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An Algebraic Circuit for a multivariate polynomial P is a computational model for constructing the polynomial P using only additions and multiplications. It is a syntactic model of computation, as opposed to the Boolean Circuit model, and hence lower bounds for this model are widely expected to be easier to prove than lower bounds for Boolean circuits. Despite this, we do not have superpolynomial lower bounds against general algebraic circuits of depth 3 (except over constant-sized finite fields) and depth 4 (over any field other than F2), while constant-depth Boolean circuit lower bounds have been known since the early 1980s. In this paper, we prove the first superpolynomial lower bounds against algebraic circuits of all constant depths over all fields of characteristic 0. We also observe that our super-polynomial lower bound for constant-depth circuits implies the first deterministic sub-exponential time algorithm for solving the Polynomial Identity Testing (PIT) problem for all small-depth circuits using the known connection between algebraic hardness and randomness. [ABSTRACT FROM AUTHOR]

The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general, priority inversion occurs in computing systems when computations that are "less important" are performed together with or ahead of those that are "more important." Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost. [ABSTRACT FROM AUTHOR]

This article discusses the urgent need to develop post-quantum cryptography in order to protect data from being compromised by future quantum computers. Public-key cryptography, which is currently used to secure information, would become ineffective if a quantum computer were able to break it. The National Institute of Standards and Technology (NIST) has launched a contest to find alternative cryptographic algorithms that are resistant to quantum attacks, and 26 algorithms have been selected for further testing. Lattice-based cryptography has emerged as a promising approach, but NIST is exploring other options to avoid relying solely on one type of algorithm. The transition to post-quantum cryptography will require time and upgrades to computer systems and protocols. [Extracted from the article]

This article investigates artificial intelligence (AI) fairness in decision-making specifically examining the limitations of this process. The article discusses eight inherent limitations in AI fairness including lack of fairness in human decision-making used to train the AI model, lack of a universal fairness definition, and limited power over the complete decision-making process.

Purpose: This paper aims to develop an algorithm to pretest an industrial portfolio on a new scale. Portfolios include complex and uncertain projects at the front-end phase. The study, therefore, proposes a procedure that helps decision-makers to handle various complex projects and defines a common scale applicable to various kinds of industrial projects. Design/methodology/approach: Decision-makers can employ the preference algorithm to reach a common understanding. To this end, the current paper posits the organization of criteria in various project sets. A sexagesimal scale is developed based on project complexity and its ability to achieve broad impact, both these factors being gauged on a five-point scale of user-friendly numberings. Findings: The proposed algorithm shows the equivalence of industrial projects in different fields. Also, the algorithm articulates the status in terms of uncertainty, complexity, risk, and value of projects. The connections between decision-makers and criteria operate on the basis of the foreseen complexity, risk, and value. It can be said that this study exemplifies and visualizes the portfolio and criteria relationship. Research limitations/implications: The procedure covers contingency exercises at the front-end phase of a portfolio and supports decisions. However, updated information can change support positions. Originality/value: The paper presents original scoring guidance for portfolio complexity on a new scale. The scaling and scoring are adjustable and calibrated using the proposed sexagesimal system. It presents an original classification of project risk and value. The main contribution is the presented algorithm which can be used to pretest industrial portfolios composed of projects that vary in both size and context. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to focus on the inclusion of the frequency behavior of grounding system effect on surge arrester (SA) model parameters' estimation. Design/methodology/approach: The grounding system impedance and its frequency behavior are the factors that have influence on the SA performance. Up to now, the grounding system impedance effect and the frequency behavior of the soil parameters have not been studied for the estimation of the parameters of the SA frequency-dependent model. In this paper, the grounding system's influence on the SA dynamic model has been simulated for rod- and counterpoise-shaped electrodes. Particle swarm optimization with a grey wolf optimization algorithm has been implemented as an optimization algorithm to adjust the parameters of the SA dynamic model. Findings: The results show that the frequency behavior of the grounding impedance and soil electrical parameters can impress the optimum parameters of the SA frequency-dependent model and should be considered for more reliable results. Also, the results evidence that the proposed optimization method provides more accurate results compared to other optimization methods. Originality/value: To the best of the authors' knowledge, this work is one of the first attempts to investigate the effect of frequency grounding system on SA frequency-dependent model parameters. [ABSTRACT FROM AUTHOR]

The article looks at how fairness can be defined and implemented in algorithmic decision-making. The author states that alternative, reasonable definitions of fairness can end up being mutually exclusive when operationalized in mathematical or algorithmic form. He discusses competing analyses of fairness in algorithms used to predict criminal behavior.

