Robust Recovery Research Articles

Adaptive weighted robust data recovery with total variation for hyperspectral image

This paper proposes an adaptive weighted robust data recovery method with total variation (TV) regularization for hyperspectral image (HSI). In the proposed method, the HSI recovery problem is modeled as a tensor robust principal component analysis optimization problem, which decomposes the received data into low-rank HSI data, outliers, and noise component, with tensor nuclear norm (TNN), ℓ1 norm, and ℓF norm constrained, respectively. Then, an adaptive weighted strategy is defined to impose on the TNN and outliers to flexibly use the priori information of singular values (SVs) and strengthen the sparsity of outliers, where the weights are adaptively conducted by the elements in SVs and outliers. Specifically, the weighted strategy in TNN retains larger SVs to determine the main information for data recovery, while penalizing smaller SVs to eliminate the influence of interference. And the weighted strategy on outliers enables the proposed method to more effectively measure the sparsity of outliers. Furthermore, TV regularization is introduced to extract the local information for data recovery by a difference operation along different modes, which also can help the proposed method avoid the detail loss caused by the weighted strategy. Experiments confirm that the proposed method significantly outperforms the existing methods.

A Distributionally Robust Defender-Attacker-Defender Model for Resilience Enhancement of Power Systems Against Malicious Cyberattacks

The cybersecurity of electric power grids is emerging as a critical challenge for the power industry in the transformation of modern power systems towards the future smart grid. It is of great importance to enhance the resilience of power systems against potential cyber threats. In this paper, a distributionally robust recovery resource allocation method based on the tri-level defender-attacker-defender (D-A-D) model is proposed to enhance the resilience of power systems in the face of malicious cyberattacks. The proposed recovery resource allocation method is able to optimally distribute the recovery resources among the substations in the grid to mitigate the impacts of successful cyberattacks during the recovery process, and improve the resilience performance of power systems against the attacks. In the proposed model, the recovery resource represents the available resource that can be expended to support the recovery efforts of the cyber-physical power system. It is expected to accelerate the recovery process of the system after successful attacks. The recovery resource may include necessary software, hardware, and labor force. Meanwhile, a distributionally robust optimization (DRO) model is proposed to address the uncertainty of the power system operation conditions, e.g., renewable energy resources (RESs). In order to verify the proposed system defense method, case studies were conducted on the IEEE Reliability Test System RTS-79. The results of the case studies show that the proposed method can provide a robust solution to recovery resource allocation for mitigating the risk of potential cyberattacks.

Fast iterative regularization by reusing data

Abstract Discrete inverse problems correspond to solving a system of equations in a stable way with respect to noise in the data. A typical approach to select a meaningful solution is to introduce a regularizer. While for most applications the regularizer is convex, in many cases it is neither smooth nor strongly convex. In this paper, we propose and study two new iterative regularization methods, based on a primal-dual algorithm, to regularize inverse problems efficiently. Our analysis, in the noise free case, provides convergence rates for the Lagrangian and the feasibility gap. In the noisy case, it provides stability bounds and early stopping rules with theoretical guarantees. The main novelty of our work is the exploitation of some a priori knowledge about the solution set: we show that the linear equations determined by the data can be used more than once along the iterations. We discuss various approaches to reuse linear equations that are at the same time consistent with our assumptions and flexible in the implementation. Finally, we illustrate our theoretical findings with numerical simulations for robust sparse recovery and image reconstruction. We confirm the efficiency of the proposed regularization approaches, comparing the results with state-of-the-art methods.

Robust Recovery of Low-Rank Matrices and Low-Tubal-Rank Tensors from Noisy Sketches

Robust Recovery of Low-Rank Matrices and Low-Tubal-Rank Tensors from Noisy Sketches

A robust adaptive-thresholding primal-dual sparse recovery for THz imaging

Terahertz time-domain spectroscopy (THz-TDS) system is a powerful tool in material spectral analysis and non-destructive testing (NDT). However, the data acquisition is time-consuming due to the point-by-point scanning process, which leads to limitations in THz imaging applications. In this paper, a robust adaptive-thresholding primal-dual sparse recovery (APSR) method is proposed to reconstruct high-resolution THz images and reduce the acquisition time. A sparsity averaging dictionary, which is a concatenate of multiple bases, is applied to improve the robustness and generalization. A reweighted scheme is also adopted to enhance the sparse solution. As a crucial consideration in the sparse recovery method, we propose a robust adaptive threshold estimator based on the median absolute deviation in the sense that the final threshold determines the reconstruction quality and the convergence rate. Meanwhile, together with the Nesterov acceleration technique, a large threshold can be applied at the beginning of the iteration to help accelerate the convergence. Numerical experiments demonstrate that the APSR method can reconstruct THz images with robustness even with low SNR (of 30 dB) and low sampling rate (of 30%) compared to conventional methods. In the real THz-TDS system, our proposed sparse imaging method can successfully recover a composite object with much fewer THz spectra and reduces the acquisition time to 30% at the cost of a relative error of 2.5%, demonstrating the efficiency of our proposed method and the penetrating ability of THz technology in the NDT applications.

