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Deadlocks are a highly undesirable situation in flexible manufacturing systems (FMSs). This article presents a direct and novel method to detect such markings by constructing the resource flow graph of a Petri net that models an FMS and to recover such markings by adding a set of recovery transitions. First, an algorithm is developed to build a new kind of directed graph called the resource flow graph of a Petri net. Resource flow graphs can well represent the competition for shared resources by different processes. Second, based on the resource flow graph, loop graphs can be found. Furthermore, partial deadlock markings can be easily detected due to their relationship with loop graphs. Then, we propose an algorithm to design a set of recovery transitions for loop graphs that are enabled at partial deadlock markings. The proposed approach can detect partial deadlock markings without generating a complete reachability graph of a Petri net and the resulting net is deadlock-free with all reachable markings by adding the obtained recovery transitions. Finally, some widely used examples are provided to demonstrate the proposed approach.

Loop closure detection (LCD) is an important portion of Simultaneous Localization and Mapping (SLAM) because of its ability to reduce accumulated position errors. In this letter, we propose a novel loop closure detection algorithm named ESA-VLAD. The crucial part of ESA-VLAD is a redesigned network with EfficientNetB0 as backbone for extracting global features, which integrates a second-order attention module in order to effectively learn the correlations between features within the feature map. A trainable Vector of Local Aggregated Descriptors (NetVLAD) is integrated in the last layer of the network to generate a compact and fixed-length global feature. Knowledge distillation strategy is adopted in training of the proposed network to accelerate the training process. For the global features, Hierarchical Navigable Small World (HNSW) is employed to retrieve the loop closure candidate images. In addition, an efficient geometrical consistency check based on local difference binary (LDB) descriptors is designed to verify loop closure matches. Experiments on several public datasets demonstrate that ESA-VLAD can obtain higher recall rates under 100% precision and less processing time per frame compared to other typical and state-of-the-art methods.

In this article, we classify the irreducible integrable modules for the loop affine-Virasoro algebra ((g°⊗C[t,t−1]⊕CK)⋊ Vir)⊗A, where A is a finitely generated commutative associative unital algebr...

Localization and mapping are key capabilities of autonomous systems. In this paper, we propose a modified Siamese network to estimate the similarity between pairs of LiDAR scans recorded by autonomous cars. This can be used to address both, loop closing for SLAM and global localization. Our approach utilizes a deep neural network exploiting different cues generated from LiDAR data. It estimates the similarity between pairs of scans using the concept of image overlap generalized to range images and furthermore provides a relative yaw angle estimate. Based on such predictions, our method is able to detect loop closures in a SLAM system or to globally localize in a given map. For loop closure detection, we use the overlap prediction as the similarity measurement to find loop closure candidates and integrate the candidate selection into an existing SLAM system to improve the mapping performance. For global localization, we propose a novel observation model using the predictions provided by OverlapNet and integrate it into a Monte-Carlo localization framework. We evaluate our approach on multiple datasets collected using different LiDAR scanners in various environments. The experimental results show that our method can effectively detect loop closures surpassing the detection performance of state-of-the-art methods and that it generalizes well to different environments. Furthermore, our method reliably localizes a vehicle in typical urban environments globally using LiDAR data collected in different seasons.

Visual simultaneous localization and mapping (vSLAM), one of the most important applications in autonomous vehicles and robots to estimate the position and pose using inexpensive visual sensors, suffers from error accumulation for long‐term navigation without loop closure detection. Recently, deep neural networks (DNNs) are leveraged to achieve high accuracy for loop closure detection, however the execution time is much slower than those employing handcrafted visual features. In this paper, a parallel loop searching and verifying method for loop closure detection with both high accuracy and high speed, which combines two parallel tasks using handcrafted and DNN features, respectively, is proposed. A fast loop searching is proposed to link the bag‐of‐words features and histogram for higher accuracy, and it splits the images into multiple grids for high parallelism; meanwhile, a DNN feature extractor is utilized for further verification. A loop state control method based on a finite state machine to control these tasks is designed, wherein the loop closure detection is described as a context‐related procedure. The framework is implemented on a real machine, and the top‐2 best accuracy and fastest execution time of 80‐543 frames per second (min: 1.84ms, and max: 12.45ms) are achieved on several public benchmarks compared with some existing algorithms.