The article discusses an algorithm designed to compute maximum flow and minimum-cost flows. It builds on the works of prior researchers to propose new theorems and provides formulas and graphs to illustrate these theorems and schemes.

The article discusses Explainable artificial intelligence (XAI) and the expectation of artificial intelligence (AI) end users to understand how AI works, including how algorithmic decision making occurs. An overview of AI end users' desire for information on AI methodology on reaching decisions, including in regard to the ethical nature of AI, is provided.

The Burrows-Wheeler Transform (BWT) is an invertible text transformation that permutes symbols of a text according to the lexicographical order of its suffixes. BWT is the main component of popular lossless compression programs (such as bzip2) as well as recent powerful compressed indexes (such as the r-index7), central in modern bioinformatics. The compressibility of BWT is quantified by the number r of equal-letter runs in the output. Despite the practical significance of BWT, no nontrivial upper bound on r is known. By contrast, the sizes of nearly all other known compression methods have been shown to be either always within a polylog n factor (where n is the length of the text) from z, the size of Lempel--Ziv (LZ77) parsing of the text, or much larger in the worst case (by an nε factor for ε > 0). In this paper, we show that r = O (z log² n) holds for every text. This result has numerous implications for text indexing and data compression; in particular: (1) it proves that many results related to BWT automatically apply to methods based on LZ77, for example, it is possible to obtain functionality of the suffix tree in O (z polylog n) space; (2) it shows that many text processing tasks can be solved in the optimal time assuming the text is compressible using LZ77 by a sufficiently large polylog n factor; and (3) it implies the first nontrivial relation between the number of runs in the BWT of the text and of its reverse. In addition, we provide an O (z polylog n)-time algorithm converting the LZ77 parsing into the run-length compressed BWT. To achieve this, we develop several new data structures and techniques of independent interest. In particular, we define compressed string synchronizing sets (generalizing the recently introduced powerful technique of string synchronizing sets11) and show how to efficiently construct them. Next, we propose a new variant of wavelet trees for sequences of long strings, establish a nontrivial bound on their size, and describe efficient construction algorithms. Finally, we develop new indexes that can be constructed directly from the LZ77 parsing and efficiently support pattern matching queries on text substrings. [ABSTRACT FROM AUTHOR]

The article focuses on how revisiting key algorithms from computing history can unlock the hidden efficiency in computer science problems. The author discusses the shortest-path problem that has been long-standing, explores the use of the Bellman-Ford algorithm, and examines the work of Aaron Bernstein, Danupon Nanongkai, and Christian Wulff-Nilsen.

Exploring a potential way to immensely speed up algorithms for the group isomorphism problem. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to analyze direct current (DC) drive stability, including parameter uncertainty and perturbation in the feedback loop, by computing disk margins. Design/methodology/approach: Although the closed-loop stability analysis of a DC drive has been presented well in the referenced papers, the effect of parameter uncertainty and perturbation in the feedback loop has not yet been discussed well. In this study, the conventional and disk-based stability margins were measured and compared for the nominal parameters of the DC drive. Subsequently, the smallest disk-based margins that destabilize the feedback loop for a given perturbation are computed and compared with normal disk margins. Findings: The disk-based margin offered by the DC drive controlled by the JAYA-PID controller is disk gain margins (DGM) = 8.41 dB and disk phase margin (DPM) = 48.410 and the smallest disk-based margin offered is DGM = 1.51 dB and DPM = 9.950. In addition, the effect of the modeled uncertainty on the disk stability margins was analyzed, and it was observed that the maximum allowable parameter uncertainty with the JAYA controller was 73% of its nominal parameters. The simulation results were validated using an experimental testbed. Originality/value: This research work is not published anywhere else. [ABSTRACT FROM AUTHOR]