Robust DNN-Based Recovery of Wideband Spectrum Signals

Robust DNN-Based Recovery of Wideband Spectrum Signals

Reliable reserve-crew scheduling for airlines

We study the practical setting in which regular- and reserve-crew schedules are dynamically maintained up to the day of executing the schedule. At each day preceding the execution of the schedule, disruptions occur due to sudden unavailability of personnel, making the planned regular and reserve-crew schedules infeasible for its execution day. This paper studies the fundamental question how to repair the schedules’ infeasibility in the days preceding the execution, taking into account labor regulations. We propose a robust repair strategy that maintains flexibility in order to cope with additional future disruptions. The flexibility in reserve-crew usage is explicitly considered through evaluating the expected shortfall of the reserve-crew schedule based on a Markov chain formulation. The core of our approach relies on iteratively solving a set-covering formulation, which we call the Robust Crew Recovery Problem, which encapsulates this flexibility notion for reserve crew usage. A tailored branch-and-price algorithm is developed for solving the Robust Crew Recovery Problem to optimality. The corresponding pricing problem is efficiently solved by a newly developed pulse algorithm. Based on actual data from a medium-sized hub-and-spoke airline, we show that embracing our approach leads to fewer flight cancellations and fewer last-minute alterations, compared to repairing disrupted schedules without considering our robust measure.

Hybrid hierarchical learning for solving complex sequential tasks using the robotic manipulation network ROMAN

AbstractSolving long sequential tasks remains a non-trivial challenge in the field of embodied artificial intelligence. Enabling a robotic system to perform diverse sequential tasks with a broad range of manipulation skills is a notable open problem and continues to be an active area of research. In this work, we present a hybrid hierarchical learning framework, the robotic manipulation network ROMAN, to address the challenge of solving multiple complex tasks over long time horizons in robotic manipulation. By integrating behavioural cloning, imitation learning and reinforcement learning, ROMAN achieves task versatility and robust failure recovery. It consists of a central manipulation network that coordinates an ensemble of various neural networks, each specializing in different recombinable subtasks to generate their correct in-sequence actions, to solve complex long-horizon manipulation tasks. Our experiments show that, by orchestrating and activating these specialized manipulation experts, ROMAN generates correct sequential activations accomplishing long sequences of sophisticated manipulation tasks and achieving adaptive behaviours beyond demonstrations, while exhibiting robustness to various sensory noises. These results highlight the significance and versatility of ROMAN’s dynamic adaptability featuring autonomous failure recovery capabilities, and underline its potential for various autonomous manipulation tasks that require adaptive motor skills.

An efficient and secure identity-based integrity auditing scheme for sensitive data with anti-replacement attack on multi-cloud storage

The rapid growth of cloud storage brings forth significant difficulties in ensuring the integrity of data owners' sensitive data. Multi-cloud storage systems, which motive to enhance trustworthiness, are vulnerable to rigorous replacement attacks. These insidious attacks involve malicious actors substituting legitimate data with counterfeit versions, leading to compromised integrity and fraudulent audit outcomes. We propose a novel identity-based data integrity auditing protocol tailored for multi-cloud storage environments to tackle this challenge. Our protocol leverages a Third-Party Auditor (TPA) to delegate data authenticity and auditing responsibilities, employing identity-based blind signatures with a robust data recovery scheme. By eliminating the need for cumbersome public key certificate management, our solution effectively protects data owners' sensitive data while ensuring confidentiality across multiple cloud providers, thereby mitigating the risk of replacement attacks. A thorough security examination shows the proposed system resists forging, replacement attacks, and data confidentiality violations. A rigorous performance experiment also reveals outstanding computational and communication efficiency that outperforms current methods in tag production, proof generation, and verification. Our protocol offers an extraordinary balance of security and efficiency, making it particularly useful for auditing patent-sensitive data in multi-cloud storage. Our proposal provides a timely and effective way to protect sensitive data and prevent attackers in multi-cloud storage settings at a time when replacement attacks pose a serious concern.

