Practical Algorithm Research Articles

Ocular Toxicities of MEK Inhibitors in Patients With Cancer: A Systematic Review and Meta-analysis.

Mitogen-activated protein kinase (MEK) inhibitors, which integrate the important signaling chain of the RAS-RAF-MEK-ERK1/2 pathway, regulate cell functions such as division and proliferation for patients with solid tumors. However, various ocular adverse effects (AEs) affect patients during clinical treatment. This systematic review aimed to assess the occurrence of AEs during treatment with MEK inhibitors plus targeted therapy or chemotherapy. A scientific literature search was conducted in PubMed, the Cochrane Library, Embase, and several Chinese databases to identify randomized controlled trials. Overall, ocular AEs were assessed as the primary end point; blurred vision, chorioretinopathy, and retinal detachment were assessed as secondary end points. Seventeen randomized controlled trials were included. Overall, the use of MEK inhibitors combined with other targeted inhibitors or chemotherapy was significantly associated with a nearly 7.3% increased risk of overall ocular toxicities vs therapy without MEK inhibitors (risk ratio [RR], 2.88; 95% CI, 1.42-5.85, P < .05). An increased risk of blurred vision (RR, 4.10; 95% CI, 2.55- 6.58; P < .05), chorioretinopathy (RR, 8.36; 95% CI, 3.42-20.47; P < .05), and retinal detachment (RR, 8.98; 95% CI, 3.92-20.57; P < .05) was demonstrated. Treatment with MEK inhibitors combined with targeted drugs or chemotherapy seems to increase overall ocular AEs. A more practical algorithm for the screening of ocular AEs was suggested to be conducted whenever new or worsening ocular toxicities occur.

Practical imaging algorithms in ultra-wideband radar systems using active aperture synthesis and stochastic probing signals

The subject of the manuscript is the algorithms for radar imaging. This research develops imaging methods and algorithms for wideband and ultrawideband active aperture synthesis systems with antenna arrays and stochastic probing signals. The use of antenna arrays makes it possible to obtain radar images without the need to move radar or antenna system in space. The use of wideband and ultra-wideband stochastic probing signals is justified by their narrow autocorrelation functions. This increased the resolution of the obtained images. The main idea of the proposed algorithms is to filter the original wideband signal into several narrowband processes. Furthermore, only the central frequencies of each narrowband signal were processed. This approach allows us to use the classical widespread methods of aperture synthesis for the case of a wideband signal. Usually, they are applicable only for narrowband signals that satisfy the condition of a quasi-monochromatic approximation. This significantly reduces the overall computational complexity of the imaging algorithm, which simplifies its further practical implementation on the existing radioelement base. Because of the simulation, a primary radar image has been obtained and the overall performance of the proposed approach to processing wideband signals has been confirmed. An increase in the quality of the obtained image when using a multiple of frequency ranges is shown. An experimental study of the effect of processing a wideband signal only at its centre frequency instead of the entire frequency band is conducted. During the experiment, the correlation functions of the signals received by two spaced receivers were obtained. As a result, the Van Cittert-Zernike theorem has been experimentally confirmed. It allows signal processing only at its centre frequency instead of the entire frequency band. Simultaneously, the prospect of expanding the bandwidth of the probing signal is indicated. It, in the presence of a wideband element base and devices for high-speed signal processing, will further increase the imaging resolution of a radar system.

Blockchain‐based reliable image copyright protection

AbstractThe indiscriminate distribution of a large number of unlicensed images through the Internet seriously harms the benefits of image owners, which prompts the study of image copyright protection solutions. Existing blockchain‐free solutions are limited to using centralized servers to store copyright information, leaving data vulnerable to being lost or tampered with by external attacks. Although the blockchain‐based image copyright protection solutions are capable of addressing the above problem, improvements are needed in choosing a suitable blockchain platform and supporting integrated copyright life cycle management. To this end, this paper designs a blockchain‐based reliable image copyright protection system named BB‐RICP using Hyperledger Fabric. For one thing, the adoption of Fabric mitigates the limitations of other blockchain platforms represented by Ethereum in terms of efficiency, economy, and availability. For another, BB‐RICP successes in achieving integrated copyright lifecycle management. In addition, BB‐RICP achieves enhanced applicability performance by firstly introducing GM algorithms recommended by Chinese national standard and adapting the more consortium blockchain‐oriented practical byzantine fault tolerance algorithm. Moreover, the adoption of spread spectrum watermarking brings convenience to users. Finally, Kubernetes is used to assist in the simulation of BB‐RICP to verify that users can operate reliably in the blockchain network.

