pwc-archive/papers-with-abstracts huggingface dataset:
A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom
Authors: Thanveer Shaik, Xiaohui Tao, Lin Li, Haoran Xie, Juan D. Velásquez
Date: 2023-06-21 00:00:00
Tasks: feature selection

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data to information to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. We present a comprehensive review of multimodal medical data fusion focused on the integration of various data modalities. The review explores different approaches such as feature selection, rule-based systems, machine learning, deep learning, and natural language processing, for fusing and analyzing multimodal data. This paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and theories, it proposes a generic framework for multimodal medical data fusion that aligns with the DIKW model. Moreover, it discusses future directions related to the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches. The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare. Our findings can guide researchers and practitioners in leveraging the power of multimodal fusion with the state-of-the-art approaches to revolutionize healthcare and improve patient outcomes.
GEO-Nav: a geometric dataset of voltage-gated sodium channels
Authors: Andrea Raffo, Ulderico Fugacci, Silvia Biasotti
Date: 2023-06-21 00:00:00
Tasks: Drug Design, Morphological Analysis

Voltage-gated sodium (Nav) channels constitute a prime target for drug design and discovery, given their implication in various diseases such as epilepsy, migraine and ataxia to name a few. In this regard, performing morphological analysis is a crucial step in comprehensively understanding their biological function and mechanism, as well as in uncovering subtle details of their mechanism that may be elusive to experimental observations. Despite their tremendous therapeutic potential, drug design resources are deficient, particularly in terms of accurate and comprehensive geometric information. This paper presents a geometric dataset of molecular surfaces that are representative of Nav channels in mammals. For each structure we provide three representations and a number of geometric measures, including length, volume and straightness of the recognized channels. To demonstrate the effective use of GEO-Nav, we have tested it on two methods belonging to two different categories of approaches: a sphere-based and a tessellation-based method.
Neural ShDF: Reviving an Efficient and Consistent Mesh Segmentation Method
Authors: Bruno Roy
Date: 2023-06-14 00:00:00
Tasks: Segmentation

Partitioning a polygonal mesh into meaningful parts can be challenging. Many applications require decomposing such structures for further processing in computer graphics. In the last decade, several methods were proposed to tackle this problem, at the cost of intensive computational times. Recently, machine learning has proven to be effective for the segmentation task on 3D structures. Nevertheless, these state-of-the-art methods are often hardly generalizable and require dividing the learned model into several specific classes of objects to avoid overfitting. We present a data-driven approach leveraging deep learning to encode a mapping function prior to mesh segmentation for multiple applications. Our network reproduces a neighborhood map using our knowledge of the \textsl{Shape Diameter Function} (SDF) method using similarities among vertex neighborhoods. Our approach is resolution-agnostic as we downsample the input meshes and query the full-resolution structure solely for neighborhood contributions. Using our predicted SDF values, we can inject the resulting structure into a graph-cut algorithm to generate an efficient and robust mesh segmentation while considerably reducing the required computation times.
Fundamental Performance Bounds for Carrier Phase Positioning in Cellular Networks
Authors: Henk Wymeersch, Rouhollah Amiri, Gonzalo Seco-Granados
Date: 2023-06-21 00:00:00

