pwc-archive/papers-with-abstracts huggingface dataset:
Seventy Years of Radar and Communications: The Road from Separation to Integration
Authors: Fan Liu, Le Zheng, Yuanhao Cui, Christos Masouros, Athina P. Petropulu, Hugh Griffiths, Yonina C. Eldar
Date: 2022-10-02 00:00:00
Tasks: Integrated sensing and communication, ISAC

Radar and communications (R&C) as key utilities of electromagnetic (EM) waves have fundamentally shaped human society and triggered the modern information age. Although R&C have been historically progressing separately, in recent decades they have been converging towards integration, forming integrated sensing and communication (ISAC) systems, giving rise to new, highly desirable capabilities in next-generation wireless networks and future radars. To better understand the essence of ISAC, this paper provides a systematic overview on the historical development of R&C from a signal processing (SP) perspective. We first interpret the duality between R&C as signals and systems, followed by an introduction of their fundamental principles. We then elaborate on the two main trends in their technological evolution, namely, the increase of frequencies and bandwidths, and the expansion of antenna arrays. We then show how the intertwined narratives of R&C evolved into ISAC, and discuss the resultant SP framework. Finally, we overview future research directions in this field.
Robust Trajectory-based Density Estimation for Geometric Structure Recovery: Theory and Applications
Authors: Turner Richmond, Namita Lokare, Qian Ge, Edgar Lobaton
Date: 2022-10-01 00:00:00
Tasks: Activity Recognition, Density Estimation, Time Series, Time Series Analysis

With the rise of the Internet of Things, strategies for effectively processing big data are essential for discovering meaningul insights. The time series datasets produced by groups of interconnected devices contain valuable underlying patterns. Recent works have extracted patterns from spatio-temporal datasets to aid in road network generation, activity recognition, and others. The speed and accuracy of the underlying geometry reconstruction are important in these applications. Existing methods such as kernel density estimation (KDE) have been used but are often computationally expensive. We propose modifying edge quadtrees to utilize their effective heirarchical structure. Our modification estimates density using a novel trajectory count function which provides mathematical guarantees on the stability of the count by enforcing an invariance to local perturbations. We evaluate our method's effectiveness at extracting the underlying geometry and representative subsample points. For verification, we compare against a KDE variant at extracting the underlying shape of noisy synthetic trajectories travelling alonng the shape. We compare map extraction from GPS traces against current methods. Our method significantly improves runtime while extracting the geometry better or at least comparably. We also compare against maxmin subsampling on an activity recognition data set and find a significant runtime improvement with comparable performance.
Diagnostic Quality Assessment for Low-Dimensional ECG Representations
Authors: Péter Kovács, Carl Böck, Thomas Tschoellitsch, Mario Huemer, Jens Meier
Date: 2022-10-01 00:00:00
Tasks: Denoising, Diagnostic

There have been several attempts to quantify the diagnostic distortion caused by algorithms that perform low-dimensional electrocardiogram (ECG) representation. However, there is no universally accepted quantitative measure that allows the diagnostic distortion arising from denoising, compression, and ECG beat representation algorithms to be determined. Hence, the main objective of this work was to develop a framework to enable biomedical engineers to efficiently and reliably assess diagnostic distortion resulting from ECG processing algorithms. We propose a semiautomatic framework for quantifying the diagnostic resemblance between original and denoised/reconstructed ECGs. Evaluation of the ECG must be done manually, but is kept simple and does not require medical training. In a case study, we quantified the agreement between raw and reconstructed (denoised) ECG recordings by means of kappa-based statistical tests. The proposed methodology takes into account that the observers may agree by chance alone. Consequently, for the case study, our statistical analysis reports the "true", beyond-chance agreement in contrast to other, less robust measures, such as simple percent agreement calculations. Our framework allows efficient assessment of clinically important diagnostic distortion, a potential side effect of ECG (pre-)processing algorithms. Accurate quantification of a possible diagnostic loss is critical to any subsequent ECG signal analysis, for instance, the detection of ischemic ST episodes in long-term ECG recordings.
Simulation of Medium-Frequency R-Mode Signal Strength
Authors: Jaewon Yu, Joon Hyo Rhee
Date: 2022-10-01 00:00:00

Assuming failure in the global navigation satellite systems due to radio frequency interference and ionospheric anomaly, an R-Mode system, a terrestrial integrated navigation system, is being actively studied for domestic deployment in South Korea. In this study, parameters for an approximate calculation of the received signal strength were obtained and applied to develop a performance simulation tool for a medium-frequency R-Mode system. As a case study, the signal strength from the Yeongju transmitter was simulated using the proposed parameters.
Smoothness of the Value Function for Optimal Consumption Model with Consumption-Wealth Utility and Borrowing Constraint
Authors: Weidong Tian, Zimu Zhu
Date: 2022-10-03 00:00:00

