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This paper investigates the weighted least squares (WLS) and minimax design problems for variable fractional delay (VFD) FIR filters. For the WLS design, the general case of incorporating arbitrary nonnegative weighting functions is considered. The optimal solution is characterized by two matrix equations. An efficient algorithm using conjugate gradient (CG) techniques is proposed to solve the WLS solution. The proposed algorithm is guaranteed to converge to the optimal solution in a finite number of iterations. Moreover, an iterative reweighted least squares (IRLS) algorithm that uses the proposed CG algorithm as its iteration core is developed for the minimax design problem. In both of the algorithms, the filter coefficients are arranged as matrices, achieving a great saving in computations and memory space. The associated computational complexity is analyzed. Some design examples are provided and comparisons with existing methods show that the proposed ones are either computationally more efficient or can obtain the better filter performance, or both. [ABSTRACT FROM AUTHOR]

The limited memory version of the Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm is the most popular quasi-Newton algorithm in machine learning and optimization. Recently, it was shown that the stochastic L-BFGS (sL-BFGS) algorithm with the variance-reduced stochastic gradient converges linearly. In this paper, we propose a new sL-BFGS algorithm by importing a proper momentum. We prove an accelerated linear convergence rate under mild conditions. The experimental results on different data sets also verify this acceleration advantage. [ABSTRACT FROM AUTHOR]

As convolutional neural networks (CNNs) become more and more diverse and complicated, acceleration of CNNs increasingly encounters a bottleneck of balancing performance, energy efficiency, and flexibility in a unified architecture. This paper proposed a Winograd-based highly efficient and dynamically Reconfigurable Accelerator (named WRA) for quickly evolving CNN models. A cost-effective convolution decomposition method (CDW) was proposed, and it extends the application of the fast Winograd algorithm. Based on CDW, a high-throughput and reconfigurable processing element (PE) array was designed to exploit the parallelism of Winograd. Besides, a highly compact memory structure employed four levels of data reuse schemes to achieve maximal data reuse and minimize external bandwidth requirement. Provided with dynamically reconfigurable capability, WRA implements CDW and other convolutions (e.g., standard convolution, depthwise separable convolution, and group convolution) on a unified hardware architecture. The WRA accelerator was implemented on a Xilinx XCVU9P platform running at 330 MHz clock frequency, controlled by a POWER8 processor via a coherent accelerator processor interface (CAPI) interface. At different configurations, WRA can provide 2.2–6.3 TOPS performance for different convolution shapes. The average performance and energy efficiency for VGG16/AlexNet/MobileNetV1/MobileNetV2 are 5288 GOP/s at 151.2 GOPs/W, 3478 GOP/s at 99.4 GOPs/W, 2674 GOP/s at 76.4 GOPs/W, and 2194 GOP/s at 62.7 GOPs/W. It achieves $1.7\times $ – $24\times $ speedup compared with the previous FPGA-based designs. [ABSTRACT FROM AUTHOR]

In this paper, fast and compact implementations for code-based signature are presented. Existing designs are either using enormous memory storage or suffering from slow issuing speed of signatures. A vastly optimized new design solving these problems is proposed by exploiting quasi-cyclic low-density generator matrix codes at different levels. In particular, this paper provides a new algorithmic enhancement of signature generation and gives detailed and optimized solutions for critical steps of this algorithm. The design presented in this paper is the fastest implementation of code-based signatures in open literature. It is shown, for instance, that our implementation of signature generation engine can generate approximately 60 000 signatures per second on a Xilinx Virtex-6 FPGA, requiring only 5992 slices and 60 memory blocks. In addition, a very compact implementation is also provided, producing 5438 signatures per second with only 18 memory blocks. [ABSTRACT FROM PUBLISHER]

Researchers from the University of Electronic Science and Technology of China have developed an intelligent inspection guidance system for urethral endoscopy. The system combines image processing, simultaneous localization and mapping (SLAM), and intelligent navigation technologies to assist doctors in conducting a quick and comprehensive examination. The system uses a generative adversarial network-based deblurring algorithm and a vascular attention-based feature extraction algorithm to improve accuracy in identifying urethral vascular features. The research concludes that the proposed method is more accurate and comprehensive, resulting in a 4.34% accuracy improvement. [Extracted from the article]

