Collaborative correlation filters for real-time tracking with spatial constraint

Accurate scale estimation of the target plays an important role in object tracking. Most state-of-the-art methods estimate the target size by employing an exhaustive scale search. These methods can achieve high accuracy but suffer significantly from large computational cost. In this paper, we first propose an adaptive scale search strategy with the scale selection factor instead of an exhaustive scale search. This proposed strategy contributes to reducing computational costs by adaptive sampling. Furthermore, the boundary effects of correlation filters are suppressed by investigating background information so that the accuracy of the proposed tracker can be boosted. Experiments’ empirical evaluations of 61 challenging benchmark sequences demonstrate that the overall tracking performance of the proposed tracker is very successfully improved. Moreover, our method obtains the top rank in performance by outperforming 17 state-of-the-art trackers on OTB2013.

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Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

10.26434/chemrxiv.13047863 ◽

2020 ◽

Author(s):

Jingbai Li ◽

Patrick Reiser ◽

André Eberhard ◽

Pascal Friederich ◽

Steven Lopez

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Excited State ◽

Adaptive Sampling ◽

Computational Cost ◽

Ground Truth ◽

Absolute Error ◽

Photochemical Reactions ◽

Computational Techniques ◽

Full Potential

Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr et al. demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH2NH2+).We have developed a Python-based code, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD), to accomplish the unprecedented 10 ns cis-trans photodynamics of trans-hexafluoro-2-butene (CF3–CH=CH–CF3) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S1 half-life (60.5 fs), the photochemical product ratio (trans: cis = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (trans → cis but not cis → trans pathways) that may offer future automated photochemical reaction discoveries.

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Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

Medicinal Chemistry ◽

10.2174/1573406414666181015151610 ◽

2019 ◽

Vol 15 (3) ◽

pp. 216-230 ◽

Cited By ~ 2

Author(s):

Abbasali Emamjomeh ◽

Javad Zahiri ◽

Mehrdad Asadian ◽

Mehrdad Behmanesh ◽

Barat A. Fakheri ◽

...

Keyword(s):

Computational Methods ◽

State Of The Art ◽

Noncoding Rnas ◽

Research Field ◽

Design Strategies ◽

Structural Prediction ◽

Computational Costs ◽

Cellular Processes ◽

Laboratory Techniques ◽

Fast Prediction

Background:Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.Objective:The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA’s roles in cellular processes and drugs design, briefly.Method:In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.Results:The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.Conclusion:ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

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Performance of Adaptive Unstructured Mesh Modelling in Idealized Advection Cases over Steep Terrains

Atmosphere ◽

10.3390/atmos9110444 ◽

2018 ◽

Vol 9 (11) ◽

pp. 444 ◽

Cited By ~ 2

Author(s):

Jinxi Li ◽

Jie Zheng ◽

Jiang Zhu ◽

Fangxin Fang ◽

Christopher. Pain ◽

...

Keyword(s):

Unstructured Mesh ◽

Computational Cost ◽

Adaptive Mesh ◽

Galerkin Finite Element Method ◽

Adaptive Meshes ◽

Galerkin Finite Element ◽

Computational Costs ◽

Cut Cell ◽

Discontinuous Galerkin Finite Element ◽

Terrain Following

Advection errors are common in basic terrain-following (TF) coordinates. Numerous methods, including the hybrid TF coordinate and smoothing vertical layers, have been proposed to reduce the advection errors. Advection errors are affected by the directions of velocity fields and the complexity of the terrain. In this study, an unstructured adaptive mesh together with the discontinuous Galerkin finite element method is employed to reduce advection errors over steep terrains. To test the capability of adaptive meshes, five two-dimensional (2D) idealized tests are conducted. Then, the results of adaptive meshes are compared with those of cut-cell and TF meshes. The results show that using adaptive meshes reduces the advection errors by one to two orders of magnitude compared to the cut-cell and TF meshes regardless of variations in velocity directions or terrain complexity. Furthermore, adaptive meshes can reduce the advection errors when the tracer moves tangentially along the terrain surface and allows the terrain to be represented without incurring in severe dispersion. Finally, the computational cost is analyzed. To achieve a given tagging criterion level, the adaptive mesh requires fewer nodes, smaller minimum mesh sizes, less runtime and lower proportion between the node numbers used for resolving the tracer and each wavelength than cut-cell and TF meshes, thus reducing the computational costs.

