Reviewerly/exHarmony huggingface dataset:
RRT*-Based Path Planning for Continuum Arms

Continuum arms are bio-inspired devices that exhibit continuous, smooth bending and generate motion through structural deformation. Rapidly-exploring random trees (RRT) is a traditional approach for performing efficient path planning. RRT approaches are usually based on exploring the configuration space (C-Space) of the robot to find a desirable work space (W-Space) path. <styled-content style="color:black" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Due to the complex kinematics and the highly non-linear mapping between the C-Space and W-Space of continuum arms, a high-quality path in the C-Space (e.g., a linear path) may not correspond to a desirable path/movement in the W-Space. Consequently, the C-Space RRT approaches that are based on C-Space cost functions do not lead to reliable and effective path planning when applied to continuum arms.</styled-content> In this letter, we propose a RRT* path planning approach for continuum arms that is based on exploring the W-Space of the robot as opposed to its C-Space. We show the successful applications of the proposed W-Space RRT* path planner in performing path planning with obstacle avoidance and in performing trajectory tracking. In all the aforementioned tasks, the quality of the paths generated by the proposed planner is superior to that of previous approaches and to its counterpart C-Space based RRT* approach, while the paths are generated in substantially less time.
Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems

Innovations in Next-Generation Sequencing are enabling generation of DNA sequence data at ever faster rates and at very low cost. Large sequencing centers typically employ hundreds of such systems. Such high-throughput and low-cost generation of data underscores the need for commensurate acceleration in downstream computational analysis of the sequencing data. A fundamental step in downstream analysis is mapping of the reads to a long reference DNA sequence, such as a reference human genome. Sequence mapping is a compute-intensive step that accounts for more than 30% of the overall time of the GATK workflow. BWA-MEM is one of the most widely used tools for sequence mapping and has tens of thousands of users. In this work, we focus on accelerating BWA-MEM through an efficient architecture aware implementation, while maintaining identical output. The volume of data requires distributed computing environment, usually deploying multicore processors. Since the application can be easily parallelized for distributed memory systems, we focus on performance improvements on a single socket multicore processor. BWA-MEM run time is dominated by three kernels, collectively responsible for more than 85% of the overall compute time. We improved the performance of these kernels by 1) improving cache reuse, 2) simplifying the algorithms, 3) replacing small fragmented memory allocations with a few large contiguous ones, 4) software prefetching, and 5) SIMD utilization wherever applicable - and massive reorganization of the source code enabling these improvements. As a result, we achieved nearly 2x, 183x, and 8x speedups on the three kernels, respectively, resulting in up to 3.5x and 2.4x speedups on end-to-end compute time over the original BWA-MEM on single thread and single socket of Intel Xeon Skylake processor. To the best of our knowledge, this is the highest reported speedup over BWA-MEM.
Dual-Scale Single Image Dehazing via Neural Augmentation

Model-based single image dehazing algorithms restore haze-free images with sharp edges and rich details for real-world hazy images at the expense of low PSNR and SSIM values for synthetic hazy images. Data-driven ones restore haze-free images with high PSNR and SSIM values for synthetic hazy images but with low contrast, and even some remaining haze for real-world hazy images. In this paper, a novel single image dehazing algorithm is introduced by combining model-based and data-driven approaches. Both transmission map and atmospheric light are first estimated by the model-based methods, and then refined by dual-scale generative adversarial networks (GANs) based approaches. The resultant algorithm forms a neural augmentation which converges very fast while the corresponding data-driven approach might not converge. Haze-free images are restored by using the estimated transmission map and atmospheric light as well as the Koschmieder’s law. Experimental results indicate that the proposed algorithm can remove haze well from real-world and synthetic hazy images.
TIAToolbox as an end-to-end library for advanced tissue image analytics

Computational pathology has seen rapid growth in recent years, driven by advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel whole-slide images, to the best of our knowledge, there is no open-source software library providing a generic end-to-end API for pathology image analysis using best practices. Most researchers have designed custom pipelines from the bottom up, restricting the development of advanced algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make computational pathology accessible to computational, biomedical, and clinical researchers.By creating modular and configurable components, we enable the implementation of computational pathology algorithms in a way that is easy to use, flexible and extensible. We consider common sub-tasks including reading whole slide image data, patch extraction, stain normalization and augmentation, model inference, and visualization. For each of these steps, we provide a user-friendly application programming interface for commonly used methods and models.We demonstrate the use of the interface to construct a full computational pathology deep-learning pipeline. We show, with the help of examples, how state-of-the-art deep-learning algorithms can be reimplemented in a streamlined manner using our library with minimal effort.We provide a usable and adaptable library with efficient, cutting-edge, and unit-tested tools for data loading, pre-processing, model inference, post-processing, and visualization. This enables a range of users to easily build upon recent deep-learning developments in the computational pathology literature.
Adaptive NN Fixed-Time Fault-Tolerant Control for Uncertain Stochastic System With Deferred Output Constraint via Self-Triggered Mechanism

