pwc-archive/papers-with-abstracts huggingface dataset:
An Examination of Demographic Differences in Obtaining Investment and Financial Planning Information
Authors: Paul Bechly
Date: 2021-04-22 00:00:00

Financial literacy and financial education are important components of modern life. The importance of financial literacy is increasing for financial consumers because of the weakening of both government and employer-based retirement systems. Unfortunately, empirical research shows that financial consumers are not fully informed and are not able to make proper choices even when appropriate information is available. More research is needed as to how financial consumers obtain investment and financial planning information. A primary data study was conducted to understand the differences between the demographic categories of gender, age, education-level, and income-level with the means of obtaining investment and financial planning information. In this research study, which selected a population from the LinkedIn platform, statistical differences between gender, age, education-level, and income-level were confirmed. These differences helped to confirm prior research in this field of study. Practical opportunities for commercial outreach to specific populations became evident through this type of research. Providers of investment and financial planning information can access their targeted audience more effectively by understanding the demographic profile of the audience, as well as the propensity of the demographic profile of the audience to respond. As this type of research is relatively easy to construct and administer, commercial outreach for providers of investment and financial planning information can be conducted in a cost-efficient and effective manner.
Complex-valued reservoir computing for aspect classification and slope-angle estimation with low computational cost and high resolution in interferometric synthetic aperture radar
Authors: Bungo Konishi, Akira Hirose, Ryo Natsuaki
Date: 2021-04-22 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Synthetic aperture radar (SAR) is widely used for ground surface classification since it utilizes information on vegetation and soil unavailable in optical observation. Image classification often employs convolutional neural networks. However, they have serious problems such as long learning time and resolution degradation in their convolution and pooling processes. In this paper, we propose complex-valued reservoir computing (CVRC) to deal with complex-valued images in interferometric SAR (InSAR). We classify InSAR image data by using CVRC successfully with a higher resolution and a lower computational cost, i.e., one-hundredth learning time and one-fifth classification time, than convolutional neural networks. We also conduct experiments on slope angle estimation. CVRC is found applicable to quantitative tasks dealing with continuous values as well as discrete classification tasks with a higher accuracy.
Enhanced Target Localization with Deployable Multiplatform Radar Nodes Based on Non-Convex Constrained Least Squares Optimization
Authors: Augusto Aubry, Paolo Braca, Antonio De Maio, Angela Marino
Date: 2021-04-22 00:00:00

A new algorithm for 3D localization in multiplatform radar networks, comprising one transmitter and multiple receivers, is proposed. To take advantage of the monostatic sensor radiation pattern features, ad-hoc constraints are imposed in the target localization process. Therefore, the localization problem is formulated as a non-convex constrained Least Squares (LS) optimization problem which is globally solved in a quasi-closed-form leveraging Karush-Kuhn-Tucker (KKT) conditions. The performance of the new algorithm is assessed in terms of Root Mean Square Error (RMSE) in comparison with the benchmark Cramer Rao Lower Bound (CRLB) and some competitors from the open literature. The results corroborate the effectiveness of the new strategy which is capable of ensuring a lower RMSE than the counterpart methodologies especially in the low Signal to Noise Ratio (SNR) regime.
A hybrid beamforming design for massive MIMO LEO satellite communications
Authors: Joan Palacios, Nuria Gonzalez-Prelcic, Carlos Mosquera, Takayuki Shimizu, Chang-Heng Wang
Date: 2021-04-22 00:00:00

5G and future cellular networks intend to incorporate low earth orbit (LEO) satellite communication systems (SatCom) to solve the coverage and availability problems that cannot be addressed by satellite-based or ground-based infrastructure alone. This integration of terrestrial and non terrestrial networks poses many technical challenges which need to be identified and addressed. To this aim, we design and simulate the downlink of a LEO SatCom compatible with 5G NR, with a special focus on the design of the beamforming codebook at the satellite side. The performance of this approach is evaluated for the link between a LEO satellite and a mobile terminal in the Ku band, assuming a realistic channel model and commercial antenna array designs, both at the satellite and the terminal. Simulation results provide insights on open research challenges related to analog codebook design and hybrid beamforming strategies, requirements of the antenna terminals to provide a given SNR, or required beam reconfiguration capabilities among others.
Low-complexity Distributed Detection with One-bit Memory Under Neyman-Pearson Criterion
Authors: Guangyang Zeng, Xiaoqiang Ren, Junfeng Wu
Date: 2021-04-22 00:00:00
Tasks: Decision Making