International audience; The polyhedral model is a high-level intermediate representation for loop nests that supports elegantly a great many loop optimizations. In a compiler, after polyhedral loop optimizations have been performed, it is necessary and difficult to regenerate sequential or parallel loop nests before continuing compilation. This paper reports on the formalization and proof of semantic preservation of such a code generator that produces sequential code from a polyhedral representation. The formalization and proofs are mechanized using the Coq proof assistant.

The similarity metric in Loop closure detection (LCD) is still considered in an old fashioned way, i.e. to pre-define a fixed distance function, leading to a limited performance. This paper proposes a general framework named LRN-LCD, i.e. a Lightweight relation network for LCD, which combines the feature extraction module and similarity metric module into a simple and lightweight network. The LRN-LCD, an end-to-end framework, can learn a non-linear deep similarity metric to detect loop closures from different scenes. Moreover, the LRN-LCD supports image sequences as input to speed up the similarity metric in real-time applications. Extensive experiments on several open datasets illustrate that LRN-LCD is more robust to strong condition variations and viewpoint variations than the mainstream methods.

Розглянуто зменшення магнітного поля кабельної лінії електропередачі за допомогою системи контурного екранування. Отримано наближені аналітичні вирази для розрахунку комплексної амплітуди та діючого значення магнітної індукції в області екранування. Запропоновано прозору фізичну інтерпретацію отриманих співвідношень і параметру γ, який вони містять. Розглянуто граничний випадок γ=1, коли довжина системи контурного екранування набагато перевищує відстань від кабельної лінії до області екранування. Для випадку довільної довжини системи контурного екранування запропоновано функцію апроксимації залежності параметра γ від відношення відстані між крайніми точками екранного контуру та областю екранування до відстані між центральним кабелем лінії електропередачі та границею області екранування. Для визначення довжини системи контурного екранування розв’язано обернену задачу. У результаті дано рекомендації щодо оцінки довжини системи контурного екранування, якщо відомо таке: гранично допустимий рівень магнітного поля, який має бути досягнуто у всій області екранування; величина струму в кабельній лінії та відстань між її кабелями; амплітуда та початкова фаза струму в системі контурного екранування, а також відстань між її проводами, які розташовано паралельно кабельній лінії; довжина області екранування та її віддаленість від кабельної лінії., This paper deals with a mitigation of a cable line magnetic field by a loop shield. Approximate analytical expressions for a phasor and rms value of a magnetic flux density in a shielding area are developed. I give a clear physical interpretation for the obtained relations and the parameter γ that they contain. The limiting case γ=1 is considered, when the loop shield length is much greater than the distance from the cable line to the shielding area. For the case of an arbitrary length of the loop shield, I propose a function to approximate the dependence of γ on the ratio of the distance between the extreme points of the loop shield and the shielding area to the distance between the central cable of the power line and the shielding area boundary. To determine the loop shield length, the inverse problem is solved. As a result, I develop recommendations for estimating the loop shield length taking into account the following: the magnetic field reference level in the shielding area; the current in the cable line and the distance between its cables; the amplitude and the phase shift of the current in the loop shield, as well as the distance between its conductors, which are parallel to the cable line; the length of the shielding area and its distance from the cable line., Рассмотрено уменьшение магнитного поля кабельной линии электропередачи при помощи системы контурного экранирования. Получены приближенные аналитические выражения для расчета комплексной амплитуды и действующего значения магнитной индукции в области экранирования. Дана прозрачная физическая интерпретация полученных соотношений и параметра γ, который они содержат. Рассмотрен предельный случай γ=1, когда длина системы контурного экранирования намного превосходит расстояние от кабельной линии до области экранирования. Для случая произвольной длины системы контурного экранирования предложена функция аппроксимации зависимости параметра γ от отношения расстояния между крайними точками экранного контура и областью экранирования к расстоянию между центральным кабелем линии электропередачи и границей области экранирования. Для определения длины системы контурного экранирования решена обратная задача. В результате даны рекомендации по оценке длины системы контурного экранирования, если известно следующее: предельно допустимый уровень магнитного поля, который должен быть достигнут во всей области экранирования; величина тока в кабельной линии и расстояние между ее кабелями; амплитуда и начальная фаза тока в системе контурного экранирования, а также расстояние между ее проводами, которые расположены параллельно кабельной линии; длина области экранирования и ее удаленность от кабельной линии.

