pwc-archive/papers-with-abstracts huggingface dataset:
Image transmission through a flexible multimode fiber by deep learning
Authors: Shachar Resisi, Sebastien M. Popoff, Yaron Bromberg
Date: 2020-11-08 00:00:00
Tasks: Deep Learning

When multimode optical fibers are perturbed, the data that is transmitted through them is scrambled. This presents a major difficulty for many possible applications, such as multimode fiber-based telecommunication and endoscopy. To overcome this challenge, a deep learning approach that generalizes over mechanical perturbations is presented. Using this approach, successful reconstruction of the input images from intensity-only measurements of speckle patterns at the output of a 1.5 meter-long randomly perturbed multimode fiber is demonstrated. The model's success is explained by hidden correlations in the speckle of random fiber conformations.
Agricultural Windfalls and the Seasonality of Political Violence in Africa
Authors: David Ubilava, Justin V. Hastings, Kadir Atalay
Date: 2022-02-16 00:00:00

When the prices of cereal grains rise, social unrest and conflict become likely. In rural areas, the predation motives of perpetrators can explain the positive relationship between prices and conflict. Predation happens at places and in periods where and when spoils to be appropriated are available. In predominantly agrarian societies, such opportune times align with the harvest season. Does the seasonality of agricultural income lead to the seasonality of conflict? We address this question by analyzing over 55 thousand incidents involving violence against civilians staged by paramilitary groups across Africa during the 1997-2020 period. We investigate the crop year pattern of violence in response to agricultural income shocks via changes in international cereal prices. We find that a year-on-year one standard deviation annual growth of the price of the major cereal grain results in a harvest-time spike in violence by militias in a one-degree cell where this cereal grain is grown. This translates to a nearly ten percent increase in violence during the early postharvest season. We observe no such change in violence by state forces or rebel groups--the other two notable actors. By further investigating the mechanisms, we show that the violence by militias is amplified after plausibly rich harvest seasons when the value of spoils to be appropriated is higher. By focusing on harvest-related seasonality of conflict, as well as actors more likely to be involved in violence against civilians, we contribute to the growing literature on the economic causes of conflict in predominantly agrarian societies.
Transparency principle for carbon emissions drives sustainable finance
Authors: Chris Kenyon, Mourad Berrahoui, Andrea Macrina
Date: 2022-02-15 00:00:00
Tasks: Financial Analysis, Management

Alignment of financial market incentives and carbon emissions disincentives is key to limiting global warming. Regulators and standards bodies have made a start by requiring some carbon-related disclosures and proposing others. Here we go further and propose a Carbon Equivalence Principle: all financial products shall contain a description of the equivalent carbon flows from greenhouse gases that the products enable, as well as their existing description in terms of cash flows. This description of the carbon flows enabled by the project shall be compatible with existing bank systems that track cashflows so that carbon flows have equal standing to cash flows. We demonstrate that this transparency alone can align incentives by applying it to project finance examples for power generation and by following through the financial analysis. The financial requirements to offset costs of carbon flows enabled in the future radically change project costs, and risk that assets become stranded, thus further increasing costs. This observation holds whichever partner in the project bears the enabled-carbon costs. Mitigating these risks requires project re-structuring to include negative emissions technologies. We also consider that sequestered carbon needs to remain sequestered permanently, e.g., for at least one hundred years. We introduce mixed financial-physical solutions to minimise this permanence cost, and price to them. This complements previous insurance-based proposals with lesser scope. For financial viability we introduce project designs that are financially net-zero, and as a consequence are carbon negative. Thus we see that adoption of the Carbon Equivalence Principle for financial products aligns incentives, requires product redesign, and is simply good financial management driving sustainability.
Fair Division with Money and Prices
Authors: Anna Bogomolnaia, Herve Moulin
Date: 2022-02-16 00:00:00
Tasks: Privacy Preserving