We consider a multi-stage distributed detection scenario, where $n$ sensors and a fusion center (FC) are deployed to accomplish a binary hypothesis test. At each time stage, local sensors generate binary messages, assumed to be spatially and temporally independent given the hypothesis, and then upload them to the FC for global detection decision making. We suppose a one-bit memory is available at the FC to store its decision history and focus on developing iterative fusion schemes. We first visit the detection problem of performing the Neyman-Pearson (N-P) test at each stage and give an optimal algorithm, called the oracle algorithm, to solve it. Structural properties and limitation of the fusion performance in the asymptotic regime are explored for the oracle algorithm. We notice the computational inefficiency of the oracle fusion and propose a low-complexity alternative, for which the likelihood ratio (LR) test threshold is tuned in connection to the fusion decision history compressed in the one-bit memory. The low-complexity algorithm greatly brings down the computational complexity at each stage from $O(4^n)$ to $O(n)$. We show that the proposed algorithm is capable of converging exponentially to the same detection probability as that of the oracle one. Moreover, the rate of convergence is shown to be asymptotically identical to that of the oracle algorithm. Finally, numerical simulations and real-world experiments demonstrate the effectiveness and efficiency of our distributed algorithm.
Tracking Cells and their Lineages via Labeled Random Finite Sets
Authors: Tran Thien Dat Nguyen, Ba-Ngu Vo, Ba-Tuong Vo, Du Yong Kim, Yu Suk Choi
Date: 2021-04-22 00:00:00

Determining the trajectories of cells and their lineages or ancestries in live-cell experiments are fundamental to the understanding of how cells behave and divide. This paper proposes novel online algorithms for jointly tracking and resolving lineages of an unknown and time-varying number of cells from time-lapse video data. Our approach involves modeling the cell ensemble as a labeled random finite set with labels representing cell identities and lineages. A spawning model is developed to take into account cell lineages and changes in cell appearance prior to division. We then derive analytic filters to propagate multi-object distributions that contain information on the current cell ensemble including their lineages. We also develop numerical implementations of the resulting multi-object filters. Experiments using simulation, synthetic cell migration video, and real time-lapse sequence, are presented to demonstrate the capability of the solutions.
Joint Time and Power Allocation for 5G NR Unlicensed Systems
Authors: Haizhou Bao, Yiming Huo, Xiaodai Dong, Chuanhe Huang
Date: 2021-04-22 00:00:00
Tasks: Fairness

The fifth-generation (5G) and beyond networks are designed to efficiently utilize the spectrum resources to meet various quality of service (QoS) requirements. The unlicensed frequency bands used by WiFi are mainly deployed for indoor applications and are not always fully occupied. The cellular industry has been working to enable cellular and WiFi coexistence. In particular, 5G New Radio in unlicensed channel spectrum (NR-U) supports the uplink and downlink transmission on the maximum channel occupation time (MCOT) duration. In this paper, we consider maximizing the total throughput of both downlink and uplink in NR-U by jointly optimizing the time and power allocation during MCOT while ensuring fair coexistence with WiFi. Fairness is guaranteed in two steps: 1) tuning the access related parameters of NR-U to achieve proportional fairness, and 2) including 3GPP fairness from the throughput perspective as a constraint in NR-U throughput maximization. Numerical analysis and simulation have demonstrated the superior performance of the proposed resource allocation algorithm compared to conventional deployment strategies.
The Maximal Eigengap Estimator for Acoustic Vector-Sensor Processing
Authors: Robert Bassett, Jacob Foster, Kay L. Gemba, Paul Leary, Kevin B. Smith
Date: 2021-04-21 00:00:00