Thispaper presents a Neural Network aimed at robust and fast visual loop detection in underwater environments. The proposal is based on an autoencoder architecture, in which the decoder part is being replaced by three fully connected layers. In order to help the proposed network to learn the features that define loop closings, two different global image descriptors to be targeted during training are proposed. Also, a method allowing unsupervised training is presented. The experiments, performed in coastal areas of Mallorca (Spain), show the validity of our proposal and compares it to previously existing methods, based on pre-engineered and learned descriptors.

Three-dimensional magnetotelluric modeling algorithm of high accuracy and high efficiency is required for data interpretation and inversion. In this paper, edge-based finite element method with unstructured mesh is used to solve 3D magnetotelluric problem. Two boundary conditions—Dirichlet boundary condition and Neumann boundary condition—are set for cross-validation and comparison. We propose an efficient parallel algorithm to speed up computation and improve efficiency. The algorithm is based on distributed matrix storage and has three levels of parallelism. The first two are process level parallelization for frequencies and matrix solving, and the last is thread-level parallelization for loop unrolling. The algorithm is validated by several model studies. Scalability tests have been performed on two distributed-memory HPC platforms, one consists of Intel Xeon E5-2660 microprocessors and the other consists of Phytium FT2000 Plus microprocessors. On Intel platform, computation time of our algorithm solving Dublin Test Model-1 with 3,756,373 edges at 21 frequencies is 365 s on 2520 cores. The speedup and efficiency are 1609 and 60% compared to 100 cores. On Phytium platform, scalability test shows that the speedup from 256 cores to 86,016 cores has been increased to 11,255.

A transformation is introduced in momentum representation to keep a covariant description at every stage of a loop computation in gravity. The procedure treats on equal footing local internal and space-time symmetries althought the complete transformation is known for the former [1] whereas in gravity we solve for the first few orders in an expansion. As an explicit application the one loop UV divergences of Hilbert-Einstein gravity with a cosmological constant and spin 0, 1/2 and 1 matter are computed with functional methods and in a field-covariant formalism.

In this paper, the authors propose to increase the efficiency of blockchain mining by using a population-based approach. Blockchain relies on solving difficult mathematical problems as proof-of-work within a network before blocks are added to the chain. Brute force approach, advocated by some as the fastest algorithm for solving partial hash collisions and implemented in Bitcoin blockchain, implies exhaustive, sequential search. It involves incrementing the nonce (number) of the header by one, then taking a double SHA-256 hash at each instance and comparing it with a target value to ascertain if lower than that target. It excessively consumes both time and power. In this paper, the authors, therefore, suggest using an inner for-loop for the population-based approach. Comparison shows that it's a slightly faster approach than brute force, with an average speed advantage of about 1.67% or 3,420 iterations per second and 73% of the time performing better. Also, we observed that the more the total particles deployed, the better the performance until a pivotal point. Furthermore, a recommendation on taming the excessive use of power by networks, like Bitcoin's, by using penalty by consensus is suggested., 6 pages, 1 table and 2 figures

Lam and Pylyavskyy introduced loop symmetric functions as a generalization of symmetric functions. They defined loop Schur functions as generating functions over semistandard tableaux with respect to a `colored weight,' and they proved a Jacobi--Trudi-style determinantal formula for these generating functions. We prove that loop Schur functions can be expressed as a ratio of `loop alternants,' extending the analogy with Schur functions. As an application, we give a new proof of the loop version of the Murnaghan--Nakayama rule., 12 pages. (v2) Added a denominator formula (Corollary 3.2) and an alternative ratio of alternants formula (Theorem 3.3); improved the exposition; changed notational conventions for $h_k^{(r)}$ and alternants

Inspired by generative adversarial network (GAN), we propose a novel unsupervised approach for loop closure detection in autonomous unmanned systems. A binary GAN model dedicated to mobile application scenarios is designed to obtain binary feature descriptors, which are further incorporated into the most commonly used Bag of Visual Words (BoVW) model for loop closure detection. Compared with those hand-crafted features like SIFT and ORB, the performance of loop closure detection in complex environments with strong viewpoint and condition changes can be greatly improved. Compared with existing supervised approach based on convolutional neural network like AlexNet and AMOSNet, the cost-expensive task of supervised data annotation is totally avoided, which make the proposed approach more practical.