A recent study conducted by researchers from the Icahn School of Medicine at Mount Sinai in New York City explores the use of advanced deep learning algorithms for magnetic resonance imaging (MRI) segmentation. Accurate MRI segmentation is crucial for diagnosing abnormalities and planning treatments, but traditional supervised learning techniques require a large amount of annotated data. The study reviews state-of-the-art algorithms that utilize a limited number of annotated samples, including self-supervised learning, generative models, few-shot learning, and semi-supervised learning. The researchers also discuss future directions in the field, such as contrastive language-image pretraining, to further improve image segmentation with limited labels. [Extracted from the article]

A report from the China University of Petroleum (East China) discusses the need for modern technology, specifically artificial intelligence, to support the diagnosis of breast cancer. The existing computer-aided diagnostic systems for breast cancer are costly and limited in their capabilities. The report proposes the use of improved algorithms and a simple integrated system for pre-diagnosis of breast cancer based on CNN and Pyqt5. The system utilizes tensorflow and pytorch frameworks and includes a human-computer interaction GUI system interface. The research has been peer-reviewed and more information can be obtained from the China University of Petroleum (East China). [Extracted from the article]

Researchers from Brigham and Women's Hospital in Boston, Massachusetts have published a report on algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation (TMS). TMS is a device-based neuromodulation technique used to treat brain diseases. The report discusses modern algorithms for real-time TMS electric field modeling, including basis representation techniques and deep neural-network-based methods. It also reviews software tools that integrate electric field modeling with navigated TMS. The research was supported by the National Institutes of Health (NIH) in the United States. [Extracted from the article]

This article discusses the use of machine learning algorithms, specifically the K-Nearest Neighbors (KNN) algorithm, in predicting vaccine targets. Traditional methods for identifying suitable antigen targets for vaccines can be time-consuming and lack scalability. The authors present an implementation of the KNN algorithm for vaccine target prediction and demonstrate its effectiveness in accurately identifying vaccine targets. The study highlights the potential of the KNN algorithm in this field and provides insights for future research. Please note that this article has not been peer-reviewed. [Extracted from the article]

Researchers from Linkoping University in Sweden have developed an enhanced chameleon swarm algorithm (ICSA) for global optimization and multi-level thresholding medical image segmentation. Medical image segmentation is crucial for disease diagnosis and analysis, but selecting optimal thresholds can be computationally intensive. The ICSA algorithm improves precision and efficiency by introducing the concept of the "best random mutation strategy" and utilizing three distribution functions for mutating search individuals. Comparative analysis shows that ICSA outperforms other metaheuristic algorithms and significantly improves image segmentation performance. This research has been peer-reviewed and published in Neural Computing and Applications. [Extracted from the article]

A study conducted at Velagapudi Ramakrishna Siddhartha Engineering College explores the use of a genetic algorithm-based decision tree to detect dementia through MRI analysis. The researchers emphasize the importance of early and accurate detection of dementia for optimal therapy and management. By analyzing a large collection of MRI images and associated clinical data, the decision tree algorithm achieved an impressive accuracy rate of 96.67%. This research contributes to the development of a non-invasive diagnostic tool for dementia detection. [Extracted from the article]

A new study conducted by researchers at Quantum University focuses on hydrocephalus, a central nervous system disorder that primarily affects infants and toddlers. The study aims to improve the accuracy of hydrocephalus diagnosis by developing an algorithm called H-detect. This algorithm utilizes image processing techniques and the CatBoost algorithm to predict hydrocephalus more effectively than traditional methods. The research concludes that the H-detect strategy outperforms conventional approaches. For more information, readers can refer to the journal article "H-Detect: an Algorithm for Early Detection of Hydrocephalus" published in Informatika i avtomatizacia. [Extracted from the article]

A study conducted by researchers at Prince Sattam bin Abdulaziz University in Saudi Arabia explores the issue of balancing generation and demand in interconnected microgrids (MGs) to address security, power quality, and reliability concerns. The study proposes an optimized fractional order load frequency control (LFC) scheme with cascaded outer and inner control loops. The proposed controller, based on a modified version of the liver cancer optimization algorithm (MLCA), achieves better disturbance rejection compared to traditional LFC methods. The study also considers the contribution of electric vehicles and the high penetration of renewables to test the stability of the proposed LFC technique. The results confirm the superior response and improved performance of the proposed controller. [Extracted from the article]