The carrier phase of cellular signals can be utilized for highly accurate positioning, with the potential for orders-of-magnitude performance improvements compared to standard time-difference-of-arrival positioning. Due to the integer ambiguities, standard performance evaluation tools such as the Cram\'er-Rao bound (CRB) are overly optimistic. In this paper, a new performance bound, called the mixed-integer CRB (MICRB) is introduced that explicitly accounts for this integer ambiguity. While computationally more complex than the standard CRB, the MICRB can accurately predict positioning performance, as verified by numerical simulations, and hence it serves as a useful guide to choose the system parameters that facilitate carrier phase positioning.
Federated Self-Learning with Weak Supervision for Speech Recognition
Authors: Milind Rao, Gopinath Chennupati, Gautam Tiwari, Anit Kumar Sahu, Anirudh Raju, Ariya Rastrow, Jasha Droppo
Date: 2023-06-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Incremental Learning, Natural Language Understanding, Self-Learning, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) models with low-footprint are increasingly being deployed on edge devices for conversational agents, which enhances privacy. We study the problem of federated continual incremental learning for recurrent neural network-transducer (RNN-T) ASR models in the privacy-enhancing scheme of learning on-device, without access to ground truth human transcripts or machine transcriptions from a stronger ASR model. In particular, we study the performance of a self-learning based scheme, with a paired teacher model updated through an exponential moving average of ASR models. Further, we propose using possibly noisy weak-supervision signals such as feedback scores and natural language understanding semantics determined from user behavior across multiple turns in a session of interactions with the conversational agent. These signals are leveraged in a multi-task policy-gradient training approach to improve the performance of self-learning for ASR. Finally, we show how catastrophic forgetting can be mitigated by combining on-device learning with a memory-replay approach using selected historical datasets. These innovations allow for 10% relative improvement in WER on new use cases with minimal degradation on other test sets in the absence of strong-supervision signals such as ground-truth transcriptions.
Learning When to Trust Which Teacher for Weakly Supervised ASR
Authors: Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke
Date: 2023-06-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic speech recognition (ASR) training can utilize multiple experts as teacher models, each trained on a specific domain or accent. Teacher models may be opaque in nature since their architecture may be not be known or their training cadence is different from that of the student ASR model. Still, the student models are updated incrementally using the pseudo-labels generated independently by the expert teachers. In this paper, we exploit supervision from multiple domain experts in training student ASR models. This training strategy is especially useful in scenarios where few or no human transcriptions are available. To that end, we propose a Smart-Weighter mechanism that selects an appropriate expert based on the input audio, and then trains the student model in an unsupervised setting. We show the efficacy of our approach using LibriSpeech and LibriLight benchmarks and find an improvement of 4 to 25\% over baselines that uniformly weight all the experts, use a single expert model, or combine experts using ROVER.
Coverage Performance of UAV-powered Sensors for Energy-neutral Networks with Recharging Stations
Authors: Oktay Cetinkaya, Mustafa Ozger, David De Roure
Date: 2023-06-21 00:00:00

The projected number of Internet of Things (IoT) sensors makes battery maintenance a challenging task. Although battery-less IoT is technologically viable, the sensors should be somehow energized, either locally or remotely. Unmanned aerial vehicles (UAVs) can respond to this quest via wireless power transfer (WPT). However, to achieve energy neutrality across the IoT networks and thus mitigate the maintenance issues, the UAVs providing energy and connectivity to IoT sensors must be supplied by recharging stations having multi-source energy harvesting (EH) capability. Yet, as these sensors rely solely on UAV-transferred power, the absence of UAVs causes sensor outages and hence loss of coverage when they visit recharging stations for battery replenishment. Hence, besides the UAV parameters (e.g., battery size and velocity), recharging duration and station density must be carefully determined to avoid these outages. To address that, this paper uses stochastic geometry to derive the coverage probability of UAV-powered sensors. Our analysis sheds light on the fundamental trade-offs and design guidelines for energy-neutral IoT networks with recharging stations in regard to the regulatory organization limitations, practical rectenna and UAV models, and the minimum power requirements of sensors.
Extended NYUSIM-based MmWave Channel Model and Simulator for RIS-Assisted Systems
Authors: Aline Habib, Israa Khaled, Ammar El Falou, Charlotte Langlais
Date: 2023-06-21 00:00:00

