pwc-archive/papers-with-abstracts huggingface dataset:
Cut-set and Stability Constrained Optimal Power Flow for Resilient Operation During Wildfires
Authors: Satyaprajna Sahoo, Anamitra Pal
Date: 2023-11-09 00:00:00
Tasks: ARC

Resilient operation of the power system during ongoing wildfires is challenging because of the uncertain ways in which the fires impact the electric power infrastructure (multiple arc-faults, complete melt-down). To address this challenge, we propose a novel cut-set and stability-constrained optimal power flow (OPF) that quickly mitigates both static and dynamic insecurities as wildfires progress through a region. First, a Feasibility Test (FT) algorithm that quickly desaturates overloaded cut-sets to prevent cascading line outages is integrated with the OPF problem. Then, the resulting formulation is combined with a data-driven transient stability analyzer that predicts the correction factors for eliminating dynamic insecurities. The proposed model considers the possibility of generation rescheduling as well as load shed. The results obtained using the IEEE 118-bus system indicate that the proposed approach alleviates vulnerability of the system to wildfires while minimizing operational cost.
Minimal Input Structural Modifications for Strongly Structural Controllability
Authors: Geethu Joseph, Shana Moothedath, Jiabin Lin
Date: 2023-11-08 00:00:00

This paper studies the problem of modifying the input matrix of a structured system to make the system strongly structurally controllable. We focus on the generalized structured systems that rely on zero/nonzero/arbitrary structure, i.e., some entries of system matrices are zeros, some are nonzero, and the remaining entries can be zero or nonzero (arbitrary). We analyze the feasibility of the problem, and if it is feasible, we reformulate it into another equivalent problem. This new formulation leads to a greedy heuristic algorithm. However, we also show that the greedy algorithm can give arbitrarily poor solutions for some special systems. Our alternative approach is a randomized Markov chain Monte Carlo-based algorithm. Unlike the greedy algorithm, this algorithm is guaranteed to converge to an optimal solution with high probability. Finally, we numerically evaluate the algorithms on random graphs to show that the algorithms perform well.
Multi-fidelity Bayesian Optimisation of Syngas Fermentation Simulators
Authors: Mahdi Eskandari, Lars Puiman, Jakob Zeitler
Date: 2023-11-06 00:00:00
Tasks: Bayesian Optimisation, Bayesian Optimization

A Bayesian optimization approach for maximizing the gas conversion rate in an industrial-scale bioreactor for syngas fermentation is presented. We have access to a high-fidelity, computational fluid dynamic (CFD) reactor model and a low-fidelity ideal-mixing-based reactor model. The goal is to maximize the gas conversion rate, with respect to the input variables (e.g., pressure, biomass concentration, gas flow rate). Due to the high cost of the CFD reactor model, a multi-fidelity Bayesian optimization algorithm is adopted to solve the optimization problem using both high and low fidelities. We first describe the problem in the context of syngas fermentation followed by our approach to solving simulator optimization using multiple fidelities. We discuss concerns regarding significant differences in fidelity cost and their impact on fidelity sampling and conclude with a discussion on the integration of real-world fermentation data.
Discrete synaptic events induce global oscillations in balanced neural networks
Authors: Denis S. Goldobin, Matteo di Volo, Alessandro Torcini
Date: 2023-11-10 00:00:00

Neural dynamics is triggered by discrete synaptic inputs of finite amplitude. However, the neural response is usually obtained within the diffusion approximation (DA) representing the synaptic inputs as Gaussian noise. We derive a mean-field formalism encompassing synaptic shot-noise for sparse balanced networks of spiking neurons. For low (high) external drives (synaptic strengths) irregular global oscillations emerge via continuous and hysteretic transitions, correctly predicted by our approach, but not from the DA. These oscillations display frequencies in biologically relevant bands.
Time-Varying Identification of Monetary Policy Shocks
Authors: Annika Camehl, Tomasz Woźniak
Date: 2023-11-10 00:00:00

We propose a new Bayesian heteroskedastic Markov-switching structural vector autoregression with data-driven time-varying identification. The model selects alternative exclusion restrictions over time and, as a condition for the search, allows to verify identification through heteroskedasticity within each regime. Based on four alternative monetary policy rules, we show that a monthly six-variable system supports time variation in US monetary policy shock identification. In the sample-dominating first regime, systematic monetary policy follows a Taylor rule extended by the term spread, effectively curbing inflation. In the second regime, occurring after 2000 and gaining more persistence after the global financial and COVID crises, it is characterized by a money-augmented Taylor rule. This regime's unconventional monetary policy provides economic stimulus, features the liquidity effect, and is complemented by a pure term spread shock. Absent the specific monetary policy of the second regime, inflation would be over one percentage point higher on average after 2008.
Empirical Review of Youth-Employment Programs in Ghana
Authors: Monica Lambon-Quayefio, Thomas Yeboah, Nkechi S. Owoo, Marjan Petreski, Catherine Koranchie, Edward Asiedu, Mohammed Zakaria, Ernest Berko, Yaw Nsiah Agyemang
Date: 2023-11-10 00:00:00