Researchers at the University of Maribor in Slovenia have developed a recycling procedure for rapid antigen tests, which were widely used during the Covid-19 epidemic. The tests, which consist of various materials including gold nanoparticles, become mass waste after use. The researchers developed an algorithm of characterization techniques to successfully identify and describe the key materials in the tests, including the gold nanoparticles. The next step is to validate the recycling process so that the gold nanoparticles and plastic can be reused in new products. [Extracted from the article]

A study conducted by researchers at Zhengzhou University of Aeronautics explores the optimization of overseas surgical warehouse locations in cross-border medical supply chain networks using parallel big data mining. The researchers propose an algorithm that takes into account factors such as cross-border transportation cost, local transportation cost, warehouse operation cost, and market value to determine the best location for overseas warehouses. By introducing the concept of time window and customer satisfaction, a multi-objective location model is established. The study concludes that the proposed algorithm effectively improves user satisfaction, optimal probability, and reduces system costs. [Extracted from the article]

A recent study conducted by researchers at Sichuan University in Chengdu, China explores the use of computer algorithms to mimic the route choices made by human players in wargames. The researchers developed a fuzzy dynamic route choice model and algorithm that takes into account the uncertain and dynamic decision-making environment of wargaming. The model was applied to a practical case and demonstrated its effectiveness in handling fuzzy and uncertain information while simulating the decision-making psychology of wargame players. This research provides a new method for automatic route choice in wargame simulations. [Extracted from the article]

Spatial Omics is a new field that is revolutionizing biological and biomedical research by analyzing biological data-based spatial information. This technology allows researchers to study the structure and function of biological systems by uncovering the spatial distribution characteristics in cells, tissues, and organs. However, the potential of Spatial Omics is hindered by the lack of available techniques and computational tools to handle the enormous multi-dimensional datasets. To address this, GigaScience Press has published a series of articles that provide open-source algorithms, tools, and applications for analyzing and interpreting spatial omics data. These resources aim to democratize the use of this technology and facilitate further research in the field. [Extracted from the article]

Researchers at Chitkara University in Punjab, India have developed an improved lung cancer segmentation technique using nature-inspired optimization approaches. The study focused on the precise identification of tumor nodules in lung cancer Computed Tomography (CT) images. The researchers integrated four nature-inspired swarm intelligent techniques with the K-means algorithm to address the visual challenges of segmentation. The results showed that the K-means algorithm with Cuckoo Search Algorithm (CSA) significantly improved the quality of lung cancer segmentation compared to other optimization approaches. This research provides a valuable tool for researchers to improve the segmentation of medical image datasets for identifying the targeted region of interest. [Extracted from the article]

A research team at the University of Chicago Medical Center has developed a new risk score, called the U.S. Candidate Risk Score (US-CRS), to predict the likelihood that a patient will die without a heart transplant. The US-CRS aims to address the limitations of the current therapy-based 6-status system by incorporating objective physiological measurements, such as levels of molecules in the blood associated with organ failure. The researchers analyzed data from over 16,900 adult heart transplant candidates to determine the relationship between the US-CRS and mortality within 6 weeks of being placed on the transplant waitlist. The US-CRS model was found to be much better at predicting mortality than the current U.S. heart allocation system. However, further validation and committee approval are needed before the US-CRS can be adopted. The researchers are also working on improving heart allocation overall and designing a fair and resilient continuous distribution system for deceased donor hearts. [Extracted from the article]