Spectrum scarcity has motivated the exploration of the millimeter-wave (mmWave) band as a key technology to cope with the ever-increasing data traffic. However, in this band, radiofrequency waves are highly susceptible to transmission loss and blockage. Recently, reconfigurable intelligent surfaces (RIS) have been proposed to transform the random nature of the propagation channel into a programmable and controllable radio environment. This innovative technique can improve mmWave coverage. However, most works consider theoretical channel models. In order to fill the gap towards a realistic RIS channel simulator, we extend the 3D statistical channel simulator NYUSIM based on extensive measurements to help model RIS-assisted mmWave systems. We validate the extended simulator analytically and via simulations. In addition, we study the received power in different configurations. Finally, we highlight the effectiveness of using RIS when the direct link is partially blocked or non-existent.
Predicting Strategic Energy Storage Behaviors
Authors: Yuexin Bian, Ningkun Zheng, Yang Zheng, Bolun Xu, Yuanyuan Shi
Date: 2023-06-20 00:00:00

Energy storage are strategic participants in electricity markets to arbitrage price differences. Future power system operators must understand and predict strategic storage arbitrage behaviors for market power monitoring and capacity adequacy planning. This paper proposes a novel data-driven approach that incorporates prior model knowledge for predicting the strategic behaviors of price-taker energy storage systems. We propose a gradient-descent method to find the storage model parameters given the historical price signals and observations. We prove that the identified model parameters will converge to the true user parameters under a class of quadratic objective and linear equality-constrained storage models. We demonstrate the effectiveness of our approach through numerical experiments with synthetic and real-world storage behavior data. The proposed approach significantly improves the accuracy of storage model identification and behavior forecasting compared to previous blackbox data-driven approaches.
System Level Evaluation of Network-Controlled Repeaters: Performance Improvement of Serving Cell and Interference Impact on Neighbor Cells
Authors: Gabriel C. M. da Silva, Erik R. B. Falcão, Victor F. Monteiro, Darlan C. Moreira, Diego A. Sousa, Tarcisio F. Maciel, Fco. Rafael M. Lima, Behrooz Makki
Date: 2023-06-20 00:00:00

Heterogeneous networks have been studied as one of the enablers of network densification. These studies have been intensified to overcome some drawbacks related to propagation in millimeter waves (mmWaves), such as severe path and penetration losses. One of the promising heterogeneous nodes is network-controlled repeater (NCR). It was proposed by the 3rd generation partnership project (3GPP) in Release 18 as a candidate solution to enhance network coverage. In this context, this work performs a system level evaluation to analyze the performance improvement that an NCR can cause in its serving cell as well as its interference impact on neighbor cells. Particularly, the results show a considerable improvement on the performance of user equipments (UEs) served by the NCR, while neighbor UEs that receive the NCR signal as interference are negatively impacted, but not enough to suffer from outage.
Symmetry's Edge in Cortical Dynamics: Multiscale Dynamics of Ensemble Excitation and Inhibition
Authors: Nima Dehghani
Date: 2023-06-21 00:00:00

Creating a quantitative theory for the cortex presents challenges and raises questions. What are the significant scales--micro, meso, or macroscopic? What are the interactions--pairwise, higher order, or mean-field? And what control parameters are relevant--noisy, dissipative, or emergent? We suggest an approach inspired by advances in understanding matter. This involves identifying invariances in neuron ensemble dynamics, searching for order parameters connecting key degrees of freedom and distinguishing macroscopic states, and pinpointing broken symmetries to uncover emergent laws when neurons interact and coordinate. Using multielectrode and multiscale neural recordings, we measure population-level ensemble Excitation/Inhibition (E/I) balance, differing from the input-level E/I balance of single neurons, to study collective behavior in large neural populations. We investigate a set of parameters that can assist us in differentiating between various functional system states (during wake/sleep cycle) and pinpointing broken symmetries that serve different information processing and memory functions. Furthermore, we identify pathological broken symmetries that result in states like seizures.
Integrative analysis of ATAC-seq and RNA-seq for cells infected by human T-cell leukemia virus type 1
Authors: Azusa Tanaka, Yasuhiro Ishitsuka, Hiroki Ohta, Norihiro Takenouchi, Masanori Nakagawa, Ki-Ryang Koh, Chiho Onishi, Hiromitsu Tanaka, Akihiro Fujimoto, Jun-ichirou Yasunaga, Masao Matsuoka
Date: 2023-06-20 00:00:00

