pwc-archive/papers-with-abstracts huggingface dataset:
Is A 15-minute City within Reach in the United States? An Investigation of Activity-Based Mobility Flows in the 12 Most Populous US Cities
Authors: Tanhua Jin, Kailai Wang, Yanan Xin, Jian Shi, Ye Hong, Frank Witlox
Date: 2023-10-22 00:00:00

Enhanced efforts in the transportation sector should be implemented to mitigate the adverse effects of CO2 emissions resulting from zoning-based planning paradigms. The innovative concept of the 15-minute city, with a focus on proximity-based planning, holds promise in minimizing unnecessary travel and advancing the progress toward achieving carbon neutrality. However, an important research question that remains insufficiently explored is: to what extent is a 15-minute city concept within reach for US cities? This paper establishes a comprehensive framework to evaluate the 15-minute city concept using SafeGraph Point of Interest (POI) check-in data in the 12 most populous US cities. The results reveal that residents are more likely to rely on cars due to the fact that most of their essential activities are located beyond convenient walking, cycling, and public transit distances. However, there is significant potential for the implementation of the 15-minute city concept, as most residents' current activities can be accommodated within a 15-minute radius by the aforementioned low-emission modes of transportation. Our findings can offer policymakers insight into how far US cities are away from the 15-minute city and the potential CO2 emission reduction they can expect if the concept is successfully implemented.
Causal clustering: design of cluster experiments under network interference
Authors: Davide Viviano, Lihua Lei, Guido Imbens, Brian Karrer, Okke Schrijvers, Liang Shi
Date: 2023-10-23 00:00:00
Tasks: Clustering

This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. We provide a framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global effect. We show that optimal clustering solves a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes simple conditions to choose between any two cluster designs, including choosing between a cluster or individual-level randomization. We illustrate the method's properties using unique network data from the universe of Facebook's users and existing data from a field experiment.
BVARs and Stochastic Volatility
Authors: Joshua Chan
Date: 2023-10-22 00:00:00

Bayesian vector autoregressions (BVARs) are the workhorse in macroeconomic forecasting. Research in the last decade has established the importance of allowing time-varying volatility to capture both secular and cyclical variations in macroeconomic uncertainty. This recognition, together with the growing availability of large datasets, has propelled a surge in recent research in building stochastic volatility models suitable for large BVARs. Some of these new models are also equipped with additional features that are especially desirable for large systems, such as order invariance -- i.e., estimates are not dependent on how the variables are ordered in the BVAR -- and robustness against COVID-19 outliers. Estimation of these large, flexible models is made possible by the recently developed equation-by-equation approach that drastically reduces the computational cost of estimating large systems. Despite these recent advances, there remains much ongoing work, such as the development of parsimonious approaches for time-varying coefficients and other types of nonlinearities in large BVARs.
Unobserved Grouped Heteroskedasticity and Fixed Effects
Authors: Jorge A. Rivero
Date: 2023-10-21 00:00:00

This paper extends the linear grouped fixed effects (GFE) panel model to allow for heteroskedasticity from a discrete latent group variable. Key features of GFE are preserved, such as individuals belonging to one of a finite number of groups and group membership is unrestricted and estimated. Ignoring group heteroskedasticity may lead to poor classification, which is detrimental to finite sample bias and standard errors of estimators. I introduce the "weighted grouped fixed effects" (WGFE) estimator that minimizes a weighted average of group sum of squared residuals. I establish $\sqrt{NT}$-consistency and normality under a concept of group separation based on second moments. A test of group homoskedasticity is discussed. A fast computation procedure is provided. Simulations show that WGFE outperforms alternatives that exclude second moment information. I demonstrate this approach by considering the link between income and democracy and the effect of unionization on earnings.
Bayesian Estimation of Panel Models under Potentially Sparse Heterogeneity
Authors: Hyungsik Roger Moon, Frank Schorfheide, Boyuan Zhang
Date: 2023-10-20 00:00:00

