pwc-archive/papers-with-abstracts huggingface dataset:
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations
Authors: Lina Zhuang, Lianru Gao, Bing Zhang, Xiyou Fu, Jose M. Bioucas-Dias
Date: 2021-03-12 00:00:00
Tasks: Anomaly Detection, Denoising, Hyperspectral Image Denoising, Image Denoising

Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels. This enables objects to be detected and the identification of materials that have subtle differences between them. However, the increase in spectral resolution often means that there is a decrease in the number of photons received in each channel, which means that the noise linked to the image formation process is greater. This degradation limits the quality of the extracted information and its potential applications. Thus, denoising is a fundamental problem in hyperspectral image (HSI) processing. As images of natural scenes with highly correlated spectral channels, HSIs are characterized by a high level of self-similarity and can be well approximated by low-rank representations. These characteristics underlie the state-of-the-art methods used in HSI denoising. However, where there are rarely occurring pixel types, the denoising performance of these methods is not optimal, and the subsequent detection of these pixels may be compromised. To address these hurdles, in this article, we introduce RhyDe (Robust hyperspectral Denoising), a powerful HSI denoiser, which implements explicit low-rank representation, promotes self-similarity, and, by using a form of collaborative sparsity, preserves rare pixels. The denoising and detection effectiveness of the proposed robust HSI denoiser is illustrated using semireal and real data.
Non-Asymptotic Performance Guarantees for Neural Estimation of $\mathsf{f}$-Divergences
Authors: Sreejith Sreekumar, Zhengxin Zhang, Ziv Goldfeld
Date: 2021-03-11 00:00:00

Statistical distances (SDs), which quantify the dissimilarity between probability distributions, are central to machine learning and statistics. A modern method for estimating such distances from data relies on parametrizing a variational form by a neural network (NN) and optimizing it. These estimators are abundantly used in practice, but corresponding performance guarantees are partial and call for further exploration. In particular, there seems to be a fundamental tradeoff between the two sources of error involved: approximation and estimation. While the former needs the NN class to be rich and expressive, the latter relies on controlling complexity. This paper explores this tradeoff by means of non-asymptotic error bounds, focusing on three popular choices of SDs -- Kullback-Leibler divergence, chi-squared divergence, and squared Hellinger distance. Our analysis relies on non-asymptotic function approximation theorems and tools from empirical process theory. Numerical results validating the theory are also provided.
Asymptotic Theory of $\ell_1$-Regularized PDE Identification from a Single Noisy Trajectory
Authors: Yuchen He, Namjoon Suh, Xiaoming Huo, Sungha Kang, Yajun Mei
Date: 2021-03-12 00:00:00

We prove the support recovery for a general class of linear and nonlinear evolutionary partial differential equation (PDE) identification from a single noisy trajectory using $\ell_1$ regularized Pseudo-Least Squares model~($\ell_1$-PsLS). In any associative $\mathbb{R}$-algebra generated by finitely many differentiation operators that contain the unknown PDE operator, applying $\ell_1$-PsLS to a given data set yields a family of candidate models with coefficients $\mathbf{c}(\lambda)$ parameterized by the regularization weight $\lambda\geq 0$. The trace of $\{\mathbf{c}(\lambda)\}_{\lambda\geq 0}$ suffers from high variance due to data noises and finite difference approximation errors. We provide a set of sufficient conditions which guarantee that, from a single trajectory data denoised by a Local-Polynomial filter, the support of $\mathbf{c}(\lambda)$ asymptotically converges to the true signed-support associated with the underlying PDE for sufficiently many data and a certain range of $\lambda$. We also show various numerical experiments to validate our theory.
Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis
Authors: Xiaowu Dai, Lexin Li
Date: 2021-03-12 00:00:00
Tasks: BIG-bench Machine Learning, Data Integration

Multimodal imaging has transformed neuroscience research. While it presents unprecedented opportunities, it also imposes serious challenges. Particularly, it is difficult to combine the merits of the interpretability attributed to a simple association model with the flexibility achieved by a highly adaptive nonlinear model. In this article, we propose an orthogonalized kernel debiased machine learning approach, which is built upon the Neyman orthogonality and a form of decomposition orthogonality, for multimodal data analysis. We target the setting that naturally arises in almost all multimodal studies, where there is a primary modality of interest, plus additional auxiliary modalities. We establish the root-$N$-consistency and asymptotic normality of the estimated primary parameter, the semi-parametric estimation efficiency, and the asymptotic validity of the confidence band of the predicted primary modality effect. Our proposal enjoys, to a good extent, both model interpretability and model flexibility. It is also considerably different from the existing statistical methods for multimodal data integration, as well as the orthogonality-based methods for high-dimensional inferences. We demonstrate the efficacy of our method through both simulations and an application to a multimodal neuroimaging study of Alzheimer's disease.
Adaptation to Unknown Situations as the Holy Grail of Learning-Based Self-Adaptive Systems: Research Directions
Authors: Ivana Dusparic, Nicolas Cardozo
Date: 2021-03-11 00:00:00