More Recent Advances in (Hyper)Graph Partitioning

In recent years, significant advances have been made in the design and evaluation of balanced (hyper)graph partitioning algorithms. We survey trends of the past decade in practical algorithms for balanced (hyper)graph partitioning together with future research directions. Our work serves as an update to a previous survey on the topic [ 29 ]. In particular, the survey extends the previous survey by also covering hypergraph partitioning and has an additional focus on parallel algorithms.

World's first practical power flow algorithm operating on today's noisy quantum computers

Power flow is an indispensable foundation for power system analytics. Under the deep penetration of renewables, modern power system analytics often becomes intractable because it needs to run an enormous amount of power flow analyses to quantify the impact of uncertainties. Unlike classical power flow methods that scale polynomially with the system size, quantum computing enables using logarithmically-scaled number of qubits to solve linear equations in power flow analysis. Thus, quantum power flow (QPF) provides a promising direction to make today's intractable power system analytics tractable. However, a major obstacle to the development of a practical quantum power flow algorithm lies in the fact that today's mainstream quantum computers are still noisy-intermediate-scale quantum (NISQ) devices whose capability is restricted by the limited number of qubits and considerable noises. To bridge this gap, Stony Brook University establishes a variational quantum power flow that allows for practical and noise-resilient power flow analysis on today's NISQ devices.

Optimal Convex Lifted Sparse Phase Retrieval and PCA With an Atomic Matrix Norm Regularizer

We present novel analysis and algorithms for solving sparse phase retrieval and sparse principal component analysis (PCA) with convex lifted matrix formulations. The key innovation is a new mixed atomic matrix norm that, when used as regularization, promotes low-rank matrices with sparse factors. We show that convex programs with this atomic norm as a regularizer provide near-optimal sample complexity and error rate guarantees for sparse phase retrieval and sparse PCA. While we do not know how to solve the convex programs exactly with an efficient algorithm, for the phase retrieval case we carefully analyze the program and its dual and thereby derive a practical heuristic algorithm. We show empirically that this practical algorithm performs similarly to existing state-of-the-art algorithms.

The Shannon Information Capacity of an Arbitrary Radiating Surface: An Electromagnetic Approach

Utilizing a cross-disciplinary approach, we explore Shannon information-theoretic characterizations of the information capacity limits of generic electromagnetic (EM) surfaces intended for possible use in wireless communication links. Our principal task is to first formulate at a general and rigorous level the EM theory of the information that can be extracted from the Maxwellian fields radiated by an arbitrarily shaped continuous surface. This is then followed by a detailed derivation and illustration of practical physics-informed algorithms for computing approximations of the Shannon capacity of surfaces with any given geometry operating in Gaussian channels. Our formalism can address both near- and far-field information capacity scenarios, with a mathematical treatment that includes a complete characterization of the source-field polarization structure, mutual coupling, and interactions.