This paper studies an optimal consumption-investment problem for an investor whose instantaneous utility depends on both consumption and wealth, and the investor faces a general borrowing constraint that the investment amount in the risky asset does not exceed an exogenous function of the wealth. We show that the value function is second-order smooth and present the optimal consumption-investment policy in a feedback form. Moreover, when the risky investment amount is bounded above by a fixed constant, we show that under certain conditions, the constraint is binding if and only if an endogenous threshold bounds the portfolio wealth, and we determine the endogenous wealth threshold with the smooth fit condition. Our results encompass several well-developed portfolio choice models and imply new applications.
Stochastic optimization on matrices and a graphon McKean-Vlasov limit
Authors: Zaid Harchaoui, Sewoong Oh, Soumik Pal, Raghav Somani, Raghavendra Tripathi
Date: 2022-10-02 00:00:00
Tasks: Stochastic Optimization

We consider stochastic gradient descents on the space of large symmetric matrices of suitable functions that are invariant under permuting the rows and columns using the same permutation. We establish deterministic limits of these random curves as the dimensions of the matrices go to infinity while the entries remain bounded. Under a ``small noise'' assumption the limit is shown to be the gradient flow of functions on graphons whose existence was established in~\cite{oh2021gradient}. We also consider limits of stochastic gradient descents with added properly scaled reflected Brownian noise. The limiting curve of graphons is characterized by a family of stochastic differential equations with reflections and can be thought of as an extension of the classical McKean-Vlasov limit for interacting diffusions to the graphon setting. The proofs introduce a family of infinite-dimensional exchangeable arrays of reflected diffusions and a novel notion of propagation of chaos for large matrices of diffusions converging to such arrays in a suitable sense.
Towards complete representation of bacterial contents in metagenomic samples
Authors: Xiaowen Feng, Heng Li
Date: 2022-09-30 00:00:00
Tasks: Diversity

Background: In the metagenome assembly of a microbiome community, we may think abundant species would be easier to assemble due to their deeper coverage. However, this conjucture is rarely tested. We often do not know how many abundant species we are missing and do not have an approach to recover these species. Results: Here we proposed k-mer based and 16S RNA based methods to measure the completeness of metagenome assembly. We showed that even with PacBio High-Fidelity (HiFi) reads, abundant species are often not assembled as high strain diversity may lead to fragmented contigs. We developed a novel algorithm to recover abundant metagenome-assembled genomes (MAGs) by identifying circular assembly subgraphs. Our algorithm is reference-free and complement to standard metagenome binning. Evaluated on 14 real datasets, it rescued many abundant species that would be missing with existing methods. Conclusions: Our work stresses the importance of metagenome completeness which is often overlooked before. Our algorithm generates more circular MAGs and moves a step closer to the complete representation of microbiome communities.
NLP-based classification of software tools for metagenomics sequencing data analysis into EDAM semantic annotation
Authors: Kaoutar Daoud Hiri, Matjaž Hren, Tomaž Curk
Date: 2022-09-30 00:00:00
Tasks: regression

Motivation: The rapid growth of metagenomics sequencing data makes metagenomics increasingly dependent on computational and statistical methods for fast and efficient analysis. Consequently, novel analysis tools for big-data metagenomics are constantly emerging. One of the biggest challenges for researchers occurs in the analysis planning stage: selecting the most suitable metagenomics software tool to gain valuable insights from sequencing data. The building process of data analysis pipelines is often laborious and time-consuming since it requires a deep and critical understanding of how to apply a particular tool to complete a specified metagenomics task. Results: We have addressed this challenge by using machine learning methods to develop a classification system of metagenomics software tools into 13 classes (11 semantic annotations of EDAM and two virus-specific classes) based on the descriptions of the tools. We trained three classifiers (Naive Bayes, Logistic Regression, and Random Forest) using 15 text feature extraction techniques (TF-IDF, GloVe, BERT-based models, and others). The manually curated dataset includes 224 software tools and contains text from the abstract and the methods section of the tools' publications. The best classification performance, with an Area Under the Precision-Recall Curve score of 0.85, is achieved using Logistic regression, BioBERT for text embedding, and text from abstracts only. The proposed system provides accurate and unified identification of metagenomics data analysis tools and tasks, which is a crucial step in the construction of metagenomics data analysis pipelines.
Nodal statistics-based equivalence relation for graph collections
Authors: Lucrezia Carboni, Michel Dojat, Sophie Achard
Date: 2022-10-03 00:00:00
Tasks: Functional Connectivity, Relation

