pwc-archive/papers-with-abstracts huggingface dataset:
Pricing commodity index options
Authors: Alberto Manzano, Emanuele Nastasi, Andrea Pallavicini, Carlos Vázquez
Date: 2022-08-02 00:00:00

We present a stochastic local volatility model for derivative contracts on commodity futures. The aim of the model is to be able to recover the prices of derivative claims both on futures contracts and on indices on futures strategies. Numerical examples for calibration and pricing are provided for the S&P GSCI Crude Oil excess-return index.
On the implied volatility of Asian options under stochastic volatility models
Authors: Elisa Alòs, Eulalia Nualart, Makar Pravosud
Date: 2022-08-02 00:00:00

In this paper we study the short-time behavior of the at-the-money implied volatility for arithmetic Asian options with fixed strike price. The asset price is assumed to follow the Black-Scholes model with a general stochastic volatility process. Using techniques of the Malliavin calculus such as the anticipating Ito's formula we first compute the level of the implied volatility of the option when the maturity converges to zero. Then, we find and short maturity asymptotic formula for the skew of the implied volatility that depends on the roughness of the volatility model. We apply our general results to the SABR model and the rough Bergomi model, and provide some numerical simulations that confirm the accurateness of the asymptotic formula for the skew.
Vertical GaN Diode BV Maximization through Rapid TCAD Simulation and ML-enabled Surrogate Model
Authors: Albert Lu, Jordan Marshall, Yifan Wang, Ming Xiao, Yuhao Zhang, Hiu Yung Wong
Date: 2022-07-18 00:00:00

In this paper, two methodologies are used to speed up the maximization of the breakdown volt-age (BV) of a vertical GaN diode that has a theoretical maximum BV of ~2100V. Firstly, we demonstrated a 5X faster accurate simulation method in Technology Computer-Aided-Design (TCAD). This allows us to find 50% more numbers of high BV (>1400V) designs at a given simulation time. Secondly, a machine learning (ML) model is developed using TCAD-generated data and used as a surrogate model for differential evolution optimization. It can inversely design an out-of-the-training-range structure with BV as high as 1887V (89% of the ideal case) compared to ~1100V designed with human domain expertise.
Application of Model Derived Charge Transfer Inefficiency Corrections to STIS Photometric CCD Data
Authors: Paul Bristow
Date: 2003-10-24 00:00:00

The STECF Calibration Enhancement effort for the Space Telescope Imaging Spectrograph (STIS) aims to improve data calibration via the application of physical modelling techniques. As part of this effort we have developed a model of the STIS CCD readout process. The model itself is described in some detail in earlier ISRs. Here we consider the application to STIS photometric data. We demonstrate that this approach can successfully remove the trails typically seen in CTE affected data and go on to ask whether this charge is being restored to the cores of detected objects in the correct quantities. We find good agreement between the simulation derived corrections and empirical corrections for a range of background and source levels. Moreover we find that, where the two differ, it is because only the simulation properly accounts for the charge distribution in the array.
Weak Lensing Measurements: A Revisited Method and Application to HST Images
Authors: Jason Rhodes, Alexandre Refregier, Ed Groth
Date: 1999-05-07 00:00:00

The weak distortions produced by gravitational lensing in the images of background galaxies provide a method to measure directly the distribution of mass in the universe. However this technique requires high precision measurements of the lensing shear and cautious corrections for systematic effects. Kaiser, Squires, & Broadhurst (1995) proposed a method to calibrate the ellipticity-shear relation in the presence of Point Spread Function (PSF) anisotropies and camera distortions. We revisit the KSB method and show that both the PSF and the camera distortions can be corrected for using source moments, as opposed to ellipticities. We clarify the applicability of some of the approximations made in this method. We derive expressions for the corrections which only involve the galaxy moments. We derive an explicit relation between the shear and the average ellipticity. We discuss the shortcomings of the method, and test its validity using numerical simulations. As an application of the method, we repeat the analysis of the HST WFPC2 camera performed by Hoekstra et al. (1998). We confirm the presence of sizable (10%) PSF ellipticities at the edge of the WFPC2 chips. We also show that the PSF ellipticity varies by as much as 2% over time. We use these measurements to correct the shape of galaxies in the HST Survey Strip (``Groth'' Strip). By considering the dependence of the ellipticities on object size, we show that, after corrections, the residual systematic uncertainty for galaxies with radii greater than 0.15 arcsec, is about 0.4%, when averaged over each chip. We discuss how these results provide good prospects for measuring weak lensing by large-scale structure with deep HST surveys.
The Stability of the Point Spread Function of the Advanced Camera for Surveys on the Hubble Space Telescope and Implications for Weak Gravitational Lensing
Authors: Jason D. Rhodes, Richard Massey, Justin Albert, Nicholas Collins, Richard S. Ellis, Catherine Heymans, Jonathan P. Gardner, Jean-Paul Kneib, Anton Koekemoer, Alexie Leauthaud, Yannick Mellier, Alexandre Refregier, James E. Taylor, Ludovic van Waerbeke
Date: 2007-02-06 00:00:00