The article considers the differential equations of a switched permanent magnet motor in which a short-circuit fault occurred in one or more turns in one of parallel stator winding branches. Owing to the occurred asymmetry of the phase quantities, symmetrical line-to-line voltages at the stator winding terminals are assumed. It is shown that turn-to-turn short-circuit faults give rise to non-sinusoidal and imbalanced phase currents and voltages at the nominal load torque on the shaft, and it should be noted that initially, a growth of the frequency and ratios of currents in the phases with an increase in the number of short-circuited turns are observed, after which the phase currents tend to decrease (with a continuing growth in the current through the short-circuited loop), and the rotor stalling occurs. The growth of motor rotation frequency and decrease of its overloading capacity take place due to a growth in the demagnetizing effect of armature reaction caused by the current through the short-circuited stator winding turns.

This paper investigates the impact of sequential content load algorithm on regional transfer in OpenSimulator framework. It, then, presents an abstract parallel content load algorithm, which is implemented using the basic parallel for and chunk partitioning constructs to exploit the parallelism of multi-core architectures. This work used a set of five example virtual worlds content to investigate the effectiveness of the proposed work and its impact on the reallocation process of regions in OpenSimulator. Simulation results revealed that the implementation using parallel for reduced the timings by 47% to 57% while the implementation using chunk partitioning minimized it by 63% to 72%. Time impact on regional transfer was reduced by 39% to 51% using parallel for while 52% to 66% using chunk partitioning.

Electrical parasitics, especially parasitic inductance, play an essential role in enhancing power module design performance through reducing voltage overshoot and switching power losses. In this paper, a new electrical parasitic extraction model is developed, inspired by the loop-based extraction method from VLSI. This model shows only within 10% error compared to the FastHenry method while being orders of magnitude more efficient in run time and memory. For the same design, this method takes less than 0.5 seconds on the same machine to achieve similar accuracy. This method also significantly reduces the number of elements in the extracted netlist, which reduces the complexity for loop evaluation a few thousand times. Utilizing the divide-and-conquer strategy, this model demonstrates many advantages over previous work in layout optimization and post-layout simulation. The model is also attractive for use with optimization routines, and therefore has been used in the latest PowerSynth layout optimization tool.

In the process of understanding and redesigning the function of proteins in modern biochemistry, protein engineers are increasingly focusing on the exploration of regions in proteins called loops. Analyzing various characteristics of these regions helps the experts to design the transfer of the desired function from one protein to another. This process is denoted as loop grafting. As this process requires extensive manual treatment and currently there is no proper visual support for it, we designed LoopGrafter: a web-based tool that provides experts with visual support through all the loop grafting pipeline steps. The tool is logically divided into several phases, starting with the definition of two input proteins and ending with a set of grafted proteins. Each phase is supported by a specific set of abstracted 2D visual representations of loaded proteins and their loops that are interactively linked with the 3D view onto proteins. By sequentially passing through the individual phases, the user is shaping the list of loops that are potential candidates for loop grafting. In the end, the actual in-silico insertion of the loop candidates from one protein to the other is performed and the results are visually presented to the user. In this way, the fully computational rational design of proteins and their loops results in newly designed protein structures that can be further assembled and tested through in-vitro experiments. LoopGrafter was designed in tight collaboration with protein engineers, and its final appearance reflects many testing iterations. We showcase the contribution of LoopGrafter on a real case scenario and provide the readers with the experts' feedback, confirming the usefulness of our tool.

In this paper, we advance a multimodal optimization music mashup creation model for loop recombination at scale. The motivation to pursue such a model is to 1) tackle current scalability limitations in state-of-the-art (brute force) models while enforcing the 2) compatibility, i.e., recombination quality, of audio loops, and 3) a pool of diverse solutions that can accommodate personal user preferences or promote different musical styles. To this end, we adopt the Artificial Immune System (AIS) opt-aiNet algorithm to efficiently compute a population of compatible and diverse mashups from loop recombinations. Optimal mashups result from local minima in a feature space that objectively represents harmonic and rhythmic compatibility. We implemented our model as a prototype application named Mixmash-AIS, and conducted an objective evaluation that tackles three dimensions: loop recombination compatibility, mashups diversity, and computational model efficiency. The conducted evaluation compares the proposed system to a standard genetic algorithm (GA) and a brute force (BF) approach. While the GA stands as the most efficient algorithm, its poor results in terms of compatibility reinforce the primacy of the AIS opt-aiNet in efficiently finding optimal compatible loop mashups. Furthermore, the AIS opt-aiNet showed to promote a diverse mashup population, outperforming both GA or BF approaches.

