pwc-archive/papers-with-abstracts huggingface dataset:
Wearable and Continuous Prediction of Passage of Time Perception for Monitoring Mental Health
Authors: Lara Orlandic, Adriana Arza Valdes, David Atienza
Date: 2021-05-03 00:00:00

A person's passage of time perception (POTP) is strongly linked to their mental state and stress response, and can therefore provide an easily quantifiable means of continuous mental health monitoring. In this work, we develop a custom experiment and Machine Learning (ML) models for predicting POTP from biomarkers acquired from wearable biosensors. We first confirm that individuals experience time passing slower than usual during fear or sadness (p = 0.046) and faster than usual during cognitive tasks (p = 2 x 10^-5). Then, we group together the experimental segments associated with fast, slow, and normal POTP, and train a ML model to classify between these states based on a person's biomarkers. The classifier had a weighted average F-1 score of 79%, with the fast-passing time class having the highest F-1 score of 93%. Next, we classify each individual's POTP regardless of the task at hand, achieving an F-1 score of 77.1% when distinguishing time passing faster rather than slower than usual. In the two classifiers, biomarkers derived from the respiration, electrocardiogram, skin conductance, and skin temperature signals contributed most to the classifier output, thus enabling real-time POTP monitoring using noninvasive, wearable biosensors.
Long-term changes in functional connectivity predict responses to intracranial stimulation of the human brain
Authors: Christoforos Papasavvas, Peter Neal Taylor, Yujiang Wang
Date: 2021-05-06 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

Targeted electrical stimulation of the brain perturbs neural networks and modulates their rhythmic activity both at the site of stimulation and at remote brain regions. Understanding, or even predicting, this neuromodulatory effect is crucial for any therapeutic use of brain stimulation. To this end, we analyzed the stimulation responses in 131 stimulation sessions across 66 patients with focal epilepsy recorded through intracranial EEG (iEEG). We considered functional and structural connectivity features as predictors of the response at every iEEG contact. Taking advantage of multiple recordings over days, we also investigated how slow changes in interictal functional connectivity (FC) ahead of the stimulation relate to stimulation responses. The results reveal that, indeed, this long-term variability of FC exhibits strong association with the stimulation-induced increases in delta and theta band power. Furthermore, we show through cross-validation that long-term variability of FC improves prediction of responses above the performance of spatial predictors alone. These findings can enhance the patient-specific design of effective neuromodulatory protocols for therapeutic interventions.
The Reconfiguration Pattern of Individual Brain Metabolic Connectome for Parkinson's Disease Identification
Authors: Weikai Li, Yongxiang Tang, Zhengxia Wang, Shuo Hu, Xin Gao
Date: 2021-04-29 00:00:00
Tasks: Diagnostic

Background: Positron Emission Tomography (PET) with 18F-fluorodeoxyglucose (18F-FDG) reveals metabolic abnormalities in Parkinson's disease (PD) at a systemic level. Previous metabolic connectome studies derived from groups of patients have failed to identify the individual neurophysiological details. We aim to establish an individual metabolic connectome method to characterize the aberrant connectivity patterns and topological alterations of the individual-level brain metabolic connectome and their diagnostic value in PD. Methods: The 18F-FDG PET data of 49 PD patients and 49 healthy controls (HCs) were recruited. Each individual's metabolic brain network was ascertained using the proposed Jensen-Shannon Divergence Similarity Estimation (JSSE) method. The intergroup difference of the individual's metabolic brain network and its global and local graph metrics were analyzed to investigate the metabolic connectome's alterations. The identification of the PD from HC individuals was used by the multiple kernel support vector machine (MK-SVM) to combine the information from connection and topological metrics. The validation was conducted using the nest leave-one-out cross-validation strategy to confirm the performance of the methods. Results: The proposed JSSE metabolic connectome method showed the most involved metabolic motor networks were PUT-PCG, THA-PCG, and SMA pathways in PD, which was similar to the typical group-level method, and yielded another detailed individual pathological connectivity in ACG-PCL, DCG-PHG and ACG pathways. These aberrant functional network measures exhibited an ideal classification performance in the identifying of PD individuals from HC individuals at an accuracy of up to 91.84%.
Quantitative Human Paleogenetics: what can ancient DNA tell us about complex trait evolution?
Authors: Evan K. Irving-Pease, Rasa Muktupavela, Michael Dannemann, Fernando Racimo
Date: 2021-05-06 00:00:00

