pwc-archive/papers-with-abstracts huggingface dataset:
Mortgage Securitization Dynamics in the Aftermath of Natural Disasters: A Reply
Authors: Amine Ouazad, Matthew E. Kahn
Date: 2023-05-12 00:00:00

Climate change poses new risks for real estate assets. Given that the majority of home buyers use a loan to pay for their homes and the majority of these loans are purchased by the Government Sponsored Enterprises (GSEs), it is important to understand how rising natural disaster risk affects the mortgage finance market. The climate securitization hypothesis (CSH) posits that, in the aftermath of natural disasters, lenders strategically react to the GSEs conforming loan securitization rules that create incentives that foster both moral hazard and adverse selection effects. The climate risks bundled into GSE mortgage-backed securities emerge because of the complex securitization chain that creates weak monitoring and screening incentives. We survey the recent theoretical literature and empirical literature exploring screening incentive effects. Using regression discontinuity methods, we test key hypotheses presented in the securitization literature with a focus on securitization dynamics immediately after major hurricanes. Our evidence supports the CSH. We address the data construction issues posed by LaCour-Little et. al. and show that their concerns do not affect our main results. Under the current rules of the game, climate risks exacerbates the established lemons problem commonly found in loan securitization markets.
Mechanism Design without Rational Expectations
Authors: Giacomo Rubbini
Date: 2023-05-12 00:00:00

Is incentive compatibility still necessary for implementation if we relax the rational expectations assumption? This paper proposes a generalized model of implementation that does not assume agents hold rational expectations and characterizes the class of solution concepts requiring Bayesian Incentive Compatibility (BIC) for full implementation. Surprisingly, for a broad class of solution concepts, full implementation of functions still requires BIC even if rational expectations do not hold. This finding implies that some classical results, such as the impossibility of efficient bilateral trade (Myerson & Satterthwaite, 1983), hold for a broader range of non-equilibrium solution concepts, confirming their relevance even in boundedly rational setups.
Hail Mary Pass: Contests with Stochastic Progress
Authors: Chang Liu
Date: 2023-05-12 00:00:00
Tasks: Position

This paper studies the equilibrium behavior in contests with stochastic progress. Participants have access to a safe action that makes progress deterministically, but they can also take risky moves that stochastically influence their progress towards the goal and thus their relative position. In the unique well-behaved Markov perfect equilibrium of this dynamic contest, the follower drops out if the leader establishes a substantial lead, but resorts to "Hail Marys" beforehand: no matter how low the return of the risky move is, the follower undertakes in an attempt to catch up. Moreover, if the risky move has a medium return (between high and low), the leader will also adopt it when the follower is close to dropping out - an interesting preemptive motive. We also examine the impact of such equilibrium behavior on the optimal prize allocation.
Revealing Patterns of Symptomatology in Parkinson's Disease: A Latent Space Analysis with 3D Convolutional Autoencoders
Authors: E. Delgado de las Heras, F. J. Martinez-Murcia, I. A. Illán, C. Jiménez-Mesa, D. Castillo-Barnes, J. Ramírez, J. M. Górriz
Date: 2023-05-11 00:00:00
Tasks: Representation Learning

This work proposes the use of 3D convolutional variational autoencoders (CVAEs) to trace the changes and symptomatology produced by neurodegeneration in Parkinson's disease (PD). In this work, we present a novel approach to detect and quantify changes in dopamine transporter (DaT) concentration and its spatial patterns using 3D CVAEs on Ioflupane (FPCIT) imaging. Our approach leverages the power of deep learning to learn a low-dimensional representation of the brain imaging data, which then is linked to different symptom categories using regression algorithms. We demonstrate the effectiveness of our approach on a dataset of PD patients and healthy controls, and show that general symptomatology (UPDRS) is linked to a d-dimensional decomposition via the CVAE with R2>0.25. Our work shows the potential of representation learning not only in early diagnosis but in understanding neurodegeneration processes and symptomatology.
Theory of Periodically Time-Variant Linear Systems
Authors: Juan I. Bonetti, Agustín C. Galletto, Mario R. Hueda
Date: 2023-05-12 00:00:00

In this work we provide a mathematical framework to describe the periodically time variant (PTV) linear systems. We study their frequency-domain features to estimate the output bandwidth, a necessary value to obtain a suitable digital representation of such systems. In addition, we derive several interesting properties enabling useful equivalences to represent, simulate and compensate PTVs.
Rate-Splitting Multiple Access: The First Prototype and Experimental Validation of its Superiority over SDMA and NOMA
Authors: Xinze Lyu, Sundar Aditya, JungHoon Kim, Bruno Clerckx
Date: 2023-05-12 00:00:00
Tasks: Fairness