For a class of nonstrict-feedback stochastic nonlinear systems with the injection and deception attacks, this article explores the problem of adaptive neural network (NN) fixed-time control ground on the self-triggered mechanism in a pioneering way. After developing the self-triggered mechanism and the delay-error-dependence function, a neural adaptive delay-constrained fault-tolerant controller is proposed by employing the backstepping technique. The self-triggered mechanism does not require an additional observer to determine the time of the data transmission, which reduces the consumption of the system resources more efficiently. In addition, the whole Lyapunov function with the delay-error-dependence term is developed to solve the deferred output constraint problem. Under the proposed controller, it can be proven that all the signals within the closed-loop system are semiglobally uniformly bounded in probability, while the convergence time is independent of the initial state and the deferred output constraint control performance is achieved. The feasibility and the superiority of the proposed control strategy are shown by some simulations.
Deep Learning and Machine Learning with Grid Search to Predict Later Occurrence of Breast Cancer Metastasis Using Clinical Data

It is important to be able to predict, for each individual patient, the likelihood of later metastatic occurrence, because the prediction can guide treatment plans tailored to a specific patient to prevent metastasis and to help avoid under-treatment or over-treatment. Deep neural network (DNN) learning, commonly referred to as deep learning, has become popular due to its success in image detection and prediction, but questions such as whether deep learning outperforms other machine learning methods when using non-image clinical data remain unanswered. Grid search has been introduced to deep learning hyperparameter tuning for the purpose of improving its prediction performance, but the effect of grid search on other machine learning methods are under-studied. In this research, we take the empirical approach to study the performance of deep learning and other machine learning methods when using non-image clinical data to predict the occurrence of breast cancer metastasis (BCM) 5, 10, or 15 years after the initial treatment. We developed prediction models using the deep feedforward neural network (DFNN) methods, as well as models using nine other machine learning methods, including naïve Bayes (NB), logistic regression (LR), support vector machine (SVM), LASSO, decision tree (DT), k-nearest neighbor (KNN), random forest (RF), AdaBoost (ADB), and XGBoost (XGB). We used grid search to tune hyperparameters for all methods. We then compared our feedforward deep learning models to the models trained using the nine other machine learning methods.Based on the mean test AUC (Area under the ROC Curve) results, DFNN ranks 6th, 4th, and 3rd when predicting 5-year, 10-year, and 15-year BCM, respectively, out of 10 methods. The top performing methods in predicting 5-year BCM are XGB (1st), RF (2nd), and KNN (3rd). For predicting 10-year BCM, the top performers are XGB (1st), RF (2nd), and NB (3rd). Finally, for 15-year BCM, the top performers are SVM (1st), LR and LASSO (tied for 2nd), and DFNN (3rd). The ensemble methods RF and XGB outperform other methods when data are less balanced, while SVM, LR, LASSO, and DFNN outperform other methods when data are more balanced. Our statistical testing results show that at a significance level of 0.05, DFNN overall performs comparably to other machine learning methods when predicting 5-year, 10-year, and 15-year BCM.Our results show that deep learning with grid search overall performs at least as well as other machine learning methods when using non-image clinical data. It is interesting to note that some of the other machine learning methods, such as XGB, RF, and SVM, are very strong competitors of DFNN when incorporating grid search. It is also worth noting that the computation time required to do grid search with DFNN is much more than that required to do grid search with the other nine machine learning methods.
Wafer-scale nanofabrication of telecom single-photon emitters in silicon

A highly promising route to scale millions of qubits is to use quantum photonic integrated circuits (PICs), where deterministic photon sources, reconfigurable optical elements, and single-photon detectors are monolithically integrated on the same silicon chip. The isolation of single-photon emitters, such as the G centers and W centers, in the optical telecommunication O-band, has recently been realized in silicon. In all previous cases, however, single-photon emitters were created uncontrollably in random locations, preventing their scalability. Here, we report the controllable fabrication of single G and W centers in silicon wafers using focused ion beams (FIB) with high probability. We also implement a scalable, broad-beam implantation protocol compatible with the complementary-metal-oxide-semiconductor (CMOS) technology to fabricate single telecom emitters at desired positions on the nanoscale. Our findings unlock a clear and easily exploitable pathway for industrial-scale photonic quantum processors with technology nodes below 100 nm.
A Centralized Strategy for Multi-Agent Exploration

