Algorithmic Aspects Research Articles

Performing SU(d) Operations and Rudimentary Algorithms in a Superconducting Transmon Qudit for d=3 and d=4

Quantum computation architecture based on $d$-level systems, or qudits, has attracted considerable attention recently due to their enlarged Hilbert space. Extensive theoretical and experimental studies have addressed aspects of algorithms and benchmarking techniques for qudit-based quantum computation and quantum information processing. Here, we report a physical realization of qudit with upto 4 embedded levels in a superconducting transmon, demonstrating high-fidelity initialization, manipulation, and simultaneous multi-level readout. In addition to constructing SU($d$) operations and benchmarking protocols for quantum state tomography, quantum process tomography, and randomized benchmarking etc, we experimentally carry out these operations for $d=3$ and $d=4$. Moreover, we perform prototypical quantum algorithms and observe outcomes consistent with expectations. Our work will hopefully stimulate further research interest in developing manipulation protocols and efficient applications for quantum processors with qudits.

Algorithm selection for protein–ligand docking: strategies and analysis on ACE

The present study investigates the use of algorithm selection for automatically choosing an algorithm for any given protein–ligand docking task. In drug discovery and design process, conceptualizing protein–ligand binding is a major problem. Targeting this problem through computational methods is beneficial in order to substantially reduce the resource and time requirements for the overall drug development process. One way of addressing protein–ligand docking is to model it as a search and optimization problem. There have been a variety of algorithmic solutions in this respect. However, there is no ultimate algorithm that can efficiently tackle this problem, both in terms of protein–ligand docking quality and speed. This argument motivates devising new algorithms, tailored to the particular protein–ligand docking scenarios. To this end, this paper reports a machine learning-based approach for improved and robust docking performance. The proposed set-up is fully automated, operating without any expert opinion or involvement both on the problem and algorithm aspects. As a case study, an empirical analysis was performed on a well-known protein, Human Angiotensin-Converting Enzyme (ACE), with 1428 ligands. For general applicability, AutoDock 4.2 was used as the docking platform. The candidate algorithms are also taken from AutoDock 4.2. Twenty-eight distinctly configured Lamarckian-Genetic Algorithm (LGA) are chosen to build an algorithm set. ALORS which is a recommender system-based algorithm selection system was preferred for automating the selection from those LGA variants on a per-instance basis. For realizing this selection automation, molecular descriptors and substructure fingerprints were employed as the features characterizing each target protein–ligand docking instance. The computational results revealed that algorithm selection outperforms all those candidate algorithms. Further assessment is reported on the algorithms space, discussing the contributions of LGA’s parameters. As it pertains to protein–ligand docking, the contributions of the aforementioned features are examined, which shed light on the critical features affecting the docking performance.

Information-theoretic and algorithmic aspects of parallel and distributed reconstruction from pooled data

In the pooled data problem the goal is to efficiently reconstruct a binary signal from additive measurements. Given a signal σ∈{0,1}n, we can query multiple entries at once and get the total number of non-zero entries in the query as a result. We assume that queries are time-consuming and therefore focus on the setting where all queries are executed in parallel. First, we propose and analyze a simple and efficient greedy reconstruction algorithm. Secondly, we derive a sharp information-theoretic threshold for the minimum number of queries required to reconstruct σ with high probability. Finally, we consider two noise models: In the noisy channel model, the result for each entry of the signal flips with a certain probability. In the noisy query model, each query result is subject to random Gaussian noise. We pin down the range of error probabilities and distributions for which our algorithm reconstructs the exact initial states with high probability. Our theoretical findings are complemented by simulations where we compare our simple algorithm with approximate message passing (AMP) that is conjectured to be optimal in a number of related problems.

Correction: Tolmachev et al. Algorithmic Aspects of Simulation of Magnetic Field Generation by Thermal Convection in a Plane Layer of Fluid. Mathematics 2023, 11, 808

There was an error in the original publication [...]