In multi-user multi-antenna communications, it is well-known in theory that Rate-Splitting Multiple Access (RSMA) can achieve a higher spectral efficiency than both Space Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA). However, an experimental evaluation of RSMA's performance, relative to SDMA and NOMA, is missing in the literature, which is essential to address the ongoing debate between RSMA and NOMA over which is better suited to handle most efficiently the available resources and interference in 6G. In this paper, we address this critical knowledge gap by realizing the first-ever RSMA prototype using software-defined radios. Through measurements using our prototype, we empirically solve the modulation and coding scheme limited sum throughput maximization problem for RSMA, SDMA and NOMA for the two-user multiple-input single-output (MISO) scenario over (a) different pairs of line-of-sight channels that vary in terms of their relative pathloss and spatial correlation, and with (b) different channel state information quality. We observe that RSMA achieves the highest sum throughput across all these cases, whereas SDMA and NOMA are effective only in some cases. Furthermore, RSMA also achieves better fairness at a higher sum throughput than both SDMA and NOMA.
Deep Learning for Asynchronous Massive Access with Data Frame Length Diversity
Authors: Yanna Bai, Wei Chen, Bo Ai, Petar Popovski
Date: 2023-05-12 00:00:00
Tasks: Action Detection, Activity Detection, compressed sensing, Deep Learning, Diversity

Grant-free non-orthogonal multiple access has been regarded as a viable approach to accommodate access for a massive number of machine-type devices with small data packets. The sporadic activation of the devices creates a multiuser setup where it is suitable to use compressed sensing in order to detect the active devices and decode their data. We consider asynchronous access of machine-type devices that send data packets of different frame sizes, leading to data length diversity. We address the composite problem of activity detection, channel estimation, and data recovery by posing it as a structured sparse recovery, having three-level sparsity caused by sporadic activity, symbol delay, and data length diversity. We approach the problem through approximate message passing with a backward propagation algorithm (AMP-BP), tailored to exploit the sparsity, and in particular the data length diversity. Moreover, we unfold the proposed AMP-BP into a model-driven network, termed learned AMP-BP (LAMP-BP), which enhances detection performance. The results show that the proposed LAMP-BP outperforms existing methods in activity detection and data recovery accuracy.
Doppler-Resilient Design of CAZAC Sequences for mmWave/THz Sensing Applications
Authors: Fan Zhang, Tianqi Mao, Zhaocheng Wang
Date: 2023-05-12 00:00:00

Ultra-high-resolution target sensing has emerged as a key enabler for various cutting-edge applications, which can be realized by utilizing the millimeter wave/terahertz frequencies. However, the extremely high operating frequency inevitably leads to significant Doppler shift effects, especially for high-mobility applications, causing the degradation of sensing performance with high false alarm rate. To this end, this paper proposes a parameter design methodology of the well-known constant amplitude zero auto correlation (CAZAC) sequences, which aims at enhancing their resilience to Doppler shifts. Specifically, we suppress the sidelobes incurred by Doppler shifts for the peak-to-sidelobe ratio (PSLR) improvement within the range of interest (RoI) of the radar range profile. The Zadoff-Chu (ZC) sequence, as a representative member in the CAZAC family, is firstly considered. The impacts of its root index on range sidelobes are investigated based on number theory. For an arbitrary-length ZC sequence, a feasible range of the root index is derived to satisfy the requirement of PSLR within the scope of RoI. Furthermore, these design guidelines are extended to a general form of CAZAC sequences, where a low-complexity heuristic algorithm is developed for PSLR improvement. Simulation results demonstrate that under severe Doppler shifts, our proposed methodology could enhance the sensing performance by lowering the false alarm rate while maintaining the same detection rate, compared with its classical counterpart.
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications
Authors: Guoshun Nan, Zhichun Li, Jinli Zhai, Qimei Cui, Gong Chen, Xin Du, Xuefei Zhang, Xiaofeng Tao, Zhu Han, Tony Q. S. Quek
Date: 2023-05-12 00:00:00
Tasks: Adversarial Robustness, Deep Learning, Semantic Communication

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare. However, robustifying semantic interpretation is challenging as ESC is extremely vulnerable to physical-layer adversarial attacks due to the openness of wireless channels and the fragileness of neural models. Toward ESC robustness in practice, we ask the following two questions: Q1: For attacks, is it possible to generate semantic-oriented physical-layer adversarial attacks that are imperceptible, input-agnostic and controllable? Q2: Can we develop a defense strategy against such semantic distortions and previously proposed adversaries? To this end, we first present MobileSC, a novel semantic communication framework that considers the computation and memory efficiency in wireless environments. Equipped with this framework, we propose SemAdv, a physical-layer adversarial perturbation generator that aims to craft semantic adversaries over the air with the abovementioned criteria, thus answering the Q1. To better characterize the realworld effects for robust training and evaluation, we further introduce a novel adversarial training method SemMixed to harden the ESC against SemAdv attacks and existing strong threats, thus answering the Q2. Extensive experiments on three public benchmarks verify the effectiveness of our proposed methods against various physical adversarial attacks. We also show some interesting findings, e.g., our MobileSC can even be more robust than classical block-wise communication systems in the low SNR regime.
Multi-State Inter-Satellite Channel Models
Authors: Eray Güven, Olfa Ben Yahia, Güneş Karabulut Kurt
Date: 2023-05-12 00:00:00