Simple approximative algorithms for free-support Wasserstein barycenters

Computing Wasserstein barycenters of discrete measures has recently attracted considerable attention due to its wide variety of applications in data science. In general, this problem is NP-hard, calling for practical approximative algorithms. In this paper, we analyze a well-known simple framework for approximating Wasserstein-{varvec{p}} barycenters, where we mainly consider the most common case {varvec{p}}={varvec{2}} and {varvec{p}}={varvec{1}}, which is not as well discussed. The framework produces sparse support solutions and shows good numerical results in the free-support setting. Depending on the desired level of accuracy, this requires only {varvec{N}}-{varvec{1}} or {varvec{N(N}}-{varvec{1)/2 }} standard two-marginal optimal transport (OT) computations between the {varvec{N}} input measures, respectively, which is fast, memory-efficient and easy to implement using any OT solver as a black box. What is more, these methods yield a relative error of at most {varvec{N}} and {varvec{2}}, respectively, for both {varvec{p}}={varvec{ 1, 2}}. We show that these bounds are practically sharp. In light of the hardness of the problem, it is not surprising that such guarantees cannot be close to optimality in general. Nevertheless, these error bounds usually turn out to be drastically lower for a given particular problem in practice and can be evaluated with almost no computational overhead, in particular without knowledge of the optimal solution. In our numerical experiments, this guaranteed errors of at most a few percent.

An Energy Sharing Mechanism Considering Network Constraints and Market Power Limitation

As the number of prosumers with distributed energy resources (DERs) grows, the conventional centralized operation scheme may suffer from conflicting interests, privacy concerns, and incentive inadequacy. In this paper, we propose an energy sharing mechanism to address the above challenges. It takes into account network constraints and fairness among prosumers. In the proposed energy sharing market, all prosumers play a generalized Nash game. The market equilibrium is proved to have nice features in a large market or when it is a variational equilibrium. To deal with the possible market failure, inefficiency, or instability in general cases, we introduce a price regulation policy to avoid market power exploitation. The improved energy sharing mechanism with price regulation can guarantee existence and uniqueness of a socially near-optimal market equilibrium. Some advantageous properties are proved, such as prosumer's individual rationality, a sharing price structure similar to the locational marginal price, and the tendency towards social optimum with an increasing number of prosumers. For implementation, a practical bidding algorithm is developed with convergence condition. Experimental results validate the theoretical outcomes and show the practicability of our model and method.

Reconstruction of Noma Sequelae: A Surgical Treatment Algorithm Developed from Lessons from 210 Cases in Ethiopia.

Facing Africa is a UK-based non-governmental organization that treats the secondary problems of Ethiopian noma survivors. Operations are performed in Addis Ababa by a visiting expert team. Postoperatively, patients are seen annually for years after the surgery. This article discusses basic principles, goals, and a practical surgical algorithm for operating on lip, cheek, and oral defects, based on 210 noma patients who were operated on in Ethiopia over a period of 11 years. The suggested algorithm has proven to work for the Facing Africa team members and is considered shareware for all surgeons to use and benefit from.

Constrained Learning With Non-Convex Losses

Though learning has become a core component of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced systems. The need to impose requirements on learning is therefore paramount, especially as it reaches critical applications in social, industrial, and medical domains. However, the non-convexity of most modern statistical problems is only exacerbated by the introduction of constraints. Whereas good unconstrained solutions can often be learned using empirical risk minimization, even obtaining a model that satisfies statistical constraints can be challenging. All the more so, a good one. In this paper, we overcome this issue by learning in the empirical dual domain, where constrained statistical learning problems become unconstrained and deterministic. We analyze the generalization properties of this approach by bounding the empirical duality gap -- i.e., the difference between our approximate, tractable solution and the solution of the original (non-convex) statistical problem -- and provide a practical constrained learning algorithm. These results establish a constrained counterpart to classical learning theory, enabling the explicit use of constraints in learning. We illustrate this theory and algorithm in rate-constrained learning applications arising in fairness and adversarial robustness.

Biologics for Nasal Polyps: Synthesizing Current Recommendations into a Practical Clinical Algorithm.