This paper introduces the maximal eigengap estimator for finding the direction of arrival of a wideband acoustic signal using a single vector-sensor. We show that in this setting narrowband cross-spectral density matrices can be combined in an optimal weighting that approximately maximizes signal-to-noise ratio across a wide frequency band. The signal subspace resulting from this optimal combination of narrowband power matrices defines the maximal eigengap estimator. We discuss the advantages of the maximal eigengap estimator over competing methods, and demonstrate its utility in a real-data application using signals collected in 2019 from an acoustic vector-sensor deployed in the Monterey Bay.
Towards 3D Metric GPR Imaging Based on DNN Noise Removal and Dielectric Estimation
Authors: Jinglun Feng, Liang Yang, Jizhong Xiao
Date: 2021-04-21 00:00:00
Tasks: GPR

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect subsurface objects (i.e., rebars, utility pipes) and reveal the underground scene. The two biggest challenges in GPR-based inspection are the GPR data collection and subsurface target imaging. To address these challenges, we propose a robotic solution that automates the GPR data collection process with a free motion pattern. It facilitates the 3D metric GPR imaging by tagging the pose information with GPR measurement in real-time. We also introduce a deep neural network (DNN) based GPR data analysis method which includes a noise removal segmentation module to clear the noise in GPR raw data and a DielectricNet to estimate the dielectric value of subsurface media in each GPR B-scan data. We use both the field and synthetic data to verify the proposed method. Experimental results demonstrate that our proposed method can achieve better performance and faster processing speed in GPR data collection and 3D GPR imaging than other methods.
Differential brain connectivity patterns while listening to breakup and rebellious songs: A functional magnetic resonance imaging study
Authors: Chia-Wei Li, Chen-Gia Tsai
Date: 2021-04-22 00:00:00
Tasks: Functional Connectivity, Hippocampus

Song appreciation involves a broad range of mental processes, and different neural networks may be activated by different song types. The aim of the present study was to show differential functional connectivity of the prefrontal cortices while listening to breakup and rebellious songs. Breakup songs describe romance and longing, whereas rebellious songs convey criticism of conventional ideas or socio-cultural norms. We hypothesized that the medial and lateral prefrontal cortices may interact with different brain regions in response to these two song types. Functional magnetic resonance imaging data of fifteen participants were collected while they were listening to two complete breakup songs and two complete rebellious songs currently popular in Taiwan. The results showed that listening to the breakup songs, compared to the rebellious songs, enhanced the coupling between the medial prefrontal cortex and several emotion-related regions, including the thalamus, caudate, amygdala, hippocampus, middle orbitofrontal cortex, and right inferior frontal gyrus. This coupling may reflect the neural processes of pain empathy, reward processing, compassion, and reappraisal in response to longing and sorrow expressed by the breakup songs. Compared to the breakup songs, listening to the rebellious songs was associated with enhanced coupling between subregions in the prefrontal and orbitofrontal cortices. These areas might work in concert to support re-evaluation of conventional ideas or socio-cultural norms as suggested by the rebellious songs. This study advanced our understanding of the integration of brain functions while processing complex information.
The external pallidum: think locally, act globally
Authors: Connor D. Courtney, Arin Pamukcu, C. Savio Chan
Date: 2021-04-21 00:00:00
Tasks: Diversity

The globus pallidus (GPe) of the basal ganglia has been underappreciated for decades due to poor understanding of its cells and circuits. The advent of molecular tools has sparked a resurgence in interest in the GPe. Here, we review a recent flurry of publications that has unveiled the complexity of the molecular landscape and cellular composition within the GPe. These discoveries have revealed that GPe neurons display a number of circuit features that do not conform to the traditional views of the basal ganglia. Consistent with its broad interconnectivity across the brain, emerging evidence suggests that the GPe plays multifaceted roles in both motor and non-motor functions. Altogether, recent data highlight cellular and functional diversity within the GPe and prompt new proposals for computational rules of the basal ganglia.
Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis
Authors: John T. Nardini, Bernadette J. Stolz, Kevin B. Flores, Heather A. Harrington, Helen M. Byrne
Date: 2021-01-02 00:00:00
Tasks: Topological Data Analysis

