Relative Performance Of Algorithms Research Articles

Derivative-free search approaches for optimization of well inflow control valves and controls

Decisions regarding problem conceptualization, search approach, and how best to parametrize optimization methods for practical application are key to successful implementation of optimization approaches within georesources field development projects. This work provides decision support regarding the application of derivative-free search approaches for concurrent optimization of inflow control valves (ICVs) and well controls. A set of state-of-the-art approaches possessing different search features is implemented over two reference cases, and their performance, resource requirements, and specific method configurations are compared across multiple problem formulations for completion design. In this study, problem formulations to optimize completion design comprise fixed ICVs and piecewise-constant well controls. The design is optimized by several derivative-free methodologies relying on parallel pattern-search (tAPPS), population-based stochastic sampling (tPSO) and trust-region interpolation-based models (tDFTR). These methodologies are tested on a heterogeneous two-dimensional case and on a realistic case based on a section of the Olympus benchmark model. Three problem formulations are applied in both cases, i.e., one formulation optimizes ICV settings only, while two joint configurations also treat producer and injector controls as variables. Various method parametrizations across the range of cases and problem formulations exploit the different search features to improve convergence, achieve final objectives and infer response surface features. The scope of this particular study treats only deterministic problem formulations. Results outline performance trade-offs between parallelizable algorithms (tAPPS, tPSO) with high total runtime search efficiency and the local-search trust-region approach (tDFTR) providing effective objective gains for a low number of cost function evaluations. tAPPS demonstrates robust performance across different problem formulations that can support exploration efforts, e.g., during a pre-drill design phase while multiple independent tDFTR runs can provide local tuning capability around established solutions in a time-constrained post-drill setting. Additional remarks regarding joint completion design optimization, comparison metrics, and relative algorithm performance given the varying problem formulations are also made.

FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs

Digital radiography is one of the most common and cost-effective standards for the diagnosis of bone fractures. For such diagnoses expert intervention is required which is time-consuming and demands rigorous training. With the recent growth of computer vision algorithms, there is a surge of interest in computer-aided diagnosis. The development of algorithms demands large datasets with proper annotations. Existing X-Ray datasets are either small or lack proper annotation, which hinders the development of machine-learning algorithms and evaluation of the relative performance of algorithms for classification, localization, and segmentation. We present FracAtlas, a new dataset of X-Ray scans curated from the images collected from 3 major hospitals in Bangladesh. Our dataset includes 4,083 images that have been manually annotated for bone fracture classification, localization, and segmentation with the help of 2 expert radiologists and an orthopedist using the open-source labeling platform, makesense.ai. There are 717 images with 922 instances of fractures. Each of the fracture instances has its own mask and bounding box, whereas the scans also have global labels for classification tasks. We believe the dataset will be a valuable resource for researchers interested in developing and evaluating machine learning algorithms for bone fracture diagnosis.

Parallel Multi-Deque Partition Dual-Deque Merge sorting algorithm using OpenMP

Quicksort is an important algorithm that uses the divide and conquer concept, and it can be run to solve any problem. The performance of the algorithm can be improved by implementing this algorithm in parallel. In this paper, the parallel sorting algorithm named the Multi-Deque Partition Dual-Deque Merge Sorting algorithm (MPDMSort) is proposed and run on a shared memory system. This algorithm contains the Multi-Deque Partitioning phase, which is a block-based parallel partitioning algorithm, and the Dual-Deque Merging phase, which is a merging algorithm without compare-and-swap operations and sorts the small data with the sorting function of the standard template library. The OpenMP library, which is an application programming interface used to develop the parallel implementation of this algorithm, is implemented in MPDMSort. Two computers (one with an Intel Xeon Gold 6142 CPU and the other with an Intel Core i7-11700 CPU) running Ubuntu Linux are used in this experiment. The results show that MPDMSort is faster than parallel balanced quicksort and multiway merge sort on the large random distribution data. A speedup of 13.81times and speedup per thread of 0.86 can be obtained. Thus, developers can use these parallel partitioning and merging algorithms to improve the performance of related algorithms.

