pwc-archive/papers-with-abstracts huggingface dataset:
Multipath-based SLAM for Non-Ideal Reflective Surfaces Exploiting Multiple-Measurement Data Association
Authors: Lukas Wielandner, Alexander Venus, Thomas Wilding, Erik Leitinger
Date: 2023-04-12 00:00:00
Tasks: Simultaneous Localization and Mapping, Single Particle Analysis

Multipath-based simultaneous localization and mapping (SLAM) is a promising approach to obtain position information of transmitters and receivers as well as information regarding the propagation environments in future mobile communication systems. Usually, specular reflections of the radio signals occurring at flat surfaces are modeled by virtual anchors (VAs) that are mirror images of the physical anchors (PAs). In existing methods for multipath-based SLAM, each VA is assumed to generate only a single measurement. However, due to imperfections of the measurement equipment such as non-calibrated antennas or model mismatch due to roughness of the reflective surfaces, there are potentially multiple multipath components (MPCs) that are associated to one single VA. In this paper, we introduce a Bayesian particle-based sum-product algorithm (SPA) for multipath-based SLAM that can cope with multiple-measurements being associated to a single VA. Furthermore, we introduce a novel statistical measurement model that is strongly related to the radio signal. It introduces additional dispersion parameters into the likelihood function to capture additional MPCs-related measurements. We demonstrate that the proposed SLAM method can robustly fuse multiple measurements per VA based on numerical simulations.
Structured Channel Estimation for RIS-Assisted THz Communications
Authors: Fazal-E-Asim, Bruno Sokal, André L. F. de Almeida, Behrooz Makki, Gábor Fodor
Date: 2023-04-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper proposes tensor-based channel estimation for reconfigurable intelligent surface (RIS)-assisted communication networks. We exploit the inherent geometrical structure of the Terahertz propagation channel, including the antenna array geometries at the base station, the RIS, and the user equipment to design a tensor-based channel estimator, referred to as the higher-dimensional rank-one approximations (HDR) method. By exploiting the geometrical structure of the combined base station-RIS-user equipment channel, the proposed HDR estimator recasts parametric channel estimation as a single sixth-order rank-one tensor approximation problem, which can be efficiently solved using higher-order singular value decomposition to deliver parallel estimates of each channel component vector. Numerical results show that the proposed method provides significantly more accurate parameter estimates than competing state-of-the-art tensor-based RIS channel estimation, Khatri-Rao factorization, and least squares methods. For higher-rank channels, the HDR method shows similar spectral efficiency compared to its competitors while having similar computational complexity to the classical least squares estimator.
Discovery and optimization of cell-type-specific DNA methylation markers for in silico deconvolution
Authors: Aleksa Krsmanovic
Date: 2023-04-12 00:00:00

DNA methylation is a significant driver of cell-type heterogeneity and has been implicated in various regulatory processes ranging from cell differentiation to imprinting. As the methyl group is embedded in the DNA molecule, assessing DNA methylation is particularly promising in liquid-biopsy-based approaches, as cell-free DNA retains information related to its cell of origin. In this work, I leverage a recently profiled collection of cell-sorted whole genome bisulfite profiles of 44 healthy cell types. The high quality and purity of such data provide an ideal basis for discovering and characterizing discriminative DNA methylation regions that could serve as a reference for computational deconvolution. First, I characterize differentially methylated regions between every pair of cell types, obtaining a meaningful measure of divergence. Pairwise differences were then aggregated to identify a set of uniquely (de)methylated regions (UMRs) for each cell type. Identified UMRs are predominantly hypomethylated and their numbers vary significantly across cell types. They are mostly located in enhancer regions and strongly support cell-type-specific characteristics. As mapping onto UMRs has proven unsuitable for deconvolution, I developed a novel approach utilizing the set cover algorithm to select discriminative regions for this purpose. Based on these regions, deconvolution was performed in two distinct approaches: a beta-value-based and a read-level one. Both approaches outperform an existing deconvolution software modeled on the same data 3-fold in terms of total deconvolution error. Surprisingly, the beta-based approach slightly outperformed the read-level one. Overall, I present an adaptable, end-to-end software solution (methylcover) for obtaining accurate cell type deconvolution, with possible future applications to non-invasive assays for disease detection and monitoring.
Altered Topological Structure of the Brain White Matter in Maltreated Children through Topological Data Analysis
Authors: Moo K. Chung, Tahmineh Azizi, Jamie L. Hanson, Andrew L. Alexander, Richard J. Davidson, Seth D. Pollak
Date: 2023-04-12 00:00:00
Tasks: Topological Data Analysis