Genetic association data from national biobanks and large-scale association studies have provided new prospects for understanding the genetic evolution of complex traits and diseases in humans. In turn, genomes from ancient human archaeological remains are now easier than ever to obtain, and provide a direct window into changes in frequencies of trait-associated alleles in the past. This has generated a new wave of studies aiming to analyse the genetic component of traits in historic and prehistoric times using ancient DNA, and to determine whether any such traits were subject to natural selection. In humans, however, issues about the portability and robustness of complex trait inference across different populations are particularly concerning when predictions are extended to individuals that died thousands of years ago, and for which little, if any, phenotypic validation is possible. In this review, we discuss the advantages of incorporating ancient genomes into studies of trait-associated variants, the need for models that can better accommodate ancient genomes into quantitative genetic frameworks, and the existing limits to inferences about complex trait evolution, particularly with respect to past populations.
Principles of seed banks: complexity emerging from dormancy
Authors: Jay T. Lennon, Frank den Hollander, Maite Wilke-Berenguer, Jochen Blath
Date: 2020-11-30 00:00:00

Across the tree of life, populations have evolved the capacity to contend with suboptimal conditions by engaging in dormancy, whereby individuals enter a reversible state of reduced metabolic activity. The resulting seed banks are complex, storing information and imparting memory that gives rise to multi-scale structures and networks spanning collections of cells to entire ecosystems. We outline the fundamental attributes and emergent phenomena associated with dormancy and seed banks, with the vision for a unifying and mathematically based framework that can address problems in the life sciences, ranging from global change to cancer biology.
Real-Time Interactive Modeling and Simulation in Biological Physics and Active Matter with Mechanica
Authors: Endre T. Somogyi, Jeffery Coulter, Fanbo Sun, Herbert M. Sauro, James A. Glazier
Date: 2021-05-06 00:00:00

Modeling and simulation (M&S) has revolutionized the way modern engineered products are designed, tested and evaluated. Yet modeling and simulation is much less frequently applied in the study of natural biological and active matter systems. Two of the greatest challenges in applying M\&S approaches to natural biological systems are (1) difficulty in specifying a model, and developing a simulation from it, and (2) tuning and optimizing parameters. Here we address the first challenge directly, and develop a software library that can lead to progress in the second.
Where are the opportunities for growth in the professional services space?
Authors: Edouard Ribes
Date: 2021-03-29 00:00:00
Tasks: Marketing

The professional services industry (legal, accounting, consulting, architectural services{\ldots}) employs an important share of the active population in mature countries. However, after decades of undisputed growth, the sector appears to be at a turning point in certain geographies. This article therefore proposes a simple framework to help diagnose where growth opportunities (if any) may lie.When applied to the US economic context, the model indicates that at a macro-economic national level the sector should stall, which concurs with the trend observed over the past decade. However, it also highlights that a few industrial sectors (e.g. the US beverage industry) still offer pockets of growth for a variety of professional expertises. Replicating and fine-tuning those findings could be interesting for practitioners to steer their marketing and business development efforts. On the other hand, the quantitative framework presented in this study could pave the way for future research in the academic community.
Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging
Authors: Nicholas Salmon, Indranil SenGupta
Date: 2021-05-05 00:00:00
Tasks: Gaussian Processes

In this paper, we introduce and analyze the fractional Barndorff-Nielsen and Shephard (BN-S) stochastic volatility model. The proposed model is based upon two desirable properties of the long-term variance process suggested by the empirical data: long-term memory and jumps. The proposed model incorporates the long-term memory and positive autocorrelation properties of fractional Brownian motion with $H>1/2$, and the jump properties of the BN-S model. We find arbitrage-free prices for variance and volatility swaps for this new model. Because fractional Brownian motion is still a Gaussian process, we derive some new expressions for the distributions of integrals of continuous Gaussian processes as we work towards an analytic expression for the prices of these swaps. The model is analyzed in connection to the quadratic hedging problem and some related analytical results are developed. The amount of derivatives required to minimize a quadratic hedging error is obtained. Finally, we provide some numerical analysis based on the VIX data. Numerical results show the efficiency of the proposed model compared to the Heston model and the classical BN-S model.
Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification
Authors: Bechir Hamdaoui, Abdurrahman Elmaghbub, Seifeddine Mejri
Date: 2021-03-02 00:00:00
Tasks: Classification, General Classification