Self-adaptive systems continuously adapt to changes in their execution environment. Capturing all possible changes to define suitable behaviour beforehand is unfeasible, or even impossible in the case of unknown changes, hence human intervention may be required. We argue that adapting to unknown situations is the ultimate challenge for self-adaptive systems. Learning-based approaches are used to learn the suitable behaviour to exhibit in the case of unknown situations, to minimize or fully remove human intervention. While such approaches can, to a certain extent, generalize existing adaptations to new situations, there is a number of breakthroughs that need to be achieved before systems can adapt to general unknown and unforeseen situations. We posit the research directions that need to be explored to achieve unanticipated adaptation from the perspective of learning-based self-adaptive systems. At minimum, systems need to define internal representations of previously unseen situations on-the-fly, extrapolate the relationship to the previously encountered situations to evolve existing adaptations, and reason about the feasibility of achieving their intrinsic goals in the new set of conditions. We close discussing whether, even when we can, we should indeed build systems that define their own behaviour and adapt their goals, without involving a human supervisor.
Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search
Authors: Jiaoyang Li, Daniel Harabor, Peter J. Stuckey, Sven Koenig
Date: 2021-03-12 00:00:00
Tasks: Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks us to plan collision-free paths for a team of cooperative agents. In this work, we show that one of the reasons why MAPF is so hard to solve is due to a phenomenon called pairwise symmetry, which occurs when two agents have many different paths to their target locations, all of which appear promising, but every combination of them results in a collision. We identify several classes of pairwise symmetries and show that each one arises commonly in practice and can produce an exponential explosion in the space of possible collision resolutions, leading to unacceptable runtimes for current state-of-the-art (bounded-sub)optimal MAPF algorithms. We propose a variety of reasoning techniques that detect the symmetries efficiently as they arise and resolve them by using specialized constraints to eliminate all permutations of pairwise colliding paths in a single branching step. We implement these ideas in the context of the leading optimal MAPF algorithm CBS and show that the addition of the symmetry reasoning techniques can have a dramatic positive effect on its performance - we report a reduction in the number of node expansions by up to four orders of magnitude and an increase in scalability by up to thirty times. These gains allow us to solve to optimality a variety of challenging MAPF instances previously considered out of reach for CBS.
Explaining Network Intrusion Detection System Using Explainable AI Framework
Authors: Shraddha Mane, Dattaraj Rao
Date: 2021-03-12 00:00:00
Tasks: BIG-bench Machine Learning, Intrusion Detection, Network Intrusion Detection

Cybersecurity is a domain where the data distribution is constantly changing with attackers exploring newer patterns to attack cyber infrastructure. Intrusion detection system is one of the important layers in cyber safety in today's world. Machine learning based network intrusion detection systems started showing effective results in recent years. With deep learning models, detection rates of network intrusion detection system are improved. More accurate the model, more the complexity and hence less the interpretability. Deep neural networks are complex and hard to interpret which makes difficult to use them in production as reasons behind their decisions are unknown. In this paper, we have used deep neural network for network intrusion detection and also proposed explainable AI framework to add transparency at every stage of machine learning pipeline. This is done by leveraging Explainable AI algorithms which focus on making ML models less of black boxes by providing explanations as to why a prediction is made. Explanations give us measurable factors as to what features influence the prediction of a cyberattack and to what degree. These explanations are generated from SHAP, LIME, Contrastive Explanations Method, ProtoDash and Boolean Decision Rules via Column Generation. We apply these approaches to NSL KDD dataset for intrusion detection system and demonstrate results.
Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies
Authors: Giovani Guizzo, Federica Sarro, Jens Krinke, Silvia Regina Vergilio
Date: 2021-03-12 00:00:00