Aiming at reducing the complexity of minimum mean square error (MMSE) detection in massive multiple-input multiple-output (MIMO) systems, this paper proposes a detection algorithm with high convergence rate and an efficient hardware architecture based on second-order Richardson iteration (SORI). In the proposed algorithm, a pre-iteration-based initialization method is presented to accelerate the convergence without extra complexity. In addition, the approximation of relaxation factor and the log-likelihood ratio (LLR) is introduced to further reduce computing load. Theoretical analysis demonstrates the advantages of the proposed algorithm in fast convergence and low complexity, and simulation results show that the proposed algorithm can efficiently approach MMSE performance. Based on this algorithm, a flexible hardware architecture is designed, which is deeply pipelined to support $128\times U$ ($8\leq U\leq 32$) massive MIMO detection with the configurable number of iterations, and a folded dual-mode systolic array (DMSA) is fully utilized to achieve the flexibility with low hardware consumption. Implemented on Xilinx Virtex-7 FPGA and SMIC 40nm CMOS technology, the proposed detector is competitive in terms of energy and area efficiency compared to state-of-the-art iterative detectors, and it can adapt to the varied channel condition and the number of users in massive MIMO systems. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) has been widely studied due to its high performance and usability for various applications such as image classification, detection, segmentation, translation, and action recognition. Thanks to the universal applications and high performance of DNN algorithm, DNN is adopted for various AI platforms, including edge/mobile devices as well as cloud servers. However, high-performance DNN requires a large amount of computation and memory access, making it challenging to implement DNN operation on edge/mobile. There have been several ways to solve these problems, including algorithms as well as hardware for DNN. Algorithms that help accelerate DNN in hardware enable much more efficient operation of high-performance AI. This article aims to provide an overview of the recent hardware and algorithm co-design schemes enabling efficient processing of DNNs. Specifically, it will provide algorithm optimization methods for DNN structure, neurons, synapses, and data types. This paper also introduces optimization methods for hardware architectures, PE array, data-path control, and microarchitecture of PE. And we will also show examples of DNN algorithm and hardware co-designed ASICs. [ABSTRACT FROM AUTHOR]

Sparsified kernel adaptive filters (SKAFs) is an attractive filtering solution with low memory and computational complexity. Most of existing SKAFs are based on the mean square error (MSE) criterion under Gaussian noise assumption for its simplicity and convenience. When the assumption deviates largely from the underlying truth, the performance of these methods could degrade significantly. In this paper, we propose a novel SKAF, named as projected kernel least mean p-power algorithm (PKLMP), based on the mean p-power error (MPE) criterion and vector projection (VP) method. We provide convergence analyses in terms of the stead-state MSE, based on a Taylor expansion method, and derive the lower and upper bounds for the steady-state excess MSE. We also conduct mean convergence analysis for PKLMP, and derive convergence conditions. To exploit the information in the desired outputs, we further derive a modified PKLMP by smoothing the desired signal. Finally, a simple and effective online variable kernel centers strategy is proposed to improve the filtering performance of the proposed KAFs. Simulation results under a static function estimation, a chaotic time-series prediction, and two real-world time-series predictions are conducted and validate the effectiveness of the proposed PKLMP algorithms. [ABSTRACT FROM AUTHOR]

Piecewise affine (PWA) models are attractive frameworks that can represent various hybrid systems with local affine submodels and polyhedral regions due to their universal approximation properties. The PWA identification problem amounts to estimating both the submodel parameters and the polyhedral partitions from data. In this paper, we propose a novel approach to address the identification problem of PWA systems such that the number of submodels, parameters of submodels, and the polyhedral partitions are obtained. In particular, a cluster-based algorithm is designed to acquire the number of submodels, the initial labeled data set, and initial parameters corresponding to each submodel. Additionally, we develop a modified self-training support vector machine algorithm to simultaneously identify the hyperplanes and parameter of each submodel with the ouputs of the cluster-based algorithm. The proposed algorithm is computationally efficient for region estimation and able to accomplish this task with only a small quantity of classified regression vectors. The effectiveness of the proposed identification approach is illustrated via simulation results. [ABSTRACT FROM AUTHOR]

To reduce multiplication operations in convolution of convolutional neural networks (CNNs), there are three widely used convolutional acceleration algorithms, i.e., Winograd, FFT and FFA. However, current accelerators based on these convolutional acceleration algorithms have issues on flexibility and efficiency. Firstly, some accelerators utilized a combination of these acceleration algorithms and employed multiple types of computational units to achieve their respective advantages. As a result, some computational units are left unused when the best-performing unit is working, which causes much area inefficiency. Secondly, current accelerators tend to choose small parameters of these convolutional acceleration algorithms to avoid unacceptable precision loss, as a result, they are hardly to support large kernel sizes and lack of flexibility. Thirdly, these acceleration algorithms are typically presented for 1-stride convolutions, consequently, few implementation considers the acceleration of large-stride convolutions, which is a major restriction to hardware flexibility. This paper proposed a stride-based convolution decomposition method (SCDM) to reform different convolution shapes (i.e., kernel sizes & strides) to an identical pattern. With the aid of SCDM, a Winograd-stretched and hardware-efficient design (WHD) is presented to utilize one uniform computational unit for the acceleration of different convolution shapes, which combines complementary performance advantages on both Winograd F(4,3)andF(4,2) units. Compared to current FFT-based or FFA-based works, WHD can stretch the use range of Winograd and simplify implementation, thereby achieving hardware flexibility and efficiency. Evaluation results show that 34.08%~55.41% operation reduction were achieved on six CNN models, while incurring a slight hardware overhead. [ABSTRACT FROM AUTHOR]

