pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Regression Discontinuity: An Event-Study Approach
Authors: Francesco Ruggieri
Date: 2023-07-26 00:00:00
Tasks: regression

I propose a novel argument to identify economically interpretable intertemporal treatment effects in dynamic regression discontinuity designs (RDDs). Specifically, I develop a dynamic potential outcomes model and reformulate two assumptions from the difference-in-differences literature, no anticipation and common trends, to attain point identification of cutoff-specific impulse responses. The estimand of each target parameter can be expressed as the sum of two static RDD contrasts, thereby allowing for nonparametric estimation and inference with standard local polynomial methods. I also propose a nonparametric approach to aggregate treatment effects across calendar time and treatment paths, leveraging a limited path independence restriction to reduce the dimensionality of the parameter space. I apply this method to estimate the dynamic effects of school district expenditure authorizations on housing prices in Wisconsin.
Using Probabilistic Stated Preference Analyses to Understand Actual Choices
Authors: Romuald Meango
Date: 2023-07-26 00:00:00
Tasks: counterfactual

Can stated preferences help in counterfactual analyses of actual choice? This research proposes a novel approach to researchers who have access to both stated choices in hypothetical scenarios and actual choices. The key idea is to use probabilistic stated choices to identify the distribution of individual unobserved heterogeneity, even in the presence of measurement error. If this unobserved heterogeneity is the source of endogeneity, the researcher can correct for its influence in a demand function estimation using actual choices, and recover causal effects. Estimation is possible with an off-the-shelf Group Fixed Effects estimator.
The Core of Bayesian Persuasion
Authors: Laura Doval, Ran Eilat
Date: 2023-07-25 00:00:00

An analyst observes the frequency with which an agent takes actions, but not the frequency with which she takes actions conditional on a payoff relevant state. In this setting, we ask when the analyst can rationalize the agent's choices as the outcome of the agent learning something about the state before taking action. Our characterization marries the obedience approach in information design (Bergemann and Morris, 2016) and the belief approach in Bayesian persuasion (Kamenica and Gentzkow, 2011) relying on a theorem by Strassen (1965) and Hall's marriage theorem. We apply our results to ring-network games and to identify conditions under which a data set is consistent with a public information structure in first-order Bayesian persuasion games.
It's Not Always the Leader's Fault: How Informed Followers Can Undermine Efficient Leadership
Authors: Panagiotis Kyriazis, Edmund Lou
Date: 2023-07-25 00:00:00
Tasks: Decision Making

Coordination facilitation and efficient decision-making are two essential components of successful leadership. In this paper, we take an informational approach and investigate how followers' information impacts coordination and efficient leadership in a model featuring a leader and a team of followers. We show that efficiency is achieved as the unique rationalizable outcome of the game when followers possess sufficiently imprecise information. In contrast, if followers have accurate information, the leader may fail to coordinate them toward the desired outcome or even take an inefficient action herself. We discuss the implications of the results for the role of leaders in the context of financial fragility and crises.
NeuroHeed: Neuro-Steered Speaker Extraction using EEG Signals
Authors: Zexu Pan, Marvin Borsdorf, Siqi Cai, Tanja Schultz, Haizhou Li
Date: 2023-07-26 00:00:00
Tasks: EEG

Humans possess the remarkable ability to selectively attend to a single speaker amidst competing voices and background noise, known as selective auditory attention. Recent studies in auditory neuroscience indicate a strong correlation between the attended speech signal and the corresponding brain's elicited neuronal activities, which the latter can be measured using affordable and non-intrusive electroencephalography (EEG) devices. In this study, we present NeuroHeed, a speaker extraction model that leverages EEG signals to establish a neuronal attractor which is temporally associated with the speech stimulus, facilitating the extraction of the attended speech signal in a cocktail party scenario. We propose both an offline and an online NeuroHeed, with the latter designed for real-time inference. In the online NeuroHeed, we additionally propose an autoregressive speaker encoder, which accumulates past extracted speech signals for self-enrollment of the attended speaker information into an auditory attractor, that retains the attentional momentum over time. Online NeuroHeed extracts the current window of the speech signals with guidance from both attractors. Experimental results demonstrate that NeuroHeed effectively extracts brain-attended speech signals, achieving high signal quality, excellent perceptual quality, and intelligibility in a two-speaker scenario.
Sound Field Estimation around a Rigid Sphere with Physics-informed Neural Network
Authors: Xingyu Chen, Fei Ma, Amy Bastine, Prasanga Samarasinghe, Huiyuan Sun
Date: 2023-07-26 00:00:00