We incorporate a version of a spike and slab prior, comprising a pointmass at zero ("spike") and a Normal distribution around zero ("slab") into a dynamic panel data framework to model coefficient heterogeneity. In addition to homogeneity and full heterogeneity, our specification can also capture sparse heterogeneity, that is, there is a core group of units that share common parameters and a set of deviators with idiosyncratic parameters. We fit a model with unobserved components to income data from the Panel Study of Income Dynamics. We find evidence for sparse heterogeneity for balanced panels composed of individuals with long employment histories.
Can the Black Lives Matter Movement Reduce Racial Disparities? Evidence from Medical Crowdfunding
Authors: Kaixin Liu, Jiwei Zhou, Junda Wang
Date: 2023-10-23 00:00:00

Using high-frequency donation records from a major medical crowdfunding site and careful difference-in-difference analysis, we demonstrate that the 2020 BLM surge decreased the fundraising gap between Black and non-Black beneficiaries by around 50\%. The reduction is largely attributed to non-Black donors. Those beneficiaries in counties with moderate BLM activities were most impacted. We construct innovative instrumental variable approaches that utilize weekends and rainfall to identify the global and local effects of BLM protests. Results suggest a broad social movement has a greater influence on charitable-giving behavior than a local event. Social media significantly magnifies the impact of protests.
Modeling Link-level Road Traffic Resilience to Extreme Weather Events Using Crowdsourced Data
Authors: Songhua Hu, Kailai Wang, Lingyao Li, Yingrui Zhao, Zhenbing He, Yunpeng, Zhang
Date: 2023-10-22 00:00:00

Climate changes lead to more frequent and intense weather events, posing escalating risks to road traffic. Crowdsourced data offer new opportunities to monitor and investigate changes in road traffic flow during extreme weather. This study utilizes diverse crowdsourced data from mobile devices and the community-driven navigation app, Waze, to examine the impact of three weather events (i.e., floods, winter storms, and fog) on road traffic. Three metrics, speed change, event duration, and area under the curve (AUC), are employed to assess link-level traffic change and recovery. In addition, a user's perceived severity is computed to evaluate link-level weather impact based on crowdsourced reports. This study evaluates a range of new data sources, and provides insights into the resilience of road traffic to extreme weather, which are crucial for disaster preparedness, response, and recovery in road transportation systems.
A prototype software framework for transferable computational health economic models and its early application in youth mental health
Authors: Matthew P Hamilton, Caroline X Gao, Glen Wiesner, Kate M Filia, Jana M Menssink, Petra Plencnerova, David G Baker, Patrick D McGorry, Alexandra Parker, Jonathan Karnon, Sue M Cotton, Cathrine Mihalopoulos
Date: 2023-10-22 00:00:00
Tasks: Attribute

We are developing an economic model to explore multiple topics in Australian youth mental health policy. We want that model to be readily transferable to other jurisdictions. We developed a software framework for authoring transparent, reusable and updatable Computational Health Economic Models (CHEMs) (the software files that implement health economic models). We specified framework user requirements of a template CHEM module that facilitates modular model implementations, a simple programming syntax and tools for authoring new CHEM modules, supplying CHEMs with data, reporting reproducible CHEM analyses, searching for CHEM modules and maintaining a CHEM project website. We implemented the framework as six development version code libraries in the programming language R that integrate with online services for software development and research data archiving. We used the framework to author five development version R libraries of CHEM modules focused on utility mapping in youth mental health. These modules provide tools for variable validation, dataset description, multi-attribute instrument scoring, construction of mapping models, reporting of mapping studies and making out of sample predictions. We assessed these CHEM module libraries as mostly meeting transparency, reusability and updatability criteria that we have previously developed, but requiring more detailed documentation and unit testing of individual modules. Our software framework has potential value as a prototype for future tools to support the development of transferable CHEMs.
Diversity Preferences, Affirmative Action and Choice Rules
Authors: Oguzhan Celebi
Date: 2023-10-22 00:00:00
Tasks: Diversity

