pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of complex circadian time series data using wavelets
Authors: Christoph Schmal, Gregor Mönke, Adrián E. Granada
Date: 2021-07-29 00:00:00
Tasks: Time Series, Time Series Analysis

Experiments that compare rhythmic properties across different genetic alterations and entrainment conditions underlie some of the most important breakthroughs in circadian biology. A robust estimation of the rhythmic properties of the circadian signals goes hand in hand with these discoveries. Widely applied traditional signal analysis methods such as fitting cosine functions or Fourier transformations rely on the assumption that oscillation periods do not change over time. However, novel high-resolution recording techniques have shown that, most commonly, circadian signals exhibit time-dependent changes of periods and amplitudes which cannot be captured with the traditional approaches. In this chapter we introduce a method to determine time-dependent properties of oscillatory signals, using the novel open-source Python-based Biological Oscillations Analysis Toolkit (pyBOAT). We show with examples how to detect rhythms, compute and interpret high-resolution time-dependent spectral results, analyze the main oscillatory component and to subsequently determine these main components time-dependent instantaneous period, amplitude and phase. We introduce step-by-step how such an analysis can be done by means of the easy-to-use point-and-click graphical user interface (GUI) provided by pyBOAT or executed within a Python programming environment. Concepts are explained using simulated signals as well as experimentally obtained time series.
Characterising menotactic behaviours in movement data using hidden Markov models
Authors: Ron R. Togunov, Andrew E. Derocher, Nicholas J. Lunn, Marie Auger-Méthé
Date: 2021-07-27 00:00:00

1. Movement is the primary means by which animals obtain resources and avoid hazards. Most movement exhibits directional bias that is related to environmental features (taxis), such as the location of food patches, predators, ocean currents, or wind. Numerous behaviours with directional bias can be characterized by maintaining orientation at an angle relative to the environmental stimuli (menotaxis), including navigation relative to sunlight or magnetic fields and energy-conserving flight across wind. However, no statistical methods exist to flexibly classify and characterise such directional bias. 2. We propose a biased correlated random walk model that can identify menotactic behaviours by predicting turning angle as a trade-off between directional persistence and directional bias relative to environmental stimuli without making a priori assumptions about the angle of bias. We apply the model within the framework of a multi-state hidden Markov model (HMM) and describe methods to remedy information loss associated with coarse environmental data to improve the classification and parameterization of directional bias. 3. Using simulation studies, we illustrate how our method more accurately classifies behavioural states compared to conventional correlated random walk HMMs that do not incorporate directional bias. We illustrate the application of these methods by identifying cross wind olfactory foraging and drifting behaviour mediated by wind-driven sea ice drift in polar bears (Ursus maritimus) from movement data collected by satellite telemetry. 4. The extensions we propose can be readily applied to movement data to identify and characterize behaviours with directional bias toward any angle, and open up new avenues to investigate more mechanistic relationships between animal movement and the environment.
Neural network approximation for superhedging prices
Authors: Francesca Biagini, Lukas Gonon, Thomas Reitsam
Date: 2021-07-29 00:00:00

This article examines neural network-based approximations for the superhedging price process of a contingent claim in a discrete time market model. First we prove that the $\alpha$-quantile hedging price converges to the superhedging price at time $0$ for $\alpha$ tending to $1$, and show that the $\alpha$-quantile hedging price can be approximated by a neural network-based price. This provides a neural network-based approximation for the superhedging price at time $0$ and also the superhedging strategy up to maturity. To obtain the superhedging price process for $t>0$, by using the Doob decomposition it is sufficient to determine the process of consumption. We show that it can be approximated by the essential supremum over a set of neural networks. Finally, we present numerical results.
Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time
Authors: Nick James, Max Menzies
Date: 2021-07-29 00:00:00

This paper introduces new methods to study behaviours among the 52 largest cryptocurrencies between 01-01-2019 and 30-06-2021. First, we explore evolutionary correlation behaviours and apply a recently proposed turning point algorithm to identify regimes in market correlation. Next, we inspect the relationship between collective dynamics and the cryptocurrency market size - revealing an inverse relationship between the size of the market and the strength of collective dynamics. We then explore the time-varying consistency of the relationships between cryptocurrencies' size and their returns and volatility. There, we demonstrate that there is greater consistency between size and volatility than size and returns. Finally, we study the spread of volatility behaviours across the market changing with time by examining the structure of Wasserstein distances between probability density functions of rolling volatility. We demonstrate a new phenomenon of increased uniformity in volatility during market crashes, which we term \emph{volatility dispersion}.
Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces
Authors: Han Lin Shang, Fearghal Kearney
Date: 2021-07-22 00:00:00
Tasks: Time Series, Time Series Analysis