The politics of structural reform in Ireland: Dominant growth coalitions, domestic business elites, and internal devaluation

This paper broadens the scope of the Growth Model (GM) research agenda beyond the study of ‘core’ business elites by investigating the influence of (oft-neglected) non-core business actors and sectoral interests in determining growth strategies. To do so it looks at a crucial case in the GM debate: Ireland. Ireland’s robust recovery from the eurozone crisis has set it apart from the rest of the eurozone periphery. For GM scholarship, Ireland’s recovery has been driven by its long-established multinational/export sector, which is relatively indifferent to the effects of austerity and internal devaluation. Why, then, was internal devaluation a central plank of Ireland’s crisis response and Economic Adjustment Programme (EAP)? Drawing on 10 original interviews with Irish business elites and policy makers and documentary evidence, this paper shows that domestic, not multinational, business elites played a key role in framing conditionality related to competitiveness and labour market reform in the Irish EAP. This challenges the concept of ‘dominant growth coalitions’ and argues that an exclusive focus on ‘core’ business elites neglects the ability of other fractions of business power to determine growth strategies. Looking beyond ‘core’ business elites makes it possible to identify transformation behind more obvious patterns of continuity.

Deep Learning Peephole LSTM Neural Network-Based Channel State Estimators for OFDM 5G and Beyond Networks

This study uses deep learning (DL) techniques for pilot-based channel estimation in orthogonal frequency division multiplexing (OFDM). Conventional channel estimators in pilot-symbol-aided OFDM systems suffer from performance degradation, especially in low signal-to-noise ratio (SNR) regions, due to noise amplification in the estimation process, intercarrier interference, a lack of primary channel data, and poor performance with few pilots, although they exhibit lower complexity and require implicit knowledge of the channel statistics. A new method for estimating channels using DL with peephole long short-term memory (peephole LSTM) is proposed. The proposed peephole LSTM-based channel state estimator is deployed online after offline training with generated datasets to track channel parameters, which enables robust recovery of transmitted data. A comparison is made between the proposed estimator and conventional LSTM and GRU-based channel state estimators using three different DL optimization techniques. Due to the outstanding learning and generalization properties of the DL-based peephole LSTM model, the suggested estimator significantly outperforms the conventional least square (LS) and minimum mean square error (MMSE) estimators, especially with a few pilots. The suggested estimator can be used without prior information on channel statistics. For this reason, it seems promising that the proposed estimator can be used to estimate the channel states of an OFDM communication system.

Efficacy of one‐time rotenone application for Westslope Cutthroat Trout restoration in Boulder Creek, Montana

AbstractObjectiveAlthough piscicides are an important tool for native fish management, our understanding of native fish population dynamics posttreatment (i.e., in putatively vacant habitat) is limited.MethodsHere, we describe long‐term trends in size and abundance of Cutthroat Trout Oncorhynchus clarkii in Lower Boulder Lake and Boulder Creek (Montana).ResultNo significant differences were found before and after treatment with rotenone in mean length and population density in Boulder Creek. Posttreatment genetic samples from Boulder Lake and upper Boulder Creek consisted entirely of the Westslope Cutthroat Trout Oncorhynchus clarkii lewisi used to refound the population. Middle and lower Boulder Creek genetic samples contained 99.4% and 98.4% Westslope Cutthroat Trout, indicating that some nonnative (Yellowstone Cutthroat Trout Oncorhynchus clarkii bouvieri hybrid) fish survived the treatment. Population demographics showed robust recovery posttreatment; however, genetic results revealed the limitations of a one‐time treatment—that is, an incomplete fish kill.ConclusionWe demonstrate that Cutthroat Trout populations achieve recovery after rotenone treatment in the long term, but one‐time rotenone treatments on large drainages often fail to totally eradicate populations. Therefore, managers should plan on multiple treatments and utilize environmental DNA to help achieve total removal of target species.

Beurling-Type Density Criteria for System Identification

This paper addresses the problem of identifying a linear time-varying (LTV) system characterized by a (possibly infinite) discrete set of delay-Doppler shifts without a lattice (or other “geometry-discretizing”) constraint on the support set. Concretely, we show that a class of such LTV systems is identifiable whenever the upper uniform Beurling density of the delay-Doppler support sets, measured “uniformly over the class”, is strictly less than 1/2. The proof of this result reveals an interesting relation between LTV system identification and interpolation in the Bargmann-Fock space. Moreover, we show that the density condition we obtain is also necessary for classes of systems invariant under time-frequency shifts and closed under a natural topology on the support sets. We furthermore find that identifiability guarantees robust recovery of the delay-Doppler support set, as well as the weights of the individual delay-Doppler shifts, both in the sense of asymptotically vanishing reconstruction error for vanishing measurement error.