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white-matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.
Amygdala and cortical gamma band responses to emotional faces depend on the attended to valence
Authors: Enya M. Weidner, Stephan Moratti, Sebastian Schindler, Philip Grewe, Christian G. Bien, Johanna Kissler
Date: 2023-04-12 00:00:00
Tasks: EEG

The amygdala is assumed to contribute to a bottom-up attentional bias during visual processing of emotional faces. Still, how its response to emotion interacts with top-down attention is not fully understood. It is also unclear if amygdala activity and scalp EEG respond to emotion and attention in a similar way. Therefore, we studied the interaction of emotion and attention during face processing in oscillatory gamma-band activity (GBA) in the amygdala and on the scalp. Amygdala signals were recorded via intracranial EEG (iEEG) in 10 patients with epilepsy. Scalp recordings were collected from 19 healthy participants. Three randomized blocks of angry, neutral, and happy faces were presented, and either negative, neutral, or positive expressions were denoted as targets. Both groups detected happy faces fastest and most accurately. During attention to negative faces, low GBA (< 90 Hz) increased specifically for angry faces both in the amygdala and over posterior scalp regions, albeit earlier on the scalp (60 ms) than in the amygdala (130 ms). From 220 ms, amygdala high GBA (117.5-145 Hz) was additionally persistently increased for both angry and neutral compared to happy faces. When neutral faces served as targets, amygdala high GBA (105-122.5 Hz) was higher for emotional than neutral faces from 160-320 ms. Attention to positive faces did not result in a differentiation between facial expressions. Present data reveal that attention-independent emotion detection in amygdala high GBA may only occur during a neutral focus of attention. Top-down threat vigilance coordinates widespread GBA, biasing stimulus processing in favor of negative faces. These results are in line with a multi-pathway model of emotion processing and help specify the role of GBA in this process by revealing how attentional focus can tune timing and amplitude of emotional GBA responses.
Starker Effekt von Schnelltests (Strong effect of rapid tests)
Authors: Jan Mohring, Michael Burger, Robert Feßler, Jochen Fiedler, Neele Leithäuser, Johanna Schneider, Michael Speckert, Jaroslaw Wlazlo
Date: 2023-04-12 00:00:00

This article is a reproduction of a Fraunhofer ITWM report from 28 June 2021 on the contribution of various non-pharmaceutical measures in breaking the 3rd Corona wave in Germany. The main finding is that testing contributed more to the containment of the pandemic in this phase than vaccination or contact restrictions. The analysis is based on a new epidemiological cohort model that represents testing, vaccination and contact restrictions by time-varying rates of detection, vaccination and contacts, respectively. Only the effectiveness of different vaccines is taken from the literature. All other parameters are automatically identified in such a way that the simulated and the published incidences and death rates match. Among these parameters are incubation time, mean duration of the infectious phase, mortality rate, as well as two contact rates and one detection rate per week. Note that we can reconstruct such a high number of parameters only because we assume that the weekly wave patterns in new infections follow real infection dynamics, periodically driven by high contact rates on weekdays and lower ones on weekends. Usually, people assume that the weekly wave patterns are just reporting artefacts and that weekly mean values are the finest usable data. One focus of the paper is to quantify the increase in detection rate due to the introduction of rapid testing in schools. For this purpose, we compare federal states that differ in the start of school tests and Easter holidays. There is a clear temporal correlation with the identified detection rates. Finally, we compare the effect of the individual non-pharmaceutical measures by replacing one by one the fitted rates of detection, vaccination and contacts by neutral ones. The increase in the simulated number of actually infected persons measures the effect of the measure ignored.
Precision Oncology, Signaling Pathways Reprogramming and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics
Authors: Manish Kumar
Date: 2023-04-11 00:00:00

