pwc-archive/papers-with-abstracts huggingface dataset:
Ranker-agnostic Contextual Position Bias Estimation
Authors: Oriol Barbany Mayor, Vito Bellini, Alexander Buchholz, Giuseppe Di Benedetto, Diego Marco Granziol, Matteo Ruffini, Yannik Stein
Date: 2021-07-28 00:00:00
Tasks: Learning-To-Rank, Position

Learning-to-rank (LTR) algorithms are ubiquitous and necessary to explore the extensive catalogs of media providers. To avoid the user examining all the results, its preferences are used to provide a subset of relatively small size. The user preferences can be inferred from the interactions with the presented content if explicit ratings are unavailable. However, directly using implicit feedback can lead to learning wrong relevance models and is known as biased LTR. The mismatch between implicit feedback and true relevances is due to various nuisances, with position bias one of the most relevant. Position bias models consider that the lack of interaction with a presented item is not only attributed to the item being irrelevant but because the item was not examined. This paper introduces a method for modeling the probability of an item being seen in different contexts, e.g., for different users, with a single estimator. Our suggested method, denoted as contextual (EM)-based regression, is ranker-agnostic and able to correctly learn the latent examination probabilities while only using implicit feedback. Our empirical results indicate that the method introduced in this paper outperforms other existing position bias estimators in terms of relative error when the examination probability varies across queries. Moreover, the estimated values provide a ranking performance boost when used to debias the implicit ranking data even if there is no context dependency on the examination probabilities.
Understanding and Generalizing Monotonic Proximity Graphs for Approximate Nearest Neighbor Search
Authors: Dantong Zhu, Minjia Zhang
Date: 2021-07-27 00:00:00
Tasks: Mathematical Proofs

Graph-based algorithms have shown great empirical potential for the approximate nearest neighbor (ANN) search problem. Currently, graph-based ANN search algorithms are designed mainly using heuristics, whereas theoretical analysis of such algorithms is quite lacking. In this paper, we study a fundamental model of proximity graphs used in graph-based ANN search, called Monotonic Relative Neighborhood Graph (MRNG), from a theoretical perspective. We use mathematical proofs to explain why proximity graphs that are built based on MRNG tend to have good searching performance. We also run experiments on MRNG and graphs generalizing MRNG to obtain a deeper understanding of the model. Our experiments give guidance on how to approximate and generalize MRNG to build proximity graphs on a large scale. In addition, we discover and study a hidden structure of MRNG called conflicting nodes, and we give theoretical evidence how conflicting nodes could be used to improve ANN search methods that are based on MRNG.
Abordagem probabilística para análise de confiabilidade de dados gerados em sequenciamentos multiplex na plataforma ABI SOLiD
Authors: Fabio M. F. Lobato, Carlos D. N. Damasceno, Péricles L. Machado, Nandamudi L. Vijaykumar, André R. dos Santos, Sylvain H. Darnet, André N. A. Gonçalves, Dayse O. de Alencar, Ádamo L. de Santana
Date: 2021-07-27 00:00:00

The next-generation sequencers such as Illumina and SOLiD platforms generate a large amount of data, commonly above 10 Gigabytes of text files. Particularly, the SOLiD platform allows the sequencing of multiple samples in a single run, called multiplex run, through a tagging system called Barcode. This feature requires a computational process for separation of the data sample because the sequencer provides a mixture of all samples in a single output. This process must be secure to avoid any harm that may scramble further analysis. In this context, realized the need to develop a probabilistic model capable of assigning a degree of confidence in the marking system used in multiplex sequencing. The results confirmed the adequacy of the model obtained, which allows, among other things, to guide a process of filtering the data and evaluation of the sequencing protocol used.
Toward AI Assistants That Let Designers Design
Authors: Sebastiaan De Peuter, Antti Oulasvirta, Samuel Kaski
Date: 2021-07-22 00:00:00