Ghana-s current youth unemployment rate is 19.7%, and the country faces a significant youth unemployment problem. While a range of youth-employment programs have been created over the years, no systematic documentation and evaluation of the impacts of these public initiatives has been undertaken. Clarifying which interventions work would guide policy makers in creating strategies and programs to address the youth-employment challenge. By complementing desk reviews with qualitative data gathered from focus-group discussions and key informant interviews, we observe that most youth-employment programs implemented in Ghana cover a broad spectrum that includes skills training, job placement matching, seed capital, and subsidies. Duplication of initiatives, lack of coordination, and few to non-existent impact evaluations of programs are the main challenges that plague these programs. For better coordination and effective policy making, a more centralized and coordinated system is needed for program design and implementation. Along the same lines, ensuring rigorous evaluation of existing youth-employment programs is necessary to provide empirical evidence of the effectiveness and efficiency of these programs.
Optimal taxation and the Domar-Musgrave effect
Authors: Brendan K. Beare, Alexis Akira Toda
Date: 2023-11-10 00:00:00

This article concerns the optimal choice of flat taxes on labor and capital income, and on consumption, in a tractable economic model. Agents manage a portfolio of bonds and physical capital while subject to idiosyncratic investment risk and random mortality. We identify the tax rates which maximize welfare in stationary equilibrium while preserving tax revenue, finding that a very large increase in welfare can be achieved by only taxing capital income and consumption. The optimal rate of capital income taxation is zero if the natural borrowing constraint is strictly binding on entrepreneurs, but may otherwise be positive and potentially large. The Domar-Musgrave effect, whereby capital income taxation with full offset provisions encourages risky investment through loss sharing, explains cases where it is optimal to tax capital income. In further analysis we study the dynamic response to the substitution of consumption taxation for labor income taxation. We find that consumption immediately drops before rising rapidly to the new stationary equilibrium, which is higher on average than initial consumption for workers but lower for entrepreneurs.
Patience ensures fairness
Authors: Florian Brandl, Andrew Mackenzie
Date: 2023-11-10 00:00:00
Tasks: Fairness

We revisit the problem of fairly allocating a sequence of time slots when agents may have different levels of patience (Mackenzie and Komornik 2023). For each number of agents, we provide a lower threshold and an upper threshold on the level of patience such that (i) if each agent is at least as patient as the lower threshold, then there is a proportional allocation, and (ii) if each agent is at least as patient as the upper threshold and moreover has weak preference for earlier time slots, then there is an envy-free allocation. In both cases, the proof is constructive.
Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images
Authors: Juhwan Lee, Justin N. Kim, Luis A. P. Dallan, Vladislav N. Zimin, Ammar Hoori, Neda S. Hassani, Mohamed H. E. Makhlouf, Giulio Guagliumi, Hiram G. Bezerra, David L. Wilson
Date: 2023-11-10 00:00:00
Tasks: Segmentation, Sensitivity, Shadow Detection, Transfer Learning

Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries. Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,theta) images. Data were augmented in a natural way by changing theta in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837+/-0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0+/-0.3%, Dice: 0.846+/-0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95+/-20.73 um), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9+/-128.0 deg / 202.0+/-121.1 deg). Our method will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.
YOLOv5s-BC: An improved YOLOv5s-based method for real-time apple detection
Authors: Jingfan Liu, Zhaobing Liu
Date: 2023-11-10 00:00:00