(abridged) We examine the spatial and temporal stability of the HST ACS Wide Field Camera (WFC) point spread function (PSF) using the two square degree COSMOS survey. We show that stochastic aliasing of the PSF necessarily occurs during `drizzling'. This aliasing is maximal if the output pixel scale is equal to the input pixel scale of 0.05''. We show that this source of PSF variation can be significantly reduced by choosing a Gaussian drizzle kernel and by setting the output pixel size to 0.03''. We show that the PSF is temporally unstable, most likely due to thermal fluctuations in the telescope's focus. We find that the primary manifestation of this thermal drift in COSMOS images is an overall slow periodic focus change. Using a modified version of TinyTim, we create undistorted stars in a 30x30 grid across the ACS WFC CCDs. These PSF models are created for telescope focus values in the range -10 microns to +5 microns, thus spanning the allowed range of telescope focus values. We then use the approximately ten well measured stars in each COSMOS field to pick the best-fit focus value for each field. The TinyTim model stars are then used to perform PSF corrections for weak lensing allowing systematics due to incorrectly modeled PSFs to be greatly reduced. We have made the software for PSF modeling using our modified version of TinyTim available to the astronomical community. We show the effects of Charge Transfer Efficiency (CTE) degradation, which distorts the object in the readout direction, mimicking a weak lensing signal. We derive a parametric correction for the effect of CTE on the shapes of objects in the COSMOS field as a function of observation date, position within the ACS WFC field, and object flux.
Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box
Authors: Raluca Chitic, Ali Osman Topal, Franck Leprevost
Date: 2022-07-21 00:00:00
Proceeding: MDPI Applied Sciences (Special Issue on Explainable Artificial Intelligence) 2022 7

Through the addition of humanly imperceptible noise to an image classified as belonging to a category ca, targeted adversarial attacks can lead Convolutional Neural Networks (CNNs) to classify a modified image as belonging to any predefined target class ct ̸ = ca. To achieve a better understanding of the inner workings of adversarial attacks, this paper analyzes the adversarial images created by two opposing attacks against 10 ImageNet-trained CNNs. A total of 2 × 437 adversarial images are created by the EA black-box evolutionary algorithm and by the BIM white-box, gradient-based attack. We inspect and compare these two sets of adversarial images through different perspectives: behaviour while looking at smaller regions, noise frequency, transferability, texture change and penultimate layer activations. We find that texture change is rather a side-effect than a means for the attacks, and that ct-relevant features only build up significantly for regions of size 56 × 56 onwards. In the penultimate CNN layers, both attacks increase the activation of the units which are positively related to ct and of the units which are negatively related to ca. In contrast to the EA’s white noise nature, BIM introduces predominantly low-frequency noise. BIM affects the original ca features more than the EA does, thus producing slightly more transferable adversarial images. However, transferability with both attacks is low, since the attacks’ ct-related information is specific to the output layers of the targeted CNN. We find that the adversarial images are actually more transferable at regions of size 56 × 56 than at full scale.
On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks
Authors: Longfei Ma, Nan Cheng, Xiucheng Wang, Ruijin Sun, Ning Lu
Date: 2022-08-02 00:00:00
Tasks: Decision Making, Dynamic neural networks, Management

On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic. In this paper, we study the on-demand wireless resource orchestration problem with the focus on the computing delay in orchestration decision-making process. Specifically, we take the decision-making delay into the optimization problem. Then, a dynamic neural network (DyNN)-based method is proposed, where the model complexity can be adjusted according to the service requirements. We further build a knowledge base representing the relationship among the service requirements, available computing resources, and the resource allocation performance. By exploiting the knowledge, the width of DyNN can be selected in a timely manner, further improving the performance of orchestration. Simulation results show that the proposed scheme significantly outperforms the traditional static neural network, and also shows sufficient flexibility in on-demand service provisioning.
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling
Authors: Xiucheng Wang, Longfei Ma, Haocheng Li, Zhisheng Yin, Tom. Luan, Nan Cheng
Date: 2022-08-02 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmitted to server at a time, while server processes tasks according to the transmission order, the problem is NP-hard. However, it is difficult for traditional optimization methods to quickly obtain the optimal solution, while approaches based on reinforcement learning face with the challenge of excessively large action space and slow convergence. In this paper, we propose a Digital Twin (DT)-assisted RL-based task scheduling method in order to improve the performance and convergence of the RL. We use DT to simulate the results of different decisions made by the agent, so that one agent can try multiple actions at a time, or, similarly, multiple agents can interact with environment in parallel in DT. In this way, the exploration efficiency of RL can be significantly improved via DT, and thus RL can converges faster and local optimality is less likely to happen. Particularly, two algorithms are designed to made task scheduling decisions, i.e., DT-assisted asynchronous Q-learning (DTAQL) and DT-assisted exploring Q-learning (DTEQL). Simulation results show that both algorithms significantly improve the convergence speed of Q-learning by increasing the exploration efficiency.
Post-hoc Interpretability based Parameter Selection for Data Oriented Nuclear Reactor Accident Diagnosis System
Authors: Chengyuan Li, Meifu Li, Zhifang Qiu
Date: 2022-08-03 00:00:00
Tasks: Diagnostic, Position