Most prior works on deep learning-based wireless device classification using radio frequency (RF) data apply off-the-shelf deep neural network (DNN) models, which were matured mainly for domains like vision and language. However, wireless RF data possesses unique characteristics that differentiate it from these other domains. For instance, RF data encompasses intermingled time and frequency features that are dictated by the underlying hardware and protocol configurations. In addition, wireless RF communication signals exhibit cyclostationarity due to repeated patterns (PHY pilots, frame prefixes, etc.) that these signals inherently contain. In this paper, we begin by explaining and showing the unsuitability as well as limitations of existing DNN feature design approaches currently proposed to be used for wireless device classification. We then present novel feature design approaches that exploit the distinct structures of the RF communication signals and the spectrum emissions caused by transmitter hardware impairments to custom-make DNN models suitable for classifying wireless devices using RF signal data. Our proposed DNN feature designs substantially improve classification robustness in terms of scalability, accuracy, signature anti-cloning, and insensitivity to environment perturbations. We end the paper by presenting other feature design strategies that have great potentials for providing further performance improvements of the DNN-based wireless device classification, and discuss the open research challenges related to these proposed strategies.
Prediction of Ultrasonic Guided Wave Propagation in Solid-fluid and their Interface under Uncertainty using Machine Learning
Authors: Subhayan De, Bhuiyan Shameem Mahmood Ebna Hai, Alireza Doostan, Markus Bause
Date: 2021-03-30 00:00:00
Tasks: Gaussian Processes, Structural Health Monitoring

Structural health monitoring (SHM) systems use the non-destructive testing principle for damage identification. As part of SHM, the propagation of ultrasonic guided waves (UGWs) is tracked and analyzed for the changes in the associated wave pattern. These changes help identify the location of a structural damage, if any. We advance existing research by accounting for uncertainty in the material and geometric properties of a structure. The physics model used in this study comprises of a monolithically coupled system of acoustic and elastic wave equations, known as the wave propagation in fluid-solid and their interface (WpFSI) problem. As the UGWs propagate in the solid, fluid, and their interface, the wave signal displacement measurements are contrasted against the benchmark pattern. For the numerical solution, we develop an efficient algorithm that successfully addresses the inherent complexity of solving the multiphysics problem under uncertainty. We present a procedure that uses Gaussian process regression and convolutional neural network for predicting the UGW propagation in a solid-fluid and their interface under uncertainty. First, a set of training images for different realizations of the uncertain parameters of the inclusion inside the structure is generated using a monolithically-coupled system of acoustic and elastic wave equations. Next, Gaussian processes trained with these images are used for predicting the propagated wave with convolutional neural networks for further enhancement to produce high-quality images of the wave patterns for new realizations of the uncertainty. The results indicate that the proposed approach provides an accurate prediction for the WpFSI problem in the presence of uncertainty.
A BFS-Tree of Ranking References for Unsupervised Manifold Learning
Authors: Daniel Carlos GuimarãesPedronette, Lucas PascottiValem, Ricardo da S.Torres
Date: 2020-09-24 00:00:00
Tasks: Benchmarking, Image Retrieval, Retrieval

Contextual information, defined in terms of the proximity of feature vectors in a feature space, has been successfully used in the construction of search services. These search systems aim to exploit such information to effectively improve ranking results, by taking into account the manifold distribution of features usually encoded. In this paper, a novel unsupervised manifold learning is proposed through a similarity representation based on ranking references. A breadth-first tree is used to represent similarity information given by ranking references and is exploited to discovery underlying similarity relationships. As a result, a more effective similarity measure is computed, which leads to more relevant objects in the returned ranked lists of search sessions. Several experiments conducted on eight public datasets, commonly used for image retrieval benchmarking, demonstrated that the proposed method achieves very high effectiveness results, which are comparable or superior to the ones produced by state-of-the-art approaches.
The Effective Methods for Intrusion Detection With Limited Network Attack Data: Multi-Task Learning and Oversampling
Authors: Yun Zhou
Date: 2020-10-06 00:00:00
Tasks: Intrusion Detection, Multi-Task Learning, Network Intrusion Detection

Recently, many anomaly intrusion detection algorithms have been developed and applied in
network security. These algorithms achieve high detection rate on many classical datasets. However, most of
them failed to address two challenges: 1) imbalanced trafc data with limited network attack, 2) multiple data
sources that are distributed in different terminals. In detail, those algorithms assume that there are sufcient
network trafc data to train their models for intrusion detection. Due to the network attack trafc is always
scarce in the real-world network, this assumption is difcult to satisfy in most cases. In this paper, we use
Multi-Task Learning (MTL) and oversampling methods to address those challenges of network intrusion
detection. Firstly, we use the MTL method to treat each terminal as a single task, and then use relevant
information between different terminals to help learn every single task. Meanwhile, we use the oversampling
method to overcome the minority problem of attacks. Through a series of experiments on the latest UNSWNB15
and CICIDS2018 datasets, this paper veries the effectiveness of MTL and oversampling methods for
network intrusion detection with limited network attack data, where they achieve more than 90% detection
rate in different experimental settings.
An equation-of-state-meter of quantum chromodynamics transition from deep learning
Authors: Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stöcker, Xin-Nian Wang
Date: 2018-01-15 00:00:00