To address the issues associated with the existing algorithms for the current apple detection, this study proposes an improved YOLOv5s-based method, named YOLOv5s-BC, for real-time apple detection, in which a series of modifications have been introduced. Firstly, a coordinate attention (CA) block has been incorporated into the backbone module to construct a new backbone network. Secondly, the original concatenation operation has been replaced with a bidirectional feature pyramid network (BiFPN) in the neck module. Lastly, a new detection head has been added to the head module, enabling the detection of smaller and more distant targets within the field of view of the robot. The proposed YOLOv5s-BC model was compared to several target detection algorithms, including YOLOv5s, YOLOv4, YOLOv3, SSD, Faster R-CNN (ResNet50), and Faster R-CNN (VGG), with significant improvements of 4.6%, 3.6%, 20.48%, 23.22%, 15.27%, and 15.59% in mAP, respectively. The detection accuracy of the proposed model is also greatly enhanced over the original YOLOv5s model. The model boasts an average detection speed of 0.018 seconds per image, and the weight size is only 16.7 Mb with 4.7 Mb smaller than that of YOLOv8s, meeting the real-time requirements for the picking robot. Furthermore, according to the heat map, our proposed model can focus more on and learn the high-level features of the target apples, and recognize the smaller target apples better than the original YOLOv5s model. Then, in other apple orchard tests, the model can detect the pickable apples in real time and correctly, illustrating a decent generalization ability.
Passive Integrated Sensing and Communication Scheme based on RF Fingerprint Information Extraction for Cell-Free RAN
Authors: Jingxuan Yu, Fan Zeng, Jiamin Li, Feiyang Liu, Pengcheng Zhu, Dongming Wang, Xiaohu You
Date: 2023-11-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper investigates how to achieve integrated sensing and communication (ISAC) based on a cell-free radio access network (CF-RAN) architecture with a minimum footprint of communication resources. We propose a new passive sensing scheme. The scheme is based on the radio frequency (RF) fingerprint learning of the RF radio unit (RRU) to build an RF fingerprint library of RRUs. The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side. The receiver extracts the channel parameters from the signal and estimates the channel environment, thus locating the reflectors in the environment. The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture. Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.
38.7 GHz Thin Film Lithium Niobate Acoustic Filter
Authors: Omar Barrera, Sinwoo Cho, Jack Kramer, Vakhtang Chulukhadze, Joshua Campbell, Ruochen Lu
Date: 2023-11-09 00:00:00

In this work, a 38.7 GHz acoustic wave ladder filter exhibiting insertion loss (IL) of 5.63 dB and 3-dB fractional bandwidth (FBW) of 17.6% is demonstrated, pushing the frequency limits of thin-film piezoelectric acoustic filter technology. The filter achieves operating frequency up to 5G millimeter wave (mmWave) frequency range 2 (FR2) bands, by thinning thin-film LiNbO3 resonators to sub-50 nm thickness. The high electromechanical coupling (k2) and quality factor (Q) of first-order antisymmetric (A1) mode resonators in 128 Y-cut lithium niobate (LiNbO3) collectively enable the first acoustic filters at mmWave. The key design consideration of electromagnetic (EM) resonances in interdigitated transducers (IDT) is addressed and mitigated. These results indicate that thin-film piezoelectric resonators could be pushed to 5G FR2 bands. Further performance enhancement and frequency scaling calls for better resonator technologies and EM-acoustic filter co-design.
Optimal dividend strategies for a catastrophe insurer
Authors: Hansjoerg Albrecher, Pablo Azcue, Nora Muler
Date: 2023-11-09 00:00:00

In this paper we study the problem of optimally paying out dividends from an insurance portfolio, when the criterion is to maximize the expected discounted dividends over the lifetime of the company and the portfolio contains claims due to natural catastrophes, modelled by a shot-noise Cox claim number process. The optimal value function of the resulting two-dimensional stochastic control problem is shown to be the smallest viscosity supersolution of a corresponding Hamilton-Jacobi-Bellman equation, and we prove that it can be uniformly approximated through a discretization of the space of the free surplus of the portfolio and the current claim intensity level. We implement the resulting numerical scheme to identify optimal dividend strategies for such a natural catastrophe insurer, and it is shown that the nature of the barrier and band strategies known from the classical models with constant Poisson claim intensity carry over in a certain way to this more general situation, leading to action and non-action regions for the dividend payments as a function of the current surplus and intensity level. We also discuss some interpretations in terms of upward potential for shareholders when including a catastrophe sector in the portfolio.
Structure of the space of folding protein sequences defined by large language models
Authors: A. Zambon, R. Zecchina, G. Tiana
Date: 2023-11-10 00:00:00

Proteins populate a manifold in the high-dimensional sequence space whose geometrical structure guides their natural evolution. Leveraging recently-developed structure prediction tools based on transformer models, we first examine the protein sequence landscape as defined by the folding score function. This landscape shares characteristics with optimization challenges encountered in machine learning and constraint satisfaction problems. Our analysis reveals that natural proteins predominantly reside in wide, flat minima within this energy landscape. To investigate further, we employ statistical mechanics algorithms specifically designed to explore regions with high local entropy in relatively flat landscapes. Our findings indicate that these specialized algorithms can identify valleys with higher entropy compared to those found using traditional methods such as Monte Carlo Markov Chains. In a proof-of-concept case, we find that these highly entropic minima exhibit significant similarities to natural sequences, especially in critical key sites and local entropy. Additionally, evaluations through Molecular Dynamics suggests that the stability of these sequences closely resembles that of natural proteins. Our tool combines advancements in machine learning and statistical physics, providing new insights into the exploration of sequence landscapes where wide, flat minima coexist alongside a majority of narrower minima.
Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection
Authors: Shi Gu, Marcelo G Mattar, Huajin Tang, Gang Pan
Date: 2023-11-10 00:00:00