Mobile robots rely heavily on the creation of the scene map and the positioning in the map in an unknown environment, but no matter what type of map is created, it is inevitably affected by cumulative errors. This presents a huge challenge for loop closure detection technology. Using traditional loop closure detection methods to perform scene recognition is difficult to extract the appearance changes caused by time, weather, or seasonal conditions in the image and deep semantic information, and the speed of extracting image features is slow, which is difficult to meet the real-time performance of robots. Because of the success of deep convolutional neural networks(CNN), it is possible to enrich the information of image features. First of all, this paper uses the pre-trained CNN model SSE-Net to extract the deep visual appearance and semantic features of the image, and obtain the feature description vector. Then, after product quantization(PQ) and encoding, the final pair of candidate frames is quickly searched and matched to obtain the most similar pair of candidate frames and judged as a loop . After the verification of the New collage dataset and the City Center dataset, this algorithm has achieved a good Precision-Recall rate and a faster speed compared with the recently proposed large-scale convolution network VGG16 method and traditional feature extraction methods.

This work presents an extension of graph-based SLAM methods to exploit the potential of 3D laser scans for loop detection. Every high-dimensional point cloud is replaced by a compact global descriptor, whereby a trained detector decides whether a loop exists. Searching for loops is performed locally in a variable space to consider the odometry drift. Since closing a wrong loop has fatal consequences, an extensive verification is performed before acceptance. The proposed algorithm is implemented as an extension of the widely used state-of-the-art library RTAB-Map, and several experiments show the improvement: During SLAM with a mobile service robot in changing indoor and outdoor campus environments, our approach improves RTAB-Map regarding total number of closed loops. Especially in the presence of significant environmental changes, which typically lead to failure, localization becomes possible by our extension. Experiments with a car in traffic (KITTI benchmark) show the general applicability of our approach. These results are comparable to the state-of-the-art LiDAR method LOAM. The developed ROS package is freely available., Comment: Accepted at AIM Conference 2021

Loop‐closure detection is important in large‐scale localisation system. However, it is still difficult in a dynamic environment. An online fast loop‐closure detection algorithm based on Deeplabv3 with MobileNetV2 (DpMn2) as network backbone and local difference binary descriptor is proposed, and the algorithm is named as DpMn2‐LDB. DpMn2 splits out common dynamic objects of images, and then uses visual geometry group network (VGG16) that is trained on place‐centric data to extract global features for nearest neighbour image retrieval. The loop‐closure matches are verified based on LDB descriptors and random sample consensus (RANSAC). Experimental results show that the proposed method can obtain a higher recall rate under 100% precision with less execution time per frame on several public datasets compared with other typical or state‐of‐the‐art algorithms.

Heterogeneous microprocessors integrate a CPU and GPU on the same chip, providing fast CPU-GPU communication and enabling cores to compute on data “in place.” This permits exploiting a finer granularity of parallelism on the integrated GPUs, and enables the use of GPUs for accelerating more complex and irregular codes. One challenge, however, is exposing enough parallelism such that both the CPU and GPU are effectively utilized to achieve maximum gain. In this article, we propose exploiting nested parallelism for integrated CPU-GPU chips. We look for loop structures in which one or more regular data parallel loops are nested within a parallel outer loop that can contain irregular code (e.g., with control divergence). By scheduling the outer loop on multiple CPU cores, multiple dynamic instances of the inner regular loop(s) can be scheduled on the GPU cores. This boosts GPU utilization and parallelizes the outer loop. We find that such nested MIMD-SIMD parallelization provides greater levels of parallelism for integrated CPU-GPU chips, and additionally there is ample opportunity to perform such parallelization in OpenMP programs. Our results show nested MIMD-SIMD parallelization provides a 16.1x and 8.67x speedup over sequential execution on a simulator and a physical machine, respectively. Our technique beats CPU-only parallelization by 4.13x and 2.40x, respectively, and GPU-only parallelization by 2.74x and 2.26x, respectively. Compared to the next-best scheme (either CPU- or GPU-only parallelization) per benchmark, our approach provides a 1.46x and 1.23x speedup for the simulator and physical machine, respectively.