This paper presents static and dynamic versions of univariate, multivariate, and multilevel functional time-series methods to forecast implied volatility surfaces in foreign exchange markets. We find that dynamic functional principal component analysis generally improves out-of-sample forecast accuracy. More specifically, the dynamic univariate functional time-series method shows the greatest improvement. Our models lead to multiple instances of statistically significant improvements in forecast accuracy for daily EUR-USD, EUR-GBP, and EUR-JPY implied volatility surfaces across various maturities, when benchmarked against established methods. A stylised trading strategy is also employed to demonstrate the potential economic benefits of our proposed approach.
Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction
Authors: Chaoran Cui, Xiaojie Li, Juan Du, Chunyun Zhang, Xiushan Nie, Meng Wang, Yilong Yin
Date: 2021-07-22 00:00:00
Tasks: Stock Trend Prediction

Predicting the future price trends of stocks is a challenging yet intriguing problem given its critical role to help investors make profitable decisions. In this paper, we present a collaborative temporal-relational modeling framework for end-to-end stock trend prediction. The temporal dynamics of stocks is firstly captured with an attention-based recurrent neural network. Then, different from existing studies relying on the pairwise correlations between stocks, we argue that stocks are naturally connected as a collective group, and introduce the hypergraph structures to jointly characterize the stock group-wise relationships of industry-belonging and fund-holding. A novel hypergraph tri-attention network (HGTAN) is proposed to augment the hypergraph convolutional networks with a hierarchical organization of intra-hyperedge, inter-hyperedge, and inter-hypergraph attention modules. In this manner, HGTAN adaptively determines the importance of nodes, hyperedges, and hypergraphs during the information propagation among stocks, so that the potential synergies between stock movements can be fully exploited. Extensive experiments on real-world data demonstrate the effectiveness of our approach. Also, the results of investment simulation show that our approach can achieve a more desirable risk-adjusted return. The data and codes of our work have been released at https://github.com/lixiaojieff/HGTAN.
Feature importance recap and stacking models for forex price prediction
Authors: Yunze Li, Yanan Xie, Chen Yu, Fangxing Yu, Bo Jiang, Matloob Khushi
Date: 2021-07-06 00:00:00
Tasks: Deep Learning, Feature Importance, feature selection

Forex trading is the largest market in terms of qutantitative trading. Traditionally, traders refer to technical analysis based on the historical data to make decisions and trade. With the development of artificial intelligent, deep learning plays a more and more important role in forex forecasting. How to use deep learning models to predict future price is the primary purpose of most researchers. Such prediction not only helps investors and traders make decisions, but also can be used for auto-trading system. In this article, we have proposed a novel approach of feature selection called 'feature importance recap' which combines the feature importance score from tree-based model with the performance of deep learning model. A stacking model is also developed to further improve the performance. Our results shows that proper feature selection approach could significantly improve the model performance, and for financial data, some features have high importance score in many models. The results of stacking model indicate that combining the predictions of some models and feed into a neural network can further improve the performance.
Profit and loss manipulations by online trading brokers
Authors: Golnaz Shahtahmassebi, Lascelles Wright
Date: 2021-07-29 00:00:00

Online trading has attracted millions of people around the world. In March 2021, it was reported there were 18 million accounts from just one broker. Historically, manipulation in financial markets is considered to be fraudulently influencing share, currency pairs or any other indices prices. This article introduces the idea that online trading platform technical issues can be considered as brokers manipulation to control traders profit and loss. More importantly it shows these technical issues are the contributing factors of the 82% risk of retail traders losing money. We identify trading platform technical issues of one of the world's leading online trading providers and calculate retail traders losses caused by these issues. To do this, we independently record each trade details using the REST API response provided by the broker. We show traders log activity files is the only way to assess any suspected profit or loss manipulation by the broker. Therefore, it is essential for any retail trader to have access to their log files. We compare our findings with broker's Trustpilot customer reviews. We illustrate how traders' profit and loss can be negatively affected by broker's platform technical issues such as not being able to close profitable trades, closing trades with delays, disappearance of trades, disappearance of profit from clients statements, profit and loss discrepancies, stop loss not being triggered, stop loss or limit order triggered too early. Although regulatory bodies try to ensure that consumers get a fair deal, these attempts are hugely insufficient in protecting retail traders. Therefore, regulatory bodies such as the FCA should take these technical issues seriously and not rely on brokers' internal investigations, because under any other circumstances, these platform manipulations would be considered as crimes and connivingly misappropriating funds.
Personalized Image Semantic Segmentation
Authors: Yu Zhang, Chang-Bin Zhang, Peng-Tao Jiang, Ming-Ming Cheng, Feng Mao
Date: 2021-07-24 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Semantic segmentation models trained on public datasets have achieved great success in recent years. However, these models didn't consider the personalization issue of segmentation though it is important in practice. In this paper, we address the problem of personalized image segmentation. The objective is to generate more accurate segmentation results on unlabeled personalized images by investigating the data's personalized traits. To open up future research in this area, we collect a large dataset containing various users' personalized images called PIS (Personalized Image Semantic Segmentation). We also survey some recent researches related to this problem and report their performance on our dataset. Furthermore, by observing the correlation among a user's personalized images, we propose a baseline method that incorporates the inter-image context when segmenting certain images. Extensive experiments show that our method outperforms the existing methods on the proposed dataset. The code and the PIS dataset will be made publicly available.
Machine Learning and Deep Learning Methods for Building Intelligent Systems in Medicine and Drug Discovery: A Comprehensive Survey
Authors: G Jignesh Chowdary, Suganya G, Premalatha M, Asnath Victy Phamila Y, Karunamurthy K
Date: 2021-07-19 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Diagnostic, Drug Discovery, Survey