Cancer is a fatal genetic disease involving unregulated cell growth and proliferation with varying underlying complexities including immune evasion, treatment resistance and recurrence, and optimized treatment is required for proper cure. Molecular studies have revealed that tumors are extremely heterogeneous in nature, leading to the complexity of cancer development, which is ultimately linked to its genetic machinery. It would require effective targeting of dysregulated molecular mechanisms involving growth factors, regulatory proteins, cell adhesion molecules, and molecules of immune system mainly driven by alterations in tumor suppressor genes and oncogenes that may vary among different cancer types. Importantly, patients with the same type of cancer respond differently to available cancer treatments, indicating the need for patient-specific treatment options. Thus, in-depth genomic studies of patients' tumors are needed to fully understand the determinants of cancer initiation and progression for effective targeted therapy. Precision oncology has evolved as a form of cancer therapy focused on genetic profiling of tumors to identify molecular alterations involved in cancer manifestation for tailored individualized treatment of the disease. Accordingly, there have been great developments in the formulation and production of anticancer agents in recent years owing to advances in molecular technologies enabling precise targeting of oncogenic pathways involved in disease progression. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of advancements in the tools and techniques associated with the process to assess its scope and importance in realizing the intended goals.
Time-frequency co-movements between commodities and economic policy uncertainty across different crises
Authors: M. Belén Arouxet, Aurelio F. Bariviera, Verónica Pastor, Victoria Vampa
Date: 2023-04-11 00:00:00
Tasks: Time Series

Commodity futures constitute an attractive asset class for portfolio managers. Propelled by their low correlation with other assets, commodities begin gaining popularity among investors, as they allow to capture diversification benefits. After more than two decades of active investing experience, this paper examines the time and frequency of spillovers between Economic Policy Uncertainty (Davis, 2016) and a broad set of commodities. The period under examination goes from December 1997 until April 2022, covering political, economic, and even health crises. We apply a wavelet coherence analysis between time series, in order to shed light on the time-frequency comovements and lead-lag relationships. This research finds a distinct impact on the commodities, depending on the nature of the crisis. In particular, during the global financial crisis and the Covid-19 crisis, comovements are stronger in most commodities.
Managing Portfolio for Maximizing Alpha and Minimizing Beta
Authors: Soumyadip Sarkar
Date: 2023-04-01 00:00:00
Tasks: Management

Portfolio management is an essential component of investment strategy that aims to maximize returns while minimizing risk. This paper explores several portfolio management strategies, including asset allocation, diversification, active management, and risk management, and their importance in optimizing portfolio performance. These strategies are examined individually and in combination to demonstrate how they can help investors maximize alpha and minimize beta. Asset allocation is the process of dividing a portfolio among different asset classes to achieve the desired level of risk and return. Diversification involves spreading investments across different securities and sectors to minimize the impact of individual security or sector-specific risks. Active management involves security selection and risk management techniques to generate excess returns while minimizing losses. Risk management strategies, such as stop-loss orders and options strategies, aim to minimize losses in adverse market conditions. The importance of combining these strategies for optimizing portfolio performance is emphasized in this paper. The proper implementation of these strategies can help investors achieve their investment goals over the long-term, while minimizing exposure to risks. A call to action for investors to utilize portfolio management strategies to maximize alpha and minimize beta is also provided.
Exploring the Determinants of Capital Adequacy in Commercial Banks: A Study of Bangladesh's Banking Sector
Authors: Md Shah Naoaj
Date: 2023-03-18 00:00:00

This study investigates the factors that influence the capital adequacy of commercial banks in Bangladesh using panel data from 28 banks over the period of 2013-2019. Three analytical methods, including the Fixed Effect model, Random Effect model, and Pooled Ordinary Least Square (POLS) method, are employed to analyze two versions of the capital adequacy ratio, namely the Capital Adequacy Ratio (CAR) and Tier 1 Capital Ratio. The study reveals that capital adequacy is significantly affected by several independent variables, with leverage and liquidity risk having a negative and positive relationship, respectively. Additionally, the study finds a positive correlation between real GDP and net profit and capital adequacy, while inflation has a negative correlation. For the Tier 1 Ratio, the study shows no significant relationship betweenleverage and liquidity risk, but a positive correlation with the number of employees, net profit, and real GDP, while a negative correlation with size and GDP deflator. Pooled OLS analysis reveals a negative correlation with leverage, size, and inflation for both CAR and Tier 1 Capital Ratio, and a positive correlation with liquidity risk, net profit, and real GDP. Based on the Hausman test, the Random Effect model is deemed moresuitable for this dataset. These findings have important implications for policymakers, investors, and bank managers in Bangladesh by providing insights into the factors that impact the capital ratios of commercial banks.
Object-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings
Authors: Alexia Toumpa, Anthony G. Cohn
Date: 2023-03-30 00:00:00
Tasks: Object, Scene Understanding