Automated detection of the P-wave arrival for real-time earthquake-early-warning-system employing conventional short-time-average/long-time-average (STA/LTA) algorithm is prone to suffer from erroneous detection due to high background noise. This paper introduces an enhanced variable step-size least mean square (EVSSLMS) algorithm for considerably improving the P-wave detection. The proposed event detection scheme employs the EVSSLMS algorithm for de-noising the seismic data followed by conventional STA/LTA algorithm to detect arrival of the P-wave. The EVSSLMS algorithm outperforms the existing trigonometric least mean square (TLMS), variable step-size LMS (VSSLMS), least mean logarithmic square (LMLS) and variable $\alpha $ -LMLS algorithms in terms of convergence speed and steady-state error and achieves a significant enhancement in detection accuracy by 42%, 33%, 25% and 23% at low signal-to-noise ratio (SNR) as compared to the existing TLMS, VSSLMS, LMLS, and variable- $\alpha $ LMLS algorithms, respectively. Moreover, a high-speed and low-complexity very-large-scale-integrated (VLSI) architecture has been implemented on both field-programmable-gate array (FPGA) and application-specific-integrated circuit (ASIC) platforms for real-time applications. Comparison of architectural performance of the proposed scheme with that of architecture designed to realize variable- $\alpha $ LMLS algorithm exhibits 36% less slice-delay-product whereas, its ASIC implementation exhibits 7.27% less area-delay-product, 9.31% less energy-per-sample with 5.2% more maximum achievable frequency. [ABSTRACT FROM AUTHOR]

This paper addresses a general method for parameter identification in distributed parameter systems (DPSs) based on a reference model and measurement information. Moreover, the developed identification method is used to deal with secure communication problems. Firstly, in combination with the historical information measured by sensors, an iterative approach based on an approximator is put forward. Meanwhile, an execution scheme for the iterative approach is provided by an optimization policy. Subsequently, by bringing new mechanisms into quantum particle swarm optimization (QPSO) algorithm to enhance its search capability, we propose quantum-leading-following-based optimization (QLFBO) algorithm, which serves the optimization policy. Further, the global convergence for QLFBO algorithm is proved combined with the parameter identification problem of DPSs, and the computational complexity on this algorithm is discussed in detail. Finally, simulation experiments on the diffusion-reaction model are carried out by the iterative approach based on QLFBO algorithm under different measurement ways. Simultaneously, secure communication depending on this model is performed in the double-scroll Chua’s circuit and a lotus image via the investigated iterative strategy and encryption-decryption scheme. Simulation results verify the validity and practicability of the developed method. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to introduce two new (3 + 1)-dimensional Boiti–Leon–Manna–Pempinelli (BLMP) equations, the first with constant coefficients and the other with time-dependent coefficients. The author obtains multiple soliton solutions and multiple complex soliton solutions for the two developed models. Design/methodology/approach: The newly developed models with constant coefficients and with time-dependent coefficients have been handled by using the simplified Hirota's method. The author also uses the complex Hirota's criteria for deriving multiple complex soliton solutions. Findings: The two developed BLMP models exhibit complete integrability for any constant coefficient and any analytic time-dependent coefficients by investigating the compatibility conditions for each developed model. Research limitations/implications: The paper presents an efficient algorithm for handling integrable equations with constant and analytic time-dependent coefficients. Practical implications: The paper presents two new integrable equations with a variety of coefficients. The author showed that integrable equations with constant and time-dependent coefficients give real and complex soliton solutions. Social implications: The paper presents useful algorithms for finding and studying integrable equations with constant and time-dependent coefficients. Originality/value: The paper presents an original work with a variety of useful findings. [ABSTRACT FROM AUTHOR]