Chronic rhinosinusitis with nasal polyps (CRSwNP) has been traditionally managed with a combination of topical and systemic medical therapy as well as endoscopic sinus surgery. The emergence of biologic therapies that target specific aspects of the inflammatory cascade has ushered in a potentially new paradigm in the management options available for CRSwNP. To summarize the current literature and recommendations supporting the use of available biologic therapies for CRSwNP and to develop an algorithm to aid clinical decision-making regarding treatment selection. A review of available literature and studies that demonstrated the clinical efficacy of biologic agents for the treatment of CRSwNP informing current CRSwNP consensus algorithms. Current biologic medications target immunoglobulin E, interleukins, or interleukin receptors implicated in the Th2 inflammatory cascade. Institution of biologic therapy is now an option for patients who have disease refractory to topical medical therapy and endoscopic sinus surgery, those who cannot tolerate surgery, or patients with other comorbid Th2 diseases. Response to treatment should be monitored at 4-6 months and 1 year after initiating therapy. Across multiple indirect comparisons, dupilumab appears to have the largest therapeutic benefit across multiple subjective and objective outcomes. The choice of therapeutic agent also depends on drug availability, patient tolerance, presence of comorbid illnesses, and cost. Biologics are emerging as an important option in the management of patients with CRSwNP. While more data is required to fully inform indications, treatment selection, and health economics related to their use, biologics may offer robust symptom relief to patients who have failed other interventions.

How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography

Recently, algorithms capable of assessing the severity of Coronary Artery Disease (CAD) in form of the Coronary Artery Disease-Reporting and Data System (CAD-RADS) grade from Coronary Computed Tomography Angiography (CCTA) scans using Deep Learning (DL) were proposed. Before considering to apply these algorithms in clinical practice, their robustness regarding different commonly used Computed Tomography (CT)-specific image formation parameters—including denoising strength, slab combination, and reconstruction kernel—needs to be evaluated. For this study, we reconstructed a data set of 500 patient CCTA scans under seven image formation parameter configurations. We select one default configuration and evaluate how varying individual parameters impacts the performance and stability of a typical algorithm for automated CAD assessment from CCTA. This algorithm consists of multiple preprocessing and a DL prediction step. We evaluate the influence of the parameter changes on the entire pipeline and additionally on only the DL step by propagating the centerline extraction results of the default configuration to all others. We consider the standard deviation of the CAD severity prediction grade difference between the default and variation configurations to assess the stability w.r.t. parameter changes. For the full pipeline we observe slight instability (± 0.226 CAD-RADS) for all variations. Predictions are more stable with centerlines propagated from the default to the variation configurations (± 0.122 CAD-RADS), especially for differing denoising strengths (± 0.046 CAD-RADS). However, stacking slabs with sharp boundaries instead of mixing slabs in overlapping regions (called true stack ± 0.313 CAD-RADS) and increasing the sharpness of the reconstruction kernel (± 0.150 CAD-RADS) leads to unstable predictions. Regarding the clinically relevant tasks of excluding CAD (called rule-out; AUC default 0.957, min 0.937) and excluding obstructive CAD (called hold-out; AUC default 0.971, min 0.964) the performance remains on a high level for all variations. Concluding, an influence of reconstruction parameters on the predictions is observed. Especially, scans reconstructed with the true stack parameter need to be treated with caution when using a DL-based method. Also, reconstruction kernels which are underrepresented in the training data increase the prediction uncertainty.

Management of Dental Patients With Mental Health Problems in Special Care Dentistry: A Practical Algorithm.

Introduction Individuals with mental health problems have poor oral health affecting their quality of life with an increased burden on their well-being. Dentists find treating them challenging due to a lack of training and awareness in modifications of dental care delivery in special needs patients. Also, polypharmacy is common in psychiatric care, further complicating dental care while prescribing routine medications as potential drug interactions must be considered. Methods Due to a lack of clinical practice guidelines in the literature and the absence of guidelines issued by dental governing bodies, we attempted to consolidate the existing challenges and propose a model for managing psychiatric special needs patients. Results and discussion Based on the current evidence, we hereby recommend 'psychiatric dental consultation liaison' (PDCL) services as the acceptable framework for the management of dental patients with mental health problems in special care dentistry. Conclusion PDCL services will favour both dentists and patients as it includes psychiatric consultation and interventions that will result in the positive execution of comprehensive dental treatment care.