Purpose: A novel asynchronous network-based model is proposed in this paper for the sentiment analysis of online public opinions. This new model provides a new approach to analyze the evolution characteristics of online public opinion sentiments in complex environment. Design/methodology/approach: Firstly, a new sentiment analysis model is proposed based on the asynchronous network theory. Then the graphical evaluation and review technique is employed and extended to design the model-based sentiment analysis algorithms. Finally, simulations and real-world case studies are given to show the effectiveness of the proposed model. Findings: The dynamics of online public opinion sentiments are determined by both personal preferences to certain topics and the complex interactive influences of environmental factors. The application of appropriate quantitative models can improve the prediction of public opinion sentiment. Practical implications: The proposed model-based algorithms provide simple but effective ways to explore the complex dynamics of online public opinions. Case studies highlight the role of government agencies in shaping sentiments of public opinions on social topics. Originality/value: This paper proposes a new asynchronous network model for the dynamic sentiment analysis of online public opinions. It extends the previous static models and provides a new way to extract opinion evolution patterns in complex environment. Applications of the proposed model provide some new insights into the online public opinion management. [ABSTRACT FROM AUTHOR]

In this paper, we describe multi-itinerary optimization (MIO)--a novel Bing Maps service that automates the process of building itineraries for multiple agents while optimizing their routes to minimize travel time or distance. MIO can be used by organizations with a fleet of vehicles and drivers, mobile salesforce, or a team of personnel in the field, to maximize workforce efficiency. It supports a variety of constraints, such as service time windows, duration, priority, pickup and delivery dependencies, and vehicle capacity. MIO also considers traffic conditions between locations, resulting in algorithmic challenges at multiple levels (e.g., calculating time-dependent travel-time distance matrices at scale and scheduling services for multiple agents). To support an end-to-end cloud service with turnaround times of a few seconds, our algorithm design targets a sweet spot between accuracy and performance. Toward that end, we build a scalable approach based on the ALNS metaheuristic. Our experiments show that accounting for traffic significantly improves solution quality: MIO finds efficient routes that avoid late arrivals, whereas traffic-agnostic approaches result in a 15% increase in the combined travel time and the lateness of an arrival. Furthermore, our approach generates itineraries with substantially higher quality than a cutting-edge heuristic (LKH), with faster running times for large instances. [ABSTRACT FROM AUTHOR]

Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates such sampled metrics in more detail and shows that they are inconsistent with their exact counterpart, in the sense that they do not persist relative statements, for example, recommender A is better than B, not even in expectation. Moreover, the smaller the sample size, the less difference there is between metrics, and for very small sample size, all metrics collapse to the AUC metric. We show that it is possible to improve the quality of the sampled metrics by applying a correction, obtained by minimizing different criteria. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. To summarize, our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the quality of the estimate. [ABSTRACT FROM AUTHOR]

Constraint satisfaction problems (CSPs) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSPs and the computation of the number of distinct elements in a data stream, also known as the zeroth frequency moment (F0) of a data stream. Constraint satisfaction problems (CSPs) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSPs and the computation of the number of distinct elements in a data stream, also known as the zeroth frequency moment (F0) of a data stream. [ABSTRACT FROM AUTHOR]

This article explores the trends in computer science research in India from 2019 to 2022 based on research coming out of leading Indian technology institutes, including Chennai Mathematical Institute, Indian Institute of Science and Indian Institute of Technology Delhi. Topics include research on automata theory, formal verification, computational complexity research, and Algorithms research.

A recent report from researchers at Birmingham Women's and Children's NHS Foundation Trust discusses the diagnosis and treatment of progressive familial intrahepatic cholestasis (PFIC), a rare liver disorder that primarily affects children. The researchers aimed to provide recommendations for the management of PFIC in clinical practice. They developed an algorithm for the diagnosis and treatment of children with suspected PFIC, which includes the use of licensed inhibitors of ileal bile acid transporters as the first-line treatment. The authors hope that these recommendations will help standardize the management of PFIC and raise awareness of current developments in the field. [Extracted from the article]

Purpose: The purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises. Design/methodology/approach: In this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm. Findings: The results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation. Practical implications: This paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing. Originality/value: This study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets. [ABSTRACT FROM AUTHOR]

The author presents a set of puzzles involving tidy towers, a colored block stacking puzzle. First, several scenarios related to the puzzle are presented and solved. Lastly, the author presents two puzzles and information is provided for readers to submit their solutions.