Coupon Filter: A Universal and Lightweight Filter Framework for More Accurate Data Stream Processing

Data stream processing plays a critical role in providing fundamental statistics for various applications, such as anomaly detection. Still, the unbalanced distribution of data streams severely affects the performance of related algorithms, which motivates the recent studies on filter structure design to enhance the existing algorithms for a more precise estimation result. However, these filters are mainly designed for frequency-based filtration, while none of them can conduct universal filtration; apparently, frequency is not the only targeted metric in practical processing tasks, metrics like cardinality and persistence are of equal importance. To cope with the issue, we propose a novel filter framework to implement universal, lightweight, and accurate filtration. The filter framework is called Coupon Filter due to the interpretation of its flow-level filtration as a coupon collection process. We prove the filtration efficiency of our filter design and formally analyze its recording process. We deploy our filter on the three typical metrics (frequency, cardinality, and persistence) to illustrate its advantages. The experimental results on real Internet traces demonstrate the effectiveness of our filter in enhancing existing stream processing approaches in terms of accuracy and throughput. All source codes are available at Github https://github.com/duyang92/coupon-filter-paper.

Classification of Scientific Documents in the Kazakh Language Using Deep Neural Networks and a Fusion of Images and Text

The rapid development of natural language processing and deep learning techniques has boosted the performance of related algorithms in several linguistic and text mining tasks. Consequently, applications such as opinion mining, fake news detection or document classification that assign documents to predefined categories have significantly benefited from pre-trained language models, word or sentence embeddings, linguistic corpora, knowledge graphs and other resources that are in abundance for the more popular languages (e.g., English, Chinese, etc.). Less represented languages, such as the Kazakh language, balkan languages, etc., still lack the necessary linguistic resources and thus the performance of the respective methods is still low. In this work, we develop a model that classifies scientific papers written in the Kazakh language using both text and image information and demonstrate that this fusion of information can be beneficial for cases of languages that have limited resources for machine learning models’ training. With this fusion, we improve the classification accuracy by 4.4499% compared to the models that use only text or only image information. The successful use of the proposed method in scientific documents’ classification paves the way for more complex classification models and more application in other domains such as news classification, sentiment analysis, etc., in the Kazakh language.

Moving Target Tracking Algorithm Based on Improved Resampling Particle Filter in UWB Environment

In this paper, a moving target tracking (MTT) algorithm based on the improved resampling particle filter (IRPF) was put forward for the reduced accuracy of particle filter (PF) due to the lack of particle diversity resulting from traditional resampling methods. In this algorithm, the influences of the likelihood probability distribution of particles on the PF accuracy were firstly analyzed to stratify the adaptive regions of particles, and a particle diversity measurement index based on stratification was proposed. After that, a threshold was set for the particle diversity after resampling. If the particle diversity failed to reach the set threshold, all new particles would be subjected to a Gaussian random walk in a preset variance matrix to improve the particle diversity. Finally, the performance of related algorithms was tested in both simulation environment and actual indoor ultrawideband (UWB) nonline-of-sight (NLOS) environment. The experimental results revealed that the nonlinear target state estimation accuracy was maximally and minimally improved by 12.83% and 1.97%, respectively, in the simulation environment, and the root mean square error (RMSE) of MTT was reduced from 17.131 cm to 10.471 cm in actual UWB NLOS environment, indicating that the IRPF algorithm can enhance the target estimation accuracy and state tracking capability, manifesting better filter performance.

Satellite-derived bathymetry using Landsat-8 and Sentinel-2A images: assessment of atmospheric correction algorithms and depth derivation models in shallow waters.

