pwc-archive/papers-with-abstracts huggingface dataset:
Integrative Data Semantics through a Model-enabled Data Stewardship
Authors: Philipp Wegner, Sebastian Schaaf, Mischa Uebachs, Daniel Domingo-Fernández, Yasamin Salimi, Stephan Gebel, Astghik Sargsyan, Colin Birkenbihl, Stephan Springstubbe, Thomas Klockgether, Juliane Fluck, Martin Hofmann-Apitius, Alpha Tom Kodamullil
Date: 2021-11-17 00:00:00

Motivation: The importance of clinical data in understanding the pathophysiology of complex disorders has prompted the launch of multiple initiatives designed to generate patient-level data from various modalities. While these studies can reveal important findings relevant to the disease, each study captures different yet complementary aspects and modalities which, when combined, generate a more comprehensive picture of disease aetiology. However, achieving this requires a global integration of data across studies, which proves to be challenging given the lack of interoperability of cohort datasets. Results: Here, we present the Data Steward Tool (DST), an application that allows for semi-automatic semantic integration of clinical data into ontologies and global data models and data standards. We demonstrate the applicability of the tool in the field of dementia research by establishing a Clinical Data Model (CDM) in this domain. The CDM currently consists of 277 common variables covering demographics (e.g. age and gender), diagnostics, neuropsychological tests, and biomarker measurements. The DST combined with this disease-specific data model shows how interoperability between multiple, heterogeneous dementia datasets can be achieved.
Mean-Variance-VaR portfolios: MIQP formulation and performance analysis
Authors: Francesco Cesarone, Manuel L Martino, Fabio Tardella
Date: 2021-11-18 00:00:00
Tasks: Management, Portfolio Optimization

Value-at-Risk is one of the most popular risk management tools in the financial industry. Over the past 20 years several attempts to include VaR in the portfolio selection process have been proposed. However, using VaR as a risk measure in portfolio optimization models leads to problems that are computationally hard to solve. In view of this, few practical applications of VaR in portfolio selection have appeared in the literature up to now. In this paper, we propose to add the VaR criterion to the classical Mean-Variance approach in order to better address the typical regulatory constraints of the financial industry. We thus obtain a portfolio selection model characterized by three criteria: expected return, variance, and VaR at a specified confidence level. The resulting optimization problem consists in minimizing variance with parametric constraints on the levels of expected return and VaR. This model can be formulated as a Mixed-Integer Quadratic Programming (MIQP) problem. An extensive empirical analysis on seven real-world datasets demonstrates the practical applicability of the proposed approach. Furthermore, the out-of-sample performance of the optimal Mean-Variance-VaR portfolios seems to be generally better than that of the optimal Mean-Variance and Mean-VaR portfolios.
Effect of the U.S.--China Trade War on Stock Markets: A Financial Contagion Perspective
Authors: Minseog Oh, Donggyu Kim
Date: 2021-11-18 00:00:00

In this paper, we investigate the effect of the U.S.--China trade war on stock markets from a financial contagion perspective, based on high-frequency financial data. Specifically, to account for risk contagion between the U.S. and China stock markets, we develop a novel jump-diffusion process. For example, we consider three channels for volatility contagion--such as integrated volatility, positive jump variation, and negative jump variation--and each stock market is able to affect the other stock market as an overnight risk factor. We develop a quasi-maximum likelihood estimator for model parameters and establish its asymptotic properties. Furthermore, to identify contagion channels and test the existence of a structural break, we propose hypothesis test procedures. From the empirical study, we find evidence of financial contagion from the U.S. to China and evidence that the risk contagion channel has changed from integrated volatility to negative jump variation.
FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance
Authors: Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, Christina Dan Wang
Date: 2021-11-07 00:00:00
Tasks: Deep Reinforcement Learning, Friction, reinforcement-learning, Reinforcement Learning (RL)

Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to obtain an agent that automatically positions to win in the market, namely \textit{to decide where to trade, at what price} and \textit{what quantity}, due to the error-prone programming and arduous debugging. In this paper, we present the first open-source framework \textit{FinRL} as a full pipeline to help quantitative traders overcome the steep learning curve. FinRL is featured with simplicity, applicability and extensibility under the key principles, \textit{full-stack framework, customization, reproducibility} and \textit{hands-on tutoring}. Embodied as a three-layer architecture with modular structures, FinRL implements fine-tuned state-of-the-art DRL algorithms and common reward functions, while alleviating the debugging workloads. Thus, we help users pipeline the strategy design at a high turnover rate. At multiple levels of time granularity, FinRL simulates various markets as training environments using historical data and live trading APIs. Being highly extensible, FinRL reserves a set of user-import interfaces and incorporates trading constraints such as market friction, market liquidity and investor's risk-aversion. Moreover, serving as practitioners' stepping stones, typical trading tasks are provided as step-by-step tutorials, e.g., stock trading, portfolio allocation, cryptocurrency trading, etc.
Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data
Authors: Arttu Arjas, Erwin J. Alles, Efthymios Maneas, Simon Arridge, Adrien Desjardins, Mikko J. Sillanpää, Andreas Hauptmann
Date: 2021-11-18 00:00:00
Tasks: 3D Object Tracking, Object Tracking