This paper introduces recently developed Aquila Optimization Algorithm specifically configured for Multi-Robot space exploration. The proposed hybrid framework "Coordinated Multi-Robot Exploration Aquila Optimizer" (CME-AO) is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based methodology, known as Aquila Optimizer (AO). A novel parallel communication protocol is also embedded to improve multi-robot space exploration process while simultaneously minimizing both the computation complexity and time. This ensures acquisition of a optimal collision-free path in a barrier-filled environment via generating a finite map. The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila Optimization technique is then incorporated to increase the overall solution accuracy. Numerous simulations under different environmental conditions were then performed to validate the effectiveness of the proposed algorithm. Algorithm consistency aspects in achieving the expected results (area explored rate and time) is demonstrated through statistical means. A perspective analysis is then performed by comparing the performance of the CME-AO algorithm with latest state of art contemporary algorithms namely conventional CME and CME-WO (CME Whale Optimizer). The comparison duly accommodates all pertinent aspects such as % area explored, number of failed runs, and time taken for map exploration for different environments. Results indicate that the proposed algorithm presents two distinct advantages over the other conventional state of the art CME based techniques a) enhanced map exploration in cluttered environment and b) significantly reduced computation complexity and execution time, with almost no fail runs. This makes the suggested methodology particularly suitable for on-board utilization in an obstacle-cluttered environment, where other techniques either fails (stuck locally) or takes longer exploration time.
Artificial intelligence and machine learning for quantum technologies

In this Perspective, the authors review how machine learning, and more broadly methods of artificial intelligence, are utilized in advancing quantum technologies, specifically the design, control, calibration and optimization of quantum devices. They also discuss open challenges in the field and potential future directions within the next decade.
Comparative Study of Differentially Private Data Synthesis Methods

When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional data synthesis techniques often rely on strong assumptions of a data intruder's behaviors and background knowledge to assess disclosure risk. Differential privacy (DP) formulates a theoretical approach for a strong and robust privacy guarantee in data release without having to model intruders' behaviors. Efforts have been made aiming to incorporate the DP concept in the data synthesis process. In this paper, we examine current DIfferentially Private Data Synthesis (DIPS) techniques for releasing individual-level surrogate data for the original data, compare the techniques conceptually and evaluate the statistical utility and inferential properties of the synthetic data via each DIPS technique through extensive simulation studies. Our work sheds light on the practical feasibility and utility of the various DIPS approaches, and suggests future research directions for DIPS.
Identification Protocols and Signature Schemes Based on Supersingular Isogeny Problems

We present signature schemes whose security relies on computational assumptions relating to isogeny graphs of supersingular elliptic curves. We give two schemes, both of them based on interactive identification protocols. The first identification protocol is due to De Feo, Jao and Plût. The second one, and the main contribution of the paper, makes novel use of an algorithm of Kohel, Lauter, Petit and Tignol for the quaternion version of the $$\ell $$-isogeny problem, for which we provide a more complete description and analysis, and is based on a more standard and potentially stronger computational problem. Both identification protocols lead to signatures that are existentially unforgeable under chosen message attacks in the random oracle model using the well-known Fiat-Shamir transform, and in the quantum random oracle model using another transform due to Unruh. A version of the first signature scheme was independently published by Yoo, Azarderakhsh, Jalali, Jao and Soukharev. This is the full version of a paper published at ASIACRYPT 2017.
Learning Filter Bank Sparsifying Transforms

Data are said to follow the transform (or analysis) sparsity model if they become sparse when acted on by a linear operator called a sparsifying transform. Several algorithms have been designed to learn such a transform directly from data, and data-adaptive sparsifying transforms have demonstrated excellent performance in signal restoration tasks. Sparsifying transforms are typically learned using small sub-regions of data called patches, but these algorithms often ignore redundant information shared between neighboring patches. We show that many existing transform and analysis sparse representations can be viewed as filter banks, thus linking the local properties of the patch-based model to the global properties of a convolutional model. We propose a new transform learning framework, where the sparsifying transform is an undecimated perfect reconstruction filter bank. Unlike previous transform learning algorithms, the filter length can be chosen independently of the number of filter bank channels. Numerical results indicate that filter bank sparsifying transforms outperform existing patch-based transform learning for image denoising while benefiting from additional flexibility in the design process.
An enhanced deadline constraint based task scheduling mechanism for cloud environment