Mutation testing is an effective approach to evaluate and strengthen software test suites, but its adoption is currently limited by the mutants' execution computational cost. Several strategies have been proposed to reduce this cost (a.k.a. mutation cost reduction strategies), however none of them has proven to be effective for all scenarios since they often need an ad-hoc manual selection and configuration depending on the software under test (SUT). In this paper, we propose a novel multi-objective evolutionary hyper-heuristic approach, dubbed Sentinel, to automate the generation of optimal cost reduction strategies for every new SUT. We evaluate Sentinel by carrying out a thorough empirical study involving 40 releases of 10 open-source real-world software systems and both baseline and state-of-the-art strategies as a benchmark. We execute a total of 4,800 experiments, and evaluate their results with both quality indicators and statistical significance tests, following the most recent best practice in the literature. The results show that strategies generated by Sentinel outperform the baseline strategies in 95% of the cases always with large effect sizes. They also obtain statistically significantly better results than state-of-the-art strategies in 88% of the cases, with large effect sizes for 95% of them. Also, our study reveals that the mutation strategies generated by Sentinel for a given software version can be used without any loss in quality for subsequently developed versions in 95% of the cases. These results show that Sentinel is able to automatically generate mutation strategies that reduce mutation testing cost without affecting its testing effectiveness (i.e. mutation score), thus taking off from the tester's shoulders the burden of manually selecting and configuring strategies for each SUT.
Your most telling friends: Propagating latent ideological features on Twitter using neighborhood coherence
Authors: Pedro Ramaciotti Morales, Jean-Philippe Cointet, Julio Laborde
Date: 2021-03-12 00:00:00

Multidimensional scaling in networks allows for the discovery of latent information about their structure by embedding nodes in some feature space. Ideological scaling for users in social networks such as Twitter is an example, but similar settings can include diverse applications in other networks and even media platforms or e-commerce. A growing literature of ideology scaling methods in social networks restricts the scaling procedure to nodes that provide interpretability of the feature space: on Twitter, it is common to consider the sub-network of parliamentarians and their followers. This allows to interpret inferred latent features as indices for ideology-related concepts inspecting the position of members of parliament. While effective in inferring meaningful features, this is generally restrained to these sub-networks, limiting interesting applications such as country-wide measurement of polarization and its evolution. We propose two methods to propagate ideological features beyond these sub-networks: one based on homophily (linked users have similar ideology), and the other on structural similarity (nodes with similar neighborhoods have similar ideologies). In our methods, we leverage the concept of neighborhood ideological coherence as a parameter for propagation. Using Twitter data, we produce an ideological scaling for 370K users, and analyze the two families of propagation methods on a population of 6.5M users. We find that, when coherence is considered, the ideology of a user is better estimated from those with similar neighborhoods, than from their immediate neighbors.
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Authors: Zahra Ghodsi, Siva Kumar Sastry Hari, Iuri Frosio, Timothy Tsai, Alejandro Troccoli, Stephen W. Keckler, Siddharth Garg, Anima Anandkumar
Date: 2021-03-12 00:00:00
Tasks: Autonomous Vehicles

Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems. We propose efficient mechanisms to both characterize and generate testing scenarios using a state-of-the-art driving simulator. For any scenario, our method generates a set of possible driving paths and identifies all the possible safe driving trajectories that can be taken starting at different times, to compute metrics that quantify the complexity of the scenario. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project, as well as adversarial scenarios generated in simulation. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident. We demonstrate a strong correlation between the proposed metrics and human intuition.
Towards Risk Modeling for Collaborative AI
Authors: Matteo Camilli, Michael Felderer, Andrea Giusti, Dominik T. Matt, Anna Perini, Barbara Russo, Angelo Susi
Date: 2021-03-12 00:00:00
Tasks: BIG-bench Machine Learning

Collaborative AI systems aim at working together with humans in a shared space to achieve a common goal. This setting imposes potentially hazardous circumstances due to contacts that could harm human beings. Thus, building such systems with strong assurances of compliance with requirements domain specific standards and regulations is of greatest importance. Challenges associated with the achievement of this goal become even more severe when such systems rely on machine learning components rather than such as top-down rule-based AI. In this paper, we introduce a risk modeling approach tailored to Collaborative AI systems. The risk model includes goals, risk events and domain specific indicators that potentially expose humans to hazards. The risk model is then leveraged to drive assurance methods that feed in turn the risk model through insights extracted from run-time evidence. Our envisioned approach is described by means of a running example in the domain of Industry 4.0, where a robotic arm endowed with a visual perception component, implemented with machine learning, collaborates with a human operator for a production-relevant task.
Weakly Supervised Real-time Image Cropping based on Aesthetic Distributions
Authors: Peng Lu, Jiahui Liu, Xujun Peng, Xiaojie Wang
Date: 2020-10-15 00:00:00
Tasks: Image Cropping