The article addresses the topic of algorithm design in the solid-state drive (SSD) model of computation, noting its limitations as well as its benefits. The author references an accompanying paper which addresses one shortcoming in particular -- the phenomenon of "write amplification" -- by proposing a more accurate theoretical model of SSDs that incorporates read, write and erase operations.

This article discusses the use of theoretical analysis to measure the performance of algorithms and investigates its shortcomings using the field of sequencing bioinformatics. The author posits that theoretical analysis applied to algorithm performance has fallen short of its stated goals of predicting the empirical performance of an algorithm and being a yardstick to drive the design of new algorithms.

Flash-based solid-state drives (SSDs) are a key component in most computer systems, thanks to their ability to support parallel I/O at sub-millisecond latency and consistently high throughput. At the same time, due to the limitations of the flash media, they perform writes out-of-place, often incurring a high internal overhead which is referred to as write amplification. Minimizing this overhead has been the focus of numerous studies by the systems research community for more than two decades. The abundance of system-level optimizations for reducing SSD write amplification, which is typically based on experimental evaluation, stands in stark contrast to the lack of theoretical algorithmic results in this problem domain. To bridge this gap, we explore the problem of reducing write amplification from an algorithmic perspective, considering it in both offline and online settings. In the offline setting, we present a near-optimal algorithm. In the online setting, we first consider algorithms that have no prior knowledge about the input and show that in this case, the greedy algorithm is optimal. Then, we design an online algorithm that uses predictions about the input. We show that when predictions are relatively accurate, our algorithm significantly improves over the greedy algorithm. We complement our theoretical findings with an empirical evaluation of our algorithms, comparing them with the state-of-the-art scheme. The results confirm that our algorithms exhibit an improved performance for a wide range of input traces. [ABSTRACT FROM AUTHOR]

This article discusses the recent areas of focus at the National Institute of Informatics (NII) in Japan. First, a look at its academic services including SINET6, a backbone network service across Japan and its data platform service, the NII Research Data Cloud. The article also details NII research in computer science with projects in innovative computer vision and engineerable artificial intelligence, as well as applied research projects including a COVID-19 pneumonia diagnosis tool.

This article discusses how the combination of humans and artificial intelligence could target misinformation and polarization, by examining two scenarios- one where computing methods can assist human fact-checkers and one where human search methods can assist in search engine algorithms for personalization. Topics include the Australian Communication and Media Authority research on misinformation in Australia, whether filter bubbles are created by personalization attempts of algorithms, and the various computational methods that can assist fact-checking to prevent misinformation.

The article explores generalization in deep learning algorithms and the role of implicit bias involved in training neural networks.

The regulatory uses are real: In 2021, more than 100 drug and biologic applications submitted to the FDA included AI/ML components. Keywords: Algorithms; Artificial Intelligence; Bioengineering; Biologics; Biotechnology; Cybersecurity; Cyborgs; Drug Development; Drug Manufacturing; Drugs and Therapies; Emerging Technologies; FDA; Genetic Engineering; Genetically-Engineered Proteins; Government Agencies Offices and Entities; Health and Medicine; Machine Learning; Office of the FDA Commissioner; Public Health; Technology; U.S. Food and Drug Administration EN Algorithms Artificial Intelligence Bioengineering Biologics Biotechnology Cybersecurity Cyborgs Drug Development Drug Manufacturing Drugs and Therapies Emerging Technologies FDA Genetic Engineering Genetically-Engineered Proteins Government Agencies Offices and Entities Health and Medicine Machine Learning Office of the FDA Commissioner Public Health Technology U.S. Food and Drug Administration 497 497 1 05/22/23 20230523 NES 230523 2023 MAY 22 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Trials Week -- By: Patrizia Cavazzoni, M.D., Director of the Center for Drug Evaluation and Research Artificial intelligence (AI) and machine learning (ML) are no longer futuristic concepts; they are now part of how we live and work. [Extracted from the article]