Food choice and the epistemic value of the consumption of recommender systems: the case of Yuka’s perceived value in France

ABSTRACT Food Recommender Systems (RecSys) are innovative knowledge systems that inform consumers of food choices according to criteria, including nutritional content, health concerns, production method, carbon footprint or other social and ethical considerations. They raise important questions at the intersection of technology accuracy and today evolving consumers’ knowledge seeking behaviours, which implies to unpack the epistemic value of food RecSys. This study investigates the drivers of the perceived value of food RSs consumption by proposing a model that establishes via PLS-SEM (n = 253) a positive relationship between the Yuka company’s food RecSys’ epistemic value and its perceived value. The model demonstrates that Yuka RecSys’ epistemic value relies on the disciplinary drivers of compatibility, self-confidence, and consumer innovativeness, and the problematising drivers of memory and learning, which come from using the application. The perceived value of food RecSys is found to relate to RecSys epistemic value beyond the functional accuracy aspects of recommendation algorithms. Results highlight the importance of developing a refined understanding of epistemic value considering the consumption of RecSys. RecSys’ developers, retailers, food manufacturers and policy makers must work on better mapping and adjusting information through consumers socialised RecSys’ usage to shape the design of the next generation RecSys.

Cloud removal using scattering model and evaluation via semi-realistic simulation

ABSTRACT Cloud removal is an essential task in remote sensing data analysis. As the image sensors are distant from the earth ground, it is likely that part of the area of interests is covered by cloud. Moreover, the atmosphere in between creates a constant haze layer upon the acquired images. To recover the ground image, we propose to use scattering model for temporal sequence of images of any scene in the framework of low rank and sparse models. We further develop its variant, which is much faster and yet more accurate. To measure the performance of different methods objectively, we develop a semi-realistic simulation method to produce cloud cover so that various methods can be quantitatively analysed, which enables detailed study of many aspects of cloud removal algorithms, including verifying the effectiveness of proposed models in comparison with the state-of-the-arts, including deep learning models, and addressing the long standing problem of the determination of regularization parameters. Theoretic analysis on the range of the sparsity regularization parameter is provided and verified numerically.

Artificial intelligence and remote patient monitoring in US healthcare market: a literature review

ABSTRACT Background: Artificial intelligence (AI) enables remote patient monitoring (RPM) which reduces costs by triaging patients to optimize hospitalization and avoid complications. The FDA regulates AI in medical devices and aims to ensure patient safety, effectiveness, and transparent AI solutions. Objectives: Identify and summarize FDA approved RPM devices to provide information for the US medical device industry based on previous approvals and the markets’ needs. Methods: We searched publicly available databases on FDA-approved RPM devices. Selection criteria were established to classify a solution as AI. Technical information was analyzed on pre-identified 16 parameters for the qualified solutions. Results: A total of 47 RPM devices were reviewed, among which 12.8% were classified as a De Novo product and the remaining devices fell under the 510(K) FDA category. The cardiovascular (74%) AI RPM solutions dominated the US market, followed by ECG-based arrhythmia detection algorithms (59.4%), and Hemodynamics and Vital Sign monitoring algorithms (21.9%). The trend observed in the FDA rejected devices was their inability to be classified into clinically relevant categories (Criteria 2 and 3). Conclusion: The market needs more innovative RPM solutions under the De Novo category, as there are very few. The transparency is low on the technical aspect of AI algorithms. The market needs AI algorithms that can effectively classify patients rather than merely improve device functionality.

3.8-Gbps Polar Belief Propagation Decoder on GPU

In this work, a high-throughput belief propagation (BP) decoder of polar codes on graphics processing unit (GPU) is proposed for software-defined communication systems. The decoder is jointly optimized from algorithm and architecture aspects. From the algorithm aspect, the storage pattern and computation flow are optimized to reduce complexity. From the architecture aspect, different granularities of parallelism are extensively exploited to achieve high throughput. Equipped with these techniques, a high-speed GPU-based BP decoder is developed, and experimental results show that the proposed decoder can improve the normalized throughput by 76.4% to 294.1% compared to the state-of-the-art GPU-based BP decoder.