Extracting liquid fertilizer from ammonia-laden pharmaceutical wastewater using Hollow fiber membrane contactors

Production of certain pharmaceuticals generates wastewater with a high ammonia load, posing economic and environmental burdens. Recovering ammonia as a nitrogen fertilizer can alleviate these burdens and is vital from a circular economy perspective. For this purpose, this study, jointly performed by a research university and a large pharma company, evaluated the use of Hollow fiber membrane contactors (HFMC). The experiments were conducted using real pharmaceutical wastewater in two separate laboratories at two process scales (bench and pilot) and two operating modes (batch and continuous). The study evaluated the effect of feed concentration, flow rate, and process configuration and described the results using a single-parameter (mass transfer coefficient K) mathematical model. We then used the model to design scaled-up batch and continuous processes, based on which we performed a detailed economic assessment. A robust and consistent ammonia removal and recovery were obtained for all the experimental conditions tested. The model adequately described the empirical results, and K was similar across all the experiments in this study, which were conducted in practically relevant conditions. Both batch and continuous process designs were found feasible, increasing process flexibility. The economic evaluation revealed that acid and base consumption, rather than the initial investment, is the most significant expense. The cost of membranes, although relatively high, had little contribution to the overall expenses. Revenues from selling the recovered nitrogen as ammonium-sulfate fertilizer reduces the evaluated net cost of the novel treatment to a minimum of $3.76 per kgN - a potential cost savings of up to 29% relative to the current treatment ($5.28). Therefore, applying HFMC to recover nitrogen from pharmaceutical wastewater is promising from both economic and environmental viewpoints.

Learning spatially variant degradation for unsupervised blind photoacoustic tomography image restoration

Photoacoustic tomography (PAT) images contain inherent distortions due to the imaging system and heterogeneous tissue properties. Improving image quality requires the removal of these system distortions. While model-based approaches and data-driven techniques have been proposed for PAT image restoration, achieving accurate and robust image recovery remains challenging. Recently, deep-learning-based image deconvolution approaches have shown promise for image recovery. However, PAT imaging presents unique challenges, including spatially varying resolution and the absence of ground truth data. Consequently, there is a pressing need for a novel learning strategy specifically tailored for PAT imaging. Herein, we propose a configurable network model named Deep hybrid Image-PSF Prior (DIPP) that builds upon the physical image degradation model of PAT. DIPP is an unsupervised and deeply learned network model that aims to extract the ideal PAT image from complex system degradation. Our DIPP framework captures the degraded information solely from the acquired PAT image, without relying on ground truth or labeled data for network training. Additionally, we can incorporate the experimentally measured Point Spread Functions (PSFs) of the specific PAT system as a reference to further enhance performance. To evaluate the algorithm’s effectiveness in addressing multiple degradations in PAT, we conduct extensive experiments using simulation images, publicly available datasets, phantom images, and in vivo small animal imaging data. Comparative analyses with classical analytical methods and state-of-the-art deep learning models demonstrate that our DIPP approach achieves significantly improved restoration results in terms of image details and contrast.

Fabrication of Poly (Trans-3-(3-Pyridyl)Acrylic Acid)/Multi-Walled Carbon Nanotubes Membrane for Electrochemically Simultaneously Detecting Catechol and Hydroquinone.

Herein, conductive polymer membrane with excellent performance was successfully fabricated by integrating carboxylated multi-walled carbon nanotubes (MWCNTs) and poly (trans-3-(3-pyridyl) acrylic acid) (PPAA) film. The drop-casting method was employed to coated MWCNTs on the glassy carbon electrode (GCE) surface, and PPAA was then electropolymerized onto the surface of the MWCNTs/GCE in order to form PPAA-MWCNTs membrane. This enables the verification of the excellent performances of proposed membrane by electrochemically determining catechol (CC) and hydroquinone (HQ) as the model. Cyclic voltammetry experiments showed that the proposed membrane exhibited an obvious electrocatalytic effect on CC and HQ, owing to the synergistic effect of PPAA and MWCNTs. Differential pulse voltammetry was adopted for simultaneous detection purposes, and an increased electrochemical responded to CC and HQ. A concentration increase was found in the range of 1.0 × 10-6 mol/L~1.0 × 10-4 mol/L, and it exhibited a good linear relationship with satisfactory detection limits of 3.17 × 10-7 mol/L for CC and 2.03 × 10-7 mol/L for HQ (S/N = 3). Additionally, this constructed membrane showed good reproducibility and stability. The final electrode was successfully applied to analyze CC and HQ in actual water samples, and it obtained robust recovery for CC with 95.2% and 98.5%, and for HQ with 97.0% and 97.3%. Overall, the constructed membrane can potentially be a good candidate for constructing electrochemical sensors in environmental analysis.