Atmospheric correction algorithm based on the interpolation of ultraviolet and shortwave infrared bands.

Traditional atmospheric correction algorithms of ocean color remote sensing are mostly based on the extrapolation of aerosol scattering radiance from a reference band (near infrared, shortwave infrared, or ultraviolet bands), which inevitably leads to the problem of extrapolation error amplification with the increase of extrapolation spectral distance. In this study, we propose a practical interpolation-based algorithm (named the UV-SWIR-AC algorithm) using three reference bands (one ultraviolet and two shortwave infrared bands) for turbid waters. According to 6SV radiative transfer simulations with 15 customized aerosol types, we establish a fitting function framework for the aerosol scattering radiance in the wavelength range of 322-1643 nm. We apply the UV-SWIR-AC algorithm to the real satellite ocean color data observed by the Second-Generation Global Imager aboard the Global Change Observation Mission-Climate (SGLI/GCOM-C) and compare the retrieved remote sensing reflectance with the in-situ data from the observation platform of Hangzhou Bay in the East China Sea and typical bays. The results show that the UV-SWIR-AC algorithm can achieve a better performance than the traditional, extrapolation-based algorithm in turbid waters. Moreover, in the typical regional analysis, this new algorithm also demonstrates a high applicability. The UV-SWIR-AC algorithm should be helpful to improve the atmospheric correction accuracy for next-generation ocean color missions (e.g., NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission and China's Haiyang-1E/F (HY-1E/F) mission) with wider spectral ranges from the ultraviolet to shortwave infrared wavelengths.

Diagnosis and treatment of infectious vaginitis: Proposal for a new algorithm.

Vaginitis is the most common gynecologic diagnosis in primary care, and most women have at least one episode during their lives. The need for standardized strategies to diagnose and treat vaginitis, both in primary care and among gynecologists, is emphasized. The Brazilian Group for Vaginal Infections (GBIV, acronym in Portuguese) aimed to update the practical approach to affected women by reviewing and discussing recent literature, and developing algorithms for diagnosis and treatment of vaginitis. A literature search within biomedical databases PubMed and SCieLo was conducted in January 2022. The available literature was evaluated by three experienced researchers, members of the GBIV, to summarize the main data and develop practical algorithms. Detailed algorithms were developed with the main goal to improve gynecological practice considering different scenarios and access to diagnostic tools, from the simplest to the most complex tests. Different age groups and specific contexts were also considered. The combination of anamnesis, gynecological examination, and complementary tests remains the basis of a proper diagnostic and therapeutic approach. Periodic updates of these algorithms are warranted as new evidence becomes available.

Advanced Information Securing by Combining Fortified Binary Image Steganography and Asymmetric Encryption Standard

In information security, image steganography is one of the information securing process. Image steganography is mostly used in medical fields for hiding medical prescription data behind the medical images. Designing steganographic algorithms for empirical cover sources, such as digital images, is very perplexing due to the lack of accurate models. The most successful approach today avoids estimating the cover source distribution because this task is infeasible for complex and non-stationary sources. Instead, the steganography problem is formulated as source coding with fidelity constraint the sender entrenches the message with minimizing the distortion. Practical algorithms that entrench near the theoretical payload–distortion bound are available for a very general class of distortion functions. With the current framework, the only task left to the sender is essential for the design of the distortion function. Here information is which wants to be confidential that information's are stored behind the image pixels. However, due to increasing crypt analysis attacks and binary image de stenographic process image steganography has very less security. So improving the security in image steganography process, the current system provide the asymmetric encryption standard for improving more security in steganography.

Breast MRI: Clinical Indications, Recommendations, and Future Applications in Breast Cancer Diagnosis.