Many interventional surgical procedures rely on medical imaging to visualise and track instruments. Such imaging methods not only need to be real-time capable, but also provide accurate and robust positional information. In ultrasound applications, typically only two-dimensional data from a linear array are available, and as such obtaining accurate positional estimation in three dimensions is non-trivial. In this work, we first train a neural network, using realistic synthetic training data, to estimate the out-of-plane offset of an object with the associated axial aberration in the reconstructed ultrasound image. The obtained estimate is then combined with a Kalman filtering approach that utilises positioning estimates obtained in previous time-frames to improve localisation robustness and reduce the impact of measurement noise. The accuracy of the proposed method is evaluated using simulations, and its practical applicability is demonstrated on experimental data obtained using a novel optical ultrasound imaging setup. Accurate and robust positional information is provided in real-time. Axial and lateral coordinates for out-of-plane objects are estimated with a mean error of 0.1mm for simulated data and a mean error of 0.2mm for experimental data. Three-dimensional localisation is most accurate for elevational distances larger than 1mm, with a maximum distance of 6mm considered for a 25mm aperture.
On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications
Authors: Johanna Rock, Tiago Azevedo, René de Jong, Daniel Ruiz-Muñoz, Partha Maji
Date: 2021-11-11 00:00:00
Tasks: Multi-class Classification, Sensor Fusion

Deep neural networks have shown great success in prediction quality while reliable and robust uncertainty estimation remains a challenge. Predictive uncertainty supplements model predictions and enables improved functionality of downstream tasks including embedded and mobile applications, such as virtual reality, augmented reality, sensor fusion, and perception. These applications often require a compromise in complexity to obtain uncertainty estimates due to very limited memory and compute resources. We tackle this problem by building upon Monte Carlo Dropout (MCDO) models using the Axolotl framework; specifically, we diversify sampled subnetworks, leverage dropout patterns, and use a branching technique to improve predictive performance while maintaining fast computations. We conduct experiments on (1) a multi-class classification task using the CIFAR10 dataset, and (2) a more complex human body segmentation task. Our results show the effectiveness of our approach by reaching close to Deep Ensemble prediction quality and uncertainty estimation, while still achieving faster inference on resource-limited mobile platforms.
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge
Authors: Pinyarash Pinyoanuntapong, Prabhu Janakaraj, Ravikumar Balakrishnan, Minwoo Lee, Chen Chen, Pu Wang
Date: 2021-10-14 00:00:00
Tasks: Edge-computing, Federated Learning, Multi-agent Reinforcement Learning

Federated learning (FL) is a distributed machine learning technology for next-generation AI systems that allows a number of workers, i.e., edge devices, collaboratively learn a shared global model while keeping their data locally to prevent privacy leakage. Enabling FL over wireless multi-hop networks can democratize AI and make it accessible in a cost-effective manner. However, the noisy bandwidth-limited multi-hop wireless connections can lead to delayed and nomadic model updates, which significantly slows down the FL convergence speed. To address such challenges, this paper aims to accelerate FL convergence over wireless edge by optimizing the multi-hop federated networking performance. In particular, the FL convergence optimization problem is formulated as a Markov decision process (MDP). To solve such MDP, multi-agent reinforcement learning (MA-RL) algorithms along with domain-specific action space refining schemes are developed, which online learn the delay-minimum forwarding paths to minimize the model exchange latency between the edge devices (i.e., workers) and the remote server. To validate the proposed solutions, FedEdge is developed and implemented, which is the first experimental framework in the literature for FL over multi-hop wireless edge computing networks. FedEdge allows us to fast prototype, deploy, and evaluate novel FL algorithms along with RL-based system optimization methods in real wireless devices. Moreover, a physical experimental testbed is implemented by customizing the widely adopted Linux wireless routers and ML computing nodes.Finally, our experimentation results on the testbed show that the proposed network-accelerated FL system can practically and significantly improve FL convergence speed, compared to the FL system empowered by the production-grade commercially available wireless networking protocol, BATMAN-Adv.
Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events
Authors: Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas
Date: 2021-10-03 00:00:00
Tasks: Meta-Learning, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