A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy-ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in quantum chromodynamics. Such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.
Automated radiology report generation using conditioned transformers
Authors: Omar Alfarghaly, Rana Khaled, Abeer Elkorany, Maha Helal, Aly Fahmy
Date: 2021-03-26 00:00:00
Tasks: Medical Report Generation, Semantic Similarity, Semantic Textual Similarity

Radiology report writing in hospitals is a time-consuming task that also requires experience from the involved radiologists. This paper proposes a deep learning model to automatically generate radiology reports given a chest x-ray image from the public IU-Xray dataset. Our work consists of three stages: (1) Fine-tune a pre-trained Chexnet to predict specific tags from the image. (2) Calculate weighted semantic features from the predicted tag's pre-trained embeddings. (3) Condition a pre-trained GPT2 model on the visual and semantic features to generate the full medical reports. We analyze the generated reports using word-overlap metrics while also adding new meaningful semantic-based similarity metrics. The proposed model, which we call CDGPT2, surpassed most non-hierarchical recurrent models and transformer-based models in quantitative metrics while being considerably faster to train. Moreover, the model does not require a specific vocabulary and can be trained on different datasets without changing the architecture. Furthermore, we include a qualitative analysis from a radiologist from Egypt's national institute of cancer which showed that 61.6% of the generated reports on the test set were expertly written, and only 10% contained false information. We represent the first work to condition a pre-trained transformer on visual and semantic features to generate medical reports and to include semantic similarity metrics in the quantitative analysis of the generated reports.
adaptive direction information in differential evolution for numerical optimization
Authors: Yiqiao Cai, Jiahai Wang, Yonghong Chen, Tian Wang, Hui Tian, Wei Luo
Date: 2016-02-02 00:00:00

Differential evolution (DE) is a powerful evolutionary
algorithm (EA) for numerical optimization. It has
been successfully used in various scientific and engineering
fields. In most of the DE algorithms, the neighborhood
and direction information are not fully and simultaneously
exploited to guide the search. Most recently, to make full use
of these information, aDEframework with neighborhood and
direction information (NDi-DE) was proposed. It was experimentally
demonstrated that NDi-DE was effective for most
of the DE algorithms. However, the performance of NDi-DE
heavily depends on the selection of direction information.
To alleviate this drawback and improve the performance of
NDi-DE, the adaptive operator selection (AOS) mechanism
is introduced into NDi-DE to adaptively select the direction
information for the specific DE mutation strategy. Therefore,
a new DE framework, adaptive direction information based
NDi-DE (aNDi-DE), is proposed in this study. With AOS,
the good balance between exploration and exploitation of
aNDi-DE can be dynamically achieved. In order to evaluate
the effectiveness of aNDi-DE, the proposed framework
is applied to the original DE algorithms, as well as several
advanced DE variants. Experimental results show that aNDi-
DE is able to adaptively select the most suitable type of direction
information for the specific DE mutation strategy during
the evolutionary process. The efficiency and robustness of
aNDi-DE are also confirmed by comparing with NDi-DE.
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTS
Authors: Kyueyeol Jung, Incheol Kim
Date: 2021-04-05 00:00:00
Tasks: Continual Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II

It is very important to learn behavioral policies that allow multiple agents to work together organically for common goals in various real-world applications. In this multi-agent reinforcement learning (MARL) environment, most existing studies have adopted centralized training with decentralized execution (CTDE) methods as in effect standard frameworks. However, this multi-agent reinforcement learning method is difficult to effectively cope with in a dynamic environment in which new environmental changes that are not experienced during training time may constantly occur in real life situations. In order to effectively cope with this dynamic environment, this paper proposes a novel multi-agent reinforcement learning system, C-COMA. C-COMA is a continual learning model that assumes actual situations from the beginning and continuously learns the cooperative behavior policies of agents without dividing the training time and execution time of the agents separately. In this paper, we demonstrate the effectiveness and excellence of the proposed model C-COMA by implementing a dynamic mini-game based on Starcraft II, a representative real-time strategy game, and conducting various experiments using this environment.