pwc-archive/papers-with-abstracts huggingface dataset:
An Improved Random Matrix Prediction Model for Manoeuvring Extended Targets
Authors: Nathan J. Bartlett, Chris Renton, Adrian G. Wills
Date: 2021-05-26 00:00:00
Tasks: Prediction

This paper proposes an improved prediction update for extended target tracking with the random matrix model. A key innovation is to employ a generalised non-central inverse Wishart distribution to model the state transition density of the target extent; resulting in a prediction update that accounts for kinematic state dependent transformations. Moreover, the proposed prediction update offers an additional tuning parameter c.f. previous works, requires only a single Kullback-Leibler divergence minimisation, and improves overall target tracking performance when compared to state-of-the-art alternatives.
Photoacoustic-monitored laser treatment for tattoo removal: a feasibility study
Authors: Yiyun Wang, Daohuai Jiang, Hengrong Lan, Feng Gao, Fei Gao
Date: 2021-05-26 00:00:00
Tasks: Prognosis

Skin blemishes and diseases have attracted increasing research interest in recent decades, due to their growing frequency of occurrence and the severity of related diseases. Various laser treatment approaches have been introduced for the alleviation and removal of skin pigmentation. The treatments' effects highly depend on the experience and prognosis of the relevant operators. But, the operation process lacks real-time feedback, which may directly reflect the extent of the treatment. In this manuscript, we report a photoacoustic-guided laser treatment method with a feasibility study, specifically for laser treatment targeting the tattoo's removal. The results well validated the feasibility of the proposed method through the experiments on phantoms and ex vivo pig skin samples.
Tropical Tree Cover in a Heterogeneous Environment: a Reaction-diffusion Model
Authors: Bert Wuyts, Alan R. Champneys, Nicolas Verschueren, Jo I. House
Date: 2018-03-20 00:00:00

Observed bimodal tree cover distributions at particular environmental conditions and theoretical models indicate that some areas in the tropics can be in either of the alternative stable vegetation states forest or savanna. However, when including spatial interaction in nonspatial differential equation models of a bistable quantity, only the state with the lowest potential energy remains stable. Our recent reaction-diffusion model of Amazonian tree cover confirmed this and was able to reproduce the observed spatial distribution of forest versus savanna satisfactorily when forced by heterogeneous environmental and anthropogenic variables, even though bistability was underestimated. These conclusions were solely based on simulation results. Here, we perform an analytical and numerical analysis of the model. We derive the Maxwell point (MP) of the homogeneous reaction-diffusion equation without savanna trees as a function of rainfall and human impact and show that the front between forest and nonforest settles at this point as long as savanna tree cover near the front remains sufficiently low. For parameters resulting in higher savanna tree cover near the front, we also find irregular forest-savanna cycles and woodland-savanna bistability, which can both explain the remaining observed bimodality.
Modeling multi-scale data via a network of networks
Authors: Shawn Gu, Meng Jiang, Pietro Hiram Guzzi, Tijana Milenkovic
Date: 2021-05-25 00:00:00
Tasks: Data Integration, Prediction

Prediction of node and graph labels are prominent network science tasks. Data analyzed in these tasks are sometimes related: entities represented by nodes in a higher-level (higher-scale) network can themselves be modeled as networks at a lower level. We argue that systems involving such entities should be integrated with a "network of networks" (NoN) representation. Then, we ask whether entity label prediction using multi-level NoN data via our proposed approaches is more accurate than using each of single-level node and graph data alone, i.e., than traditional node label prediction on the higher-level network and graph label prediction on the lower-level networks. To obtain data, we develop the first synthetic NoN generator and construct a real biological NoN. We evaluate accuracy of considered approaches when predicting artificial labels from the synthetic NoNs and proteins' functions from the biological NoN. For the synthetic NoNs, our NoN approaches outperform or are as good as node- and network-level ones depending on the NoN properties. For the biological NoN, our NoN approaches outperform the single-level approaches for just under half of the protein functions, and for 30% of the functions, only our NoN approaches make meaningful predictions, while node- and network-level ones achieve random accuracy. So, NoN-based data integration is important.
How Life Works: Darwinian Evolution of Proteins
Authors: J. C. Phillips
Date: 2021-04-21 00:00:00
Tasks: Ingenuity