In this study, we present a meta-learning model to adapt the predictions of the network's capacity between viewers who participate in a live video streaming event. We propose the MELANIE model, where an event is formulated as a Markov Decision Process, performing meta-learning on reinforcement learning tasks. By considering a new event as a task, we design an actor-critic learning scheme to compute the optimal policy on estimating the viewers' high-bandwidth connections. To ensure fast adaptation to new connections or changes among viewers during an event, we implement a prioritized replay memory buffer based on the Kullback-Leibler divergence of the reward/throughput of the viewers' connections. Moreover, we adopt a model-agnostic meta-learning framework to generate a global model from past events. As viewers scarcely participate in several events, the challenge resides on how to account for the low structural similarity of different events. To combat this issue, we design a graph signature buffer to calculate the structural similarities of several streaming events and adjust the training of the global model accordingly. We evaluate the proposed model on the link weight prediction task on three real-world datasets of live video streaming events. Our experiments demonstrate the effectiveness of our proposed model, with an average relative gain of 25% against state-of-the-art strategies. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/melanie
Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach
Authors: Sulaiman Khan, Suleman Khan, Yasir Ali, Muhammad Khalid, Zahid Ullah, Shahid Mumtaz
Date: 2021-10-07 00:00:00
Tasks: Decision Making, Management

In the current era, the next-generation networks like 5th generation (5G) and 6th generation (6G) networks require high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key elements for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for a better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of a convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. The overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.
Distributed Proximal Policy Optimization for Contention-Based Spectrum Access
Authors: Akash Doshi, Jeffrey G. Andrews
Date: 2021-10-07 00:00:00
Tasks: Fairness

The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access that go beyond traditional carrier sensing. We develop a novel distributed implementation of a policy gradient method known as Proximal Policy Optimization modelled on a two stage Markov decision process that enables such an intelligent approach, and still achieves decentralized contention-based medium access. In each time slot, a base station (BS) uses information from spectrum sensing and reception quality to autonomously decide whether or not to transmit on a given resource, with the goal of maximizing proportional fairness network-wide. Empirically, we find the proportional fairness reward accumulated by the policy gradient approach to be significantly higher than even a genie-aided adaptive energy detection threshold. This is further validated by the improved sum and maximum user throughputs achieved by our approach.
Μεταφορά κωδίκων της MATLAB σε προγράμματα C++ για την επίλυση Κανονικών Διαφορικών Εξισώσεων Porting MATLAB routines to C++ for the numerical integration of Ordinary Differential Equations
Authors: Θεοδωρακόπουλος Αχιλλέας-Κωνσταντίνος Βασιλείου
Date: 2015-10-01 00:00:00
Proceeding: ΘΕΟΔΩΡΑΚΟΠΟΥΛΟΣ ΑΧΙΛΛΕΑΣ ηλεκτρολόγος μηχ. και μηχ. υπολογιστών 2015 10
Tasks: Numerical Integration

Στο πλαίσιο της διπλωματικής αυτής εργασίας επιχειρείται ο συγκερασμός της αξιοπιστίας και των δυνατοτήτων που παρέχει η ώριμη ρουτίνα ολοκλήρωσης προβλημάτων αρχικών συνθηκών ode15s του MATLAB με την ταχύτητα που προσφέρει η C++. Για το σκοπό αυτό δημιουργήθηκε σε γλώσσα C++ ένα αλγοριθμικά πιστό αντίγραφο της ode15s και των λοιπών αναγκαίων ρουτίνων, συμπεριλαμβάνοντας όλες τις παρεχόμενες δυνατότητες. Η επιλογή του ολοκληρωτή ode15s έναντι του δημοφιλέστερου ode45 εδράζει στη δυνατότητα του πρώτου να αντιμετωπίζει άκαμπτα προβλήματα και παράλληλα να επιδεικνύει υψηλή επίδοση ακόμη κι όταν χρησιμοποιείται σε μη άκαμπτα προβλήματα. Ο κώδικας έχει δομηθεί με τρόπο που διευκολύνει την προσθήκη νέων ολοκληρωτών του MATLAB όπως η ode45, ode113 κοκ.
Τhis M.Sc. thesis attempts to unify the reliability and capabilities of ode15s, a mature initial value problem integration routine offered by MATLAB, with the high-speed properties of the C++ programming language. In this direction, ode15s and all relevant routines required were ported in C++ yielding an algorithmically identical C++ version of ode15s, which includes all functionality available in MATLAB. The choice of ode15s over the more popular ode45 is justified by the ability of the former to integrate stiff problems while exhibiting only a slight performance impact when applied to non-stiff problems. The source code has been structured to facilitate the addition of other MATLAB integrators such as ode45, ode113 etc.
A Backtracking-Based Algorithm for Computing Hypertree-Decompositions
Authors: Georg Gottlob, Marko Samer
Date: 2007-01-14 00:00:00