Predefined mymargin finite-time output containment control problem for nonlinear multi-agent systems with multiple dynamical leaders under directed topology is investigated, where the outputs of followers can converge to the predefined convex hull formed by the multiple leaders within a finite time, and the leaders can have unknown control inputs. Firstly, for the directed topological structure among the followers, a distributed adaptive observer is designed to estimate the whole states of all the leaders under the influences of the leaders’ unknown inputs. By utilizing Hardy’s inequality and common Lyapunov theory, the finite-time convergence of the proposed observer is proved. On the basis of this conclusion, a predefined distributed containment control protocol including the desired convex combinations of the leaders is developed for each follower by using the given weights. Then an algorithm is proposed to design the control parameters in the proposed containment control protocol. With the help of the output regulation theory, the finite-time output containment criterion for nonlinear multi-agent systems in the presence of the leaders’ unknown inputs is derived. Finally, a numerical simulation example is presented to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Single constant multiplication (SCM) and multiple constant multiplications (MCM) are among the most popular schemes used for low-complexity shift-add implementation of finite impulse response (FIR) filters. While SCM is used in the direct form realization of FIR filters, MCM is used in the transposed direct form structures. Very often, the hybrid form FIR filters where the sub-sections are implemented by fixed-size MCM blocks provide better area, time, and power efficiency than those of traditional MCM and SCM based implementations. To have an efficient hybrid form filter, in this paper, we have performed a detailed complexity analysis in terms of the hardware and time consumed by the hybrid form structures. We find that the existing hybrid form structures lead to an undesirable increase of complexity in the structural-adder block. Therefore, to have a more efficient implementation, a variable size partitioning approach is proposed in this paper. It is shown that the proposed approach consumes less area and provides nearly 11% reduction of critical path delay, 40% reduction of power consumption, 15% reduction of area-delay product, 52% reduction of energy-delay product, and 42% reduction of power-area product, on an average, over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Most results in the field of algorithm design are single algorithms that solve single problems. In this paper we discuss multidimensional divide-anti-conquer, an algorithmic paradigm that can be instantiated in many different ways to yield a number of algorithms and data structures for multidimensional problems. We use this paradigm to give best-known solutions to such problems as the ECDF, maximal range searching, closest pair, and all nearest neighbor problems The contributions of the paper are on two levels. On the first level are the particular algorithms and data structures given by applying the paradigm. On the second level is the more novel contribution of this paper a detailed study of an algorithmic paradigm that is specific enough to be described precisely yet general enough to solve a wide variety of problems. [ABSTRACT FROM AUTHOR]

The article discusses the development of an algorithm for whole number multiplication that may be the fastest such algorithm, developed by mathematics researchers Joris van der Hooven and David Harvey. According to the article, the algorithm involves the rounding of complex numbers within Fourier analysis in order to achieve precision and speed.

Signal reconstruction problem (SRP) is an important optimization problem where the objective is to identify a solution to an underdetermined system of linear equations that is closest to a given prior. It has a substantial number of applications in diverse areas, such as network traffic engineering, medical image reconstruction, acoustics, astronomy, and many more. Unfortunately, most of the common approaches for solving SRP do not scale to large problem sizes. We propose a novel and scalable algorithm for solving this critical problem. Specifically, we make four major contributions. First, we propose a dual formulation of the problem and develop the Direct algorithm that is significantly more efficient than the state of the art. Second, we show how adapting database techniques developed for scalable similarity joins provides a substantial speedup over Direct. Third, we describe several practical techniques that allow our algorithm to scale—on a single machine—to settings that are orders of magnitude larger than previously studied. Finally, we use the database techniques of materialization and reuse to extend our result to dynamic settings where the input to the SRP changes. Extensive experiments on realworld and synthetic data confirm the efficiency, effectiveness, and scalability of our proposal. [ABSTRACT FROM AUTHOR]

Products and services that rely on machine learning—computer programs that constantly absorb new data and adapt their decisions in response—don’t always make ethical or accurate choices. Sometimes they cause investment losses, for instance, or biased hiring or car accidents. And as such offerings proliferate across markets, the companies creating them face major new risks. Executives need to understand and mitigate the technology’s potential downside. Machine learning can go wrong in a number of ways. Because the systems make decisions based on probabilities, some errors are always possible. Their environments may evolve in unanticipated ways, creating disconnects between the data they were trained with and the data they’re currently fed. And their complexity can make it hard to determine whether or why they made a mistake. A key question executives must answer is whether it’s better to allow smart offerings to continuously evolve or to “lock” their algorithms and periodically update them. In addition, every offering will need to be appropriately tested before and after rollout and regularly monitored to make sure it’s performing as intended. [ABSTRACT FROM AUTHOR]

