pwc-archive/papers-with-abstracts huggingface dataset:
Integrating Generic Sensor Fusion Algorithms with Sound State Representations through Encapsulation of Manifolds
Authors: Christoph Hertzberg, René Wagner, Udo Frese, Lutz Schröder
Date: 2011-07-06 00:00:00
Tasks: Sensor Fusion

Common estimation algorithms, such as least squares estimation or the Kalman filter, operate on a state in a state space S that is represented as a real-valued vector. However, for many quantities, most notably orientations in 3D, S is not a vector space, but a so-called manifold, i.e. it behaves like a vector space locally but has a more complex global topological structure. For integrating these quantities, several ad-hoc approaches have been proposed. Here, we present a principled solution to this problem where the structure of the manifold S is encapsulated by two operators, state displacement [+]:S x R^n --> S and its inverse [-]: S x S --> R^n. These operators provide a local vector-space view \delta; --> x [+] \delta; around a given state x. Generic estimation algorithms can then work on the manifold S mainly by replacing +/- with [+]/[-] where appropriate. We analyze these operators axiomatically, and demonstrate their use in least-squares estimation and the Unscented Kalman Filter. Moreover, we exploit the idea of encapsulation from a software engineering perspective in the Manifold Toolkit, where the [+]/[-] operators mediate between a "flat-vector" view for the generic algorithm and a "named-members" view for the problem specific functions.
Can maker-taker fees prevent algorithmic cooperation in market making?
Authors: Bingyan Han
Date: 2022-11-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

In a semi-realistic market simulator, independent reinforcement learning algorithms may facilitate market makers to maintain wide spreads even without communication. This unexpected outcome challenges the current antitrust law framework. We study the effectiveness of maker-taker fee models in preventing cooperation via algorithms. After modeling market making as a repeated general-sum game, we experimentally show that the relation between net transaction costs and maker rebates is not necessarily monotone. Besides an upper bound on taker fees, we may also need a lower bound on maker rebates to destabilize the cooperation. We also consider the taker-maker model and the effects of mid-price volatility, inventory risk, and the number of agents.
SmartShots: An Optimization Approach for Generating Videos with Data Visualizations Embedded
Authors: Tan Tang, JUNXIU TANG, JIEWEN LAI, LU YING, and YINGCAI WU, Zhejiang University LINGYUN YU, Xi’an Jiaotong-Liverpool University PEIRAN REN, Alibaba Group
Date: 2000-03-04 00:00:00
Proceeding: ACM Transactions on Interactive Intelligent Systems 2000 3
Tasks: Rhythm, Video Editing

Videos are well-received methods for storytellers to communicate various narratives. To further engage viewers, we introduce a novel visual medium where data visualizations are embedded into videos to present data
insights. However, creating such data-driven videos requires professional video editing skills, data visualization knowledge, and even design talents. To ease the difficulty, we propose an optimization method and
develop SmartShots, which facilitates the automatic integration of in-video visualizations. For its development, we first collaborated with experts from different backgrounds, including information visualization,
design, and video production. Our discussions led to a design space that summarizes crucial design considerations along three dimensions: visualization, embedded layout, and rhythm. Based on that, we formulated
an optimization problem that aims to address two challenges: (1) embedding visualizations while considering
both contextual relevance and aesthetic principles and (2) generating videos by assembling multi-media materials. We show how SmartShots solves this optimization problem and demonstrate its usage in three cases.
Finally, we report the results of semi-structured interviews with experts and amateur users on the usability
of SmartShots.
Bridge the Gap Between Architecture Spaces via A Cross-Domain Predictor
Authors: Yuqiao Liu, Yehui Tang ~Yehui_Tang1, Zeqiong Lv, Yunhe Wang, Yanan sun
Date: 2022-11-01 00:00:00
Proceeding: NeurIPS 2022 11
Tasks: GPU, Neural Architecture Search