Image cropping is an effective tool to edit and manipulate images to achieve better aesthetic quality. Most existing cropping approaches rely on the two-step paradigm where multiple candidate cropping areas are proposed initially and the optimal cropping window is determined based on some quality criteria for these candidates af- terwards. The obvious disadvantage of this mechanism is its low efficiency due to the huge searching space of candidate crops. In order to tackle this problem, a weakly supervised cropping frame- work is proposed, where the distribution dissimilarity between high quality images and cropped images is used to guide the coordinate predictor’s training and the ground truths of cropping windows are not required by the proposed method. Meanwhile, to improve the cropping performance, a saliency loss is also designed in the proposed framework to force the neural network to focus more on the interested objects in the image. Under this framework, the im- ages can be cropped effectively by the trained coordinate predictor in a one-pass favor without multiple candidates proposals, which ensures the high efficiency of the proposed system . Also, based on the proposed framework, many existing distribution dissimi- larity measurements can be applied to train the image cropping system with high flexibility, such as likelihood based and diver- gence based distribution dissimilarity measure proposed in this work. The experiments on the public databases show that the pro- posed cropping method achieves the state-of-the-art accuracy, and the high computation efficiency as fast as 285 FPS is also obtained.
A Mean Field Game Approach to Equilibrium Pricing with Market Clearing Condition
Authors: Masaaki Fujii, Akihiko Takahashi
Date: 2020-03-06 00:00:00
Tasks: Form

In this work, we study an equilibrium-based continuous asset pricing problem which seeks to form a price process endogenously by requiring it to balance the flow of sales-and-purchase orders in the exchange market, where a large number of agents are interacting through the market price. Adopting a mean field game (MFG) approach, we find a special form of forward-backward stochastic differential equations of McKean-Vlasov type with common noise whose solution provides a good approximate of the market price. We show the convergence of the net order flow to zero in the large N-limit and get the order of convergence in N under some conditions. We also extend the model to a setup with multiple populations where the agents within each population share the same cost and coefficient functions but they can be different population by population.
Research of Segmented 8bit Voltage-Mode R-2R Ladder DAC
Authors: You Li
Date: 2013-05-19 00:00:00

Random mismatch errors in the resistor networks are one of the dominant nonlinearity sources for high resolution and high accuracy resistor DACs. This paper applies the theory of ordered element matching in a high resolution segmented R-2R DAC. It can achieve high matching accuracy by regrouping the resistors in the MSB array according to their resistance ranks obtained by the INL test. The implementation only requires adding some additional digital circuits to the typical design. A behavioral model of 18-bit segmented R-2R DAC is created in MATLAB. The statistical results show a significant resistor area reduction compared with state of the art.
Advisors with Hidden Motives
Authors: Paula Onuchic
Date: 2021-03-12 00:00:00
Tasks: Informativeness

I study a model of advisors with hidden motives: a seller discloses information about an object's value to a potential buyer, who doesn't know the object's value or how profitable the object's sale is to the seller (the seller's motives). I characterize optimal disclosure rules, used by the seller to steer sales from lower- to higher-profitability objects. I investigate the effects of a mandated transparency policy, which reveals the seller's motives to the buyer. I show that, by removing the seller's steering incentive, transparency can dissuade the seller from disclosing information about the object's value, and from acquiring that information in the first place. This result refines our understanding of effective regulation in advice markets, and links it to the commitment protocol in the advisor-advisee relation.
A closed-form approximation for pricing geometric Istanbul options
Authors: Mohamed Amine Kacef, Kamal Boukhetala
Date: 2021-03-12 00:00:00
Tasks: Form

The Istanbul options were first introduced by Michel Jacques in 1997. These derivatives are considered as an extension of the Asian options. In this paper, we propose an analytical approximation formula for a geometric Istanbul call option (GIC) under the Black-Scholes model. Our approximate pricing formula is obtained in closed-form using a second-order Taylor expansion. We compare our theoretical results with those of Monte-Carlo simulations using the control variates method. Finally, we study the effects of changes in the price of the underlying asset on the value of GIC.
Unlocking Extra Value from Grid Batteries Using Advanced Models
Authors: Jorn M. Reniers, Grietus Mulder, David A. Howey
Date: 2020-09-24 00:00:00
Tasks: energy trading

Lithium-ion batteries are increasingly being deployed in liberalised electricity systems, where their use is driven by economic optimisation in a specific market context. However, battery degradation depends strongly on operational profile, and this is particularly variable in energy trading applications. Here, we present results from a year-long experiment where pairs of batteries were cycled with profiles calculated by solving an economic optimisation problem for wholesale energy trading, including a physically-motivated degradation model as a constraint. The results confirm the conclusions of previous simulations and show that this approach can increase revenue by 20% whilst simultaneously decreasing degradation by 30% compared to existing methods. Analysis of the data shows that conventional approaches cannot increase the number of cycles a battery can manage over its lifetime, but the physics-based approach increases the lifetime both in terms of years and number of cycles, as well as the revenue per year, increasing the possible lifetime revenue by 70%. Finally, the results demonstrate the economic impact of model inaccuracies, showing that the physics-based model can reduce the discrepancy in the overall business case from 170% to 13%. There is potential to unlock significant extra performance using control engineering incorporating physical models of battery ageing.