Memristor-Based Spectral Decomposition of Matrices and Its Applications

The recently developed memristor technology allows for extremely fast implementation of a number of important matrix operations and algorithms. Moreover, the existence of fast matrix-vector operations offers the opportunity to design new matrix algorithms that exploit these operations. Here, we focus on the spectral decomposition of matrices, a task that plays an important role in a wide variety of applications from different engineering and scientific fields, including network science, control theory, advanced dynamics, and quantum mechanics. While there are a number of algorithms designed to find eigenvalues and eigenvectors of a matrix, these methods often suffer from poor running time performance. In this work, we present an algorithm for finding eigenvalues and eigenvectors that is designed to be used on memristor crossbar arrays. Although this algorithm can be implemented in a non-memristive system, its fast running time relies on the availability of extremely fast matrix-vector multiplication, as is offered by a memristor crossbar array. In this paper, we (1) show the running time improvements of existing eigendecomposition algorithms when matrix-vector multiplications are performed on a memristor crossbar array, and (2) present <monospace>EigSweep</monospace>, a novel, fully-parallel, fast and flexible eigendecomposition algorithm that gives an improvement in running time over traditional eigendecomposition algorithms when all are accelerated by a memristor crossbar. We discuss algorithmic aspects as well as hardware-related aspects of the implementation of <monospace>EigSweep</monospace>, and perform an extensive experimental analysis on real-world and synthetic matrices.

Sampling from the Gibbs Distribution in Congestion Games

Logit dynamics is a form of randomized game dynamics in which players have a bias toward strategic deviations that give a higher improvement in cost. It is used extensively in practice. In congestion (or potential) games, the dynamics converge to the so-called Gibbs distribution over the set of all strategy profiles when interpreted as a Markov chain. In general, logit dynamics can converge slowly to the Gibbs distribution, but beyond that, not much is known about its algorithmic aspects, nor that of the Gibbs distribution. In this work, we are interested in the following two questions for congestion games: (i) Is there an efficient algorithm for sampling from the Gibbs distribution? (ii) If yes, does there also exist natural randomized dynamics that converge quickly to the Gibbs distribution? We first study these questions in extension parallel congestion games, a well-studied special case of symmetric network congestion games. As our main result, we show that there is a simple variation on the logit dynamics that converges quickly to the Gibbs distribution in such games. We also address the first question for the class of so-called capacitated uniform congestion games and the second question for max cut games played on a complete graph. To prove our results, we rely on the recent breakthrough work of Anari et al. (2019) regarding the approximate sampling of a base of a matroid according to a strongly log-concave probability distribution.

Predictors of Survival in Veterans with Head and Neck Cancer Treated Surgically.

Background: Squamous cell carcinoma (SCC) accounts for 90% of all head and neck cancers. In veterans, the prevalence of head and neck SCC is nearly twice as high compared with the civilian population. Neck dissection plays an important role in the treatment algorithm for patients with head and neck SCC. The aim of this manuscript was to investigate predictors of survival in patients with head and neck SCC who underwent curative treatment. Methods: Patients with head and neck SCC who underwent treatment with curative intent were included in this study. Data collected included clinical-demographic characteristics, tumor characteristics, and outcome. The primary endpoint was 3-year overall survival (OS), and the secondary endpoints were disease recurrence and distant metastases. Results: A total of 149 patients met inclusion criteria, and most patients were treated with surgery plus adjuvant chemoradiation (52%). The 3-year OS for the entire cohort was 55.7%. There was no statistically significant difference in mortality when comparing the various treatment types. Black patients (hazard ratio [HR] = 1.70, P = .023) and other non-white patients (HR = 3.88, P = .027) had worse 3-year OS compared with white patients. Advanced tumor classification (T4a) was also associated with worse 3-year OS (HR = 3.088, P = .003) and increased risk of cancer recurrence or distant metastases (HR = 3.34, P = .013). Conclusions: Risk factors linked to poor survival among this cohort of veterans with head and neck SCC included non-white race and advanced tumor classification. Neck dissection remains an integral aspect of the treatment algorithm for SCC of the head and neck and can provide regional control of malignant disease.