I study the relationship between diversity preferences and the choice rules implemented by institutions, with a particular focus on the affirmative action policies. I characterize the choice rules that can be rationalized by diversity preferences and demonstrate that the recently rescinded affirmative action mechanism used to allocate government positions in India cannot be rationalized. I show that if institutions evaluate diversity without considering intersectionality of identities, their choices cannot satisfy the crucial substitutes condition. I characterize choice rules that satisfy the substitutes condition and are rationalizable by preferences that are separable in diversity and match quality domains.
Prompt-driven Target Speech Diarization
Authors: Yidi Jiang, Zhengyang Chen, Ruijie Tao, Liqun Deng, Yanmin Qian, Haizhou Li
Date: 2023-10-23 00:00:00
Tasks: Action Detection, Activity Detection

We introduce a novel task named `target speech diarization', which seeks to determine `when target event occurred' within an audio signal. We devise a neural architecture called Prompt-driven Target Speech Diarization (PTSD), that works with diverse prompts that specify the target speech events of interest. We train and evaluate PTSD using sim2spk, sim3spk and sim4spk datasets, which are derived from the Librispeech. We show that the proposed framework accurately localizes target speech events. Furthermore, our framework exhibits versatility through its impressive performance in three diarization-related tasks: target speaker voice activity detection, overlapped speech detection and gender diarization. In particular, PTSD achieves comparable performance to specialized models across these tasks on both real and simulated data. This work serves as a reference benchmark and provides valuable insights into prompt-driven target speech processing.
A Study on Prosodic Entrainment in Relation to Therapist Empathy in Counseling Conversation
Authors: Dehua Tao, Tan Lee, Harold Chui, Sarah Luk
Date: 2023-10-22 00:00:00
Tasks: Relation

Counseling is carried out as spoken conversation between a therapist and a client. The empathy level expressed by the therapist is considered an important index of the quality of counseling and often assessed by an observer or the client. This research investigates the entrainment of speech prosody in relation to subjectively rated empathy. Experimental results show that the entrainment of intensity is more influential to empathy observation than that of pitch or speech rate in client-therapist interaction. The observer and the client have different perceptions of therapist empathy with the same entrained phenomena in pitch and intensity. The client's intention to make adjustment on pitch variation and intensity of speech is considered an indicator of the client's perception of counseling quality.
Modeling Intrapersonal and Interpersonal Influences for Automatic Estimation of Therapist Empathy in Counseling Conversation
Authors: Dehua Tao, Tan Lee, Harold Chui, Sarah Luk
Date: 2023-10-22 00:00:00

Counseling is usually conducted through spoken conversation between a therapist and a client. The empathy level of therapist is a key indicator of outcomes. Presuming that therapist's empathy expression is shaped by their past behavior and their perception of the client's behavior, we propose a model to estimate the therapist empathy by considering both intrapersonal and interpersonal influences. These dynamic influences are captured by applying an attention mechanism to the therapist turn and the historical turns of both therapist and client. Our findings suggest that the integration of dynamic influences enhances empathy level estimation. The influence-derived embedding should constitute a minor portion of the target turn representation for optimal empathy estimation. The client's turns (interpersonal influence) slightly surpass the therapist's own turns (intrapersonal influence) in empathy estimation effectiveness. It is noted that concentrating exclusively on recent historical turns can significantly impact the estimation of therapist empathy.
SwG-former: A Sliding-Window Graph Convolutional Network for Simultaneous Spatial-Temporal Information Extraction in Sound Event Localization and Detection
Authors: Weiming Huang, Qinghua Huang, Liyan Ma, Chuan Wang
Date: 2023-10-21 00:00:00
Tasks: Event Detection, Sound Event Detection, Sound Event Localization and Detection, Temporal Information Extraction