Purpose: This paper aims to deal with the optimal shape design of a class of permanent magnet motors by minimizing multiple objectives according to an original interpretation of Pareto optimality. The proposed method solves a many-objective problems characterized by five objective functions and five design variables with evolution strategy algorithms, classically used for single- and multi-objective (two objective functions) optimization problems. Design/methodology/approach: Two approaches are proposed in the paper: the All-Objectives (AO) and the Many-Objectives (MO) optimization approach. The former is based on a single-objective optimization of a preference function, i.e. a normalized weighted sum. In contrast, in the MO a multi-objective optimization algorithm is applied to the minimization of a weight-free preference function and simultaneously to a maximization of the distance of the current solution from the prototype. The optimizations are based on an equivalent circuit model of the Permanent Magnet (PM) motor, but the results are assessed by means of finite element analyses (FEAs). Findings: An extensive study of the solutions obtained by means of the different optimization approaches is provided by means of post-processing analyses. Both the approaches find non-dominated solutions with respect to the prototype that are substantially improving the initial solution. The points of strength along with the weakness points of each solution with respect to the prototype are analysed in depth. Practical implications: The paper gives a good guide to the designers of electric motors, focussed on a shape design optimization. Originality/value: Considering simultaneously five objective functions in an automated optimal design procedure is challenging. The proposed approach, based on a well-known and established optimization algorithm, but exploiting a new concept of degree of conflict, can lead to new results in the field of automated optimal design in a many-objective context. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to further advance the multiple space/time subdomain framework with model reduction. Existing linear multistep (LMS) methods that are second-order time accurate, and useful for practical applications, have a significant limitation. They do not account for separable controllable numerical dissipation of the primary variables. Furthermore, they have little or no significant choices of altogether different algorithms that can be integrated in a single analysis to mitigate numerical oscillations that may occur. In lieu of such limitations, under the generalized single-step single-solve (GS4) umbrella, several of the deficiencies are circumvented. Design/methodology/approach: The GS4 framework encompasses a wide variety of LMS schemes that are all second-order time accurate and offers controllable numerical dissipation. Unlike existing state-of-art, the present framework permits implicit–implicit and implicit–explicit coupling of algorithms via differential algebraic equations (DAE). As further advancement, this study embeds proper orthogonal decomposition (POD) to further reduce model sizes. This study also uses an iterative convergence check in acquiring sufficient snapshot data to adequately capture the physics to prescribed accuracy requirements. Simple linear/nonlinear transient numerical examples are presented to provide proof of concept. Findings: The present DAE-GS4-POD framework has the flexibility of using different spatial methods and different time integration algorithms in altogether different subdomains in conjunction with the POD to advance and improve the computational efficiency. Originality/value: The novelty of this paper is the addition of reduced order modeling features, how it applies to the previous DAE-GS4 framework and the improvement of the computational efficiency. The proposed framework/tool kit provides all the needed flexibility, robustness and adaptability for engineering computations. [ABSTRACT FROM AUTHOR]

Purpose: Monitoring and diagnosis of fault cases for squirrel cage induction motors can be implemented using the multiple coupled circuit model. However, the identification of the associated model parameters for a specific machine is problematic. Up to now, the main options are measurement and test procedures or the use of finite element method analyses. However, these approaches are very costly and not suitable for use in an industrial application. The purpose of this paper is a practical parameter identification based on optimization methods and a comparison of different algorithms for this task. Design/methodology/approach: Population-based metaheuristics are used to determine the parameters for the multiple coupled circuit model. For this purpose, a search space for the required parameters is defined without an elaborate analytical approach. Subsequently, a genetic algorithm, the differential evolution algorithm and particle swarm optimization are tested and compared. The algorithms use the weighted mean squared error (MSE) between the real measured data of stator currents as well as speed and the simulation results of the model as a fitness function. Findings: The results of the parameter identification show that the applied methodology generally works and all three optimization algorithms fulfill the task. The differential evolution algorithm performs best, with a weighted MSE of 2.62, the lowest error after 1,000 simulations. In addition, this algorithm achieves the lowest overall error of all algorithms after only 740 simulations. The determined parameters do not completely match the parameters of the real machine, but still result in a very good reproduction of the dynamic behavior of the induction motor with squirrel cage. Originality/value: The value of the presented method lies in the application of condition-based maintenance of electric drives in the industry, which is performed based on the multiple coupled circuit model. With a parameterized model, various healthy as well as faulty states can be calculated and thus, in the future, monitoring and diagnosis of faults of the respective motor can be performed. Essential for this, however, are the parameters adapted to the respective machine. With the described method, an automated parameter identification can be realized without great effort as a basis for an intelligent and condition-oriented maintenance. [ABSTRACT FROM AUTHOR]