AI for supporting designers needs to be rethought. It should aim to cooperate, not automate, by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers' goals and then help them without being needlessly disruptive. We present AI-assisted design: a framework for creating such AI, built around generative user models which enable reasoning about designers' goals, reasoning, and capabilities.
Interactive Storytelling for Children: A Case-study of Design and Development Considerations for Ethical Conversational AI
Authors: ennifer Chubba, Sondess Missaouib, Shauna Concannonc, Liam Maloneyb, James Alfred Walker
Date: 2021-07-20 00:00:00
Tasks: Decision Making, Ethics

Conversational Artificial Intelligence (CAI) systems and Intelligent Personal Assistants (IPA), such as Alexa, Cortana, Google Home and Siri are becoming ubiquitous in our lives, including those of children, the implications of which is receiving increased attention, specifically with respect to the effects of these systems on children's cognitive, social and linguistic development. Recent advances address the implications of CAI with respect to privacy, safety, security, and access. However, there is a need to connect and embed the ethical and technical aspects in the design. Using a case-study of a research and development project focused on the use of CAI in storytelling for children, this paper reflects on the social context within a specific case of technology development, as substantiated and supported by argumentation from within the literature. It describes the decision making process behind the recommendations made on this case for their adoption in the creative industries. Further research that engages with developers and stakeholders in the ethics of storytelling through CAI is highlighted as a matter of urgency.
Operationally-Safe Peer-to-Peer Energy Trading in Distribution Grids: A Game-Theoretic Market-Clearing Mechanism
Authors: Giuseppe Belgioioso, Wicak Ananduta, Sergio Grammatico, Carlos Ocampo-Martinez
Date: 2021-07-28 00:00:00
Tasks: energy trading

In future distribution grids, prosumers (i.e., energy consumers with storage and/or production capabilities) will trade energy with each other and with the main grid. To ensure an efficient and safe operation of energy trading, in this paper, we formulate a peer-to-peer energy market of prosumers as a generalized aggregative game, in which a network operator is only responsible for the operational constraints of the system. We design a distributed market-clearing mechanism with convergence guarantee to an economically-efficient and operationally-safe configuration (i.e., a variational generalized Nash equilibrium). Numerical studies on the IEEE 37-bus testcase show the scalability of the proposed approach and suggest that active participation in the market is beneficial for both prosumers and the network operator.
A Complete End-To-End Open Source Toolchain for the Versatile Video Coding (VVC) Standard
Authors: Adam Wieckowski, Christian Lehmann, Benjamin Bross, Detlev Marpe, Thibaud Biatek, Mickael Raulet, Jean Le Feuvre
Date: 2021-07-28 00:00:00

Versatile Video Coding (VVC) is the most recent international video coding standard jointly developed by ITU-T and ISO/IEC, which has been finalized in July 2020. VVC allows for significant bit-rate reductions around 50% for the same subjective video quality compared to its predecessor, High Efficiency Video Coding (HEVC). One year after finalization, VVC support in devices and chipsets is still under development, which is aligned with the typical development cycles of new video coding standards. This paper presents open-source software packages that allow building a complete VVC end-to-end toolchain already one year after its finalization. This includes the Fraunhofer HHI VVenC library for fast and efficient VVC encoding as well as HHI's VVdeC library for live decoding. An experimental integration of VVC in the GPAC software tools and FFmpeg media framework allows packaging VVC bitstreams, e.g. encoded with VVenC, in MP4 file format and using DASH for content creation and streaming. The integration of VVdeC allows playback on the receiver. Given these packages, step-by-step tutorials are provided for two possible application scenarios: VVC file encoding plus playback and adaptive streaming with DASH.
Deep learning based cough detection camera using enhanced features
Authors: Gyeong-Tae Lee, Hyeonuk Nam, Seong-Hu Kim, Sang-Min Choi, Youngkey Kim, Yong-Hwa Park
Date: 2021-07-28 00:00:00
Tasks: Data Augmentation, Deep Learning