We review the development of thermodynamic protein hydropathic scaling theory, starting from backgrounds in mathematics and statistical mechanics, and leading to biomedical applications. Darwinian evolution has organized each protein family in different ways, but dynamical hydropathic scaling theory is both simple and effective in providing readily transferable dynamical insights for many proteins represented in the uncounted amino acid sequences, as well as the 90 thousand static structures contained in the online Protein Data Base. Critical point theory is general, and recently it has proved to be the most effective way of describing protein networks that have evolved towards nearly perfect functionality in given environments, self-organized criticality. Darwinian evolutionary patterns are governed by common dynamical hydropathic scaling principles, which can be quantified using scales that have been developed bioinformatically by studying thousands of static PDB structures. The most effective dynamical scales involve hydropathic globular sculpting interactions averaged over length scales centered on domain dimensions. A central feature of dynamical hydropathic scaling theory is the characteristic domain length associated with a given protein functionality. Evolution has functioned in such a way that the minimal critical length scale established so far is about nine amino acids, but in some cases it is much larger. Some ingenuity is needed to find this primary length scale, as shown by the examples discussed here. Often a survey of the Darwinian evolution of a protein sequence suggests a means of determining the critical length scale. The evolution of Coronavirus is an interesting application; it identifies critical mutations.
Optimizing the location of vaccination sites to stop a zoonotic epidemic
Authors: Ricardo Castillo-Neyra, Bhaswar Bhattacharya, Aris Saxena, Brinkley Raynor, Elvis Diaz, Gian Franco Condori, Maria Rieders, Michael Z. Levy
Date: 2021-05-25 00:00:00
Tasks: regression

The mainstay of canine rabies control is fixed point mass dog vaccination campaigns (MDVC). However, in some regions, ideal vaccination coverage in dogs is not obtained due to low participation in the MDVC. Travel distance to the vaccination sites has been identified as an important barrier to participation. We aim to increase MDVC participation by optimally placing fixed point vaccination locations to minimize walking distance to the nearest vaccination location. We quantified participation probability based on walking distance to the nearest vaccination point using a Poisson regression model. The regression was fit with survey data collected from 2016-2019. We then used a computational recursive interchange technique to solve the facility location problem to find a set of optimal placements of fixed point vaccination locations. Finally, we compared predicted participation of optimally placed vaccination sites to historical participation data from surveys collected from 2016-2019. We identified the p-median algorithm to solve the facility location problem as ideal for fixed point vaccination placement. We found a predicted increase in MDVC participation if vaccination locations are placed optimally. We also found a more even vaccination coverage with optimized vaccination sites; however, the workload in some optimized locations increased significantly. We developed a data-driven computational algorithm to combat an ongoing rabies epidemic by optimally using limited resources to maximize vaccination coverage. The main positive effects we expect if this algorithm is to be implemented would be increased overall vaccination coverage and increased spatial evenness of coverage. A potential negative effect could be the presence of long waiting lines as participation increases.
Assessing asset-liability risk with neural networks
Authors: Patrick Cheridito, John Ery, Mario V. Wüthrich
Date: 2021-05-26 00:00:00

We introduce a neural network approach for assessing the risk of a portfolio of assets and liabilities over a given time period. This requires a conditional valuation of the portfolio given the state of the world at a later time, a problem that is particularly challenging if the portfolio contains structured products or complex insurance contracts which do not admit closed form valuation formulas. We illustrate the method on different examples from banking and insurance. We focus on value-at-risk and expected shortfall, but the approach also works for other risk measures.
Deep Kernel Gaussian Process Based Financial Market Predictions
Authors: Yong Shi, Wei Dai, Wen Long, Bo Li
Date: 2021-05-26 00:00:00

The Gaussian Process with a deep kernel is an extension of the classic GP regression model and this extended model usually constructs a new kernel function by deploying deep learning techniques like long short-term memory networks. A Gaussian Process with the kernel learned by LSTM, abbreviated as GP-LSTM, has the advantage of capturing the complex dependency of financial sequential data, while retaining the ability of probabilistic inference. However, the deep kernel Gaussian Process has not been applied to forecast the conditional returns and volatility in financial market to the best of our knowledge. In this paper, grid search algorithm, used for performing hyper-parameter optimization, is integrated with GP-LSTM to predict both the conditional mean and volatility of stock returns, which are then combined together to calculate the conditional Sharpe Ratio for constructing a long-short portfolio. The experiments are performed on a dataset covering all constituents of Shenzhen Stock Exchange Component Index. Based on empirical results, we find that the GP-LSTM model can provide more accurate forecasts in stock returns and volatility, which are jointly evaluated by the performance of constructed portfolios. Further sub-period analysis of the experiment results indicates that the superiority of GP-LSTM model over the benchmark models stems from better performance in highly volatile periods.
Effects of COVID-19 Vaccine Developments and Rollout on the Capital Market -- A Case Study
Authors: Maximilian Vierlboeck, Roshanak Rose Nilchiani
Date: 2021-05-26 00:00:00