Node role explainability in complex networks is very difficult, yet is crucial in different application domains such as social science, neurosciences or computer science. Many efforts have been made on the quantification of hubs revealing particular nodes in a network using a given structural property. Yet, in several applications, when multiple instances of networks are available and several structural properties appear to be relevant, the identification of node roles remains largely unexplored. Inspired by the node automorphically equivalence relation, we define an equivalence relation on graph nodes associated with any collection of nodal statistics (i.e. any functions on the node-set). This allows us to define new graph global measures: the power coefficient, and the orthogonality score to evaluate the parsimony and heterogeneity of a given nodal statistics collection. In addition, we introduce a new method based on structural patterns to compare graphs that have the same vertices set. This method assigns a value to a node to determine its role distinctiveness in a graph family. Extensive numerical results of our method are conducted on both generative graph models and real data concerning human brain functional connectivity. The differences in nodal statistics are shown to be dependent on the underlying graph structure. Comparisons between generative models and real networks combining two different nodal statistics reveal the complexity of human brain functional connectivity with differences at both global and nodal levels. Using a group of 200 healthy controls connectivity networks, our method computes high correspondence scores among the whole population, to detect homotopy, and finally quantify differences between comatose patients and healthy controls.
Failure of neuron network coherence induced by SARS-CoV-2-infected astrocytes
Authors: Sergey V. Stasenko, Alexander E. Hramov, Victor B. Kazantsev
Date: 2022-10-03 00:00:00

Coherent activations of brain neuron networks underlay many physiological functions associated with various behavioral states. These synchronous fluctuations in the electrical activity of the brain are also referred to as brain rhythms. At the cellular level, the rhythmicity can be induced by various mechanisms of intrinsic oscillations in neurons or network circulation of excitation between synaptically coupled neurons. One of the specific mechanisms concerns the activity of brain astrocytes that accompany neurons and can coherently modulate synaptic contacts of neighboring neurons, synchronizing their activity. Recent studies have shown that coronavirus infection (Covid-19), entering the central nervous system and infecting astrocytes, causes various metabolic disorders. Specifically, Covid-19 can depress the synthesis of astrocytic glutamate and GABA. It is also known that in the postcovid state, patients may suffer from symptoms of anxiety and impaired cognitive functions, which may be a consequence of disturbed brain rhythms. We propose a mathematical model of a spiking neural network accompanied by astrocytes capable to generate quasi-synchronous rhythmic bursting discharges. The model predicts that if the astrocytes are infected, and the release of glutamate is depressed, then normal burst rhythmicity suffers dramatically. Interestingly, in some cases, the failure of network coherence may be intermittent with intervals of normal rhythmicity, or the synchronization can completely disappears.
Exact solution of the Susceptible-Infectious-Recovered (SIR) epidemic model
Authors: Norio Yoshida
Date: 2022-10-02 00:00:00

Exact solution of the Susceptible-Infectious-Recovered (SIR) epidemic model is derived, and various properties of solution are obtained directly from the exact solution. It is shown that there exists an exact solution of an initial value problem for SIR differential system and that the parametric form of the exact solution satisfies some linear differential system.
Competitive exclusion in a model with seasonality: three species cannot coexist in an ecosystem with two seasons
Authors: Hwai-Ray Tung, Rick Durrett
Date: 2022-10-01 00:00:00

Chan, Durrett, and Lanchier introduced a multitype contact process with temporal heterogeneity involving two species competing for space on the d-dimensional integer lattice. Time is divided into two seasons. They proved that there is an open set of the parameters for which both species can coexist when their dispersal range is sufficiently large. Numerical simulations suggested that three species can coexist in the presence of two seasons. The main point of this paper is to prove that this conjecture is incorrect. To do this we prove results for a more general ODE model and contrast its behavior with other related systems that have been studied in order to understand the competitive exclusion principle.
Structured interactions as a stabilizing mechanism for competitive ecosystems
Authors: Violeta Calleja-Solanas, Nagi Khalil, Jesús Gómez-Gardeñes, Emilio Hernández-García, Sandro Meloni
Date: 2020-12-29 00:00:00