Coughing is a typical symptom of COVID-19. To detect and localize coughing sounds remotely, a convolutional neural network (CNN) based deep learning model was developed in this work and integrated with a sound camera for the visualization of the cough sounds. The cough detection model is a binary classifier of which the input is a two second acoustic feature and the output is one of two inferences (Cough or Others). Data augmentation was performed on the collected audio files to alleviate class imbalance and reflect various background noises in practical environments. For effective featuring of the cough sound, conventional features such as spectrograms, mel-scaled spectrograms, and mel-frequency cepstral coefficients (MFCC) were reinforced by utilizing their velocity (V) and acceleration (A) maps in this work. VGGNet, GoogLeNet, and ResNet were simplified to binary classifiers, and were named V-net, G-net, and R-net, respectively. To find the best combination of features and networks, training was performed for a total of 39 cases and the performance was confirmed using the test F1 score. Finally, a test F1 score of 91.9% (test accuracy of 97.2%) was achieved from G-net with the MFCC-V-A feature (named Spectroflow), an acoustic feature effective for use in cough detection. The trained cough detection model was integrated with a sound camera (i.e., one that visualizes sound sources using a beamforming microphone array). In a pilot test, the cough detection camera detected coughing sounds with an F1 score of 90.0% (accuracy of 96.0%), and the cough location in the camera image was tracked in real time.
Minimum Structural Sensor Placement for Switched Linear Time-Invariant Systems and Unknown Inputs
Authors: Emily A. Reed, Guilherme Ramos, Paul Bogdan, Sérgio Pequito
Date: 2021-07-28 00:00:00

In this paper, we study the structural state and input observability of continuous-time switched linear time-invariant systems and unknown inputs. First, we provide necessary and sufficient conditions for their structural state and input observability that can be efficiently verified in $O((m(n+p))^2)$, where $n$ is the number of state variables, $p$ is the number of unknown inputs, and $m$ is the number of modes. Moreover, we address the minimum sensor placement problem for these systems by adopting a feed-forward analysis and by providing an algorithm with a computational complexity of $ O((m(n+p)+\alpha)^{2.373})$, where $\alpha$ is the number of target strongly connected components of the system's digraph representation. Lastly, we explore different assumptions on both the system and unknown inputs (latent space) dynamics that add more structure to the problem, and thereby, enable us to render algorithms with lower computational complexity, which are suitable for implementation in large-scale systems.
Collision-free Formation Control of Multiple Nano-quadrotors
Authors: Anh Tung Nguyen, Ji-Won Lee, Thanh Binh Nguyen, Sung Kyung Hong
Date: 2021-07-28 00:00:00
Tasks: Collision Avoidance

The utilisation of unmanned aerial vehicles has witnessed significant growth in real-world applications including surveillance tasks, military missions, and transportation deliveries. This letter investigates practical problems of formation control for multiple nano-quadrotor systems. To be more specific, the first aim of this work is to develop a theoretical framework for the time-varying formation flight of the multi-quadrotor system regarding anti-collisions. In order to achieve this goal, the finite cut-off potential function is devoted to avoiding collisions among vehicles in the group as well as between vehicles and an obstacle. The control algorithm navigates the group of nano-quadrotors to asymptotically reach an anticipated time-varying formation. The second aim is to implement the proposed algorithm on Crazyflies nanoquadrotors, one of the most ubiquitous indoor experimentation platforms. Several practical scenarios are conducted to tendentiously expose anti-collision abilities among group members as well as between vehicles and an obstacle. The experimental outcomes validate the effectiveness of the proposed method in the formation tracking and the collision avoidance of multiple nano-quadrotors.
A Highly Linear and Flexible FPGA-Based Time-to-Digital Converter
Authors: Yuanyuan Hua, Danial Chitnis
Date: 2021-07-27 00:00:00