In this work, we present a scheduling mechanism for deadline sensitive task or lease. It is very impelling and challenging to allocate on-demand resources within the deadline rather than rejecting the lease. The work studies the insight of existing backfilling algorithm that is used to schedule deadline sensitive lease in the Open Nebula cloud platform. In backfilling mechanism a lease is selected in First Come First Serve (FCFS) to be backfilled in which some ideal resources can be found out and allocated to other leases. Mainly, the scheduling performance of backfilling algorithm could be better when there are conflicts among the similar leases to backfill. Moreover, backfilling algorithm doesn’t allow to schedule a new deadline sensitive task during execution. The proposed work addresses such types of issues and improves the scheduling performances with respect to maximize the task acceptance ratio and reduce the task rejection ratio. Finally, the performances are compared with the existing mechanisms that are quite good.
Oversampling for Imbalanced Data via Optimal Transport

The issue of data imbalance occurs in many real-world applications especially in medical diagnosis, where normal cases are usually much more than the abnormal cases. To alleviate this issue, one of the most important approaches is the oversampling method, which seeks to synthesize minority class samples to balance the numbers of different classes. However, existing methods barely consider global geometric information involved in the distribution of minority class samples, and thus may incur distribution mismatching between real and synthetic samples. In this paper, relying on optimal transport (Villani 2008), we propose an oversampling method by exploiting global geometric information of data to make synthetic samples follow a similar distribution to that of minority class samples. Moreover, we introduce a novel regularization based on synthetic samples and shift the distribution of minority class samples according to loss information. Experiments on toy and real-world data sets demonstrate the efficacy of our proposed method in terms of multiple metrics.
Frame and Feature-Context Video Super-Resolution

For video super-resolution, current state-of-the-art approaches either process multiple low-resolution (LR) frames to produce each output high-resolution (HR) frame separately in a sliding window fashion or recurrently exploit the previously estimated HR frames to super-resolve the following frame. The main weaknesses of these approaches are: 1) separately generating each output frame may obtain high-quality HR estimates while resulting in unsatisfactory flickering artifacts, and 2) combining previously generated HR frames can produce temporally consistent results in the case of short information flow, but it will cause significant jitter and jagged artifacts because the previous super-resolving errors are constantly accumulated to the subsequent frames.In this paper, we propose a fully end-to-end trainable frame and feature-context video super-resolution (FFCVSR) network that consists of two key sub-networks: local network and context network, where the first one explicitly utilizes a sequence of consecutive LR frames to generate local feature and local SR frame, and the other combines the outputs of local network and the previously estimated HR frames and features to super-resolve the subsequent frame. Our approach takes full advantage of the inter-frame information from multiple LR frames and the context information from previously predicted HR frames, producing temporally consistent highquality results while maintaining real-time speed by directly reusing previous features and frames. Extensive evaluations and comparisons demonstrate that our approach produces state-of-the-art results on a standard benchmark dataset, with advantages in terms of accuracy, efficiency, and visual quality over the existing approaches.
Chaotic genetic algorithm and the effects of entropy in performance optimization

This work proposes a new edge about the Chaotic Genetic Algorithm (CGA) and the importance of the entropy in the initial population. Inspired by chaos theory the CGA uses chaotic maps to modify the stochastic parameters of Genetic Algorithm (GA). The algorithm modifies the parameters of the initial population using chaotic series and then analyzes the entropy of such population. This strategy exhibits the relationship between entropy and performance optimization in complex search spaces. Our study includes the optimization of nine benchmark functions using eight different chaotic maps for each of the benchmark functions. The numerical experiment demonstrates a direct relation between entropy and performance of the algorithm.
Interpreting a major component from a mixed DNA profile with an unknown number of minor contributors

Modern interpretation strategies typically require an assignment of the number of contributors (N) to a DNA profile. This can prove to be a difficult task, particularly when dealing with higher order mixtures or mixtures where one or more contributors have donated low amounts of DNA. Differences in the assigned N at interpretation can lead to differences in the likelihood ration (LR). If the number of contributors cannot reasonably be assigned, then an interpretation of the profile may not be able to be progressed. In this study, we investigate mixed DNA profiles of varying complexity and interpret them altering the assigned N. We assign LRs for true- and non- contributors and compare the results given different assignments of N over a range of mixture proportions. When a component of a mixture had a proportion of at least 10%, a ratio of at least 1.5:1 to the next highest component, and a DNA amount (as determined by STRmix™) of at least 50 rfu, the LR of the component for a true contributor was not significantly affected by varying N and was therefore suitable for interpretation and the assignment of an LR. LRs produced for minor contributors were found to vary significantly as the assigned N was changed. These heuristics may be used to identify profiles suitable for interpretation.