Acquiring knowledge about object interactions and affordances can facilitate scene understanding and human-robot collaboration tasks. As humans tend to use objects in many different ways depending on the scene and the objects' availability, learning object affordances in everyday-life scenarios is a challenging task, particularly in the presence of an open set of interactions and objects. We address the problem of affordance categorization for class-agnostic objects with an open set of interactions; we achieve this by learning similarities between object interactions in an unsupervised way and thus inducing clusters of object affordances. A novel depth-informed qualitative spatial representation is proposed for the construction of Activity Graphs (AGs), which abstract from the continuous representation of spatio-temporal interactions in RGB-D videos. These AGs are clustered to obtain groups of objects with similar affordances. Our experiments in a real-world scenario demonstrate that our method learns to create object affordance clusters with a high V-measure even in cluttered scenes. The proposed approach handles object occlusions by capturing effectively possible interactions and without imposing any object or scene constraints.
KubeEdge-Sedna v0.3: Towards Next-Generation Automatically Customized AI Engineering Scheme
Authors: Zimu Zheng
Date: 2023-03-08 00:00:00
Tasks: Lifelong learning, Transfer Learning

The scale of the global edge AI market continues to grow. The current technical challenges that hinder the large-scale replication of edge AI are mainly small samples on the edge and heterogeneity of edge data. In addition, edge AI customers often have requirements for data security compliance and offline autonomy of edge AI services. Based on the lifelong learning method in the academic world, we formally define the problem of edge-cloud collaborative lifelong learning for the first time, and release the industry's first open-source edge-cloud collaborative lifelong learning. Edge-cloud collaborative lifelong learning adapts to data heterogeneity at different edge locations through (1) multi-task transfer learning to achieve accurate prediction of "thousands of people and thousands of faces"; (2) incremental processing of unknown tasks, the more systems learn and the smarter systems are with small samples, gradually realize AI engineering and automation; (3) Use the cloud-side knowledge base to remember new situational knowledge to avoid catastrophic forgetting; (4) The edge-cloud collaborative architecture enables data security compliance and edge AI services to be offline autonomy while applying cloud resources. This work hopes to help fundamentally solve the above-mentioned challenges of edge-cloud collaborative machine learning.
Prediction of Chronic Kidney Disease - A Machine Learning Perspective
Authors: Pankaj Chittora, Sandeep Chaurasia, Prasun Chakrabarti, Gaurav Kumawat, Tulika Chakrabarti, Zbigniew Leonowicz, Michał Jasiński, Łukasz Jasiński, Radomir Gono, Elżbieta Jasińska, Vadim Bolshev
Date: 2021-01-22 00:00:00
Proceeding: journal 2021 1
Tasks: Disease Prediction, feature selection, Prediction, regression

Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is
required as soon as possible. Machine learning technique has become reliable for medical treatment. With
the help of a machine learning classifier algorithms, the doctor can detect the disease on time. For this
perspective, Chronic Kidney Disease prediction has been discussed in this article. Chronic Kidney Disease
dataset has been taken from the UCI repository. Seven classifier algorithms have been applied in this research
such as artificial neural network, C5.0, Chi-square Automatic interaction detector, logistic regression, linear
support vector machine with penalty L1 & with penalty L2 and random tree. The important feature selection
technique was also applied to the dataset. For each classifier, the results have been computed based on
(i) full features, (ii) correlation-based feature selection, (iii) Wrapper method feature selection, (iv) Least
absolute shrinkage and selection operator regression, (v) synthetic minority over-sampling technique with
least absolute shrinkage and selection operator regression selected features, (vi) synthetic minority oversampling technique with full features. From the results, it is marked that LSVM with penalty L2 is giving
the highest accuracy of 98.86% in synthetic minority over-sampling technique with full features. Along
with accuracy, precision, recall, F-measure, area under the curve and GINI coefficient have been computed
and compared results of various algorithms have been shown in the graph. Least absolute shrinkage and
selection operator regression selected features with synthetic minority over-sampling technique gave the best
after synthetic minority over-sampling technique with full features. In the synthetic minority over-sampling
technique with least absolute shrinkage and selection operator selected features, again linear support vector
machine gave the highest accuracy of 98.46%. Along with machine learning models one deep neural network
has been applied on the same dataset and it has been noted that deep neural network achieved the highest
accuracy of 99.6%.