Neural Architecture Search (NAS) can automatically design promising neural architectures without artificial experience. Though it achieves great success, prohibitively high search cost is required to find a high-performance architecture, which blocks its practical implementation. Neural predictor can directly evaluate the performance of neural networks based on their architectures and thereby save much budget. However, existing neural predictors require substantial annotated architectures trained from scratch, which still consume many computational resources. To solve this issue, we propose a Cross-Domain Predictor (CDP), which is trained based on the existing NAS benchmark datasets (e.g., NAS-Bench-101), but can be used to find high-performance architectures in large-scale search spaces. Particularly, we propose a progressive subspace adaptation strategy to address the domain discrepancy between the source architecture space and the target space. Considering the large difference between two architecture spaces, an assistant space is developed to smooth the transfer process. Compared with existing NAS methods, the proposed CDP is much more efficient. For example, CDP only requires the search cost of 0.1 GPU Days to find architectures with 76.9% top-1 accuracy on ImageNet and 97.51% on CIFAR-10.
Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space
Authors: Shuo Li, Fang Liu, Zehua Hao, Kaibo Zhao, Licheng Jia
Date: 2022-10-21 00:00:00
Proceeding: European Conference on Computer Vision 2022 10
Tasks: Classification, Clustering, Few-Shot Image Classification, image-classification, Image Classification, Unsupervised Few-Shot Image Classification

Most few-shot image classification methods are trained based on tasks. Usually, tasks are built on base classes with a large number of labeled images, which consumes large effort. Unsupervised few-shot image classification methods do not need labeled images, because they require tasks to be built on unlabeled images. In order to efficiently build tasks with unlabeled images, we propose a novel single-stage clustering method: Learning Features into Clustering Space (LF2CS), which first set a separable clustering space by fixing the clustering centers and then use a learnable model to learn features into the clustering space. Based on our LF2CS, we put forward an image sampling and c-way k-shot task building method. With this, we propose a novel unsupervised few-shot image classification method, which jointly learns the learnable model, clustering and few-shot image classification. Experiments and visualization show that our LF2CS has a strong ability to generalize to the novel categories. From the perspective of image sampling, we implement four baselines according to how to build tasks. We conduct experiments on the Omniglot, miniImageNet, tieredImageNet and CIFARFS datasets based on the Conv-4 and ResNet-12 backbones. Experimental results show that
ours outperform the state-of-the-art methods.
On conditional distortion risk measures under uncertainty
Authors: Shuo Gong, Yijun Hu, Linxiao Wei
Date: 2022-11-01 00:00:00
Tasks: Position

Model uncertainty has been one prominent issue both in the theory of risk measures and in practice such as financial risk management and regulation. Motivated by this observation, in this paper, we take a new perspective to describe the model uncertainty, and thus propose a new class of risk measures under model uncertainty. More precisely, we use an auxiliary random variable to describe the model uncertainty. We first establish a conditional distortion risk measure under an auxiliary random variable. Then we axiomatically characterize it by proposing a set of new axioms. Moreover, its coherence and dual representation are investigated. Finally, we make comparisons with some known risk measures such as weighted value at risk (WVaR), range value at risk (RVaR) and $\sQ-$ mixture of ES. One advantage of our modeling is in its flexibility, as the auxiliary random variable can describe various contexts including model uncertainty. To illustrate the proposed framework, we also deduce new risk measures in the presence of background risk.This paper provides some theoretical results about risk measures under model uncertainty, being expected to make meaningful complement to the study of risk measures under model uncertainty.
Phonocardiogram Classification Using 1-Dimensional Inception Time Convolutional Neural Networks
Authors: Bjørn-Jostein Singstad, Antony M. Gitau, Markus Kreutzer Johnsen, Johan Ravn, Lars Ailo Bongo, Henrik Schirmer
Date: 2022-09-07 00:00:00
Proceeding: Computing in Cardiology 2022 9
Tasks: Classify murmurs, Phonocardiogram Classification, Predict clinical outcome

Murmurs are sounds caused by turbulent blood flow that are often the first sign of structural heart disease. These sounds are detected by auscultating the heart using a stethoscope, or more recently by a phonocardiogram (PCG). We aim to identify the presence, absence, or unclear cases of murmurs, as well as predict normal or abnormal clinical outcome from PCG recordings using machine learning. 

We trained and tested two 1-dimensional convolutional neural networks (CNN) on a PCG data set from a pediatric population of 1568 individuals. One model predicted murmurs, while the other model predicted clinical outcomes. Both models were trained to give recording-wise predictions, while the final predictions were given for every patient (patient-wise predictions).

This paper describes our participation in the George B. Moody PhysioNet Challenge 2022 whose objective was to identify heart murmurs and clinical outcome from PCGs. Our team, Simulab, trained a clinical outcome classifier that achieved a challenge cost score of 12419 (ranked 14th out of 39 teams) and the murmur classifier achieved a weighted accuracy of 0.593 (ranked 30th out of 40 teams) on the test set.
Using deep convolutional neural networks to predict patients age based on ECGs from an independent test cohort
Authors: Bjørn-Jostein Singstad, Belal Tavashi
Date: 2022-10-06 00:00:00
Proceeding: medRxiv 2022 10
Tasks: Age Estimation, Rhythm

Electrocardiography is one of the most frequently used methods to evaluate cardiovascular diseases. However, the last decade has shown that deep convolutional neural networks (CNN) can extract information from the electrocardiogram (ECG) that goes beyond traditional diagnostics, such as predicting a persons age. In this study, we trained two different 1-dimensional CNNs on open datasets to predict age from a persons ECG.