Accurate estimation of the sound field around a rigid sphere necessitates adequate sampling on the sphere, which may not always be possible. To overcome this challenge, this paper proposes a method for sound field estimation based on a physics-informed neural network. This approach integrates physical knowledge into the architecture and training process of the network. In contrast to other learning-based methods, the proposed method incorporates additional constraints derived from the Helmholtz equation and the zero radial velocity condition on the rigid sphere. Consequently, it can generate physically feasible estimations without requiring a large dataset. In contrast to the spherical harmonic-based method, the proposed approach has better fitting abilities and circumvents the ill condition caused by truncation. Simulation results demonstrate the effectiveness of the proposed method in achieving accurate sound field estimations from limited measurements, outperforming the spherical harmonic method and plane-wave decomposition method.
Understanding and Tackling Scattering and Reflective Flare for Mobile Camera Systems
Authors: Fengbo Lan, Chang Wen Chen
Date: 2023-07-26 00:00:00
Tasks: Denoising, Flare Removal

The rise of mobile devices has spurred advancements in camera technology and image quality. However, mobile photography still faces issues like scattering and reflective flares. While previous research has acknowledged the negative impact of the mobile devices' internal image signal processing pipeline (ISP) on image quality, the specific ISP operations that hinder flare removal have not been fully identified. In addition, current solutions only partially address ISP-related deterioration due to a lack of comprehensive raw image datasets for flare study. To bridge these research gaps, we introduce a new raw image dataset tailored for mobile camera systems, focusing on eliminating flare. This dataset encompasses over 2,000 high-quality, full-resolution raw image pairs for scattering flare, and 1,200 for reflective flare, captured across various real-world scenarios, mobile devices, and camera settings. It is designed to enhance the generalizability of flare removal algorithms across a wide spectrum of conditions. Through detailed experiments, we have identified that ISP operations, such as denoising, compression, and sharpening, may either improve or obstruct flare removal, offering critical insights into optimizing ISP configurations for better flare mitigation. Our dataset is poised to advance the understanding of flare-related challenges, enabling more precise incorporation of flare removal steps into the ISP.
Video Decoding Energy Estimation Using Processor Events
Authors: Christian Herglotz, André Kaup
Date: 2023-07-26 00:00:00
Tasks: Decoder

In this paper, we show that processor events like instruction counts or cache misses can be used to accurately estimate the processing energy of software video decoders. Therefore, we perform energy measurements on an ARM-based evaluation platform and count processor level events using a dedicated profiling software. Measurements are performed for various codecs and decoder implementations to prove the general viability of our observations. Using the estimation method proposed in this paper, the true decoding energy for various recent video coding standards including HEVC and VP9 can be estimated with a mean estimation error that is smaller than 6%.
Multi-UAV Enabled Integrated Sensing and Wireless Powered Communication: A Robust Multi-Objective Approach
Authors: Omid Rezaei, Mohammad Mahdi Naghsh, Seyed Mohammad Karbasi, Mohammad Mahdi Nayebi
Date: 2023-07-26 00:00:00
Tasks: Integrated sensing and communication, ISAC, Scheduling

In this paper, we consider an integrated sensing and communication (ISAC) system with wireless power transfer (WPT) where multiple unmanned aerial vehicle (UAV)-based radars serve multiple clusters of energy-limited communication users in addition to their sensing functionality. In this architecture, the radars sense the environment in phase 1 (namely sensing phase) and meanwhile, the communications users (nodes) harvest and store the energy from the radar transmit signals. The stored energy is then used for information transmission from the nodes to UAVs in phase 2, i.e., uplink phase. Performance of the radar systems depends on the transmit signals as well as the receive filters; the energy of the transmit signals also affects the communication network because it serves as the source of uplink powers. Therefore, we cast a multi-objective design problem addressing performance of both radar and communication systems via optimizing UAV trajectories, radar transmit waveforms, radar receive filters, time scheduling and uplink powers. The design problem is further formulated as a robust non-convex optimization problem taking into account the the user location uncertainty. Hence, we devise a method based on alternating optimization followed by concepts of fractional programming, S-procedure, and tricky majorization-minimization (MM) technique to tackle it. Numerical examples illustrate the effectiveness of the proposed method for different scenarios.
A Phase-Coded Time-Domain Interleaved OTFS Waveform with Improved Ambiguity Function
Authors: Jiajun Zhu, Yanqun Tang, Chao Yang, Chi Zhang, Haoran Yin, Jiaojiao Xiong, Yuhua Chen
Date: 2023-07-26 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is a significant application scenario in future wireless communication networks, and sensing capability of a waveform is always evaluated by the ambiguity function. To enhance the sensing performance of the orthogonal time frequency space (OTFS) waveform, we propose a novel time-domain interleaved cyclic-shifted P4-coded OTFS (TICP4-OTFS) with improved ambiguity function. TICP4-OTFS can achieve superior autocorrelation features in both the time and frequency domains by exploiting the multicarrier-like form of OTFS after interleaved and the favorable autocorrelation attributes of the P4 code. Furthermore, we present the vectorized formulation of TICP4-OTFS modulation as well as its signal structure in each domain. Numerical simulations show that our proposed TICP4-OTFS waveform outperforms OTFS with a narrower mainlobe as well as lower and more distant sidelobes in terms of delay and Doppler-dimensional ambiguity functions, and an instance of range estimation using pulse compression is illustrated to exhibit the proposed waveform\u2019s greater resolution. Besides, TICP4-OTFS achieves better performance of bit error rate for communication in low signal-to-noise ratio (SNR) scenarios.
Distributed Filtering Design with Enhanced Resilience to Coordinated Byzantine Attacks
Authors: Ashkan Moradi, Vinay Chakravarthi Gogineni, Naveen K. D. Venkategowda, Stefan Werner
Date: 2023-07-26 00:00:00