We must divide a finite number of indivisible goods and cash transfers between agents with quasi-linear but otherwise arbitrary utilities over the subsets of goods. We compare two division rules with cognitively feasible and privacy preserving individual messages. In Sell&Buy agents bid for the role of Seller or Buyer: with two agents the smallest bid defines the Seller who then charges any a price constrained only by her winning bid. In Divide&Choose agents bid for the role of Divider, then everyone bids on the shares of the Divider's partition. S&B dominates D&C on two counts: its guaranteed utility in the worst case rewards (resp. penalises) more subadditive (resp. superadditive) utilities; playing safe is never ambiguous and is also better placed to collect a larger share of the efficient surplus.
On the optimality of full disclosure
Authors: Emiliano Catonini, Sergey Stepanov
Date: 2022-02-16 00:00:00

A privately-informed sender can commit to any disclosure policy towards a receiver. We show that full disclosure is optimal under a sufficient condition with some desirable properties. First, it speaks directly to the utility functions of the parties, as opposed to the indirect utility function of the sender; this makes it easily interpretable and verifiable. Second, it does not require the sender's payoff to be a function of the posterior mean. Third, it is weaker than the known conditions for some special cases. With this, we show that full disclosure is optimal under modeling assumptions commonly used in principal-agent papers.
Increasing loudness in audio signals: a perceptually motivated approach to preserve audio quality
Authors: A. Jeannerot, N. de Koeijer, P. Martínez-Nuevo, M. B. Møller, J. Dyreby, P. Prandoni
Date: 2022-02-16 00:00:00

We present a method to maintain the subjective perception of volume of audio signals and, at the same time, reduce their absolute peak value. We focus on achieving this without compromising the perceived audio quality. This is specially useful, for example, to maximize the perceived reproduction level of loudspeakers where simply amplifying the signal amplitude, and hence their peak value, is limited due to already constrained physical designs. In particular, we minimize the absolute peak value subject to a constraint based on auditory masking. This limits the perceptual difference between the original and the modified signals. Moreover, this constraint can be tuned and allows to control the resulting audio quality. We show results comparing loudness and audio quality as a function of peak reduction. These results suggest that our method presents the best trade-off between loudness and audio quality when compared against classical methods based on compression and clipping.
Modelling multi-cell edge video analytics
Authors: Jaume Anguera Peris, Viktoria Fodor
Date: 2022-02-16 00:00:00
Tasks: Fairness

Edge intelligence is a scalable solution for analyzing distributed data, but it cannot provide reliable services in large-scale cellular networks unless the inherent aspects of fading and interference are also taken into consideration. In this paper, we present the first mathematical framework for modelling edge video analytics in multi-cell cellular systems. We derive the expressions for the coverage probability, the ergodic capacity, the probability of successfully completing the video analytics within a target delay requirement, and the effective frame rate. We also analyze the effect of the system parameters on the accuracy of the detection algorithm, the supported frame rate at the edge server, and the system fairness.
A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis
Authors: Khouloud Abdelli, Helmut Griesser, Carsten Tropschug, Stephan Pachnicke
Date: 2022-02-16 00:00:00
Tasks: Fault Diagnosis

A novel multitask learning approach based on stacked bidirectional long short-term memory (BiLSTM) networks and convolutional neural networks (CNN) for detecting, locating, characterizing, and identifying fiber faults is proposed. It outperforms conventionally employed techniques.
Direction of Arrival Estimation and Phase-Correction for Non-Coherent Sub-Arrays: A Convex Optimization Approach
Authors: Tom Tirer, Oded Bialer
Date: 2022-02-15 00:00:00
Tasks: Direction of Arrival Estimation