Satellite-derived bathymetry (SDB) has an extensive prospect in nearshore bathymetry for its high efficiency and low costs. Atmospheric correction and bathymetric modeling are critical processes in SDB, and examining the performance of related algorithms and models will contribute to the formulation of reliable bathymetry strategies. This study explored the effectiveness of three general atmospheric correction algorithms, namely Second Simulation of a Satellite Signal in the Solar Spectrum (6S), Atmospheric correction for OLI 'lite' (ACOLITE), and QUick Atmospheric Correction (QUAC), in depth retrieval from Landsat-8 and Sentinel-2A images using different SDB models over Ganquan Island and Oahu Island. The bathymetric Light Detection and Ranging (LiDAR) data was used for SDB model training and accuracy verification. The results indicated that the three atmospheric correction algorithms could provide effective corrections for SDB. For the SDB models except log-transformed band ratio model (LBR) and support vector machine (SVM), the impact of different atmospheric corrections on bathymetry was basically the same. Furthermore, we assessed the performance of six different SDB models: Lyzenga's model (LM), generalized additive model (GAM), LBR, SVM, multilayer perceptron (MLP), and random forest (RF). The bathymetric accuracy, consistency of bathymetric maps and generalization ability were considered for the assessment. Given sufficient training data, the accuracy of the machine learning models (SVM, MLP, RF) was generally superior to that of the empirical inversion models (LM, GAM, LBR), with the root mean square error (RMSE) varied between 0.735 m to 1.177 m. MLP achieved the best accuracy and consistency. When the depth was deeper than 15 m, the bathymetry error of all the SDB models increased sharply, and LM, LBR and SVM reached the upper limit of depth retrieval capability at 20-25 m. In addition, LM and LBR were demonstrated to have better adaptability in heterogeneous environment without training data.

Effective node selection technique towards sparse learning

Neural networks are getting wider and deeper to achieve state-of-the-art results in various machine learning domains. Such networks result in complex structures, high model size, and computational costs. Moreover, these networks are failing to adapt to new data due to their isolation in the specific domain-target space. To tackle these issues, we propose a sparse learning method to train the existing network on new classes by selecting non-crucial parameters from the network. Sparse learning also manages to keep the performance of existing classes with no additional network structure and memory costs by employing an effective node selection technique, which analyzes and selects unimportant parameters by using information theory in the neuron distribution of the fully connected layers. Our method could learn up to 40% novel classes without notable loss in the accuracy of existing classes. Through experiments, we show how a sparse learning method competes with state-of-the-art methods in terms of accuracy and even surpasses the performance of related algorithms in terms of efficiency in memory, processing speed, and overall training time. Importantly, our method can be implemented in both small and large applications, and we justify this by using well-known networks such as LeNet, AlexNet, and VGG-16.

Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference

This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29-30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical performance was analyzed in simulated market environments. This allows consideration of market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition.

Postnatal gestational age estimation using newborn screening blood spots: a proposed validation protocol

BackgroundKnowledge of gestational age (GA) is critical for guiding neonatal care and quantifying regional burdens of preterm birth. In settings where access to ultrasound dating is limited, postnatal estimates are...

Cache efficient functional algorithms

The widely studied I/O and ideal-cache models were developed to account for the large difference in costs to access memory at different levels of the memory hierarchy. Both models are based on a two level memory hierarchy with a fixed size fast memory (cache) of size M , and an unbounded slow memory organized in blocks of size B. The cost measure is based purely on the number of block transfers between the primary and secondary memory. All other operations are free. Many algorithms have been analyzed in these models and indeed these models predict the relative performance of algorithms much more accurately than the standard Random Access Machine (RAM) model. The models, however, require specifying algorithms at a very low level, requiring the user to carefully lay out their data in arrays in memory and manage their own memory allocation. We present a cost model for analyzing the memory efficiency of algorithms expressed in a simple functional language. We show how some algorithms written in standard forms using just lists and trees (no arrays) and requiring no explicit memory layout or memory management are efficient in the model. We then describe an implementation of the language and show provable bounds for mapping the cost in our model to the cost in the ideal-cache model. These bounds imply that purely functional programs based on lists and trees with no special attention to any details of memory layout can be asymptotically as efficient as the carefully designed imperative I/O efficient algorithms. For example we describe an o ( n/B log M/B n/B ) cost sorting algorithm, which is optimal in the ideal cache and I/O models.<!-- END_PAGE_1 -->