How large ecosystems can create and maintain the remarkable biodiversity we see in nature is probably one of the biggest open questions in science, attracting attention from different fields, from Theoretical Ecology to Mathematics and Physics. In this context, modeling the stable coexistence of species competing for limited resources is a particularly challenging task. From a mathematical point of view, coexistence in competitive dynamics can be achieved when dominance among species forms intransitive loops. However, these relationships usually lead to species' relative abundances neutrally cycling without converging to a stable equilibrium. Although in recent years several mechanisms have been proposed, models able to explain species coexistence in competitive communities are still limited. Here we identify locality in the interactions as one of the simplest mechanisms leading to stable species coexistence. We consider a simplified ecosystem where individuals of each species lay on a spatial network and interactions are possible only between nodes within a certain distance. Varying such distance allows to interpolate between local and global competition. Our results demonstrate, within the scope of our model, that species coexist reaching a stable equilibrium when two conditions are met: individuals are embedded in space and can only interact with other individuals within a short distance. On the contrary, when one of these ingredients is missing, large oscillations and neutral cycles emerge.
Context-Aware Compilation of DNN Training Pipelines across Edge and Cloud
Authors: Dixi Yao, Liyao Xiang, Zifan Wang, Jiayu Xu, Chao Li, Xinbing Wang
Date: 2021-12-30 00:00:00
Proceeding: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021 12
Tasks: Feature Compression, Image Classification, Image Generation, Image-text Classification, Recommendation Systems, text-classification, Text Classification

Empowered by machine learning, edge devices including smartphones, wearable, and IoT devices have become growingly intelligent, raising conflicts with the limited resource. On-device model personalization is particularly hard as training models on edge devices is highly resource-intensive. In this work, we propose a novel training pipeline across the edge and the cloud, by taking advantage of the powerful cloud while keeping data local at the edge. Highlights of the design incorporate the parallel execution enabled by our feature replay, reduced communication cost by our error-feedback feature compression, as well as the context-aware deployment decision engine. Working as an integrated system, the proposed pipeline training framework not only significantly speeds up training, but also incurs little accuracy loss or additional memory/energy overhead. We test our system in a variety of settings including WiFi, 5G, household IoT, and on different training tasks such as image/text classification, image generation, to demonstrate its advantage over the state-of-the-art. Experimental results show that our system not only adapts well to, but also draws on the varying contexts, delivering a practical and efficient solution to edge-cloud model training.
Digital financial services and open banking innovation: are banks becoming invisible?
Authors: Valeria Stefanelli, Francesco Manta, Pierluigi Toma
Date: 2022-10-03 00:00:00
Tasks: Decision Making, Management

Digitalization in many economic sectors drove the financial system to adapt to new paradigms of technological transformation. Moreover, the extant regulatory framework forced the financial system to reconsider its business models and its relationship with the market. Such reasons generated also in the banking sector a new model of competition within the ecosystem never seen before in this sector. The new ecosystem of banks and financial institutions lacks a common framework that not only synthesizes the development lines of open innovation in the banking sector, but also regarding the planification of strategic choices and organisation within the new ecosystems. The present study aims to inquire the strategic positioning of European banks toward their digital transformation strategies, by analysing the decision-making processes that occurred between 2015 and 2019. A qualitative analysis on partnerships and the adoption of Application Programming Interfaces (APIs) development in support of new service models was carried out. Results have relevant policy implications for regulators, linked to the business evolution and the risks of outsourcing, and managerial implications for the followers, specifically on the plan of service integration to diversify and boost their activities in the segment of customer relationship management and care, providing a better user experience.
Multiclass Sentiment Prediction for Stock Trading
Authors: Marshall R. McCraw
Date: 2022-09-28 00:00:00
Tasks: Prediction

Python was used to download and format NewsAPI article data relating to 400 publicly traded, low cap. Biotech companies. Crowd-sourcing was used to label a subset of this data to then train and evaluate a variety of models to classify the public sentiment of each company. The best performing models were then used to show that trading entirely off public sentiment could provide market beating returns.
Embedding-based neural network for investment return prediction
Authors: Jianlong Zhu, Dan Xian, Fengxiao, Yichen Nie
Date: 2022-09-26 00:00:00
Tasks: Prediction

In addition to being familiar with policies, high investment returns also require extensive knowledge of relevant industry knowledge and news. In addition, it is necessary to leverage relevant theories for investment to make decisions, thereby amplifying investment returns. A effective investment return estimate can feedback the future rate of return of investment behavior. In recent years, deep learning are developing rapidly, and investment return prediction based on deep learning has become an emerging research topic. This paper proposes an embedding-based dual branch approach to predict an investment's return. This approach leverages embedding to encode the investment id into a low-dimensional dense vector, thereby mapping high-dimensional data to a low-dimensional manifold, so that highdimensional features can be represented competitively. In addition, the dual branch model realizes the decoupling of features by separately encoding different information in the two branches. In addition, the swish activation function further improves the model performance. Our approach are validated on the Ubiquant Market Prediction dataset. The results demonstrate the superiority of our approach compared to Xgboost, Lightgbm and Catboost.