Human T-cell leukemia virus type 1 (HTLV-1) causes adult T-cell leukemia (ATL) and HTLV-1-associated myelopathy (HAM) after a long latent period in a fraction of infected individuals. These HTLV-1-infected cells typically have phenotypes similar to that of CD4${^+}$ T cells, but the cell status is not well understood. To extract the inherent information of HTLV-1-infected CD4$^+$ cells, we integratively analyzed the ATAC-seq and RNA-seq data of infected cells. Compared to CD4${^+}$ T cells from healthy donors, we found anomalous chromatin accessibility in HTLV-1-infected CD4${^+}$ cells derived from ATL cases in terms of location and sample-to-sample fluctuations in open chromatin regions. Further, by focusing on systematically selected genes near the open chromatin regions, all the gene expressions in ATL cases were found to be distinct from those of healthy CD4$^+$ T cells. Based on a further analysis of chromatin accessibility, we detected TLL1 (Tolloid Like 1) as one of the key genes that exhibit unique gene expressions in ATL cases. A luciferase assay indicated that TLL1 has a strong regulatory effect on TGF-$\beta$. Overall, this study provides results about the status of HTLV-1 infected cells, which are qualitatively consistent across the different scales of chromatin accessibility, transcription, and immunophenotype.
A Nonparametric Test of $m$th-degree Inverse Stochastic Dominance
Authors: Hongyi Jiang, Zhenting Sun, Shiyun Hu
Date: 2023-06-21 00:00:00

This paper proposes a nonparametric test for $m$th-degree inverse stochastic dominance which is a powerful tool for ranking distribution functions according to social welfare. We construct the test based on empirical process theory. The test is shown to be asymptotically size controlled and consistent. The good finite sample properties of the test are illustrated via Monte Carlo simulations. We apply our test to the inequality growth in the United Kingdom from 1995 to 2010.
Difference-in-Differences with Interference
Authors: Ruonan Xu
Date: 2023-06-21 00:00:00

In many scenarios, such as the evaluation of place-based policies, potential outcomes are not only dependent upon the unit's own treatment but also its neighbors' treatment. Despite this, "difference-in-differences" (DID) type estimators typically ignore such interference among neighbors. I show in this paper that the canonical DID estimators generally fail to identify interesting causal effects in the presence of neighborhood interference. To incorporate interference structure into DID estimation, I propose doubly robust estimators for the direct average treatment effect on the treated as well as the average spillover effects under a modified parallel trends assumption. I later relax common restrictions in the literature, such as immediate neighborhood interference and correctly specified spillover functions. Moreover, robust inference is discussed based on the asymptotic distribution of the proposed estimators.
The Impact of Customer Online Satisfaction on Stock Returns: Evidence from the E-commerce Reviews in China
Authors: Zhi Su, Danni Wu, Zhenkun Zhou, Junran Wu, Libo Yin
Date: 2023-06-21 00:00:00

This paper investigates the significance of consumer opinions in relation to value in China's A-share market. By analyzing a large dataset comprising over 18 million product reviews by customers on JD.com, we demonstrate that sentiments expressed in consumer reviews can influence stock returns, indicating that consumer opinions contain valuable information that can impact the stock market. Our findings show that Customer Negative Sentiment Tendency (CNST) and One-Star Tendency (OST) have a negative effect on expected stock returns, even after controlling for firm characteristics such as market risk, illiquidity, idiosyncratic volatility, and asset growth. Further analysis reveals that the predictive power of CNST is stronger in firms with high sentiment conditions, growth companies, and firms with lower accounting transparency. We also find that CNST negatively predicts revenue surprises, earnings surprises, and cash flow shocks. These results suggest that online satisfaction derived from big data analysis of customer reviews contains novel information about firms' fundamentals.
The Skill-Task Matching Model: Mechanism, Model Structure, and Algorithm
Authors: Da Xie, WeiGuo Yang
Date: 2023-06-21 00:00:00
Tasks: Decision Making, model