The models were trained and validated using 10 seconds long 12-lead ECG records, resampled to 100Hz. 59355 ECGs were used for training and cross-validation, while 21748 ECGs from a separate cohort were used as the test set. We compared the performance achieved on the cross-validation with the performance on the test set. Furthermore, we used cardiologist-annotated cardiovascular conditions to categorize the patients in the test set in order to assess whether some cardiac condition leads to greater discrepancies between CNN-predicted age and chronological age.

The best CNN model, using an Inception Time architecture, showed a significant drop in performance, in terms of mean absolute error (MAE), from cross-validation on the training set (7.90 ± 0.04 years) to the performance on the test set (8.3 years). On the other hand, the mean squared error (MSE) improved from the training set (117.5 ± 2.7 years^2) to the test set (111 years^2). We also observed that the cardiovascular condition that showed the highest deviation between predicted and biological age, in terms of MAE, was the patients with pacing rhythm (10.5 years), while the patients with prolonged QT-interval had the smallest deviation (7.4 years) in terms of MAE.

This work contributes to existing knowledge of age prediction using deep CNNs on ECGs by showing how a trained model performs on a test set from a separate cohort to that used in the training set.
Text Mining Methodologies with R: An Application to Central Bank Texts
Authors: Jonathan Benchimol, Sophia Kazinnik, Yossi Saadon
Date: 2022-06-15 00:00:00
Proceeding: Machine Learning with Applications 2022 6

We review several existing text analysis methodologies and explain their formal application processes using the open-source software R and relevant packages. Several text mining applications to analyze central bank texts are presented.
Reliability-Aware Deployment of DNNs on In-Memory Analog Computing Architectures
Authors: Md Hasibul Amin, Mohammed Elbtity, Ramtin Zand
Date: 2022-10-02 00:00:00

Conventional in-memory computing (IMC) architectures consist of analog memristive crossbars to accelerate matrix-vector multiplication (MVM), and digital functional units to realize nonlinear vector (NLV) operations in deep neural networks (DNNs). These designs, however, require energy-hungry signal conversion units which can dissipate more than 95% of the total power of the system. In-Memory Analog Computing (IMAC) circuits, on the other hand, remove the need for signal converters by realizing both MVM and NLV operations in the analog domain leading to significant energy savings. However, they are more susceptible to reliability challenges such as interconnect parasitic and noise. Here, we introduce a practical approach to deploy large matrices in DNNs onto multiple smaller IMAC subarrays to alleviate the impacts of noise and parasitics while keeping the computation in the analog domain.
Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta
Authors: Simone Saitta, Ludovica Maga, Chloe Armour, Emiliano Votta, Declan P. O'Regan, M. Yousuf Salmasi, Thanos Athanasiou, Jonathan W. Weinsaft, Xiao Yun Xu, Selene Pirola, Alberto Redaelli
Date: 2022-11-01 00:00:00

Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoracic aortic aneurysms (ATAA). To accurately reproduce hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements still makes researchers resort to idealized BCs. In this study we generated and thoroughly characterized a large dataset of synthetic 4D aortic velocity profiles suitable to be used as BCs for CFD simulations. 4D flow MRI scans of 30 subjects with ATAA were processed to extract cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. We built a data-driven generative model of 4D aortic velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.
CarbonTag: A Browser-Based Method for Approximating Energy Consumption of Online Ads
Authors: José González Cabañas, Patricia Callejo, Rubén Cuevas, Steffen Svatberg, Tommy Torjesen, Ángel Cuevas, Antonio Pastor, Mikko Kotila
Date: 2022-10-25 00:00:00