Various companies have developed vaccines to combat the pandemic caused 2020 by the virus COVID-19. Such vaccines and the distribution can have a major impact on the success of pharmaceutical companies, which in turn can show itself in their valuation and stock price. This poses the question if and how the trends or popularity of the companies might be connected to the value and stock price of said entities. To gain some insight into these questions, the work at hand looks at five COVID vaccine development companies and evaluates their correlations over the development of the vaccine as well as after the rollout start. The process was conducted by using python including various libraries. The result of this analysis was that there is a significant correlation between the Google Trend data and the respective stock prices (retrieved from yahoo! Finance) of the companies on average, where the time during the development of the drugs is more positively correlated and the post-rollout periods show a shift to a slightly negative inclining correlation. Furthermore, it was found that the smaller companies based on their market cap show a higher price volatility overall. In addition, higher average trend scores and thus popularity values were found after the rollout of the respective companies. In conclusion, a correlations between the trend data and the financial values have been found and corroborate the plots of the data. Due to the small size of the sample, the result cannot yet be considered statistically significant, but possibility for expansion exists and is already being worked on.
Bitcoin: Like a Satellite or Always Hardcore? A Core-Satellite Identification in the Cryptocurrency Market
Authors: Christoph J. Börner, Ingo Hoffmann, Jonas Krettek, Lars M. Kürzinger, Tim Schmitz
Date: 2021-05-26 00:00:00

Cryptocurrencies (CCs) become more interesting for institutional investors' strategic asset allocation and will be a fixed component of professional portfolios in future. This asset class differs from established assets especially in terms of the severe manifestation of statistical parameters. The question arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components, based on modern methods from image / pattern recognition.
On the Return Distributions of a Basket of Cryptocurrencies and Subsequent Implications
Authors: Christoph J. Börner, Ingo Hoffmann, Jonas Krettek, Lars M. Kürzinger, Tim Schmitz
Date: 2021-05-26 00:00:00

This paper evaluates and assesses the risk associated with capital allocation in cryptocurrencies (CCs). In this regard, we take a basket of 27 CCs and the CC index EWCI$^-$ into account. After considering a series of statistical tests we find the stable distribution (SDI) to be the most appropriate to model the body of CCs returns. However, as we find the SDI to possess less favorable properties in the tail area for high quantiles, the generalized Pareto distribution is adapted for a more precise risk assessment. We use a combination of both distributions to calculate the Value at Risk and the Conditional Value at Risk, indicating two subgroups of CCs with differing risk characteristics.
Detection and Accurate Localization of Circular Fiducials under Highly Challenging Conditions
Authors: Lilian Calvet, Pierre Gurdjos, Carsten Griwodz, Simone Gasparini
Date: 2016-06-23 00:00:00
Tasks: Position

Using fiducial markers ensures reliable detection and identification of planar features in images. Fiducials are used in a wide range of applications, especially when a reliable visual reference is needed, e.g., to track the camera in cluttered or textureless environments. A marker designed for such applications must be robust to partial occlusions, varying distances and angles of view, and fast camera motions. In this paper, we present a robust, highly accurate fiducial system, whose markers consist of concentric rings, along with its theoretical foundations. Relying on projective properties, it allows to robustly localize the imaged markerand to accurately detect the position of the image of the (common) circle center. We demonstrate that our system can detect and accurately localize these circular fiducials under very challenging conditions and the experimental results reveal that it outperforms other recent fiducial systems.
Information to Wisdom: Commonsense Knowledge Extraction and Compilation
Authors: Simon Razniewski, Niket Tandon, Aparna S. Varde
Date: 2021-03-04 00:00:00
Tasks: Knowledge Base Construction

Commonsense knowledge is a foundational cornerstone of artificial intelligence applications. Whereas information extraction and knowledge base construction for instance-oriented assertions, such as Brad Pitt's birth date, or Angelina Jolie's movie awards, has received much attention, commonsense knowledge on general concepts (politicians, bicycles, printers) and activities (eating pizza, fixing printers) has only been tackled recently. In this tutorial we present state-of-the-art methodologies towards the compilation and consolidation of such commonsense knowledge (CSK). We cover text-extraction-based, multi-modal and Transformer-based techniques, with special focus on the issues of web search and ranking, as of relevance to the WSDM community.
Authorship Attribution Using Text Distortion
Authors: Efstathios Stamatatos
Date: 2017-04-03 00:00:00
Tasks: Authorship Attribution, Authorship Verification

