From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration

In this paper, we propose a novel approach to the rank minimization problem, termed rank residual constraint (RRC) model. Different from existing low-rank based approaches, such as the well-known nuclear norm minimization (NNM) and the weighted nuclear norm minimization (WNNM), which estimate the underlying low-rank matrix directly from the corrupted observations, we progressively approximate the underlying low-rank matrix via minimizing the rank residual. Through integrating the image nonlocal self-similarity (NSS) prior with the proposed RRC model, we apply it to image restoration tasks, including image denoising and image compression artifacts reduction. Towards this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image. In this manner, both the reference and the estimated image are updated gradually and jointly in each iteration. Based on the group-based sparse representation model, we further provide a theoretical analysis on the feasibility of the proposed RRC model. Experimental results demonstrate that the proposed RRC model outperforms many state-of-the-art schemes in both the objective and perceptual quality

Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization

Despite extensive studies, the underlying reason as to why overparameterized neural networks can generalize remains elusive. Existing theory shows that common stochastic optimizers prefer flatter minimizers of the training loss, and thus a natural potential explanation is that flatness implies generalization. This work critically examines this explanation. Through theoretical and empirical investigation, we identify the following three scenarios for two-layer ReLU networks: (1) flatness provably implies generalization; (2) there exist non-generalizing flattest models and sharpness minimization algorithms fail to generalize, and (3) perhaps most surprisingly, there exist non-generalizing flattest models, but sharpness minimization algorithms still generalize. Our results suggest that the relationship between sharpness and generalization subtly depends on the data distributions and the model architectures and sharpness minimization algorithms do not only minimize sharpness to achieve better generalization. This calls for the search for other explanations for the generalization of over-parameterized neural networks.Comment: 34 pages,11 figure

Deciphering Charging Status, Absolute Quantum Efficiency, and Absorption Cross Section of MultiCarrier States in Single Colloidal Quantum Dot

Upon photo- or electrical-excitation, colloidal quantum dots (QDs) are often found in multi-carrier states due to multi-photon absorption and photo-charging of the QDs. While many of these multi-carrier states are observed in single-dot spectroscopy, their properties are not well studied due to random charging/discharging, emission intensity intermittency, and uncontrolled surface defects of single QD. Here we report in-situ deciphering the charging status, and precisely assessing the absorption cross section, and determining the absolute emission quantum yield of mono-exciton and biexciton states for neutral, positively-charged, and negatively-charged single core/shell CdSe/CdS QD. We uncover very different photon statistics of the three charge states in single QD and unambiguously identify their charge sign together with the information of their photoluminescence decay dynamics. We then show their distinct photoluminescence saturation behaviors and evaluated the absolute values of absorption cross sections and quantum efficiencies of monoexcitons and biexcitons. We demonstrate that addition of an extra hole or electron in a QD changes not only its emission properties but also varies its absorption cross section

Bipolar plasma vaporization using plasma-cutting and plasma-loop electrodes versus cold-knife transurethral incision for the treatment of posterior urethral stricture: a prospective, randomized study

OBJECTIVE: Evaluate the efficiency and safety of bipolar plasma vaporization using plasma-cutting and plasma-loop electrodes for the treatment of posterior urethral stricture. Compare the outcomes following bipolar plasma vaporization with conventional cold-knife urethrotomy. METHODS: A randomized trial was performed to compare patient outcomes from the bipolar and cold-knife groups. All patients were assessed at 6 and 12 months postoperatively via urethrography and uroflowmetry. At the end of the first postoperative year, ureteroscopy was performed to evaluate the efficacy of the procedure. The mean follow-up time was 13.9 months (range: 12 to 21 months). If re-stenosis was not identified by both urethrography and ureteroscopy, the procedure was considered “successful”. RESULTS: Fifty-three male patients with posterior urethral strictures were selected and randomly divided into two groups: bipolar group (n=27) or cold-knife group (n=26). Patients in the bipolar group experienced a shorter operative time compared to the cold-knife group (23.45±7.64 hours vs 33.45±5.45 hours, respectively). The 12-month postoperative Qmax was faster in the bipolar group than in the cold-knife group (15.54±2.78 ml/sec vs 18.25±2.12 ml/sec, respectively). In the bipolar group, the recurrence-free rate was 81.5% at a mean follow-up time of 13.9 months. In the cold-knife group, the recurrence-free rate was 53.8%. CONCLUSIONS: The application of bipolar plasma-cutting and plasma-loop electrodes for the management of urethral stricture disease is a safe and reliable method that minimizes the morbidity of urethral stricture resection. The advantages include a lower recurrence rate and shorter operative time compared to the cold-knife technique

Unmanned aerial vehicle inspection routing and scheduling for engineering management

Technological advances in unmanned aerial vehicles (UAVs) have enabled the extensive application of UAVs in various industrial domains. For example, UAV-based inspection in engineering management is a more efficient means of searching for hidden dangers in high-risk construction environments than traditional inspections in the artifactual field. Against the above background, this paper investigates the optimization of the UAV inspection routing and scheduling problem. A mixed-integer linear programming model is devised to optimize decisions on the assignment of inspection tasks, the monitoring sequence schedule, and charge decisions. The comprehensive consideration of no-fly zones, monitoring-interval time windows and multiple monitoring rounds distinguish the devised problem from the typical vehicle routing problem and make the mathematical model intractable for a commercial solver in the case of large-scale instances. Thus, a tailored variable neighborhood search metaheuristic is designed to solve the model efficiently. Extensive numerical experiments are conducted to validate the efficiency of the proposed algorithm for large-scale and real-scale instances. In addition, sensitivity experiments and a case study based on an engineering project are conducted to derive insights that will enable an engineering manager to improve the efficiency of inspection works

The Power of Triply Complementary Priors for Image Compressive Sensing

Recent works that utilized deep models have achieved superior results in various image restoration applications. Such approach is typically supervised which requires a corpus of training images with distribution similar to the images to be recovered. On the other hand, the shallow methods which are usually unsupervised remain promising performance in many inverse problems, \eg, image compressive sensing (CS), as they can effectively leverage non-local self-similarity priors of natural images. However, most of such methods are patch-based leading to the restored images with various ringing artifacts due to naive patch aggregation. Using either approach alone usually limits performance and generalizability in image restoration tasks. In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely \textit{external} and \textit{internal}, \textit{deep} and \textit{shallow}, and \textit{local} and \textit{non-local} priors. We then propose a novel hybrid plug-and-play (H-PnP) framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-PnP based image CS problem. Extensive experimental results demonstrate that the proposed H-PnP algorithm significantly outperforms the state-of-the-art techniques for image CS recovery such as SCSNet and WNNM