The threshold theorem is a seminal result in the field of quantum computing asserting that arbitrarily long quantum computations can be performed on a faulty quantum computer provided that the noise level is below some constant threshold. This remarkable result comes at the price of increasing the number of qubits (quantum bits) by a large factor that scales polylogarithmically with the size of the quantum computation we wish to realize. Minimizing the space overhead for fault-tolerant quantum computation is a pressing challenge that is crucial to benefit from the computational potential of quantum devices. In this paper, we study the asymptotic scaling of the space overhead needed for fault-tolerant quantum computation. We show that the polylogarithmic factor in the standard threshold theorem is in fact not needed and that there is a fault-tolerant construction that uses a number of qubits that is only a constant factor more than the number of qubits of the ideal computation. This result was conjectured by Gottesman who suggested to replace the concatenated codes from the standard threshold theorem by quantum error-correcting codes with a constant encoding rate. The main challenge was then to find an appropriate family of quantum codes together with an efficient classical decoding algorithm working even with a noisy syndrome. The efficiency constraint is crucial here: bear in mind that qubits are inherently noisy and that faults keep accumulating during the decoding process. The role of the decoder is therefore to keep the number of errors under control during the whole computation. On a technical level, our main contribution is the analysis of the small-set-flip decoding algorithm applied to the family of quantum expander codes. We show that it can be parallelized to run in constant time while correcting sufficiently many errors on both the qubits and the syndrome to keep the error under control. These tools can be seen as a quantum generalization of the bit-flip algorithm applied to the (classical) expander codes of Sipser and Spielman. [ABSTRACT FROM AUTHOR]

Memory leaks in web applications are pervasive and difficult to debug. Leaks degrade responsiveness by increasing garbage collection costs and can even lead to browser tab crashes. Previous leak detection approaches designed for conventional applications are ineffective in the browser environment. Tracking down leaks currently requires intensive manual effort by web developers, which is often unsuccessful. This paper introduces BLeak (Browser Leak debugger), the first system for automatically debugging memory leaks in web applications. BLeak’s algorithms leverage the observation that in modern web applications, users often repeatedly return to the same (approximate) visual state (e.g., the inbox view in Gmail). Sustained growth between round trips is a strong indicator of a memory leak. To use BLeak, a developer writes a short script (17–73 LOC on our benchmarks) to drive a web application in round trips to the same visual state. BLeak then automatically generates a list of leaks found along with their root causes, ranked by return on investment. Guided by BLeak, we identify and fix over 50 memory leaks in popular libraries and apps including Airbnb, AngularJS, Google Analytics, Google Maps SDK, and jQuery. BLeak’s median precision is 100%; fixing the leaks it identifies reduces heap growth by an average of 94%, saving from 0.5MB to 8MB per round trip. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed consensus seeking of multi-agent systems (MASs) with discrete-time control updating and intermittent communications among agents. Compared with existing linearly coupled protocols, a nonlinear coupled Zeno-free event-triggered controller is first proposed, which is further to project the static and dynamic triggering mechanisms exploited by using the deadband control method. Then, the node-based nonlinear coupled adaptive event-triggered controller with online self-tuning of time-varying coupling weight and its corresponding to static and dynamic deadband-based event-triggered mechanisms are designed, respectively. The exploited adaptive event-triggered controller does not rely on any global information of interaction structure and is implemented in a fully distributed fashion. In addition, two dynamic proposals not only cover existing static strategies as special cases, but also show that the minimal inter-execution time of dynamic one is not smaller than that of static one. Theoretical analysis shows that the proposed static and dynamic deadband-based event-triggered mechanisms can not only ensure the average consensus with Zeno-freeness, but also achieve the data reduction of communication and control. Finally, the proposed algorithms applied to circuit implementation are corroborated to prove its practical merits and validity. [ABSTRACT FROM AUTHOR]

The article discusses the relationship between model counting and streaming algorithms, and it mentions the differences between the idea of efficiency for model counting and the idea of estimating the amount of distinct elements in a given data stream. Data streams and a hashing-based technique for model counting are assessed, as well as a Satisfiability problem.