Authorship attribution is associated with important applications in forensics and humanities research. A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre. In this paper, we present a novel method that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures. The proposed method attempts to mask topic-specific information that is not related to the personal style of authors. Based on experiments on two main tasks in authorship attribution, closed-set attribution and authorship verification, we demonstrate that the proposed approach can enhance existing methods especially under cross-topic conditions, where the training and test corpora do not match in topic.
High Resolution, Deep Imaging Using Confocal Time-of-flight Diffuse Optical Tomography
Authors: Yongyi Zhao, Ankit Raghuram, Hyun K. Kim, Andreas H. Hielscher, Jacob T. Robinson, Ashok Veeraraghavan
Date: 2021-01-27 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Light scattering by tissue severely limits how deep beneath the surface one can image, and the spatial resolution one can obtain from these images. Diffuse optical tomography (DOT) is one of the most powerful techniques for imaging deep within tissue -- well beyond the conventional $\sim$10-15 mean scattering lengths tolerated by ballistic imaging techniques such as confocal and two-photon microscopy. Unfortunately, existing DOT systems are limited, achieving only centimeter-scale resolution. Furthermore, they suffer from slow acquisition times and slow reconstruction speeds making real-time imaging infeasible. We show that time-of-flight diffuse optical tomography (ToF-DOT) and its confocal variant (CToF-DOT), by exploiting the photon travel time information, allow us to achieve millimeter spatial resolution in the highly scattered diffusion regime ($> 50 $ mean free paths). In addition, we demonstrate two additional innovations: focusing on confocal measurements, and multiplexing the illumination sources allow us to significantly reduce the measurement acquisition time. Finally, we rely on a novel convolutional approximation that allows us to develop a fast reconstruction algorithm, achieving a 100$\times$ speedup in reconstruction time compared to traditional DOT reconstruction techniques. Together, we believe that these technical advances serve as the first step towards real-time, millimeter resolution, deep tissue imaging using DOT.
Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading
Authors: Zhihan Zhou, Liqian Ma, Han Liu
Date: 2021-05-26 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Articles, Event Detection, Event-Driven Trading, Stock Market Prediction, Stock Prediction, Stock Price Prediction, Stock Trend Prediction, Text-Based Stock Prediction

In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles. Unlike existing models that utilize textual features (e.g., bag-of-words) and sentiments to directly make stock predictions, we consider corporate events as the driving force behind stock movements and aim to profit from the temporary stock mispricing that may occur when corporate events take place. The core of the proposed strategy is a bi-level event detection model. The low-level event detector identifies events' existences from each token, while the high-level event detector incorporates the entire article's representation and the low-level detected results to discover events at the article-level. We also develop an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark. EDT includes 9721 news articles with token-level event labels as well as 303893 news articles with minute-level timestamps and comprehensive stock price labels. Experiments on EDT indicate that the proposed strategy outperforms all the baselines in winning rate, excess returns over the market, and the average return on each transaction.
Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking
Authors: Fiorenzo Artoni a, Chiara Fanciullacci a, C, Federica Bertolucci c, Alessandro Panarese a, Scott Makeig d, Silvestro Micera a, B, 1, Carmelo Chisari
Date: 2017-10-01 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sentence

In lower mammals, locomotion seems to be mainly regulated by subcortical and spinal networks. On the contrary,
recent evidence suggests that in humans the motor cortex is also significantly engaged during complex locomotion
tasks. However, a detailed understanding of cortical contribution to locomotion is still lacking especially during
stereotyped activities. Here, we show that cortical motor areas finely control leg muscle activation during
treadmill stereotyped walking. Using a novel technique based on a combination of Reliable Independent
Component Analysis, source localization and effective connectivity, and by combining electroencephalographic
(EEG) and electromyographic (EMG) recordings in able-bodied adults we were able to examine for the first time
cortical activation patterns and cortico-muscular connectivity including information flow direction. Results not
only provided evidence of cortical activity associated with locomotion, but demonstrated significant causal
unidirectional drive from contralateral motor cortex to muscles in the swing leg. These insights overturn the
traditional view that human cortex has a limited role in the control of stereotyped locomotion, and suggest useful
hypotheses concerning mechanisms underlying gait under other conditions.
One sentence summary: Motor cortex proactively drives contralateral swing leg muscles during treadmill walking,
counter to the traditional view of stereotyped human locomotion
Evaluation of concept drift adaptation for acoustic scene classifier based on Kernel Density Drift Detection and Combine Merge Gaussian Mixture Model
Authors: Ibnu Daqiqil Id, Masanobu Abe, Sunao Hara
Date: 2021-05-27 00:00:00
Tasks: Drift Detection

Based on the experimental results, all concepts drift types have their respective hyperparameter configurations. Simple and gradual concept drift have similar pattern which requires a smaller {\alpha} value than recurring concept drift because, in this type of drift, a new concept appear continuously, so it needs a high-frequency model adaptation. However, in recurring concepts, the new concept may repeat in the future, so the lower frequency adaptation is better. Furthermore, high-frequency model adaptation could lead to an overfitting problem. Implementing CMGMM component pruning mechanism help to control the number of the active component and improve model performance.