The advancement in satellite networks (SatNets) offers vast opportunities for large-scale connectivity and flexibility. The intersection of inter-satellite communication (ISC) and the developments in rate-hungry enhanced mobile broadband (eMBB) services have resulted in potential ultra-dense deployments such as mega-constellations. The integration of inter-satellite links (ISL) with high spectrum bands requires a well-defined channel model to anticipate possible challenges and provide solutions. In this research, we address the key concerns for defining SatNets channels and establish a comprehensive node-to-node ISL channel model for the Markov state model. The model takes into consideration satellite mobility, celestial body radiation, and cumulative thermal noise. Using this model, we analyze the bit error rate, outage, and achievable capacity of various ISC cases and propose a state-based power allocation method to improve their performance.
Sensing User's Channel and Location with Terahertz Extra-Large Reconfigurable Intelligent Surface under Hybrid-Field Beam Squint Effect
Authors: Zhuoran Li, Zhen Gao, Tuan Li
Date: 2023-05-12 00:00:00
Tasks: Super-Resolution

This paper investigates the sensing of user's uplink channel and location in terahertz extra-large reconfigurable intelligent surface (XL-RIS) systems, where the unique hybrid far-near field effect and the beam squint effect caused by the XL array aperture as well as the XL bandwidth are overcome. Specifically, we first propose a joint channel and location sensing scheme, which consists of a location-assisted generalized multiple measurement vector orthogonal matching pursuit (LA-GMMV-OMP) algorithm for channel estimation (CE) and a complete dictionary based localization (CDL) scheme, where a frequency selective polar-domain redundant dictionary is proposed to overcome the hybrid field beam squint effect. The CE module outputs coarse on-grid angle estimation (respectively observed from the BS and RIS) to the localization module, which returns the fine off-grid angle estimation to improve CE. Particularly, with RIS, CDL can obtain user's location via line intersection, and a polar-domain gradient descent (PGD) algorithm at the base station is proposed to achieve the off-grid angle estimation with super-resolution accuracy. Additionally, to further reduce the sensing overhead, we propose a partial dictionary-based localization scheme, which is decoupled from CE, where RIS is served as an anchor to lock the user on the hyperbola according to time difference of arrival and the user's off-grid location can be obtained by using the proposed PGD algorithm. Simulation results demonstrate the superiority of the two proposed localization schemes and the proposed CE scheme over state-of-the-art baseline approaches.
Survey on Integrated Sensing and Communication Performance Modeling and Use Cases Feasibility
Authors: Silvio Mandelli, Marcus Henninger, Maximilian Bauhofer, Thorsten Wild
Date: 2023-05-11 00:00:00
Tasks: Integrated sensing and communication, ISAC

As the research community starts to address the* key features of 6G cellular standards, one of the agreed bridge topics to be studied already in 5G advanced releases is Integrated Sensing and Communication (ISAC). The first efforts of the research community are focusing on ISAC enablers, fundamental limits, and first demonstrators, that show that the time has come for the deployment of sensing functionalities in cellular standards. This survey paper takes a needed step towards ISAC deployment, providing an analytical toolkit to model cellular systems' sensing performance, accounting for both their fundamental and practical constraints. We then elaborate on the likely features of 6G systems to provide the feasible sensing key performance indicators (KPIs) in the frequency ranges spanned by cellular networks, including the potential new bands available in 6G, the Frequency Range 3 (FR3). We further validate our framework by visually investigating ISAC constraints with simulation examples. Finally, we assess the feasibility of few selected scenarios that can be enabled by ISAC, highlighting in each of them the limiting factor and, thus, which gaps should be filled by the research and standardization communities in the next years.
MCRB on DOA Estimation for Automotive MIMO Radar in the Presence of Multipath
Authors: Moshe Levy-Israel, Igal Bilik, Joseph Tabrikian
Date: 2023-05-11 00:00:00
Tasks: Autonomous Driving