We prove that loop-erased random walks on the finite pre-Sierpinski gaskets can be extended to a loop-erased random walk on the infinite pre-Sierpinski gasket by using the ‘erasing-larger-loops-first’ method, and obtain the asymptotic behavior of the walk as the number of steps increases, in particular, the displacement exponent and a law of the iterated logarithm.

In the literature, loop fusion is an effective optimization technique which tries to enhance parallelizing compilers’ performance via memory hierarchy management, and all its competing criteria create an NP-hard problem. This paper proposes an evolutionary algorithm that aims to achieve a profitable loop order which maximizes fusion taking into account register size, parallelism and data reuse advancement. Besides, this method preserves prerequisite relations between the loops by encoding each distinct loop sequence as the shortest common supersequence (SCS) of the related dependence graph. Regarding the related optimization methods that only focus on fusion, this set of metrics, an evolutionary algorithm and also the shortest common supersequence problem have not been considered before in this area. Despite all the envisaged complexities, experimental results confirm the accuracy and advantage of the proposed approach. But due to evolutionary methods effect on raising the compilation time, the proposed algorithm is only applicable when this issue is not prominent, in comparison with the quality of the outcome.

RNA three-dimensional (3D) structures are critical for RNA cellular functions. However, structure prediction for large and complex RNAs remains a challenge, which hampers our understanding of RNA structure-function relationship. We here report a new web server, the VfoldLA server (http://rna.physics.missouri.edu/vfoldLA), for the prediction of RNA 3D structures from nucleotide sequences and base-pair information (2D structure). This server is based on the recently developed VfoldLA, a model that classifies the single-stranded loops (junctions) into four different types and according to the loop-helix connections, assembles RNA 3D structures from the loop/junction templates. The VfoldLA web server provides a user-friendly online interface for a fully automated prediction of putative 3D RNA structures using VfoldLA. With a single-RNA or RNA-RNA complex sequence and 2D structure as input, the server generates structure(s) with the JSmol visualization along with a downloadable PDB file. The output result may serve as useful scaffolds for future structure refinement studies.

Although topographic mapping missions and geological surveys carried out by Autonomous Underwater Vehicles (AUVs) are becoming increasingly prevalent, the lack of precise navigation in these scenarios still limits their application. This paper deals with the problems of long-term underwater navigation for AUVs and provides new mapping techniques by developing a Bathymetric Simultaneous Localisation And Mapping (BSLAM) method based on graph SLAM technology. To considerably reduce the calculation cost, the trajectory of the AUV is divided into various submaps based on Differences of Normals (DoN). Loop closures between submaps are obtained by terrain matching; meanwhile, maximum likelihood terrain estimation is also introduced to build weak data association within the submap. Assisted by one weight voting method for loop closures, the global and local trajectory corrections work together to provide an accurate navigation solution for AUVs with weak data association and inaccurate loop closures. The viability, accuracy and real-time performance of the proposed algorithm are verified with data collected onboard, including an 8 km planned track recorded at a speed of 4 knots in Qingdao, China.

Data-intensive applications are becoming increasingly popular. However, only a few of them with high volume can afford dedicated hardware acceleration (such as Neural Network Processor, or NPU) or platform-specific software implementation (such as Tensorflow running on GPU). In this paper, we propose a hardware and software transparent framework for the acceleration of general-purpose data-intensive applications. Our framework is based on a key insight that most data-intensive applications spend the vast majority of their execution time on some inner loops with abundant opportunities for Data-Level Parallelism (DLP). In particular, we propose SALAD, a static analyzer for loop acceleration by exploiting DLP in hot loops under the LLVM (LLVM compiler infrastructure) framework. In contrast to traditional DLP exploration techniques, SALAD is both software and architectural transparent, without the need to change either the source code or binary code, and does not need vectorized instruction set architecture (ISA) extensions. Instead, it directly works on the program binary code and generates a profile for DLP opportunities in the binary. This profile will be fed to the hardware accelerator transparently to speed up execution. With the experiments result, we estimate that the DLP information provided by SALAD could result in 3.6x-60.2x speedups on a set of benchmarks, depending on their inherent DLP.