This article aims to provide an updated overview of the indications for diagnostic breast magnetic resonance imaging (MRI), discusses the available and novel imaging exams proposed for breast cancer detection, and discusses considerations when performing breast MRI in the clinical setting. Breast MRI is superior in identifying lesions in women with a very high risk of breast cancer or average risk with dense breasts. Moreover, the application of breast MRI has benefits in numerous other clinical cases as well; e.g., the assessment of the extent of disease, evaluation of response to neoadjuvant therapy (NAT), evaluation of lymph nodes and primary occult tumor, evaluation of lesions suspicious of Paget's disease, and suspicious discharge and breast implants. Breast cancer is the most frequently detected tumor among women around the globe and is often diagnosed as a result of abnormal findings on mammography. Although effective multimodal therapies significantly decline mortality rates, breast cancer remains one of the leading causes of cancer death. A proactive approach to identifying suspicious breast lesions at early stages can enhance the efficacy of anti-cancer treatments, improve patient recovery, and significantly improve long-term survival. However, the currently applied mammography to detect breast cancer has its limitations. High false-positive and false-negative rates are observed in women with dense breasts. Since approximately half of the screening population comprises women with dense breasts, mammography is often incorrectly used. The application of breast MRI should significantly impact the correct cases of breast abnormality detection in women. Radiomics provides valuable data obtained from breast MRI, further improving breast cancer diagnosis. Introducing these constantly evolving algorithms in clinical practice will lead to the right breast detection tool, optimized surveillance program, and individualized breast cancer treatment.

A Practical Algorithm to Solve the Near-Congruence Problem for Rigid Molecules and Clusters

We present an improved algorithm to solve the near-congruence problem for rigid molecules and clusters based on the iterative application of assignment and alignment steps with biased Euclidean costs. The algorithm is formulated as a quasi-local optimization procedure with each optimization step involving a linear assignment (LAP) and a singular value decomposition (SVD). The efficiency of the algorithm is increased by up to 5 orders of magnitude with respect to the original unbiased noniterative method and can be applied to systems with hundreds or thousands of atoms, outperforming all state-of-the-art methods published so far in the literature. The Fortran implementation of the algorithm is available as an open source library (https://github.com/qcuaeh/molalignlib) and is suitable to be used in global optimization methods for the identification of local minima or basins.

EBMICQL: Improving Efficiency of Blockchain Miner Pools via Incremental and Continuous Q-Learning Framework

Blockchain mining pools assist in reducing computational load on individual miner nodes via distributing mining tasks. This distribution must be done in a non-redundant manner, so that each miner is able to calculate block hashes with optimum efficiency. To perform this task, a wide variety of mining optimization methods are proposed by researchers, and most of them distribute mining tasks via statistical request processing models. These models segregate mining requests into non-redundant sets, each of which will be processed by individual miners. But this division of requests follows a static procedure, and does not consider miner specific parameters for set creation, due to which overall efficiency of the underlying model is limited, which reduces its mining performance under real-time scenarios. To overcome this issue, an Incremental &amp; Continuous Q-Learning Framework for generation of miner-specific task groups is proposed in this text. The model initially uses a Genetic Algorithm (GA) method to improve individual miner performance, and then applies Q-Learning to individual mining requests. The Reason for selecting GA model is that it assists in maintaining better speed-to-power (S2P) ratio by optimization of miner resources that are utilized during computations. While, the reason for selecting Q-Learning Model is that it is able to continuously identify miners performance, and create performance-based mining pools at a per-miner level. Due to application of Q-Learning, the model is able to assign capability specific mining tasks to individual miner nodes. Because of this capability-driven approach, the model is able to maximize efficiency of mining, while maintaining its QoS performance. The model was tested on different consensus methods including Practical Byzantine Fault Tolerance Algorithm (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and Delegated PoS (DPoS), and its performance was evaluated in terms of mining delay, miner efficiency, number of redundant calculations per miner, and energy efficiency for mining nodes. It was observed that the proposed GA based Q-Learning Model was able to reduce mining delay by 4.9%, improve miners efficiency by 7.4%, reduce number of redundant computations by 3.5%, and reduce energy required for mining by 7.1% when compared with various state-of-the-art mining optimization techniques. Similar performance improvement was observed when the model was applied on different blockchain deployments, thus indicating better scalability and deployment capability for multiple application scenarios.