Estimating the direction of arrival (DOA) of sources is an important problem in aerospace and vehicular communication, localization and radar. In this paper, we consider a challenging multi-source DOA estimation task, where the receiving antenna array is composed of non-coherent sub-arrays, i.e., sub-arrays that observe different unknown phase shifts at every snapshot (e.g., due to waiving the demanding synchronization of local oscillators across the entire array). We formulate this problem as the reconstruction of joint sparse and low-rank matrices, and solve the problem's convex relaxation. To scale the optimization complexity with the number of snapshots better than general-purpose solvers, we design an optimization scheme, based on integrating the alternating direction method of multipliers and the accelerated proximal gradient techniques, that exploits the structure of the problem. While the DOAs can be estimated from the solution of the aforementioned convex problem, we further show how an improvement is obtained if, instead, one estimates from this solution only the sub-arrays' phase shifts. This is done using another, computationally-light, convex relaxation that is practically tight. Using the estimated phase shifts, "phase-corrected" observations are created and a final plain ("coherent") DOA estimation method can be applied. Numerical experiments show the performance advantages of the proposed strategies over existing methods.
An SMP-Based Algorithm for Solving the Constrained Utility Maximization Problem via Deep Learning
Authors: Kristof Wiedermann
Date: 2022-02-15 00:00:00

We consider the utility maximization problem under convex constraints with regard to theoretical results which allow the formulation of algorithmic solvers which make use of deep learning techniques. In particular for the case of random coefficients, we prove a stochastic maximum principle (SMP), which also holds for utility functions $U$ with $\mathrm{id}_{\mathbb{R}^{+}} \cdot U'$ being not necessarily nonincreasing, like the power utility functions, thereby generalizing the SMP proved by Li and Zheng (2018). We use this SMP together with the strong duality property for defining a new algorithm, which we call deep primal SMP algorithm. Numerical examples illustrate the effectiveness of the proposed algorithm - in particular for higher-dimensional problems and problems with random coefficients, which are either path dependent or satisfy their own SDEs. Moreover, our numerical experiments for constrained problems show that the novel deep primal SMP algorithm overcomes the deep SMP algorithm's (see Davey and Zheng (2021)) weakness of erroneously producing the value of the corresponding unconstrained problem. Furthermore, in contrast to the deep controlled 2BSDE algorithm from Davey and Zheng (2021), this algorithm is also applicable to problems with path dependent coefficients. As the deep primal SMP algorithm even yields the most accurate results in many of our studied problems, we can highly recommend its usage. Moreover, we propose a learning procedure based on epochs which improved the results of our algorithm even further. Implementing a semi-recurrent network architecture for the control process turned out to be also a valuable advancement.
Paraphrasing Magritte's Observation
Authors: Jesus Malo
Date: 2022-02-11 00:00:00

Contrast Sensitivity of the human visual system can be explained from certain low-level vision tasks (like retinal noise and optical blur removal), but not from others (like chromatic adaptation or pure reconstruction after simple bottlenecks). This conclusion still holds even under substantial change in stimulus statistics, as for instance considering cartoon-like images as opposed to natural images (Li et al. Journal of Vision, 2022, Preprint arXiv:2103.00481). In this note we present a method to generate original cartoon-like images compatible with the statistical training used in (Li et al., 2022). Following the classical observation in (Magritte, 1929), the stimuli generated by the proposed method certainly are not what they represent: Ceci n'est pas une pipe. The clear distinction between representation (the stimuli generated by the proposed method) and reality (the actual object) avoids eventual problems for the use of the generated stimuli in academic, non-profit, publications.
Recommendations for extending the GFF3 specification for improved interoperability of genomic data
Authors: Surya Saha, Scott Cain, Ethalinda K. S. Cannon, Nathan Dunn, Andrew Farmer, Zhi-Liang Hu, Gareth Maslen, Sierra Moxon, Christopher J Mungall, Rex Nelson, Monica F. Poelchau
Date: 2022-02-15 00:00:00