With the advancements in computer technology, there is a rapid development of intelligent systems to understand the complex relationships in data to make predictions and classifications. Artificail Intelligence based framework is rapidly revolutionizing the healthcare industry. These intelligent systems are built with machine learning and deep learning based robust models for early diagnosis of diseases and demonstrates a promising supplementary diagnostic method for frontline clinical doctors and surgeons. Machine Learning and Deep Learning based systems can streamline and simplify the steps involved in diagnosis of diseases from clinical and image-based data, thus providing significant clinician support and workflow optimization. They mimic human cognition and are even capable of diagnosing diseases that cannot be diagnosed with human intelligence. This paper focuses on the survey of machine learning and deep learning applications in across 16 medical specialties, namely Dental medicine, Haematology, Surgery, Cardiology, Pulmonology, Orthopedics, Radiology, Oncology, General medicine, Psychiatry, Endocrinology, Neurology, Dermatology, Hepatology, Nephrology, Ophthalmology, and Drug discovery. In this paper along with the survey, we discuss the advancements of medical practices with these systems and also the impact of these systems on medical professionals.
Multi-layered Cepstrum for Instantaneous Frequency Estimation
Authors: Chin-Yun Yu, Li Su
Date: 2019-02-01 00:00:00

We propose the multi-layered cepstrum (MLC) method to estimate multiple fundamental frequencies (MF0) of a signal under challenging contamination such as high-pass filter noise. Taking the operation of cepstrum (i.e., Fourier transform, filtering, and nonlinear activation) recursively, MLC is shown as an efficient method to enhance MF0 saliency in a step-by-step manner. Evaluation on a real-world polyphonic music dataset under both normal and low-fidelity conditions demonstrates the potential of MLC.
Comparative Investigation of Learning Algorithms for Image Classification with Small Dataset
Authors: Iqbal Imran; Odesanmi Gbenga Abiodun; Wang Jianxiang; Liu Li
Date: 2021-06-11 00:00:00
Proceeding: 6 2021 6
Tasks: image-classification, Image Classification

Increase in popularity of deep learning in various research areas leads to use it in resolving image classification problems. The objective of this research is to compare and to find learning algorithms which perform better for image classification task with small dataset. We have also tuned the hyperparameters associated with optimizers and models to improve performance. First, we performed several experiments using eight learning algorithms to come closer to optimal values of hyperparameters. Then, we executed twenty-four final experiments with near optimum values of hyperparameters to find the best learning algorithm. Experimental results showed that the AdaGrad learning algorithm achieves better accuracy, lesser training time, as well as fewer memory utilization compared to the rest of the learning algorithms.
Mid-space-independent deformable image registration
Authors: I. Aganj, J. E. Iglesias, M. Reuter, M. R. Sabuncu, B. Fischl
Date: 2017-05-15 00:00:00
Proceeding: NeuroImage 2017 5
Tasks: Deformable Medical Image Registration, Image Registration, Medical Image Registration