Secure connected domination and secure total domination in unit disk graphs and rectangle graphs

Given a graph G with vertex set V, a secure connected (resp. total) dominating set of G is a connected (resp. total) dominating set S⊆V with the property that for each u∈V∖S, there exists v∈S adjacent to u such that (S∪{u})∖{v} is a connected (resp. total) dominating set of G. The minimum secure connected dominating set (or, for short, MSCDS) (resp. minimum secure total dominating set (or, for short, MSTDS)) problem is to find an MSCDS (resp. MSTDS) in a given graph.In this paper, we initiate to consider complexity and algorithmic aspects of the MSCDS problem and the MSTDS problem in unit disk graphs and rectangle graphs. Firstly, we show that the decision version of the MSCDS problem is NP-complete in unit square graphs and unit disk graphs. Then we show that the decision version of the MSTDS problem is NP-complete even in grid graphs (a subclass of unit square graphs and unit disk graphs). Secondly, we give linear-time constant-approximation algorithms for the two problems in unit square graphs, unit disk graphs and unit-height rectangle graphs. Thirdly, we propose a PTAS for the MSTDS problem in unit square graphs and unit disk graphs. Finally, we show that the two problems in proper rectangle graphs are APX-hard. Further we give an explicit lower bound 1.00147 on efficient approximability for the two problems in proper rectangle graphs unless P=NP.

Complexity, algorithmic, and computational aspects of a dial-a-ride type problem

In dial-a-ride systems involving autonomous vehicles circulating along a circuit, a fleet of vehicles must serve clients who request rides between stations of the circuit such that the total number of pick-up and drop-off operations is minimized. In this paper, we focus on a unitary variant where each client requests a single place in the vehicles and all the clients must be served within a single tour of the circuit. Such unitary variant induces a combinatorial optimization problem for which we introduce a nontrivial special case that is polynomially solvable when the capacity of each vehicle is at most 2 but it is NP-Hard otherwise. We also study the polytope associated with the solutions to this problem. We introduce new families of valid inequalities and give necessary and sufficient conditions under which they are facet-defining. Based on these inequalities, we devise an efficient branch-and-cut algorithm that outperforms the state-of-the-art commercial solvers.

Application of Augmented Echo State Networks and Genetic Algorithm to Improve Short-Term Wind Speed Forecasting

The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable output power forecasts to set up the optimal operating point of the electrical system. In previous research, the authors developed an evolutionary approach algorithm called RCDESIGN to optimize the hyperparameters and topology of Echo State Networks (ESN), and applied the model in different time series forecasting, including wind speed. In this paper, RCDESIGN was modified in some aspects of the genetic algorithm, and now it optimizes an ESN with augmented states (ESN-AS) and has been called RCDESIGN-AS. The evolutionary algorithm allows the search for the best parameters and topology of the recurrent neural network to be performed simultaneously. In addition, RCDESIGN-AS has the important characteristic of requiring little computational effort and processing time since it is not necessary for the eigenvalues of the reservoir weight matrix to be reduced and also due to the fact that the augmented states make it possible to reduce the number of neurons in the reservoir. The method was applied for wind speed forecasting with a 24-h ahead horizon using real data of wind speed from five cities in the Northeast Region of Brazil. All results obtained with the proposed method overcame forecasting performed by the persistence method, obtaining prediction gains ranging from 60% to 80% in relation to this reference method. In some datasets, the proposed method also yielded better results than the traditional ESN, showing that RCDESIGN-AS can be a powerful tool for wind-speed forecasting and possibly for other types of time series.

Software-Defined Radio Implementation and Performance Evaluation of Frequency-Modulated Antipodal Chaos Shift Keying Communication System

This paper is devoted to software-defined radio (SDR) implementation of frequency modulated antipodal chaos shift keying (FM-ACSK) transceiver and presents results of prototype testing in real conditions. This novel and perspective class of spread-spectrum communication systems employs chaotic synchronization for the acquisition and tracking of the analog chaotic spreading code and does not need resource-demanding cross-correlation. The main motivation of the given work is to assess the performance of FM-ACSK in real conditions and demonstrate that chaotic synchronization can be considered an efficient spread-spectrum demodulation method. The work focuses on the real-time implementation aspects of the modulation-demodulation algorithms, forward error correction (FEC) and symbol timing synchronization approach in MATLAB Simulink. The performance of the presented prototype is assessed via extensive testing, which includes measurement of bit error ratio (BER) in single-user and multi-user scenarios, estimation of carrier frequency offset (CFO) impact and image transmission over-the-air between two independent sites and comparison with classical frequency hopping spread spectrum (FHSS). The paper shows that the presented class of the spread spectrum communication systems demonstrates good performance in low signal-to-noise ratio (SNR) conditions and in terms of BER significantly outperforms the classic spread-spectrum modulation schemes which employ correlation-based detection.

Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review

Deep learning (DL) is the new high-profile technology in medical artificial intelligence (AI) for building screening and diagnosing algorithms for various diseases. The eye provides a window for observing neurovascular pathophysiological changes. Previous studies have proposed that ocular manifestations indicate systemic conditions, revealing a new route in disease screening and management. There have been multiple DL models developed for identifying systemic diseases based on ocular data. However, the methods and results varied immensely across studies. This systematic review aims to summarize the existing studies and provide an overview of the present and future aspects of DL-based algorithms for screening systemic diseases based on ophthalmic examinations. We performed a thorough search in PubMed®, Embase, and Web of Science for English-language articles published until August 2022. Among the 2873 articles collected, 62 were included for analysis and quality assessment. The selected studies mainly utilized eye appearance, retinal data, and eye movements as model input and covered a wide range of systemic diseases such as cardiovascular diseases, neurodegenerative diseases, and systemic health features. Despite the decent performance reported, most models lack disease specificity and public generalizability for real-world application. This review concludes the pros and cons and discusses the prospect of implementing AI based on ocular data in real-world clinical scenarios.

Advances in brain-computer interface based on high-frequency steady-state visual evoked potential

Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.

Selected aspects of constrained conforming approximation in higher-order FEM

This paper responds to numerous questions and inquiries related to constrained approximation (approximation with hanging nodes) in higher-order finite element methods that we received during the ESCO 2022 conference. We discuss explicit constraints enforced on linear algebra level as well as implicit constraints which are built into the basis functions of the finite element space, and selected additional mathematical and algorithmic aspects of conforming higher-order finite element approximations with one-level and arbitrary-level hanging nodes.

Practical aspects of managing patients with cardiogenic shock

Cardiogenic shock is the leading cause of death among patients with acute coronary syndrome. This pathology is characterized by high rates of inhospital and annual mortality. In Russian literature, data on the prevalence, diagnosis and treatment of patients with cardiogenic shock are limited. Therefore, the main aim of this publication is to increase the awareness of specialists about modern approaches to the diagnosis and treatment of this condition. This review discusses in detail the main causes of cardiogenic shock, aspects of pathophysiology, modern classification, diagnosis, and algorithms for pharmacological and non-drug therapy in patients with cardiogenic shock.

Algorithmic Aspects of Simulation of Magnetic Field Generation by Thermal Convection in a Plane Layer of Fluid

We present an algorithm for numerical solution of the equations of magnetohydrodynamics describing the convective dynamo in a plane horizontal layer rotating about an arbitrary axis under geophysically sound boundary conditions. While in many respects we pursue the general approach that was followed by other authors, our main focus is on the accuracy of simulations, especially in the small scales. We employ the Galerkin method. We use products of linear combinations (each involving two to five terms) of Chebyshev polynomials in the vertical Cartesian space variable and Fourier harmonics in the horizontal variables for space discretisation of the toroidal and poloidal potentials of the flow (satisfying the no-slip conditions on the horizontal boundaries) and magnetic field (for which the boundary conditions mimick the presence of a dielectric over the fluid layer and an electrically conducting bottom boundary), and of the deviation of temperature from the steady-state linear profile. For the chosen coefficients in the linear combinations, the products satisfy the respective boundary conditions and constitute non-orthogonal bases in the weighted Lebesgue space. Determining coefficients in the expansion of a given function in such a basis (for instance, for computing the time derivatives of these coefficients) requires solving linear systems of equations for band matrices. Several algorithms for determining the coefficients, which are exploiting algebraically precise relations, have been developed, and their efficiency and accuracy have been numerically investigated for exponentially decaying solutions (encountered when simulating convective regimes which are spatially resolved sufficiently well). For the boundary conditions satisfied by the toroidal component of the flow, our algorithm outperforms the shuttle method, but the latter proves superior when solving the problem for the conditions characterising the poloidal component. While the conditions for the magnetic field on the horizontal boundaries are quite specific, our algorithms for the no-slip boundary conditions are general-purpose and can be applied for treating other boundary-value problems in which the zero value must be admitted on the boundary.