We distinguished between the expected and actual profit of a firm. We proposed that, beyond maximizing profit, a firm's goal also encompasses minimizing the gap between expected and actual profit. Firms strive to enhance their capability to transform projects into reality through a process of trial and error, evident as a cyclical iterative optimization process. To characterize this iterative mechanism, we developed the Skill-Task Matching Model, extending the task approach in both multidimensional and iterative manners. We vectorized jobs and employees into task and skill vector spaces, respectively, while treating production techniques as a skill-task matching matrix and business strategy as a task value vector. In our model, the process of stabilizing production techniques and optimizing business strategies corresponds to the recalibration of parameters within the skill-task matching matrix and the task value vector. We constructed a feed-forward neural network algorithm to run this model and demonstrated how it can augment operational efficiency.
Disentangling Revealed Preference From Rationalization by a Preference
Authors: Pablo Schenone
Date: 2023-06-20 00:00:00
Tasks: Relation

The weak axiom of revealed preference (WARP) ensures that the revealed preference (i) is a preference relation (i.e., it is complete and transitive) and (ii) rationalizes the choices. However, when WARP fails, either one of these two properties is violated, but it is unclear which one it is. We provide an alternative characterization of WARP by showing that WARP is equivalent to the conjunction of two axioms each of which separately guarantees (i) and (ii).
Finite Beam Depth Analysis for Large Arrays
Authors: Alva Kosasih, Emil Björnson
Date: 2023-06-21 00:00:00

Most wireless communication systems operate in the far-field region of antennas and antenna arrays, where waves are planar and beams have infinite depth. When antenna arrays become electrically large, it is possible that the receiver is in the radiative near-field of the transmitter, and vice versa. Recent works have shown that near-field beamforming exhibits a finite depth, which enables a new depth-based spatial multiplexing paradigm. In this paper, we explore how the shape and size of an array determine the near-field beam behaviors. In particular, we investigate the $3$ dB beam depth (BD), defined as the range of distances where the gain is greater than half of the peak gain. We derive analytical gain and BD expressions and prove how they depend on the aperture area and length. For non-broadside transmissions, we find that the BD increases as the transmitter approaches the end-fire direction of the array. Furthermore, it is sufficient to characterize the BD for a broadside transmitter, as the beam pattern with a non-broadside transmitter can be approximated by that of a smaller/projected array with a broadside transmitter. Our analysis demonstrates that the BD can be ordered from smallest to largest as ULA, circular, and square arrays.
SmartPUR: An Autonomous PUR Transmission Solution for Mobile C-IoT Devices
Authors: Gautham Prasad, Nadhem Rojbi, Lutz Lampe, Gus Vos
Date: 2023-06-21 00:00:00
Tasks: valid

Cellular Internet-of-things (C-IoT) user equipments (UEs) typically transmit frequent but small amounts of uplink data to the base station. Undergoing a traditional random access procedure (RAP) to transmit a small but frequent data presents a considerable overhead. As an antidote, preconfigured uplink resources (PURs) are typically used in newer UEs, where the devices are allocated uplink resources beforehand to transmit on without following the RAP. A prerequisite for transmitting on PURs is that the UEs must use a valid timing advance (TA) so that they do not interfere with transmissions of other nodes in adjacent resources. One solution to this end is to validate the previously held TA by the UE to ensure that it is still valid. While this validation is trivial for stationary UEs, mobile UEs often encounter conditions where the previous TA is no longer valid and a new one is to be requested by falling back on legacy RAP. This limits the applicability of PURs in mobile UEs. To counter this drawback and ensure a near-universal adoption of transmitting on PURs, we propose new machine learning aided solutions for validation and prediction of TA for UEs of any type of mobility. We conduct comprehensive simulation evaluations across different types of communication environments to demonstrate that our proposed solutions provide up to a 98.7% accuracy in predicting the TA.