motion systems. Advantages of the proposed control design approach are demonstrated in simulation using a high-fidelity model of a moving-magnet planar actuator system, which exhibits position dependency in both actuation and sensing.