During applying data-oriented diagnosis systems to distinguishing the type of and evaluating the severity of nuclear power plant initial events, it is of vital importance to decide which parameters to be used as the system input. However, although several diagnosis systems have already achieved acceptable performance in diagnosis precision and speed, hardly have the researchers discussed the method of monitoring point choosing and its layout. For this reason, redundant measuring data are used to train the diagnostic model, leading to high uncertainty of the classification, extra training time consumption, and higher probability of overfitting while training. In this study, a method of choosing thermal hydraulics parameters of a nuclear power plant is proposed, using the theory of post-hoc interpretability theory in deep learning. At the start, a novel Time-sequential Residual Convolutional Neural Network (TRES-CNN) diagnosis model is introduced to identify the position and hydrodynamic diameter of breaks in LOCA, using 38 parameters manually chosen on HPR1000 empirically. Afterwards, post-hoc interpretability methods are applied to evaluate the attributions of diagnosis model's outputs, deciding which 15 parameters to be more decisive in diagnosing LOCA details. The results show that the TRES-CNN based diagnostic model successfully predicts the position and size of breaks in LOCA via selected 15 parameters of HPR1000, with 25% of time consumption while training the model compared the process using total 38 parameters. In addition, the relative diagnostic accuracy error is within 1.5 percent compared with the model using parameters chosen empirically, which can be regarded as the same amount of diagnostic reliability.
Learning Prior Feature and Attention Enhanced Image Inpainting
Authors: Chenjie Cao, Qiaole Dong, Yanwei Fu
Date: 2022-08-03 00:00:00
Tasks: Image Inpainting, Image Restoration, object-detection, Object Detection

Many recent inpainting works have achieved impressive results by leveraging Deep Neural Networks (DNNs) to model various prior information for image restoration. Unfortunately, the performance of these methods is largely limited by the representation ability of vanilla Convolutional Neural Networks (CNNs) backbones.On the other hand, Vision Transformers (ViT) with self-supervised pre-training have shown great potential for many visual recognition and object detection tasks. A natural question is whether the inpainting task can be greatly benefited from the ViT backbone? However, it is nontrivial to directly replace the new backbones in inpainting networks, as the inpainting is an inverse problem fundamentally different from the recognition tasks. To this end, this paper incorporates the pre-training based Masked AutoEncoder (MAE) into the inpainting model, which enjoys richer informative priors to enhance the inpainting process. Moreover, we propose to use attention priors from MAE to make the inpainting model learn more long-distance dependencies between masked and unmasked regions. Sufficient ablations have been discussed about the inpainting and the self-supervised pre-training models in this paper. Besides, experiments on both Places2 and FFHQ demonstrate the effectiveness of our proposed model. Codes and pre-trained models are released in https://github.com/ewrfcas/MAE-FAR.
AdaCat: Adaptive Categorical Discretization for Autoregressive Models
Authors: Qiyang Li, Ajay Jain, Pieter Abbeel
Date: 2022-08-03 00:00:00
Tasks: Density Estimation, Offline RL

Autoregressive generative models can estimate complex continuous data distributions, like trajectory rollouts in an RL environment, image intensities, and audio. Most state-of-the-art models discretize continuous data into several bins and use categorical distributions over the bins to approximate the continuous data distribution. The advantage is that the categorical distribution can easily express multiple modes and are straightforward to optimize. However, such approximation cannot express sharp changes in density without using significantly more bins, making it parameter inefficient. We propose an efficient, expressive, multimodal parameterization called Adaptive Categorical Discretization (AdaCat). AdaCat discretizes each dimension of an autoregressive model adaptively, which allows the model to allocate density to fine intervals of interest, improving parameter efficiency. AdaCat generalizes both categoricals and quantile-based regression. AdaCat is a simple add-on to any discretization-based distribution estimator. In experiments, AdaCat improves density estimation for real-world tabular data, images, audio, and trajectories, and improves planning in model-based offline RL.
MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training
Authors: De-An Huang, Zhiding Yu, Anima Anandkumar
Date: 2022-08-03 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation, Video Instance Segmentation