Cache and I/O efficent functional algorithms

The widely studied I/O and ideal-cache models were developed to account for the large difference in costs to access memory at different levels of the memory hierarchy. Both models are based on a two level memory hierarchy with a fixed size primary memory(cache) of size M, an unbounded secondary memory organized in blocks of size B. The cost measure is based purely on the number of block transfers between the primary and secondary memory. All other operations are free. Many algorithms have been analyzed in these models and indeed these models predict the relative performance of algorithms much more accurately than the standard RAM model. The models, however, require specifying algorithms at a very low level requiring the user to carefully lay out their data in arrays in memory and manage their own memory allocation. In this paper we present a cost model for analyzing the memory efficiency of algorithms expressed in a simple functional language. We show how some algorithms written in standard forms using just lists and trees (no arrays) and requiring no explicit memory layout or memory management are efficient in the model. We then describe an implementation of the language and show provable bounds for mapping the cost in our model to the cost in the ideal-cache model. These bound imply that purely functional programs based on lists and trees with no special attention to any details of memory layout can be as asymptotically as efficient as the carefully designed imperative I/O efficient algorithms. For example we describe an O(n_B logM/Bn_B)cost sorting algorithm, which is optimal in the ideal cache and I/O models.

A survey of distance‐based wireless sensor network localization techniques

PurposeLocalization is a fundamental problem in wireless sensor networks. In many applications, sensor location information is critical for data processing and meaning. While the global positioning system (GPS) can be used to determine mote locations with meter precision, the high hardware cost and energy requirements of GPS receivers often prohibit the ubiquitous use of GPS for location estimates. This high cost (in terms of hardware price and energy consumption) of GPS has motivated researchers to develop localization protocols that determine mote locations based on cheap hardware and localization algorithms. The purpose of this paper is to present a comprehensive review of wireless sensor network localization techniques, and provide a detailed overview for several distance‐based localization algorithms.Design/methodology/approachTo provide a detailed summary of wireless sensor network localization algorithms, the authors outline a tiered classification system in which they first classify algorithms as distributed, distributed‐centralized, or centralized. From this broad classification, the paper then further categorizes localization algorithms using their protocol techniques. By utilizing this classification system, the authors are able to provide a survey of several wireless sensor network localization algorithms and summarize relative algorithm performance based on the algorithms' classification.FindingsThere are numerous localization algorithms available and the performance of these algorithms is dependent on network configuration, environmental variables, and the ranging method implemented. When selecting a localization algorithm, it is important to understand basic algorithm operation and expected performance. This tier‐based algorithm classification system can be used to gain a high‐level understanding of algorithm performance and energy consumption based on known algorithm characteristics.Originality/valueLocalization is a widely researched field and given the quantity of localization algorithms that currently exist, it is impossible to present a complete review of every published algorithm. Instead, the paper presents a holistic view of the current state of localization research and a detailed review of ten representative distance‐based algorithms that have diverse characteristics and methods. This review presents a new classification structure that may help researchers understand, at a high‐level, the expected performance and energy consumption of algorithms not explicitly addressed by our work.

Algorithms for the de novo sequencing of peptides from tandem mass spectra

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field.

The incremental method for fast computing the rough fuzzy approximations

Abstract The lower and upper approximations are basic concepts in rough fuzzy set theory. The effective computation of approximations is very important for improving the performance of related algorithms. This paper proposed and proved two incremental methods for fast computing the rough fuzzy approximations, one starts from the boundary set, the other is based on the cut sets of a fuzzy set. Then some illustrative examples are conducted. Consequently, two algorithms corresponding to the two incremental methods are put forward respectively. In order to test the efficiency of algorithms, some experiments are made on a large soybean data set from UCI. The experimental results show that the two incremental methods effectively reduce the computing time in comparison with the traditional non-incremental method [1].

Quantitative Imaging to Assess Tumor Response to Therapy: Common Themes of Measurement, Truth Data, and Error Sources

RATIONALE: Early detection of tumor response to therapy is a key goal. Finding measurement algorithms capable of early detection of tumor response could individualize therapy treatment as well as reduce the cost of bringing new drugs to market. On an individual basis, the urgency arises from the desire to prevent continued treatment of the patient with a high-cost and/or high-risk regimen with no demonstrated individual benefit and rapidly switch the patient to an alternative efficacious therapy for that patient. In the context of bringing new drugs to market, such algorithms could demonstrate efficacy in much smaller populations, which would allow phase 3 trials to achieve statistically significant decisions with fewer subjects in shorter trials. MATERIALS AND METHODS: This consensus-based article describes multiple, image modality-independent means to assess the relative performance of algorithms for measuring tumor change in response to therapy. In this setting, we describe specifically the example of measurement of tumor volume change from anatomic imaging as well as provide an overview of other promising generic analytic methods that can be used to assess change in heterogeneous tumors. To support assessment of the relative performance of algorithms for measuring small tumor change, data sources of truth are required. RESULTS: Very short interval clinical imaging examinations and phantom scans provide known truth for comparative evaluation of algorithms. CONCLUSIONS: For a given category of measurement methods, the algorithm that has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change.