This paper proposes a Byzantine-resilient consensus-based distributed filter (BR-CDF) wherein network agents employ partial sharing of state parameters. We characterize the performance and convergence of the BR-CDF and study the impact of a coordinated data falsification attack. Our analysis shows that sharing merely a fraction of the states improves robustness against coordinated Byzantine attacks. In addition, we model the optimal attack strategy as an optimization problem where Byzantine agents design their attack covariance or the sequence of shared fractions to maximize the network-wide mean squared error (MSE). Numerical results demonstrate the accuracy of the proposed BR-CDF and its robustness against Byzantine attacks. Furthermore, the simulation results show that the influence of the covariance design is more pronounced when agents exchange larger portions of their states with neighbors. In contrast, the performance is more sensitive to the sequence of shared fractions when smaller portions are exchanged.
Interest rate convexity in a Gaussian framework
Authors: Antoine Jacquier, Mugad Oumgari
Date: 2023-07-26 00:00:00

The contributions of this paper are twofold: we define and investigate the properties of a short rate model driven by a general Gaussian Volterra process and, after defining precisely a notion of convexity adjustment, derive explicit formulae for it.
Unravelling the fluorescence kinetics of light-harvesting proteins with simulated measurements
Authors: Callum Gray, Lekshmi Kailas, Peter G. Adams, Christopher D. P. Duffy
Date: 2023-07-26 00:00:00

The plant light-harvesting pigment-protein complex LHCII is the major antenna sub-unit of PSII and is generally (though not universally) accepted to play a role in photoprotective energy dissipation under high light conditions, a process known Non-Photochemical Quenching (NPQ). The underlying mechanisms of energy trapping and dissipation within LHCII are still debated. Various proposed models differ considerably in their molecular and kinetic detail, but are often based on different interpretations of very similar transient absorption measurements of isolated complexes. Here we present a simulated measurement of the fluorescence decay kinetics of quenched LHCII aggregates to determine whether this relatively simple measurement can discriminate between different potential NPQ mechanisms. We simulate not just the underlying physics (excitation, energy migration, quenching and singlet-singlet annihilation) but also the signal detection and typical experimental data analysis. Comparing this to a selection of published fluorescence decay kinetics we find that: (1) Different proposed quenching mechanisms produce noticeably different fluorescence kinetics even at low (annihilation free) excitation density, though the degree of difference is dependent on pulse width. (2) Measured decay kinetics are consistent with most LHCII trimers becoming relatively slow excitation quenchers. A small sub-population of very fast quenchers produces kinetics which do not resemble any observed measurement. (3) It is necessary to consider at least two distinct quenching mechanisms in order to accurately reproduce experimental kinetics, supporting the idea that NPQ is not a simple binary switch switch.
Learning heterogeneous delays in a layer of spiking neurons for fast motion detection
Authors: Antoine Grimaldi, Laurent U Perrinet
Date: 2023-07-26 00:00:00
Tasks: Motion Detection

The precise timing of spikes emitted by neurons plays a crucial role in shaping the response of efferent biological neurons. This temporal dimension of neural activity holds significant importance in understanding information processing in neurobiology, especially for the performance of neuromorphic hardware, such as event-based cameras. Nonetheless, many artificial neural models disregard this critical temporal dimension of neural activity. In this study, we present a model designed to efficiently detect temporal spiking motifs using a layer of spiking neurons equipped with heterogeneous synaptic delays. Our model capitalizes on the diverse synaptic delays present on the dendritic tree, enabling specific arrangements of temporally precise synaptic inputs to synchronize upon reaching the basal dendritic tree. We formalize this process as a time-invariant logistic regression, which can be trained using labeled data. To demonstrate its practical efficacy, we apply the model to naturalistic videos transformed into event streams, simulating the output of the biological retina or event-based cameras. To evaluate the robustness of the model in detecting visual motion, we conduct experiments by selectively pruning weights and demonstrate that the model remains efficient even under significantly reduced workloads. In conclusion, by providing a comprehensive, event-driven computational building block, the incorporation of heterogeneous delays has the potential to greatly improve the performance of future spiking neural network algorithms, particularly in the context of neuromorphic chips.
The Impact of Genomic Variation on Function (IGVF) Consortium
Authors: IGVF Consortium
Date: 2023-07-24 00:00:00
Tasks: Benchmarking