Keywords: Acid-Base Imbalance; Algorithms; Computers; Diabetes Complications; Diabetes Mellitus; Diabetic Ketoacidosis; Glucose Metabolism Disorders; Health and Medicine; Hospitals; Nutritional and Metabolic Diseases and Conditions; Peptide Hormones; Peptide Proteins; Proinsulin EN Acid-Base Imbalance Algorithms Computers Diabetes Complications Diabetes Mellitus Diabetic Ketoacidosis Glucose Metabolism Disorders Health and Medicine Hospitals Nutritional and Metabolic Diseases and Conditions Peptide Hormones Peptide Proteins Proinsulin 53 53 1 04/17/23 20230420 NES 230420 2023 APR 17 (NewsRx) -- By a News Reporter-Staff News Editor at Diabetes Week -- New study results on diabetic ketoacidosis have been published. Acid-Base Imbalance, Algorithms, Computers, Diabetes Complications, Diabetes Mellitus, Diabetic Ketoacidosis, Glucose Metabolism Disorders, Health and Medicine, Hospitals, Nutritional and Metabolic Diseases and Conditions, Peptide Hormones, Peptide Proteins, Proinsulin. [Extracted from the article]

This paper is a survey of methods for solving smooth, (strongly) monotone stochastic variational inequalities. To begin with, we present the deterministic foundation from which the stochastic methods eventually evolved. Then we review methods for the general stochastic formulation, and look at the fnite-sum setup. The last parts of the paper are devoted to various recent (not necessarily stochastic) advances in algorithms for variational inequalities. [ABSTRACT FROM AUTHOR]

This article presents information on how algorithms can be utilized to properly disperse natural disaster aid from governments and relief organizations. Topics include the need for automation and data analysis in order to assist in delivering aid in a timely and appropriate manner, how to prevent fraud in relief aid, and also how algorithms can make initial assessments for qualification of need in disaster situations. Organizations highlighted in the article include the American Red Cross, GiveDirectly, and the United Kingdom’s Department for Work & Pensions.

The article focuses on the search for algorithms that can reduce the time needed for multiplication problems within the fields of machine learning (ML), deep learning (DL), and artificial intelligence (AI). Topics discussed include the history of matrix multiplication and the data science group DeepMind and their ML approach AlphaTensor.

An important problem in terrain analysis is modeling how water flows across a terrain and creates floods by filling up depressions. In this paper, we study a number of flood-risk related problems: given a terrain Σ, represented as a triangulated xy-monotone surface with n vertices, a rain distribution, and a volume of rain ψ, determine which portions of Σ are flooded. We give an overview of efficient algorithms for these problems as well as explore the efficacy and efficiency of these algorithms on real terrains. [ABSTRACT FROM AUTHOR]

Coordinate Rotation Digital Computer (CORDIC) is a robust iterative algorithm that computes many transcendental mathematical functions. This paper proposes a reconfigurable CORDIC hardware design and FPGA realization that includes all possible configurations of the CORDIC algorithm. The proposed architecture is introduced in two approaches: multiplier-less and single multiplier approaches, each with its advantages. Compared to recent related works, the proposed implementation overpasses them in the included number of configurations. Additionally, it demonstrates efficient hardware utilization and suitability for potential applications. Furthermore, the proposed design is applied to a memristive chaotic system with different transcendental functions computed using the proposed reconfigurable block. The memristive system design is realized on the Artix-7 FPGA board, yielding throughputs of 0.4483 and 0.3972 Gbit/s for the two approaches of reconfigurable CORDIC. [ABSTRACT FROM AUTHOR]