While advances in artificial intelligence and neuroscience have enabled the emergence of neural networks capable of learning a wide variety of tasks, our understanding of the temporal dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian Feedforward (HebbFF) neural networks in tasks of continual familiarity detection. Drawing inspiration from the field of network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. In particular, we find that the emergence of network modularity is a salient predictor of performance, and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological realms.
CESPED: a new benchmark for supervised particle pose estimation in Cryo-EM
Authors: Ruben Sanchez-Garcia, Michael Saur, Javier Vargas, Carl Poelking, Charlotte M Deane
Date: 2023-11-10 00:00:00
Tasks: Pose Estimation

Cryo-EM is a powerful tool for understanding macromolecular structures, yet current methods for structure reconstruction are slow and computationally demanding. To accelerate research on pose estimation, we present CESPED, a new dataset specifically designed for Supervised Pose Estimation in Cryo-EM. Alongside CESPED, we provide a PyTorch package to simplify Cryo-EM data handling and model evaluation. We evaluated the performance of a baseline model, Image2Sphere, on CESPED, which showed promising results but also highlighted the need for further improvements. Additionally, we illustrate the potential of deep learning-based pose estimators to generalise across different samples, suggesting a promising path toward more efficient processing strategies. CESPED is available at https://github.com/oxpig/cesped.
Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models
Authors: Gang Hu
Date: 2023-11-09 00:00:00
Tasks: Algorithmic Trading, Q-Learning

This study enhances a Deep Q-Network (DQN) trading model by incorporating advanced techniques like Prioritized Experience Replay, Regularized Q-Learning, Noisy Networks, Dueling, and Double DQN. Extensive tests on assets like BTC/USD and AAPL demonstrate superior performance compared to the original model, with marked increases in returns and Sharpe Ratio, indicating improved risk-adjusted rewards. Notably, convolutional neural network (CNN) architectures, both 1D and 2D, significantly boost returns, suggesting their effectiveness in market trend analysis. Across instruments, these enhancements have yielded stable and high gains, eclipsing the baseline and highlighting the potential of CNNs in trading systems. The study suggests that applying sophisticated deep learning within reinforcement learning can greatly enhance automated trading, urging further exploration into advanced methods for broader financial applicability. The findings advocate for the continued evolution of AI in finance.
Price-mediated contagion with endogenous market liquidity
Authors: Zhiyu Cao, Zachary Feinstein
Date: 2023-11-10 00:00:00

Price-mediated contagion occurs when a positive feedback loop develops following a drop in asset prices which forces banks and other financial institutions to sell their holdings. Prior studies of such events fix the level of market liquidity without regards to the level of stress applied to the system. This paper introduces a framework to understand price-mediated contagion in a system where the capacity of the market to absorb liquidated assets is determined endogenously. In doing so, we construct a joint clearing system in interbank payments, asset prices, and market liquidity. We establish mild assumptions which guarantee the existence of greatest and least clearing solutions. We conclude with detailed numerical case studies which demonstrate the, potentially severe, repercussions of endogenizing the market liquidity on system risk.
Mobile Internet Quality Estimation using Self-Tuning Kernel Regression
Authors: Hanyang Jiang, Henry Shaowu Yuchi, Elizabeth Belding, Ellen Zegura, Yao Xie
Date: 2023-11-04 00:00:00
Tasks: regression, Weather Forecasting

Modeling and estimation for spatial data are ubiquitous in real life, frequently appearing in weather forecasting, pollution detection, and agriculture. Spatial data analysis often involves processing datasets of enormous scale. In this work, we focus on large-scale internet-quality open datasets from Ookla. We look into estimating mobile (cellular) internet quality at the scale of a state in the United States. In particular, we aim to conduct estimation based on highly {\it imbalanced} data: Most of the samples are concentrated in limited areas, while very few are available in the rest, posing significant challenges to modeling efforts. We propose a new adaptive kernel regression approach that employs self-tuning kernels to alleviate the adverse effects of data imbalance in this problem. Through comparative experimentation on two distinct mobile network measurement datasets, we demonstrate that the proposed self-tuning kernel regression method produces more accurate predictions, with the potential to be applied in other applications.