Sound event localization and detection (SELD) involves sound event detection (SED) and direction of arrival (DoA) estimation tasks. SED mainly relies on temporal dependencies to distinguish different sound classes, while DoA estimation depends on spatial correlations to estimate source directions. This paper addresses the need to simultaneously extract spatial-temporal information in audio signals to improve SELD performance. A novel block, the sliding-window graph-former (SwG-former), is designed to learn temporal context information of sound events based on their spatial correlations. The SwG-former block transforms audio signals into a graph representation and constructs graph vertices to capture higher abstraction levels for spatial correlations. It uses different-sized sliding windows to adapt various sound event durations and aggregates temporal features with similar spatial information while incorporating multi-head self-attention (MHSA) to model global information. Furthermore, as the cornerstone of message passing, a robust Conv2dAgg function is proposed and embedded into the block to aggregate the features of neighbor vertices. As a result, a SwG-former model, which stacks the SwG-former blocks, demonstrates superior performance compared to recent advanced SELD models. The SwG-former block is also integrated into the event-independent network version 2 (EINV2), called SwG-EINV2, which surpasses the state-of-the-art (SOTA) methods under the same acoustic environment.
Convex Hull Prediction Methods for Bitrate Ladder Construction: Design, Evaluation, and Comparison
Authors: Ahmed Telili, Wassim Hamidouche, Hadi Amirpour, Sid Ahmed Fezza, Luce Morin, Christian Timmerer
Date: 2023-10-23 00:00:00

HTTP adaptive streaming (HAS) has emerged as a prevalent approach for over-the-top (OTT) video streaming services due to its ability to deliver a seamless user experience. A fundamental component of HAS is the bitrate ladder, which comprises a set of encoding parameters (e.g., bitrate-resolution pairs) used to encode the source video into multiple representations. This adaptive bitrate ladder enables the client's video player to dynamically adjust the quality of the video stream in real-time based on fluctuations in network conditions, ensuring uninterrupted playback by selecting the most suitable representation for the available bandwidth. The most straightforward approach involves using a fixed bitrate ladder for all videos, consisting of pre-determined bitrate-resolution pairs known as one-size-fits-all. Conversely, the most reliable technique relies on intensively encoding all resolutions over a wide range of bitrates to build the convex hull, thereby optimizing the bitrate ladder by selecting the representations from the convex hull for each specific video. Several techniques have been proposed to predict content-based ladders without performing a costly, exhaustive search encoding. This paper provides a comprehensive review of various convex hull prediction methods, including both conventional and learning-based approaches. Furthermore, we conduct a benchmark study of several handcrafted- and DL-based approaches for predicting content-optimized convex hulls across multiple codec settings. The considered methods are evaluated on our proposed large-scale dataset, which includes 300 UHD video shots encoded with software and hardware encoders using three state-of-the-art video standards, including AVC/H.264, HEVC/H.265, and VVC/H.266, at various bitrate points. Our analysis provides valuable insights and establishes baseline performance for future research in this field.
AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons
Authors: Gowtham Krishnan Murugesan, Diana McCrumb, Mariam Aboian, Tej Verma, Rahul Soni, Fatima Memon, Keyvan Farahani, Linmin Pei, Ulrike Wagner, Andrey Y. Fedorov, David Clunie, Stephen Moore, Jeff Van Oss
Date: 2023-10-23 00:00:00
Tasks: Cloud Computing, Computed Tomography (CT)