Time-to-Digital Converters (TDCs) are major components for the measurements of time intervals. Recent developments in Field-Programmable Gate Array (FPGA) have enabled the opportunity to implement high-performance TDCs, which were only possible using dedicated hardware. In order to eliminate empty histogram bins and achieve a higher level of linearity, FPGA-based TDCs typically apply compensation methods either using multiple delay lines consuming more resources or post-processing, leading to a permanent loss of temporal information. We propose a novel TDC with a single delay line and without compensation to realize a highly linear TDC by encoding the states of the delay lines instead of the thermometer code used in the conventional TDCs. The experimental results show our states-based approach achieves an improved Differential Non-Linearity (DNL) of [-0.998, -1.533] for time resolution of 5.00 ps, [-0.44,0.49] for 10.04 ps, [-0.16, 0.19] for 21.65 ps, [-0.10, 0.11] for 43.87 ps, [-0.06, 0.07] for 64.12 ps, and [-0.07, 0.05] for 87.73 ps, whilst no empty bins have been observed. To our knowledge, the achieved raw linearity together with the zero empty bins and a simple delay line structure exceeds previously reported of the FPGA-based TDCs.
Dropping mortality by increasing connectivity in plant epidemics
Authors: Ignacio Taguas, José A. Capitán, Juan C. Nuño
Date: 2021-07-28 00:00:00

Pathogen introduction in plant communities can cause serious impact and biodiversity losses that may take long time to manage and restore. Effective control of epidemic spreading in the wild is a problem of paramount importance, because of its implications in conservation and potential economic losses. Understanding the mechanisms that hinder pathogen's propagation is, therefore, crucial. Usual modelization approaches in epidemic spreading are based in compartmentalized models, without keeping track of pathogen's concentrations during spreading. In this contribution we present and fully analyze a dynamical model for plant epidemic spreading based on pathogen's abundances. The model, which is defined on top of network substrates, is amenable to a deep mathematical analysis in the absence of a limit in the amount of pathogen a plant can tolerate before dying. In the presence of such death threshold, we observe that the fraction of dead plants peaks at intermediate values of network's connectivity, and mortality decreases for large average degrees. We discuss the implications of our results as mechanisms to halt infection propagation.
Lifespan associations of resting-state brain functional networks with ADHD symptoms
Authors: Rong Wang, Yongchen Fan, Ying Wu, Yu-Feng Zang, Changsong Zhou
Date: 2021-07-28 00:00:00

Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms that underlie its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition (NSP) approach to study the resting-state brain functional networks of ADHD patients (n=97) and healthy controls (HCs, n=97) across the lifespan (7-50 years). Compared to the linear lifespan associations of brain functional segregation and integration with age in HCs, ADHD patients have a quadratic association in the whole brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a linear association. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD. Our findings reveal a lifespan association of brain networks with ADHD symptoms and provide potential shared neural bases of distinct ADHD symptoms in children and adults.
The mystery of energy compensation
Authors: Lewis G. Halsey
Date: 2021-07-26 00:00:00
Tasks: energy management, Management

The received wisdom on how activity affects energy expenditure is that the more activity is undertaken, the more calories will have been burned by the end of the day. Yet traditional hunter-gatherers, who lead physically hard lives, burn no more calories each day than western populations living in labour-saving environments. Indeed, there is now a wealth of data, both for humans and other animals, demonstrating that long-term lifestyle changes involving increases in exercise or other physical activities do not result in commensurate increases in daily energy expenditure (DEE). This is because humans and other animals exhibit a degree of energy compensation at the organismal level, ameliorating some of the increases in DEE that would occur from the increased activity by decreasing the energy expended on other biological processes. And energy compensation can be sizable, reaching many hundreds of calories in humans. But the processes that are downregulated in the long-term to achieve energy compensation are far from clear, particularly in humans. We do not know how energy compensation is achieved. My review here of the literature on relevant exercise intervention studies, for both humans and other species, indicates conflict regarding the role that basal metabolic rate (BMR) or low level activity such as fidgeting play, if any, particularly once changes in body composition are factored out. In situations where BMR and low-level activity are not major components of energy compensation, what then drives it? I discuss how changes in mitochondrial efficiency and changes in circadian fluctuations in BMR may contribute to our understanding of energy management. Currently unexplored, these mechanisms and others may provide important insights into the mystery of how energy compensation is achieved.
MobilityCoins -- A new currency for the multimodal urban transportation system
Authors: Klaus Bogenberger, Philipp Blum, Florian Dandl, Lisa-Sophie Hamm, Allister Loder, Patrick Malcolm, Martin Margreiter, Natalie Sautter
Date: 2021-07-28 00:00:00
Tasks: Management