Energy is today the most critical environmental challenge. The amount of carbon emissions contributing to climate change is significantly influenced by both the production and consumption of energy. Measuring and reducing the energy consumption of services is a crucial step toward reducing adverse environmental effects caused by carbon emissions. Millions of websites rely on online advertisements to generate revenue, with most websites earning most or all of their revenues from ads. As a result, hundreds of billions of online ads are delivered daily to internet users to be rendered in their browsers. Both the delivery and rendering of each ad consume energy. This study investigates how much energy online ads use in the rendering process and offers a way for predicting it as part of rendering the ad. To the best of the authors' knowledge, this is the first study to calculate the energy usage of single advertisements in the rendering process. Our research further introduces different levels of consumption by which online ads can be classified based on energy efficiency. This classification will allow advertisers to add energy efficiency metrics and optimize campaigns towards consuming less possible.
AI Explainability and Governance in Smart Energy Systems: A Review
Authors: Roba Alsaigh, Rashid Mehmood, Iyad Katib
Date: 2022-10-24 00:00:00
Tasks: Decision Making, Management

Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper provides a review of AI explainability and governance in smart energy systems. We collect 3,568 relevant papers from the Scopus database, automatically discover 15 parameters or themes for AI governance in energy and elaborate the research landscape by reviewing over 150 papers and providing temporal progressions of the research. The methodology for discovering parameters or themes is based on "deep journalism", our data-driven deep learning-based big data analytics approach to automatically discover and analyse cross-sectional multi-perspective information to enable better decision-making and develop better instruments for governance. The findings show that research on AI explainability in energy systems is segmented and narrowly focussed on a few AI traits and energy system problems. This paper deepens our knowledge of AI governance in energy and is expected to help governments, industry, academics, energy prosumers, and other stakeholders to understand the landscape of AI in the energy sector, leading to better design, operations, utilisation, and risk management of energy systems.
Artificial Intelligence and Arms Control
Authors: Paul Scharre, Megan Lamberth
Date: 2022-10-22 00:00:00

Potential advancements in artificial intelligence (AI) could have profound implications for how countries research and develop weapons systems, and how militaries deploy those systems on the battlefield. The idea of AI-enabled military systems has motivated some activists to call for restrictions or bans on some weapon systems, while others have argued that AI may be too diffuse to control. This paper argues that while a ban on all military applications of AI is likely infeasible, there may be specific cases where arms control is possible. Throughout history, the international community has attempted to ban or regulate weapons or military systems for a variety of reasons. This paper analyzes both successes and failures and offers several criteria that seem to influence why arms control works in some cases and not others. We argue that success or failure depends on the desirability (i.e., a weapon's military value versus its perceived horribleness) and feasibility (i.e., sociopolitical factors that influence its success) of arms control. Based on these criteria, and the historical record of past attempts at arms control, we analyze the potential for AI arms control in the future and offer recommendations for what policymakers can do today.
How fair were COVID-19 restriction decisions? A data-driven investigation of England using the dominance-based rough sets approach
Authors: Edward Abel, Sajid Siraj
Date: 2022-10-21 00:00:00
Tasks: Decision Making, Fairness

During the COVID-19 pandemic, several countries have taken the approach of tiered restrictions which has remained a point of debate due to a lack of transparency. Using the dominance-based rough set approach, we identify patterns in the COVID-19 data pertaining to the UK government's tiered restrictions allocation system. These insights from the analysis are translated into "if-then" type rules, which can easily be interpreted by policy makers. The differences in the rules extracted from different geographical areas suggest inconsistencies in the allocations of tiers in these areas. We found that the differences delineated an overall north south divide in England, however, this divide was driven mostly by London. Based on our analysis, we demonstrate the usefulness of the dominance-based rough sets approach for investigating the fairness and explainabilty of decision making regarding COVID-19 restrictions. The proposed approach and analysis could provide a more transparent approach to localised public health restrictions, which can help ensure greater conformity to the public safety rules.
Verifying a stochastic model for the spread of a SARS-CoV-2-like infection: opportunities and limitations
Authors: Marco Roveri, Franc Ivankovic, Luigi Palopoli, Daniele Fontanelli
Date: 2022-10-31 00:00:00

There is a growing interest in modeling and analyzing the spread of diseases like the SARS-CoV-2 infection using stochastic models. These models are typically analyzed quantitatively and are not often subject to validation using formal verification approaches, nor leverage policy syntheses and analysis techniques developed in formal verification. In this paper, we take a Markovian stochastic model for the spread of a SARSCoV-2-like infection. A state of this model represents the number of subjects in different health conditions. The considered model considers the different parameters that may have an impact on the spread of the disease and exposes the various decision variables that can be used to control it. We show that the modeling of the problem within state-of-the-art model checkers is feasible and it opens several opportunities. However, there are severe limitations due to i) the espressivity of the existing stochastic model checkers on one side, and ii) the size of the resulting Markovian model even for small population sizes.