Angiogenesis is the process by which blood vessels form from pre-existing vessels. It plays a key role in many biological processes, including embryonic development and wound healing, and contributes to many diseases including cancer and rheumatoid arthritis. The structure of the resulting vessel networks determines their ability to deliver nutrients and remove waste products from biological tissues. Here we simulate the Anderson-Chaplain model of angiogenesis at different parameter values and quantify the vessel architectures of the resulting synthetic data. Specifically, we propose a topological data analysis (TDA) pipeline for systematic analysis of the model. TDA is a vibrant and relatively new field of computational mathematics for studying the shape of data. We compute topological and standard descriptors of model simulations generated by different parameter values. We show that TDA of model simulation data stratifies parameter space into regions with similar vessel morphology. The methodologies proposed here are widely applicable to other synthetic and experimental data including wound healing, development, and plant biology.
Prevention of bacterial adhesion to zwitterionic biocompatible mesoporous glasses
Authors: S Sanchez-Salcedo, A Garcia, M Vallet-Regi
Date: 2021-04-22 00:00:00

Novel materials, based on Mesoporous Bioactive Glasses (MBGs) in the ternary system SiO2-CaO-P2O5, decorated with (3-aminopropyl)triethoxysilane (APTES) and subsequently with amino acid Lysine (Lys), by post-grafting method on the external surface of the glasses (named MBG-NH2 and MBG-Lys), are reported. The surface functionalization with organic groups did not damage the mesoporous network and their structural and textural properties were also preserved despite the high solubility of MBG matrices. The incorporation of Lys confers a zwitterionic nature to these MBG materials due to the presence of adjacent amine and carboxylic groups in the external surface. At physiologic pH, this coexistence of basic amine and carboxilic acid groups from anchored Lys provided zero surface charge named zwitterionic effect. This behaviour could give rise to potential applications of antibacterial adhesion. Therefore, in order to assess the influence of zwitterionic nature in in vitro bacterial adhesion, studies were carried out with Staphylococcus aureus. It was demonstrated that the efficient interaction of these zwitterionic pairs onto the MBG surfaces reduced bacterial adhesion up to 99.9% compared to bare MBGs. In order to test the suitability of zwitterionic MBGs materials as bone grafts, their cytocompatibility was investigated in vitro with MC3T3-E1 preosteoblasts. These findings suggested that the proposed surface functionalization strategy provided MBG materials with notable antibacterial adhesion properties, hence making these materials promising candidates for local bone infection therapy.
Induction of VEGF secretion from bone marrow stromal cell line (ST-2) by the dissolution products of mesoporous silica glass particles containing CuO and SrO
Authors: Preethi Balasubramanian, Antonio J. Salinas, Sandra Sanchez-Salcedo, Rainer Detsch, Maria Vallet-Regi, Aldo R. Boccaccini
Date: 2021-04-22 00:00:00

Certain biomaterials are capable of inducing the secretion of Vascular Endothelial Growth Factor (VEGF) from cells exposed to their biochemical influence, which plays a vital role in stimulating angiogenesis. Looking for this capacity, in this study three porous glasses were synthesized and characterized. The objective of this study was to determine the concentration of the glass particles that, being out of the cytotoxic range, could increase VEGF secretion. The viability of cultivated bone marrow stromal cells (ST-2) was assessed. The samples were examined with light microscopy (LM) after the histochemical staining for haematoxylin and eosin (HE). The biological activity of glasses was evaluated in terms of the influence of the Cu2+ and Sr2+ ions on the cells. The dissolution products of CuSr-1 and CuSr-2.5 produced the highest secretion of VEGF from ST-2 cells after 48 h of incubation. The combination of Cu2+ and Sr2+ lays the foundation for engineering a bioactive glass than can lead to vascularized, functional bone tissue when used in bone regeneration applications.
Self-immolative chemistry in nanomedicine
Authors: M Gisbert-Garzaran, M Manzano, M Vallet-Regi
Date: 2021-04-22 00:00:00