Reevaluation of color constancy algorithm performance

The relative performance of color constancy algorithms is evaluated. We highlight some problems with previous algorithm evaluation and define more appropriate testing procedures. We discuss how best to measure algorithm accuracy on a single image as well as suitable methods for summarizing errors over a set of images. We also discuss how the relative performance of two or more algorithms should best be compared, and we define an experimental framework for testing algorithms. We reevaluate the performance of six color constancy algorithms using the procedures that we set out and show that this leads to a significant change in the conclusions that we draw about relative algorithm performance as compared with those from previous work.

Sell-Side Algorithmic Offerings

This study examines anecdotal and the empirical evidence available to date regarding the relative performance of algorithms. While there are clear efficiency gains from the use of algorithms, the performance we have experienced to date in our use has been adequate, but not impressive. Beyond the anecdotal, more reliable and comprehensive empirical data has unfortunately been very limited. The most ambitious study so far was completed by ITG in the spring of 2005. While the study is a good first step, unfortunately it raises more questions than it answers due in part to the limitations of the empirical data, but also to some unjustifiable leaps in logic. For instance, the benchmark being aimed for by the trades in the control group data set is unknown, yet comparisons are unfairly made to executions where the strategies employed are known. Similarly, when the absolute performance of VWAP algorithmic trades is cited, a two basis point average miss is lauded whereas we would conclude instead that the equivalent? of a penny miss per share is underwhelming as well as a confirmation that active strategies are on average beating passive, algorithmic VWAP strategies because it9s a zero sum game with all trades together creating the VWAP on the day. Clearly, a lot more digging needs to be done before any real conclusions can be drawn about the relative performance of algorithms. <b>TOPICS:</b>Statistical methods, equity portfolio management

Signal Source Separation in the Analysis of Neural Activity in Brain

This research examines the spatial and temporal characteristics of the responses to stimulation of the barrel cortex in anesthetized rats using optical imaging with particular emphasis on methods of analysis which reduce the effects of low-frequency oscillations on localization of the activated cortical region. Image sequences were captured using a light source with a narrow bandwidth of wavelength (590 ± 2 nm). On each trial image data were collected at 15 Hz and stored over a 12-s period starting 8 s before stimulation onset. Stimulation was for 1 s using an oscillating whisker vibrator (∼1-mm deflection at 5 Hz). For each subject a total of 30 experimental trials were collected and averaged. There was an interstimulus interval of 26 s. The trial-averaged data were analyzed using two related signal source separation algorithms. Both algorithms use a weak model of the expected temporal response as a filter to exclude contributions from low-frequency baseline oscillations which we call the V-signal. We found that both algorithms successfully separated most of the effects of the V-signal from the response to the stimulation. The performance of the algorithms compared favorably with the performance of related algorithms without weak constraints and the “ratio of means” strategy used by C. H. Chen-Bee et al. (1996b, J. Neurosci. Methods 68:28–37; C. H. Chen-Bee et al., 2000, J. Neurosci. Methods 97:157–173).

A robust visual method for assessing the relative performance of edge-detection algorithms

A new method for evaluating edge detection algorithms is presented and applied to measure the relative performance of algorithms by Canny, Nalwa-Binford, Iverson-Zucker, Bergholm, and Rothwell. The basic measure of performance is a visual rating score which indicates the perceived quality of the edges for identifying an object. The process of evaluating edge detection algorithms with this performance measure requires the collection of a set of gray-scale images, optimizing the input parameters for each algorithm, conducting visual evaluation experiments and applying statistical analysis methods. The novel aspect of this work is the use of a visual task and real images of complex scenes in evaluating edge detectors. The method is appealing because, by definition, the results agree with visual evaluations of the edge images.