This paper presents an extensive study of linear and logistic regression algorithms implemented with 1T1R memristor crossbars arrays. Using a sophisticated simulation platform that wraps circuit-level simulations of 1T1R crossbars and physics-based models of RRAM (memristors), we elucidate the impact of device variability on algorithm accuracy, convergence rate and precision. Moreover, a smart pulsing strategy is proposed for practical implementation of synaptic weight updates that can accelerate training in real crossbar architectures. Stochastic multi-variable linear regression shows robustness to memristor variability in terms of prediction accuracy but reveals impact on convergence rate and precision. Similarly, the stochastic logistic regression crossbar implementation reveals immunity to memristor variability as determined by negligible effects on image classification accuracy but indicates an impact on training performance manifested as reduced convergence rate and degraded precision. [ABSTRACT FROM AUTHOR]

The article provides a perspective on the use of algorithms in maintaining maximum flow through computing networks. Offers a critique of various max-flow theorems and algorithms. Discusses prior research into max-flow applications. Mentions the hope for future breakthroughs in developing scalable max-flow algorithms.

This article details the concept of critical path tracing to analyze latency in complex distributed systems. Critical path tracing works by compiling a list of the steps responsible for the slowest path of request processing and then optimizing those steps to reduce latency overall. Topics include remote procedure call or RPC telemetry, central process unit of CPU profiler analysis, and distributed tracing analysis. A detailed look at how critical path tracing works is included.

This article discusses computational modeling and simulation, with a focus on the development of mathematical software with an emphasis on how it needs to grow in the future. Topics include the optimization necessary to both the mathematical models and algorithms that support computational science software development, with a focus on decreasing operations, packaging software, and performance portability.

The article discusses the development of the algorithm known as norms to address the statistic problem known as nearest neighbor, referencing papers in the "Annual Symposium on Foundations of Computer Science" and "Proceedings of the ACM Symposium on Theory of Computing" journals. An overview of researchers' designing of normed spaces is provided. The uses of data analysis and expander graphs, including in regard to measuring the distance from data points, are discussed.

Keywords: Chicago; State:Illinois; United States; North and Central America; Algorithms; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning EN Chicago State:Illinois United States North and Central America Algorithms Cyborgs Emerging Technologies Health and Medicine Machine Learning 380 380 1 05/22/23 20230526 NES 230526 2023 MAY 28 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Devices & Surgical Technology Week -- Fresh data on Machine Learning are presented in a new report. Machine learning algorithms may be used to compare the risk of failure of specific patient-procedure combinations in the treatment of cartilage defects of the knee. [Extracted from the article]

Since chaotic cryptography has a long-term problem of dynamic degradation, this paper presents proof that chaotic systems resist dynamic degradation through theoretical analysis. Based on this proof, a novel one-dimensional two-parameter with a wide-range system mixed coupled map lattice model (TWMCML) is given. The evaluation of TWMCML shows that the system has the characteristics of strong chaos, high sensitivity, broader parameter ranges and wider chaos range, which helps to enhance the security of chaotic sequences. Based on the excellent performance of TWMCML, it is applied to the newly proposed encryption algorithm. The algorithm realizes double protection of private images under the premise of ensuring efficiency and safety. First, the important information of the image is extracted by edge detection technology. Then the important area is scrambled by the three-dimensional bit-level coupled XOR method. Finally, the global image is more fully confused by the dynamic index diffusion formula. The simulation experiment verified the effectiveness of the algorithm for grayscale and color images. Security tests show that the application of TWMCML makes the encryption algorithm have a better ability to overcome conventional attacks. [ABSTRACT FROM AUTHOR]

A new report from the University of Florence in Italy provides an extensive review of monitoring methods for electrical power lines, with a focus on high-voltage and medium-voltage systems. The objective of these techniques is to prevent catastrophic failures by detecting partial damage or deterioration of components and allowing for organized maintenance operations. The paper discusses the coordination of protection devices and the implementation of artificial intelligence algorithms to improve the reliability of the network. It also highlights diagnostic techniques, protection devices, and prognostic methods, emphasizing the role of artificial intelligence and offering guidelines for choosing between different approaches. [Extracted from the article]