The paper describes a scheme for symbolic manipulation of index expressions which arise as a by-product of the symbolic manipulation of expressions in the matrix calculi described by the authors in a previous paper. This scheme attempts program optimization by transforming the original algorithm rather than the machine code. The goal is to automatically generate code for handling the tedious address calculations necessitated by complicated data structures. The paper is therefore preoccupied with "indexing by position." The relationship of "indexing by name" and "indexing by position" is discussed. [ABSTRACT FROM AUTHOR]

A comprehensive system for transformational grammar has been designed and implemented on the IBM 360/67 computer. The system deals with the transformational model of syntax, along the lines of Chomsky's Aspects of the Theory of Syntax. The major innovations include a full, formal description of the syntax of a transformational grammar, a directed random phrase structure generator, a lexical insertion algorithm, an extended definition of analysis, and simple problem-oriented programming language in which the algorithm for application of transformations can be expressed. In this paper we present the system as a whole, first discussing the general attitudes underlying the development of the system, then outlining the system and discussing its more important special features. References are given to papers which consider some particular aspect of the system in detail. [ABSTRACT FROM AUTHOR]

The article examines the timeline for the development of post-quantum cryptography. Warnings from the U.S. National Security Agency and the U.S. National Institute of Standards and Technology to develop public-key cryptography resistant to the quantum computers of the future are reiterated and alleged challenges associated with the transition of cryptography standards are outlined.

A multi-purpose mechanical platform to track moving objects in three-dimensional space has been developed. It is composed of one main microcontroller board that processes all system data, two cameras, three motors, and one secondary microcontroller board to position a platform with three degrees of freedom. The system computes the optical flow and moves the cameras accordingly, tracking motion within the visual scene. The platform operates autonomously. To the best of our knowledge, there are no similar systems reported with low-resolution image sensors and low-cost microcontrollers. Existing solutions rely on personal computers and advanced FPGAs to process image data. This article concludes that the optical flow operation is efficient even using an image sensor with very low resolution. Thus, the system complexity and image data processing are alleviated significantly. The platform can be easily adapted to different application scenarios by adding new peripherals, sensors, or image processing algorithms. A detailed description of the system design and experimental results are provided. [ABSTRACT FROM AUTHOR]

This paper investigates the secure state estimation problem of multiagent systems with misbehaving agents. Compared with the existing works which only consider malicious behaviors generated elaborately, a class of faulty behaviors generated randomly is also taken into account. By assuming that faulty behaviors cannot be altered by the adversaries, the notion of orthogonal complement matrix is introduced to characterize the state observability of the multiagent systems with both faulty and malicious agents. It is shown that more misbehaving agents can be tolerated by distinguishing faulty agents from malicious ones. Then, for single agent, two algorithms are provided to estimate the state based on its own value and the measurements obtained from its neighbors. Finally, two simulations are provided to illustrate the correctness and effectiveness of the proposed conditions and methods. [ABSTRACT FROM AUTHOR]

Learning from imbalanced data is among the most popular topics in the contemporary machine learning. However, the vast majority of attention in this field is given to binary problems, while their much more difficult multiclass counterparts are relatively unexplored. Handling data sets with multiple skewed classes poses various challenges and calls for a better understanding of the relationship among classes. In this paper, we propose multiclass radial-based oversampling (MC-RBO), a novel data-sampling algorithm dedicated to multiclass problems. The main novelty of our method lies in using potential functions for generating artificial instances. We take into account information coming from all of the classes, contrary to existing multiclass oversampling approaches that use only minority class characteristics. The process of artificial instance generation is guided by exploring areas where the value of the mutual class distribution is very small. This way, we ensure a smart oversampling procedure that can cope with difficult data distributions and alleviate the shortcomings of existing methods. The usefulness of the MC-RBO algorithm is evaluated on the basis of extensive experimental study and backed-up with a thorough statistical analysis. Obtained results show that by taking into account information coming from all of the classes and conducting a smart oversampling, we can significantly improve the process of learning from multiclass imbalanced data. [ABSTRACT FROM AUTHOR]