The GFF3 format is a common, flexible tab-delimited format representing the structure and function of genes or other mapped features (https://github.com/The-Sequence-Ontology/Specifications/blob/master/gff3.md). However, with increasing re-use of annotation data, this flexibility has become an obstacle for standardized downstream processing. Common software packages that export annotations in GFF3 format model the same data and metadata in different notations, which puts the burden on end-users to interpret the data model. The AgBioData consortium is a group of genomics, genetics and breeding databases and partners working towards shared practices and standards. Providing concrete guidelines for generating GFF3, and creating a standard representation of the most common biological data types would provide a major increase in efficiency for AgBioData databases and the genomics research community that use the GFF3 format in their daily operations. The AgBioData GFF3 working group has developed recommendations to solve common problems in the GFF3 format. We suggest improvements for each of the GFF3 fields, as well as the special cases of modeling functional annotations, and standard protein-coding genes. We welcome further discussion of these recommendations. We request the genomics and bioinformatics community to utilize the github repository (https://github.com/NAL-i5K/AgBioData_GFF3_recommendation) to provide feedback via issues or pull requests.
Optimal market completion through financial derivatives with applications to volatility risk
Authors: Matt Davison, Marcos Escobar-Anel, Yichen Zhu
Date: 2022-02-16 00:00:00

This paper investigates the optimal choices of financial derivatives to complete a financial market in the framework of stochastic volatility (SV) models. We introduce an efficient and accurate simulation-based method, applicable to generalized diffusion models, to approximate the optimal derivatives-based portfolio strategy. We build upon the double optimization approach (i.e. expected utility maximization and risk exposure minimization) proposed in Escobar-Anel et al. (2022); demonstrating that strangle options are the best choices for market completion within equity options. Furthermore, we explore the benefit of using volatility index derivatives and conclude that they could be more convenient substitutes when only long-term maturity equity options are available.
Toward an efficient hybrid method for pricing barrier options on assets with stochastic volatility
Authors: Alexander Lipton, Artur Sepp
Date: 2022-02-16 00:00:00

We combine the one-dimensional Monte Carlo simulation and the semi-analytical one-dimensional heat potential method to design an efficient technique for pricing barrier options on assets with correlated stochastic volatility. Our approach to barrier options valuation utilizes two loops. First we run the outer loop by generating volatility paths via the Monte Carlo method. Second, we condition the price dynamics on a given volatility path and apply the method of heat potentials to solve the conditional problem in closed-form in the inner loop. We illustrate the accuracy and efficacy of our semi-analytical approach by comparing it with the two-dimensional Monte Carlo simulation and a hybrid method, which combines the finite-difference technique for the inner loop and the Monte Carlo simulation for the outer loop. We apply our method for computation of state probabilities (Green function), survival probabilities, and values of call options with barriers. Our approach provides better accuracy and is orders of magnitude faster than the existing methods. s a by-product of our analysis, we generalize Willard's (1997) conditioning formula for valuation of path-independent options to path-dependent options and derive a novel expression for the joint probability density for the value of drifted Brownian motion and its running minimum.
Solving Multi-Period Financial Planning Models: Combining Monte Carlo Tree Search and Neural Networks
Authors: Afşar Onat Aydınhan, Xiaoyue Li, John M. Mulvey
Date: 2022-02-15 00:00:00

This paper introduces the MCTS algorithm to the financial world and focuses on solving significant multi-period financial planning models by combining a Monte Carlo Tree Search algorithm with a deep neural network. The MCTS provides an advanced start for the neural network so that the combined method outperforms either approach alone, yielding competitive results. Several innovations improve the computations, including a variant of the upper confidence bound applied to trees (UTC) and a special lookup search. We compare the two-step algorithm with employing dynamic programs/neural networks. Both approaches solve regime switching models with 50-time steps and transaction costs with twelve asset categories. Heretofore, these problems have been outside the range of solvable optimization models via traditional algorithms.
Typography-MNIST (TMNIST): an MNIST-Style Image Dataset to Categorize Glyphs and Font-Styles
Authors: Nimish Magre, Nicholas Brown
Date: 2022-02-12 00:00:00