The National Cancer Institute (NCI) Image Data Commons (IDC) offers publicly available cancer radiology collections for cloud computing, crucial for developing advanced imaging tools and algorithms. Despite their potential, these collections are minimally annotated; only 4% of DICOM studies in collections considered in the project had existing segmentation annotations. This project increases the quantity of segmentations in various IDC collections. We produced high-quality, AI-generated imaging annotations dataset of tissues, organs, and/or cancers for 11 distinct IDC image collections. These collections contain images from a variety of modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). The collections cover various body parts, such as the chest, breast, kidneys, prostate, and liver. A portion of the AI annotations were reviewed and corrected by a radiologist to assess the performance of the AI models. Both the AI's and the radiologist's annotations were encoded in conformance to the Digital Imaging and Communications in Medicine (DICOM) standard, allowing for seamless integration into the IDC collections as third-party analysis collections. All the models, images and annotations are publicly accessible.
Joint Non-Linear MRI Inversion with Diffusion Priors
Authors: Moritz Erlacher, Martin Zach
Date: 2023-10-23 00:00:00
Tasks: Diagnostic, Sensitivity

Magnetic resonance imaging (MRI) is a potent diagnostic tool, but suffers from long examination times. To accelerate the process, modern MRI machines typically utilize multiple coils that acquire sub-sampled data in parallel. Data-driven reconstruction approaches, in particular diffusion models, recently achieved remarkable success in reconstructing these data, but typically rely on estimating the coil sensitivities in an off-line step. This suffers from potential movement and misalignment artifacts and limits the application to Cartesian sampling trajectories. To obviate the need for off-line sensitivity estimation, we propose to jointly estimate the sensitivity maps with the image. In particular, we utilize a diffusion model -- trained on magnitude images only -- to generate high-fidelity images while imposing spatial smoothness of the sensitivity maps in the reverse diffusion. The proposed approach demonstrates consistent qualitative and quantitative performance across different sub-sampling patterns. In addition, experiments indicate a good fit of the estimated coil sensitivities.
GDOP Based BS Selection for Positioning in mmWave 5G NR Networks
Authors: A. Indika Perera, K. B. Shashika Manosha, Nandana Rajatheva, Matti Latva-aho
Date: 2023-10-23 00:00:00

The fifth-generation (5G) of mobile communication supported by millimetre-wave (mmWave) technology and higher base station (BS) densification facilitate to enhance user equipment (UE) positioning. Therefore, 5G cellular system is designed with many positioning measurements and special positioning reference signals with a multitude of configurations for a variety of use cases, expecting stringent positioning accuracies. One of the major factors that the accuracy of a particular position estimate depends on is the geometry of the nodes in the system, which could be measured with the geometric dilution of precision (GDOP). Hence in this paper, we investigate the time difference of arrival (TDOA) measurements based UE positioning accuracy improvement, exploiting the geometric distribution of BSs in mixed LOS and NLOS environment. We propose a BS selection algorithm for UE positioning based on the GDOP of the BSs participating in the positioning process. Simulations are conducted for indoor and outdoor scenarios that use antenna arrays with beam-based mmWave NR communication. Results demonstrate that the proposed BS selection can achieve higher positioning accuracy with fewer radio resources compared to the other BS selection methods.
An introduction to radar Automatic Target Recognition (ATR) technology in ground-based radar systems
Authors: Jiangkun Gong, Jun Yan, Deyong Kong, Deren Li
Date: 2023-10-23 00:00:00

This paper presents a brief examination of Automatic Target Recognition (ATR) technology within ground-based radar systems. It offers a lucid comprehension of the ATR concept, delves into its historical milestones, and categorizes ATR methods according to different scattering regions. By incorporating ATR solutions into radar systems, this study demonstrates the expansion of radar detection ranges and the enhancement of tracking capabilities, leading to superior situational awareness. Drawing insights from the Russo-Ukrainian War, the paper highlights three pressing radar applications that urgently necessitate ATR technology: detecting stealth aircraft, countering small drones, and implementing anti-jamming measures. Anticipating the next wave of radar ATR research, the study predicts a surge in cognitive radar and machine learning (ML)-driven algorithms. These emerging methodologies aspire to confront challenges associated with system adaptation, real-time recognition, and environmental adaptability. Ultimately, ATR stands poised to revolutionize conventional radar systems, ushering in an era of 4D sensing capabilities.