In this paper, we propose an auto-tuning (AT) system by adapting the A64 Scalable Vector Extension for SPIRAL to generate discrete Fourier transform (DFT) implementations. The performance of our method is evaluated using the Supercomputer "Flow" at Nagoya University. The A64 scalable vector extension applied DFT codes are up to 1.98 times faster than scalar DFT codes and up to 3.63 times higher in terms of the SIMD instruction rate. In addition, we obtain a factor of maximum speedup 2.32 by adapting proposed AT system for loop unrolling.

Place recognition in colonoscopy is needed for various reasons. 1) If a certain region needs to be rechecked during an endoscopy, the endoscopist needs to re-localize the camera accurately to the region of interest. 2) Place recognition is needed for same-patient follow-up colonoscopy to localize the region where a polyp was cut off. 3) Recent development in colonoscopic 3D reconstruction needs place recognition to establish long-range correspondence, e.g., for loop closure. However, traditional image retrieval techniques do not generalize well in colonic images. Moreover, although place recognition or instance-level image retrieval is a widely researched topic in computer vision and several benchmarks have been published for it, there has been no specific research or benchmarks in endoscopic images, which are significantly different from common images used in traditional computer vision tasks. In this paper we present a testing dataset with manually labeled groundtruth which comprises 10126 images from 20 colonoscopic subsequences. We perform an extensive evaluation on different existing place recognition techniques using different metrics.

Visual place recognition is the task of recognizing same places of query images in a set of database images, despite potential condition changes due to time of day, weather or seasons. It is important for loop closure detection in SLAM and candidate selection for global localization. Many approaches in the literature perform computationally inefficient full image comparisons between queries and all database images. There is still a lack of suited methods for efficient place recognition that allow a fast, sparse comparison of only the most promising image pairs without any loss in performance. While this is partially given by ANN-based methods, they trade speed for precision and additional memory consumption, and many cannot find arbitrary numbers of matching database images in case of loops in the database. In this paper, we propose a novel fast sequence-based method for efficient place recognition that can be applied online. It uses relocalization to recover from sequence losses, and exploits usually available but often unused intra-database similarities for a potential detection of all matching database images for each query in case of loops or stops in the database. We performed extensive experimental evaluations over five datasets and 21 sequence combinations, and show that our method outperforms two state-of-the-art approaches and even full image comparisons in many cases, while providing a good tradeoff between performance and percentage of evaluated image pairs. Source code for Matlab will be provided with publication of this paper., Accepted for publication at International Conference on Robotics and Automation (ICRA) 2021. This is the submitted version

The loop cutset solving algorithm in the Bayesian network is particularly important for Bayesian inference. This paper proposes an algorithm for solving the approximate minimum loop cutset based on the loop cutting contribution index. Compared with the existing algorithms, the algorithm uses the loop cutting contribution index of nodes and node-pairs to analyze nodes from a global perspective, and select loop cutset candidates with node-pair as the unit. The algorithm uses the parameter μ to control the range of node pairs, and the parameter ω to control the selection conditions of the node pairs, so that the algorithm can adjust the parameters according to the size of the Bayesian networks, which ensures computational efficiency. The numerical experiments show that the calculation efficiency of the algorithm is significantly improved when it is consistent with the accuracy of the existing algorithm, the experiments also studied the influence of parameter settings on calculation efficiency using trend analysis and two-way analysis of variance. The loop cutset solving algorithm based on the loop cutting contribution index uses the node-pair as the unit to solve the loop cutset, which helps to improve the efficiency of Bayesian inference and Bayesian network structure analysis.