We present Typography-MNIST (TMNIST), a dataset comprising of 565,292 MNIST-style grayscale images representing 1,812 unique glyphs in varied styles of 1,355 Google-fonts. The glyph-list contains common characters from over 150 of the modern and historical language scripts with symbol sets, and each font-style represents varying subsets of the total unique glyphs. The dataset has been developed as part of the CognitiveType project which aims to develop eye-tracking tools for real-time mapping of type to cognition and to create computational tools that allow for the easy design of typefaces with cognitive properties such as readability. The dataset and scripts to generate MNIST-style images for glyphs in different font styles are freely available at https://github.com/aiskunks/CognitiveType.
Matching-Based Task Offloading for Vehicular Edge Computing
Authors: Pengju Liu; Junluo Li; Zhongwei Sun
Date: 2019-02-22 00:00:00
Proceeding: IEEE Access: Volume 7 2019 2
Tasks: Edge-computing

Vehicular edge computing has emerged as a promising technology to accommodate the tremendous demand for data storage and computational resources in vehicular networks. By processing the massive workload tasks in the proximity of vehicles, the quality of service can be guaranteed. However, how to determine the task offloading strategy under various constraints of resource and delay is still an open issue. In this paper, we study the task offloading problem from a matching perspective and aim to optimize the total network delay. The task offloading delay model is derived based on three different velocity models, i.e., a constant velocity model, vehicle-following model, and traveling-time statistical model. Next, we propose a pricing-based one-to-one matching algorithm and pricing-based one-to-many matching algorithms for the task offloading. The proposed algorithm is validated based on three different simulation scenarios, i.e., straight road, the urban road with the traffic light, and crooked road, which are extracted from the realistic road topologies in Beijing and Guangdong, China. The simulation results confirm that significant delay decreasing can be achieved by the proposed algorithm.
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better Performance
Authors: Andreas Pentaliotis, Marco Wiering
Date: 2021-02-01 00:00:00
Proceeding: International Conference on Agents and Artificial Intelligence 2021 2
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Q-learning is a reinforcement learning algorithm that has overestimation bias, because it learns the optimal action values by using a target that maximizes over uncertain action-value estimates. Although the overestima-tion bias of Q-learning is generally considered harmful, a recent study suggests that it could be either harmful or helpful depending on the reinforcement learning problem. In this paper, we propose a new Q-learning variant, called Variation-resistant Q-learning, to control and utilize estimation bias for better performance. Firstly, we present the tabular version of the algorithm and mathematically prove its convergence. Secondly, we combine the algorithm with function approximation. Finally, we present empirical results from three different experiments, in which we compared the performance of Variation-resistant Q-learning, Q-learning, and Double Q-learning. The empirical results show that Variation-resistant Q-learning can control and utilize estimation bias for better performance in the experimental tasks.
ROSNet: A WMN based Framework using UAVs and Ground Nodes for Post-Disaster Management
Authors: Neelabhro Roy, Sauranil Debarshi, P.B. Sujit
Date: 2021-12-22 00:00:00
Proceeding: IEEE Humanitarian Technology Conference (R10-HTC) 2021 12
Tasks: Management

This paper presents a wireless mesh network-based solution utilizing stationary and mobile ground/UAV nodes for setting up a post disaster communication infrastructure. The network is ad hoc and establishes connectivity over a large area without the use of a cellular network or the internet. We propose a complete end-to-end framework, where we deploy an android application on smartphones at the user-end, the wireless mesh network as the backbone of communication, and a graphical user interface (GUI) at a base station for facilitating situational awareness. The software stack is based on the Robot Operating System for broadcasting user information to the base station, and also for controlling the UAVs remotely. The GUI also incorporates a node recommendation strategy for selecting the optimal nodes. The framework is evaluated using UAVs and a semi-autonomous car, and the experimental results show that the proposed system could be indispensable in providing large-scale connectivity in post-disaster management.