We propose MinVIS, a minimal video instance segmentation (VIS) framework that achieves state-of-the-art VIS performance with neither video-based architectures nor training procedures. By only training a query-based image instance segmentation model, MinVIS outperforms the previous best result on the challenging Occluded VIS dataset by over 10% AP. Since MinVIS treats frames in training videos as independent images, we can drastically sub-sample the annotated frames in training videos without any modifications. With only 1% of labeled frames, MinVIS outperforms or is comparable to fully-supervised state-of-the-art approaches on YouTube-VIS 2019/2021. Our key observation is that queries trained to be discriminative between intra-frame object instances are temporally consistent and can be used to track instances without any manually designed heuristics. MinVIS thus has the following inference pipeline: we first apply the trained query-based image instance segmentation to video frames independently. The segmented instances are then tracked by bipartite matching of the corresponding queries. This inference is done in an online fashion and does not need to process the whole video at once. MinVIS thus has the practical advantages of reducing both the labeling costs and the memory requirements, while not sacrificing the VIS performance. Code is available at: https://github.com/NVlabs/MinVIS
Quantum-Inspired Tensor Neural Networks for Partial Differential Equations
Authors: Raj Patel, Chia-Wei Hsing, Serkan Sahin, Saeed S. Jahromi, Samuel Palmer, Shivam Sharma, Christophe Michel, Vincent Porte, Mustafa Abid, Stephane Aubert, Pierre Castellani, Chi-Guhn Lee, Samuel Mugel, Roman Orus
Date: 2022-08-03 00:00:00
Tasks: Deep Learning

Partial Differential Equations (PDEs) are used to model a variety of dynamical systems in science and engineering. Recent advances in deep learning have enabled us to solve them in a higher dimension by addressing the curse of dimensionality in new ways. However, deep learning methods are constrained by training time and memory. To tackle these shortcomings, we implement Tensor Neural Networks (TNN), a quantum-inspired neural network architecture that leverages Tensor Network ideas to improve upon deep learning approaches. We demonstrate that TNN provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. We benchmark TNN by applying them to solve parabolic PDEs, specifically the Black-Scholes-Barenblatt equation, widely used in financial pricing theory, empirically showing the advantages of TNN over DNN. Further examples, such as the Hamilton-Jacobi-Bellman equation, are also discussed.
On the independence between phenomenal consciousness and computational intelligence
Authors: Eduardo C. Garrido Merchán, Sara Lumbreras
Date: 2022-08-03 00:00:00

Consciousness and intelligence are properties commonly understood as dependent by folk psychology and society in general. The term artificial intelligence and the kind of problems that it managed to solve in the recent years has been shown as an argument to establish that machines experience some sort of consciousness. Following the analogy of Russell, if a machine is able to do what a conscious human being does, the likelihood that the machine is conscious increases. However, the social implications of this analogy are catastrophic. Concretely, if rights are given to entities that can solve the kind of problems that a neurotypical person can, does the machine have potentially more rights that a person that has a disability? For example, the autistic syndrome disorder spectrum can make a person unable to solve the kind of problems that a machine solves. We believe that the obvious answer is no, as problem solving does not imply consciousness. Consequently, we will argue in this paper how phenomenal consciousness and, at least, computational intelligence are independent and why machines do not possess phenomenal consciousness, although they can potentially develop a higher computational intelligence that human beings. In order to do so, we try to formulate an objective measure of computational intelligence and study how it presents in human beings, animals and machines. Analogously, we study phenomenal consciousness as a dichotomous variable and how it is distributed in humans, animals and machines. As phenomenal consciousness and computational intelligence are independent, this fact has critical implications for society that we also analyze in this work.
Multimodal sensor fusion in the latent representation space
Authors: Robert J. Piechocki, Xiaoyang Wang, Mohammud J. Bocus
Date: 2022-08-03 00:00:00
Tasks: compressed sensing, Denoising, Sensor Fusion

A new method for multimodal sensor fusion is introduced. The technique relies on a two-stage process. In the first stage, a multimodal generative model is constructed from unlabelled training data. In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion tasks. The method also handles cases where observations are accessed only via subsampling i.e. compressed sensing. We demonstrate the effectiveness and excellent performance on a range of multimodal fusion experiments such as multisensory classification, denoising, and recovery from subsampled observations.