Self-Immolative Chemistry is based on the cascade of disassembling reactions triggered by the adequate stimulation and leading to the sequential release of the smaller constituent elements. This review will focus on the possibilities that this type of chemistry offers to nanomedicine research, which is an area where the stimuli responsive behavior is always targeted. There are some examples on the use of self-immolative chemistry for prodrugs or nanoparticles for drug delivery, but there is still an exciting land of opportunities waiting to be explored. This review aims at revising what has been done so far, but, most importantly, it aims at inspiring new research of self-immolative chemistry on nanomedicine.
Approximate option pricing formula for Barndorff-Nielsen and Shephard model
Authors: Takuji Arai
Date: 2021-04-22 00:00:00

For the Barndorff-Nielsen and Shephard model, we present approximate expressions of call option prices based on the decomposition formula developed by Arai (2021). Besides, some numerical experiments are also implemented to make sure how effective our approximations are.
HMM-Free Encoder Pre-Training for Streaming RNN Transducer
Authors: Lu Huang, Jingyu Sun, Yufeng Tang, JunFeng Hou, Jinkun Chen, Jun Zhang, Zejun Ma
Date: 2021-04-02 00:00:00
Tasks: Speech Recognition

This work describes an encoder pre-training procedure using frame-wise label to improve the training of streaming recurrent neural network transducer (RNN-T) model. Streaming RNN-T trained from scratch usually performs worse than non-streaming RNN-T. Although it is common to address this issue through pre-training components of RNN-T with other criteria or frame-wise alignment guidance, the alignment is not easily available in end-to-end manner. In this work, frame-wise alignment, used to pre-train streaming RNN-T's encoder, is generated without using a HMM-based system. Therefore an all-neural framework equipping HMM-free encoder pre-training is constructed. This is achieved by expanding the spikes of CTC model to their left/right blank frames, and two expanding strategies are proposed. To our best knowledge, this is the first work to simulate HMM-based frame-wise label using CTC model for pre-training. Experiments conducted on LibriSpeech and MLS English tasks show the proposed pre-training procedure, compared with random initialization, reduces the WER by relatively 5%~11% and the emission latency by 60 ms. Besides, the method is lexicon-free, so it is friendly to new languages without manually designed lexicon.
How emoji and word embedding helps to unveil emotional transitions during online messaging
Authors: Moeen Mostafavi, Michael D. Porter
Date: 2021-03-23 00:00:00
Tasks: Chatbot

During online chats, body-language and vocal characteristics are not part of the communication mechanism making it challenging to facilitate an accurate interpretation of feelings, emotions, and attitudes. The use of emojis to express emotional feeling is an alternative approach in these types of communication. In this project, we focus on modeling a customer's emotion in an online messaging session with a chatbot. We use Affect Control Theory (ACT) to predict emotional change during the interaction. To let the customer use emojis, we also extend the affective dictionaries used by ACT. For this purpose, we mapped Emoji2vec embedding to the affective space. Our framework can find emotional change during messaging and how a customer's reaction is changed accordingly.
Voice Privacy with Smart Digital Assistants in Educational Settings
Authors: Mohammad Niknazar, Aditya Vempaty, Ravi Kokku
Date: 2021-03-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speaker Identification, speech-recognition, Speech Recognition

The emergence of voice-assistant devices ushers in delightful user experiences not just on the smart home front, but also in diverse educational environments from classrooms to personalized-learning/tutoring. However, the use of voice as an interaction modality also could result in exposure of user's identity, and hinders the broader adoption of voice interfaces; this is especially important in environments where children are present and their voice privacy needs to be protected. To this end, building on state-of-the-art techniques proposed in the literature, we design and evaluate a practical and efficient framework for voice privacy at the source. The approach combines speaker identification (SID) and speech conversion methods to randomly disguise the identity of users right on the device that records the speech, while ensuring that the transformed utterances of users can still be successfully transcribed by Automatic Speech Recognition (ASR) solutions. We evaluate the ASR performance of the conversion in terms of word error rate and show the promise of this framework in preserving the content of the input speech.