The MobilityCoin is a new, all-encompassing currency for the management of the multimodal urban transportation system. MobilityCoins includes and replaces various existing transport policy instruments while also incentivizing a shift to more sustainable modes as well as empowering the public to vote for infrastructure measures.
Renewable Energy Targets and Unintended Storage Cycling: Implications for Energy Modeling
Authors: Martin Kittel, Wolf-Peter Schill
Date: 2021-07-28 00:00:00

To decarbonize the economy, many governments have set targets for the use of renewable energy sources. These are often formulated as relative shares of electricity demand or supply. Implementing respective constraints in energy models is a surprisingly delicate issue. They may cause a modeling artifact of excessive electricity storage use. We introduce this phenomenon as 'unintended storage cycling', which can be detected in case of simultaneous storage charging and discharging. In this paper, we provide an analytical representation of different approaches for implementing minimum renewable share constraints in models, and show how these may lead to unintended storage cycling. Using a parsimonious optimization model, we quantify related distortions of optimal dispatch and investment decisions as well as market prices, and identify important drivers of the phenomenon. Finally, we provide recommendations on how to avoid the distorting effects of unintended storage cycling in energy modeling.
Towards an enabling environment for social accountability in Bangladesh
Authors: Hossain Ahmed Taufiq
Date: 2021-07-28 00:00:00

Social accountability refers to promoting good governance by making ruling elites more responsive. In Bangladesh, where bureaucracy and legislature operate with little effective accountability or checks and balances, traditional horizontal or vertical accountability proved to be very blunt and weak. In the presence of such faulty mechanisms, ordinary citizens access to information is frequently denied, and their voices are kept mute. It impasses the formation of an enabling environment, where activists and civil society institutions representing the ordinary peoples interest are actively discouraged. They become vulnerable to retribution. Social accountability, on the other hand, provides an enabling environment for activists and civil society institutions to operate freely. Thus, leaders and administration become more accountable to people. An enabling environment means providing legal protection, enhancing the availability of information and increasing citizen voice, strengthening institutional and public service capacities and directing incentives that foster accountability. Donors allocate significant shares of resources to encouraging civil society to partner with elites rather than holding them accountable. This paper advocate for a stronger legal environment to protect critical civil society and whistle-blowers, and for independent grant-makers tasked with building strong, self-regulating social accountability institutions. Key Words: Accountability, Legal Protection, Efficiency, Civil Society, Responsiveness
Combining physics-based modeling and deep learning for ultrasound elastography
Authors: Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Date: 2021-07-28 00:00:00
Tasks: Deep Learning

Ultrasound elasticity images which enable the visualization of quantitative maps of tissue stiffness can be reconstructed by solving an inverse problem. Classical model-based approaches for ultrasound elastography use deterministic finite element methods (FEMs) to incorporate the governing physical laws resulting in poor performance in noisy conditions. Moreover, these approaches utilize fixed regularizers for various tissue patterns while appropriate data-adaptive priors might be required for capturing the complex spatial elasticity distribution. In this regard, we propose a joint model-based and learning-based framework for estimating the elasticity distribution by solving a regularized optimization problem. We present an integrated objective function composed of a statistical physics-based forward model and a data-driven regularizer to leverage deep neural networks for learning the underlying elasticity prior. This constrained optimization problem is solved using the gradient descent (GD) method and the gradient of regularizer is simply replaced by the residual of the trained denoiser network for having an explicit objective function with reduced computation time.