Reading the Women’s Sentimental Novel: A Romance

In “Reading the Women’s Sentimental Novel: A Romance,” Tabitha Sparks considers a large and diffuse body of mass-market fiction written by and for Victorian women. She argues that the author-focused interpretive approach that underwrites the study of the canon neglects the attraction of formula fiction, and even the robust recovery efforts of Victorian scholars have largely avoided a taxonomic reading of these novels. In an effort to uncover their objectives and appeal, Sparks reads periodical reviews and discussions of the professional woman writer to better understand the commercial – not artistic – standards assigned to the prosaic “lady’s novel.” She examines a subset of novels by Annie S. Swan, Sarah Doudney, Emily Jolly, and Adeline Sergeant to uncover a repeated subplot in which these novels’ heroines become best-selling authors. The complete elision of the content of their best-sellers reveals and confirms the sentimental novel’s self-conscious withdrawal from the literary caste wars of the day.

Airborne radar forward‐looking image enhancing algorithm based on generative adversarial networks

AbstractRadar forward‐looking imaging is gaining significance in various applications like battlefield reconnaissance, target surveillance, and precision guidance. Although synthetic aperture radar techniques provide high azimuth resolution but faced limitations in forward‐looking area due to the poor Doppler resolution and the “left‐right” ambiguity problem. Recently, generative adversarial networks have been extensively used for image motion blur removal. This letter proposes an end‐to‐end forward‐looking image enhancing network using generative adversarial network to produce high‐resolution images, improving the efficiency, and quality of imaging. Compared to conventional methods such as the deconvolution‐based methods, this algorithm eliminates the need for design and iterative processes of the observation matrix. Simulated and real radar data validate that this approach offers robust recovery and better performance.

Ensuring resilience: Integrating IT disaster recovery planning and business continuity for sustainable information technology operations

Information Technology (IT) Disaster Recovery Planning (IT DRP) and Business Continuity (BC) are essential components of an organization’s overall resilience strategy. IT DRP focuses on the recovery and restoration of IT systems, infrastructure, and services in the event of a disruptive incident or disaster, aiming to minimize downtime and data loss. BC, on the other hand, encompasses a broader perspective, addressing the organization's ability to maintain essential operations and deliver critical services during and after a disruption. This paper provides an overview of IT DRP and BC, highlighting their importance, challenges, and strategies. It also identifies the research gaps and future research scope in these areas. The findings indicate that both IT DRP and BC face challenges in their effective implementation. These challenges include the evolving nature of technology, increasing complexity of IT systems, budget constraints, organizational resistance to change, and the need for skilled personnel. Overcoming these challenges requires a comprehensive understanding of the organization’s IT infrastructure, risk assessment, and the development of robust recovery strategies and plans. It is also noted that despite considerable research on IT DRP and BC, there are several research gaps that deserve attention. These include the development of advanced technologies and tools for more efficient recovery and continuity, the integration of IT DRP and BC with overall organizational risk management, the impact of emerging technologies such as cloud computing and virtualization on recovery strategies, and the evaluation of the effectiveness and cost-efficiency of different IT DRP and BC strategies.

Tensor Compressive Sensing Fused Low-Rankness and Local-Smoothness

A plethora of previous studies indicates that making full use of multifarious intrinsic properties of primordial data is a valid pathway to recover original images from their degraded observations. Typically, both low-rankness and local-smoothness broadly exist in real-world tensor data such as hyperspectral images and videos. Modeling based on both properties has received a great deal of attention, whereas most studies concentrate on experimental performance, and theoretical investigations are still lacking. In this paper, we study the tensor compressive sensing problem based on the tensor correlated total variation, which is a new regularizer used to simultaneously capture both properties existing in the same dataset. The new regularizer has the outstanding advantage of not using a trade-off parameter to balance the two properties. The obtained theories provide a robust recovery guarantee, where the error bound shows that our model certainly benefits from both properties in ground-truth data adaptively. Moreover, based on the ADMM update procedure, we design an algorithm with a global convergence guarantee to solve this model. At last, we carry out experiments to apply our model to hyperspectral image and video restoration problems. The experimental results show that our method is prominently better than many other competing ones. Our code and Supplementary Material are available at https://github.com/fsliuxl/cs-tctv.