Autonomous driving and advanced active safety features require accurate high-resolution sensing capabilities. Automotive radars are the key component of the vehicle sensing suit. However, when these radars operate in proximity to flat surfaces, such as roads and guardrails, they experience a multipath phenomenon that can degrade the accuracy of the direction-of-arrival (DOA) estimation. Presence of multipath leads to misspecification in the radar data model, resulting in estimation performance degradation, which cannot be reliably predicted by conventional performance bounds. In this paper, the misspecified Cram\'er-Rao bound (MCRB), which accounts for model misspecification, is derived for the problem of DOA estimation in the presence of multipath which is ignored by the estimator. Analytical relations between the MCRB and the Cram\'er-Rao bound are established, and the DOA estimation performance degradation due to multipath is investigated. The results show that the MCRB reliably predicts the asymptotic performance of the misspecified maximum-likelihood estimator and therefore, can serve as an efficient tool for automotive radar performance evaluation and system design.
In Silico Approaches to Deliver Better Antibodies by Design: The Past, the Present and the Future
Authors: Andreas Evers, Shipra Malhotra, Vanita D. Sood
Date: 2023-05-12 00:00:00
Tasks: Drug Design

The recognition of the importance of drug-like properties beyond potency to reduce clinical attrition of biologics has driven significant progress in the development of in vitro and in silico tools for developability assessment of antibody sequences. It is now routine to identify and eliminate or optimize antibody hits with poor developability profiles. To further accelerate discovery timelines and reduce clinical and non-clinical development attrition rates, more proactive in silico approaches to design sequence spaces with favorable developability profiles are required. From pragmatically front-loading structure based drug design for developability, to combining next generation sequencing with machine learning to shape screening libraries, to adapting the use of artificial intelligence and deep learning for immunoglobulins, we review herein progressively more proactive approaches to developability by design.
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
Authors: Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma
Date: 2023-05-11 00:00:00
Tasks: 3D Molecule Generation

Deep generative models have recently achieved superior performance in 3D molecule generation. Most of them first generate atoms and then add chemical bonds based on the generated atoms in a post-processing manner. However, there might be no corresponding bond solution for the temporally generated atoms as their locations are generated without considering potential bonds. We define this problem as the atom-bond inconsistency problem and claim it is the main reason for current approaches to generating unrealistic 3D molecules. To overcome this problem, we propose a new diffusion model called MolDiff which can generate atoms and bonds simultaneously while still maintaining their consistency by explicitly modeling the dependence between their relationships. We evaluated the generation ability of our proposed model and the quality of the generated molecules using criteria related to both geometry and chemical properties. The empirical studies showed that our model outperforms previous approaches, achieving a three-fold improvement in success rate and generating molecules with significantly better quality.
Semantic Processing of Political Words in Naturalistic Information Differs by Political Orientation
Authors: Shuhei Kitamura, Aya S. Ihara
Date: 2023-05-12 00:00:00

Worldviews may differ significantly according to political orientation. Even a single word can have a completely different meaning depending on political orientation. However, no direct evidence has been obtained on differences in the semantic processing of single words in naturalistic information between individuals with different political orientations. The present study aimed to fill this gap. We measured electroencephalographic signals while participants with different political orientations listened to naturalistic content. Responses for moral-, ideology-, and policy-related words between and within the participant groups were then compared. Within-group comparisons showed that right-leaning participants reacted more to moral-related words than to policy-related words, while left-leaning participants reacted more to policy-related words than to moral-related words. In addition, between-group comparisons also showed that neural responses for moral-related words were greater in right-leaning participants than in left-leaning participants and those for policy-related words were lesser in right-leaning participants than in neutral participants. There was a significant correlation between the predicted and self-reported political orientations. In summary, the study found that people with different political orientations differ in semantic processing at the level of a single word. These findings have implications for understanding the mechanisms of political polarization and for making policy messages more effective.
VoDEx: a Python library for time annotation and management of volumetric functional imaging data
Authors: Anna Nadtochiy, Peter Luu, Scott E. Fraser, Thai V. Truong
Date: 2023-05-11 00:00:00
Tasks: Management, Retrieval

Summary: In functional imaging studies, accurately synchronizing the time course of experimental manipulations and stimulus presentations with resulting imaging data is crucial for analysis. Current software tools lack such functionality, requiring manual processing of the experimental and imaging data, which is error-prone and potentially non-reproducible. We present VoDEx, an open-source Python library that streamlines the data management and analysis of functional imaging data. VoDEx synchronizes the experimental timeline and events (eg. presented stimuli, recorded behavior) with imaging data. VoDEx provides tools for logging and storing the timeline annotation, and enables retrieval of imaging data based on specific time-based and manipulation-based experimental conditions. Availability and Implementation: VoDEx is an open-source Python library and can be installed via the "pip install" command. It is released under a BSD license, and its source code is publicly accessible on GitHub https://github.com/LemonJust/vodex. A graphical interface is available as a napari-vodex plugin, which can be installed through the napari plugins menu or using "pip install." The source code for the napari plugin is available on GitHub https://github.com/LemonJust/napari-vodex.