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations.
American options in time-dependent one-factor models: Semi-analytic pricing, numerical methods and ML support
Authors: Andrey Itkin, Dmitry Muravey
Date: 2023-07-26 00:00:00

Semi-analytical pricing of American options in a time-dependent Ornstein-Uhlenbeck model was presented in [Carr, Itkin, 2020]. It was shown that to obtain these prices one needs to solve (numerically) a nonlinear Volterra integral equation of the second kind to find the exercise boundary (which is a function of the time only). Once this is done, the option prices follow. It was also shown that computationally this method is as efficient as the forward finite difference solver while providing better accuracy and stability. Later this approach called "the Generalized Integral transform" method has been significantly extended by the authors (also, in cooperation with Peter Carr and Alex Lipton) to various time-dependent one factor, and stochastic volatility models as applied to pricing barrier options. However, for American options, despite possible, this was not explicitly reported anywhere. In this paper our goal is to fill this gap and also discuss which numerical method (including those in machine learning) could be efficient to solve the corresponding Volterra integral equations.
Capital Structure Theories and its Practice, A study with reference to select NSE listed public sectors banks, India
Authors: Kurada T S S Satyanarayana, Addada Narasimha Rao
Date: 2023-07-26 00:00:00
Tasks: Management

Among the various factors affecting the firms positioning and performance in modern day markets, capital structure of the firm has its own way of expressing itself as a crucial one. With the rapid changes in technology, firms are being pushed onto a paradigm that is burdening the capital management process. Hence the study of capital structure changes gives the investors an insight into firm's behavior and intrinsic goals. These changes will vary for firms in different sectors. This work considers the banking sector, which has a unique capital structure for the given regulations of its operations in India. The capital structure behavioral changes in a few public sector banks are studied in this paper. A theoretical framework has been developed from the popular capital structure theories and hypotheses are derived from them accordingly. The main idea is to validate different theories with real time performance of the select banks from 2011 to 2022. Using statistical techniques like regression and correlation, tested hypotheses have resulted in establishing the relation between debt component and financial performance variables of the select banks which are helping in understanding the theories in practice.
Macroscopic Market Making
Authors: Ivan Guo, Shijia Jin, Kihun Nam
Date: 2023-07-26 00:00:00
Tasks: Point Processes

We propose a macroscopic market making model \`a la Avellaneda-Stoikov, using continuous processes for orders instead of discrete point processes. The model intends to bridge the gap between market making and optimal execution problems, while shedding light on the influence of order flows on the optimal strategies. We demonstrate our model through three problems. The study provides a comprehensive analysis from Markovian to non-Markovian noises and from linear to non-linear intensity functions, encompassing both bounded and unbounded coefficients. Mathematically, the contribution lies in the existence and uniqueness of the optimal control, guaranteed by the well-posedness of the strong solution to the Hamilton-Jacobi-Bellman equation and the (non-)Lipschitz forward-backward stochastic differential equation. Finally, the model's applications to price impact and optimal execution are discussed.
Multi-Factor Inception: What to Do with All of These Features?
Authors: Tom Liu, Stefan Zohren
Date: 2023-07-25 00:00:00
Tasks: All

Cryptocurrency trading represents a nascent field of research, with growing adoption in industry. Aided by its decentralised nature, many metrics describing cryptocurrencies are accessible with a simple Google search and update frequently, usually at least on a daily basis. This presents a promising opportunity for data-driven systematic trading research, where limited historical data can be augmented with additional features, such as hashrate or Google Trends. However, one question naturally arises: how to effectively select and process these features? In this paper, we introduce Multi-Factor Inception Networks (MFIN), an end-to-end framework for systematic trading with multiple assets and factors. MFINs extend Deep Inception Networks (DIN) to operate in a multi-factor context. Similar to DINs, MFIN models automatically learn features from returns data and output position sizes that optimise portfolio Sharpe ratio. Compared to a range of rule-based momentum and reversion strategies, MFINs learn an uncorrelated, higher-Sharpe strategy that is not captured by traditional, hand-crafted factors. In particular, MFIN models continue to achieve consistent returns over the most recent years (2022-2023), where traditional strategies and the wider cryptocurrency market have underperformed.