Class imbalance is a challenging problem in many classification tasks. It induces biased classification results for minority classes that contain less training samples than others. Most existing approaches aim to remedy the imbalanced number of instances among categories by resampling the majority and minority classes accordingly. However, the imbalanced level of difficulty of recognizing different categories is also crucial, especially for distinguishing samples with many classes. For example, in the task of clinical skin disease recognition, several rare diseases have a small number of training samples, but they are easy to diagnose because of their distinct visual properties. On the other hand, some common skin diseases, e.g., eczema, are hard to recognize due to the lack of special symptoms. To address this problem, we propose a self-paced balance learning (SPBL) algorithm in this paper. Specifically, we introduce a comprehensive metric termed the complexity of image category that is a combination of both sample number and recognition difficulty. First, the complexity is initialized using the model of the first pace, where the pace indicates one iteration in the self-paced learning paradigm. We then assign each class a penalty weight that is larger for more complex categories and smaller for easier ones, after which the curriculum is reconstructed by rearranging the training samples. Consequently, the model can iteratively learn discriminative representations via balancing the complexity in each pace. Experimental results on the SD-198 and SD-260 benchmark data sets demonstrate that the proposed SPBL algorithm performs favorably against the state-of-the-art methods. We also demonstrate the effectiveness of the SPBL algorithm’s generalization capacity on various tasks, such as indoor scene image recognition and object classification. [ABSTRACT FROM AUTHOR]

In this paper, $H_\infty $ control problem is investigated by off-policy integral reinforcement learning (IRL) method for the nonlinear systems with completely unknown dynamics, disturbances, and constrained-input. Firstly, according to a model-based policy iteration (PI) algorithm, a model-free algorithm is proposed based on the derived iterative equation, and the equivalence of model-based PI algorithm and model-free algorithm is proven. Then, the model-free algorithm is implemented by off-policy IRL technology to solve the Hamilton-Jacobi-Isaacs (HJI) equation with the collected system data by the least-square approach, where three neural networks (NNs) are constructed to approximate the value function, control and the disturbance. Finally, our proposed methods are applied to stabilize an autonomous third-order Chua’s chaotic circuit system and a non-autonomous second-order memristive chaotic circuit system to illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a new method to identify and compensate for the I/Q imbalance in wideband millimeter-wave transmitters using a single undersampling analog-to-digital converter (ADC) is proposed. The proposed method alleviates the need for an I/Q demodulator; hence, simplifying the structure and calibration procedure of the required transmitter-observation receiver (TOR). In addition, an algorithm for estimating the delay and phase offsets between the transmitter and the single-ADC TOR is devised. The presented algorithm improves upon the exhaustive search methods employed in the literature. Measurement results using an off-the-shelf I/Q modulator, driven by an 800 MHz test signal, show about a 5% reduction in the root normalized mean-square error (RNMSE) of its output signal when the proposed method is utilized. Further measurements were conducted to assess the performance of this method, in conjunction with digital predistortion (DPD), when a power amplifier (PA) is added to the transmitter chain. When compared with the application of DPD only, the I/Q imbalance correction allowed additional reductions of about 4% in the RNMSE and 1.5 dB in the adjacent-channel power ratio of the PA output signal. These improvements were obtained with ADC sampling rates as low as 20 times below the Nyquist rate. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose an enhanced algorithm and used to decision-making that specifically focuses on the choice of a domain in the calculation of degree of greyness according to the principle of grey numbers operation. The domain means the emerging background of interval grey numbers, it is vital for the operational mechanism of such interval grey numbers. However, the criteria of selection of domain always remain same that is not only for the calculated grey numbers but also for the resultant grey numbers, which can be assumed as unrealistic up to a certain extent. Design/methodology/approach: The existence of interval grey number operation based on kernel and the degree of greyness containing two calculation aspects, which are kernel and the degree of greyness. For the degree of greyness, it includes concepts of domain and calculation of the domain. The concepts of a domain are defined. The enhanced algorithm is also comprised of four deductive theorems and eight rules that are linked to the properties of the enhanced algorithm of the interval grey numbers based on the kernel and the degree of greyness. Findings: Aiming to improve the algorithm of the degree of greyness for interval grey numbers, based on the variation of domain in the operation process, the degree of greyness of the operation result is defined in this paper, and the specific expressions for algebraic operations are given, which is relevant to the kernel, the degree of greyness and the domain. Then, these expressions are used to the algorithm of interval grey numbers based on the kernel and the degree of greyness, improving the accuracy of the operation results. Originality/value: The enhanced algorithm in this paper can effectively reduce the loss of information in the operation process, so as to avoid the situation where the decision values are the same and scientific decisions cannot be made during the grey evaluation and decision-making process. [ABSTRACT FROM AUTHOR]