Aligning images in a mid-space is a common approach to ensuring that deformable image registration is symmetric – that it does not depend on the arbitrary ordering of the input images. The results are, however, generally dependent on the mathematical definition of the mid-space. In particular, the set of possible solutions is typically restricted by the constraints that are enforced on the transformations to prevent the mid-space from drifting too far from the native image spaces. The use of an implicit atlas has been proposed as an approach to mid-space image registration. In this work, we show that when the atlas is aligned to each image in the native image space, the data term of implicit-atlas-based deformable registration is inherently independent of the mid-space. In addition, we show that the regularization term can be reformulated independently of the mid-space as well. We derive a new symmetric cost function that only depends on the transformation morphing the images to each other, rather than to the atlas. This eliminates the need for anti-drift constraints, thereby expanding the space of allowable deformations. We provide an implementation scheme for the proposed framework, and validate it through diffeomorphic registration experiments on brain magnetic resonance images.
Quantification of structural brain connectivity via a conductance model
Authors: A. Frau-Pascual, M. Fogarty, B. Fischl, A. Yendiki, I. Aganj
Date: 2019-04-01 00:00:00
Proceeding: NeuroImage 2019 4
Tasks: Diffusion  MRI, Functional Connectivity

Connectomics has proved promising in quantifying and understanding the effects of development, aging and an array of diseases on the brain. In this work, we propose a new structural connectivity measure from diffusion MRI that allows us to incorporate direct brain connections, as well as indirect ones that would not be otherwise accounted for by standard techniques and that may be key for the better understanding of function from structure. From our experiments on the Human Connectome Project dataset, we find that our measure of structural connectivity better correlates with functional connectivity than streamline tractography does, meaning that it provides new structural information related to function. Through additional experiments on the ADNI-2 dataset, we demonstrate the ability of this new measure to better discriminate different stages of Alzheimer's disease. Our findings suggest that this measure is useful in the study of the normal brain structure, and for quantifying the effects of disease on the brain structure.
Optimus: An Efficient Dynamic Resource Scheduler for Deep Learning Clusters
Authors: Yanghua Peng
Date: 2018-04-26 00:00:00
Proceeding: EuroSys 2018 4
Tasks: CPU, Deep Learning, GPU, Machine Translation, Scheduling, speech-recognition, Speech Recognition

Deep learning workloads are common in today’s production clusters due to the proliferation of deep learning-driven AI services
(e.g., speech recognition, machine translation). A deep learning
training job is resource-intensive and time-consuming. Efficient
resource scheduling is the key to the maximal performance of a
deep learning cluster. Existing cluster schedulers are largely not tailored to deep learning jobs, and typically specifying a fixed amount
of resources for each job, prohibiting high resource efficiency and
job performance. This paper proposes Optimus, a customized job
scheduler for deep learning clusters, which minimizes job training
time based on online resource-performance models. Optimus uses
online fitting to predict model convergence during training and
sets up performance models to accurately estimate training speed
as a function of allocated resources in each job. Based on the models,
a simple yet effective method is designed and used for dynamically
allocating resources and placing deep learning tasks to minimize
job completion time. We implement Optimus on top of Kubernetes,
a cluster manager for container orchestration, and experiment on a
deep learning cluster with 7 CPU servers and 6 GPU servers, running 9 training jobs using the MXNet framework. Results show that
Optimus outperforms representative cluster schedulers by about
139% and 63% in terms of job completion time and makespan, respectively
“What Do Your Friends Think?”: Efficient Polling Methods for Networks Using Friendship Paradoxhttps://ieeexplore.ieee.org/abstract/document/8832260
Authors: Buddhika Nettasinghe, Vikram Krishnamurthy
Date: 2019-09-11 00:00:00
Proceeding: IEEE Transactions on Knowledge and Data Engineering 2019 9

This paper deals with randomized polling of a social network. In the case of forecasting the outcome of an election between two candidates A and B, classical intent polling asks randomly sampled individuals: who will you vote for? Expectation polling asks: who do you think will win? In this paper, we propose a novel neighborhood expectation polling (NEP) strategy that asks randomly sampled individuals: what is your estimate of the fraction of votes for A? Therefore, in NEP, sampled individuals will naturally look at their neighbors (defined by the underlying social network graph) when answering this question. Hence, the mean squared error (MSE) of NEP methods rely on selecting the optimal set of samples from the network. To this end, we propose two NEP algorithms for the following cases: (i) the social network graph is not known but, random walks (sequential exploration) can be performed on the graph, and (ii) the social network graph is unknown but, uniformly sampled nodes from the network are available. For both cases, algorithms based on a graph theoretic consequence called friendship paradox are proposed. Theoretical results on the dependence of the MSE of the algorithms on the properties of the network are established. Numerical results on real and synthetic data sets are provided to illustrate the performance of the algorithms.
Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications
Authors: Zhiwu Lu, Horace H. S. Ip, Yuxin Peng
Date: 2011-09-22 00:00:00
Tasks: Retrieval

This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal multimedia retrieval. Extensive results have shown the superior performance of our approach.