Hypertree decompositions of hypergraphs are a generalization of tree decompositions of graphs. The corresponding hypertree-width is a measure for the cyclicity and therefore tractability of the encoded computation problem. Many NP-hard decision and computation problems are known to be tractable on instances whose structure corresponds to hypergraphs of bounded hypertree-width. Intuitively, the smaller the hypertree-width, the faster the computation problem can be solved. In this paper, we present the new backtracking-based algorithm det-k-decomp for computing hypertree decompositions of small width. Our benchmark evaluations have shown that det-k-decomp significantly outperforms opt-k-decomp, the only exact hypertree decomposition algorithm so far. Even compared to the best heuristic algorithm, we obtained competitive results as long as the hypergraphs are not too large.
Fast Computation of Moore-Penrose Inverse Matrices
Authors: Pierre Courrieu
Date: 2008-04-30 00:00:00

Many neural learning algorithms require to solve large least square systems in order to obtain synaptic weights. Moore-Penrose inverse matrices allow for solving such systems, even with rank deficiency, and they provide minimum-norm vectors of synaptic weights, which contribute to the regularization of the input-output mapping. It is thus of interest to develop fast and accurate algorithms for computing Moore-Penrose inverse matrices. In this paper, an algorithm based on a full rank Cholesky factorization is proposed. The resulting pseudoinverse matrices are similar to those provided by other algorithms. However the computation time is substantially shorter, particularly for large systems.
A Family of Rank Similarity Measures based on Maximized Effectiveness Difference
Authors: Luchen Tan, Clarke L. A. Clarke
Date: 2014-08-15 00:00:00
Tasks: Retrieval

Rank similarity measures provide a method for quantifying differences between search engine results without the need for relevance judgments. For example, the providers of a search service might use such measures to estimate the impact of a proposed algorithmic change across a large number of queries - perhaps millions - identifying those queries where the impact is greatest. In this paper, we propose and validate a family of rank similarity measures, each derived from an associated effectiveness measure. Each member of the family is based on the maximization of effectiveness difference under this associated measure. Computing this maximized effectiveness difference (MED) requires the solution of an optimization problem that varies in difficulty, depending on the associated measure. We present solutions for several standard effectiveness measures, including nDCG, MAP, and ERR. Through an experimental validation, we show that MED reveals meaningful differences between retrieval runs. Mathematically, MED is a metric, regardless of the associated measure. Prior work has established a number of other desiderata for rank similarity in the context of search, and we demonstrate that MED satisfies these requirements. Unlike previous proposals, MED allows us to directly translate assumptions about user behavior from any established effectiveness measure to create a corresponding rank similarity measure. In addition, MED cleanly accommodates partial relevance judgments, and if complete relevance information is available, it reduces to a simple difference between effectiveness values.
Sentence encoding for Dialogue Act classification
Authors: Nathan Duran, Steve Battle, Jim Smith
Date: 2021-11-02 00:00:00
Proceeding: Natural Language Engineering 2021 11
Tasks: Classification, Dialog Act Classification, Dialogue Act Classification, Sentence, Transfer Learning

In this study, we investigate the process of generating single-sentence representations for the purpose of Dialogue Act (DA) classification, including several aspects of text pre-processing and input representation which are often overlooked or underreported within the literature, for example, the number of words to keep in the vocabulary or input sequences. We assess each of these with respect to two DA-labelled corpora, using a range of supervised models, which represent those most frequently applied to the task. Additionally, we compare context-free word embedding models with that of transfer learning via pre-trained language models, including several based on the transformer architecture, such as Bidirectional Encoder Representations from Transformers (BERT) and XLNET, which have thus far not been widely explored for the DA classification task. Our findings indicate that these text pre-processing considerations do have a statistically significant effect on classification accuracy. Notably, we found that viable input sequence lengths, and vocabulary sizes, can be much smaller than is typically used in DA classification experiments, yielding no significant improvements beyond certain thresholds. We also show that in some cases the contextual sentence representations generated by language models do not reliably outperform supervised methods. Though BERT, and its derivative models, do represent a significant improvement over supervised approaches, and much of the previous work on DA classification.