Enabling fully autonomous robots capable of navigating and exploring large-scale, unknown and complex environments has been at the core of robotics research for several decades. A key requirement in autonomous exploration is building accurate and consistent maps of the unknown environment that can be used for reliable navigation. Loop closure detection, the ability to assert that a robot has returned to a previously visited location, is crucial for consistent mapping as it reduces the drift caused by error accumulation in the estimated robot trajectory. Moreover, in multi-robot systems, loop closures enable merging local maps obtained by a team of robots into a consistent global map of the environment. In this paper, we present a degeneracy-aware and drift-resilient loop closing method to improve place recognition and resolve 3D location ambiguities for simultaneous localization and mapping (SLAM) in GPS-denied, large-scale and perceptually-degraded environments. More specifically, we focus on SLAM in subterranean environments (e.g., lava tubes, caves, and mines) that represent examples of complex and ambiguous environments where current methods have inadequate performance., Accepted for publication in Journal of Intelligent and Robotic Systems, 2021

Informacije iz centra boja u mozgu istodobno putuju do područja mozga koja su odgovorna za otkrivanje pokreta, oblika, prijelaza i ostalog. Ova paralelna upotreba boje u više područja mozga sugerira izuzetnu važnost naše reakcije na boju, naročito u slučajevima kada su u pitanju djeca koja nisu još razvila mehanizam zadrške svojih emocija. Cilj istraživanja provedenog u kontekstu diplomskog rada usmjeren je otkrivanju potencijalne povezanosti boje pozadine korištenog programskom jeziku i emocija razvijenih kod djece u šestom razredu osnovne škole. Rezultati istraživanja otkrivaju da boja pozadine programskog jezika za vrijeme programiranja ima važan učinak na spektar razvijenih emocija dvanaestogodišnje djece. Instrumenti istraživanja su pridonijeli detaljnom uvidu u emocije učenika prije i poslije provedenog istraživanja u kojem se koristio Python kao programski jezik te gradivo koje se odnosilo na FOR petlju i IF-ELSE grananje., Information from the colour centre in the brain simultaneously travel to areas of the brain that are responsible for detecting movement, shape, transitions and more. This parallel use of colour in multiple areas of the brain suggests that our response to colour is greatly significant, especially when it comes to children who have not yet created a mechanism for retaining their emotions. The aim of this research, conducted as a part of the thesis, was to show potential relationship between background colours and emotions of children in the sixth grade of primary school developed while programming. Research reveals that a background colour of the programming language has an important effect on the range of emotions of twelve-year-old children. The research instruments enabled detailed insight into students' emotions before and after the research in which Python as programming language was used as well as FOR loop and IF-ELSE branching as learning material have been teaching.

Using a for loop with an ArrayList is pretty straightforward, but you need to work with two particular methods of the ArrayList class in order to loop through all of the elements.

Code completion aims at speeding up code writing by predicting the next code token(s) the developer is likely to write. Works in this field focused on improving the accuracy of the generated predictions, with substantial leaps forward made possible by deep learning (DL) models. However, code completion techniques are mostly evaluated in the scenario of predicting the next token to type, with few exceptions pushing the boundaries to the prediction of an entire code statement. Thus, little is known about the performance of state-of-the-art code completion approaches in more challenging scenarios in which, for example, an entire code block must be generated. We present a large-scale study exploring the capabilities of state-of-the-art Transformer-based models in supporting code completion at different granularity levels, including single tokens, one or multiple entire statements, up to entire code blocks (e.g., the iterated block of a for loop). We experimented with several variants of two recently proposed Transformer-based models, namely RoBERTa and the Text-To-Text Transfer Transformer (T5), for the task of code completion. The achieved results show that Transformer-based models, and in particular the T5, represent a viable solution for code completion, with perfect predictions ranging from ~29%, obtained when asking the model to guess entire blocks, up to ~69%, reached in the simpler scenario of few tokens masked from the same code statement., arXiv admin note: substantial text overlap with arXiv:2103.07115

Current approaches to visual place recognition for loop closure do not provide information about confidence of decisions. In this work we present an algorithm for place recognition on the basis of graph-based decisions on deep embeddings and blur detections. The graph constructed in advance permits together with information about the room category an inference on usefulness of place recognition, and in particular, it enables the evaluation the confidence of final decision. We demonstrate experimentally that thanks to proposed blur detection the accuracy of scene recognition is much higher. We evaluate performance of place recognition on the basis of manually selected places for recognition with corresponding sets of relevant and irrelevant images. The algorithm has been evaluated on large dataset for visual place recognition that contains both images with severe (unknown) blurs and sharp images. Images with 6-DOF viewpoint variations were recorded using a humanoid robot.

The for loop runs for a specific number of times, based on a special variable called the iterator. The iterator's value, conditional test result, and modifications for each execution of the loop (called the step) controls the runtime of the loop. All three of these items are defined at the beginning of the for loop.