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Faster Motif Counting via Succinct Color Coding and Adaptive Sampling

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3447397 ◽

2021 ◽

Vol 15 (6) ◽

pp. 1-27

Author(s):

Marco Bressan ◽

Stefano Leucci ◽

Alessandro Panconesi

Keyword(s):

Adaptive Sampling ◽

Relative Frequency ◽

State Of The Art ◽

Color Coding ◽

Input Graph ◽

Large Graphs ◽

Running Time ◽

Uniform Sampling ◽

Current State ◽

Connected Subgraphs

We address the problem of computing the distribution of induced connected subgraphs, aka graphlets or motifs , in large graphs. The current state-of-the-art algorithms estimate the motif counts via uniform sampling by leveraging the color coding technique by Alon, Yuster, and Zwick. In this work, we extend the applicability of this approach by introducing a set of algorithmic optimizations and techniques that reduce the running time and space usage of color coding and improve the accuracy of the counts. To this end, we first show how to optimize color coding to efficiently build a compact table of a representative subsample of all graphlets in the input graph. For 8-node motifs, we can build such a table in one hour for a graph with 65M nodes and 1.8B edges, which is times larger than the state of the art. We then introduce a novel adaptive sampling scheme that breaks the “additive error barrier” of uniform sampling, guaranteeing multiplicative approximations instead of just additive ones. This allows us to count not only the most frequent motifs, but also extremely rare ones. For instance, on one graph we accurately count nearly 10.000 distinct 8-node motifs whose relative frequency is so small that uniform sampling would literally take centuries to find them. Our results show that color coding is still the most promising approach to scalable motif counting.

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Adaptive sampling of potential-field data: A direct approach to compressive inversion

Geophysics ◽

10.1190/geo2013-0087.1 ◽

2014 ◽

Vol 79 (1) ◽

pp. IM1-IM9 ◽

Cited By ~ 20

Author(s):

Nathan Leon Foks ◽

Richard Krahenbuhl ◽

Yaoguo Li

Keyword(s):

Potential Field ◽

Adaptive Sampling ◽

Field Data ◽

Computational Cost ◽

Large Field ◽

Magnetic Data ◽

Data Sampling ◽

Data Types ◽

Data Set ◽

Potential Field Data

Compressive inversion uses computational algorithms that decrease the time and storage needs of a traditional inverse problem. Most compression approaches focus on the model domain, and very few, other than traditional downsampling focus on the data domain for potential-field applications. To further the compression in the data domain, a direct and practical approach to the adaptive downsampling of potential-field data for large inversion problems has been developed. The approach is formulated to significantly reduce the quantity of data in relatively smooth or quiet regions of the data set, while preserving the signal anomalies that contain the relevant target information. Two major benefits arise from this form of compressive inversion. First, because the approach compresses the problem in the data domain, it can be applied immediately without the addition of, or modification to, existing inversion software. Second, as most industry software use some form of model or sensitivity compression, the addition of this adaptive data sampling creates a complete compressive inversion methodology whereby the reduction of computational cost is achieved simultaneously in the model and data domains. We applied the method to a synthetic magnetic data set and two large field magnetic data sets; however, the method is also applicable to other data types. Our results showed that the relevant model information is maintained after inversion despite using 1%–5% of the data.

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Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models

Symmetry ◽

10.3390/sym13030511 ◽

2021 ◽

Vol 13 (3) ◽

pp. 511

Author(s):

Syed Mohammad Minhaz Hossain ◽

Kaushik Deb ◽

Pranab Kumar Dhar ◽

Takeshi Koshiba

Keyword(s):

State Of The Art ◽

Computational Cost ◽

Region Of Interest ◽

Number Of Clusters ◽

Plant Leaf ◽

Leaf Disease ◽

Automatic Initialization ◽

Adequate Accuracy ◽

Model Size ◽

High Computational Cost

Proper plant leaf disease (PLD) detection is challenging in complex backgrounds and under different capture conditions. For this reason, initially, modified adaptive centroid-based segmentation (ACS) is used to trace the proper region of interest (ROI). Automatic initialization of the number of clusters (K) using modified ACS before recognition increases tracing ROI’s scalability even for symmetrical features in various plants. Besides, convolutional neural network (CNN)-based PLD recognition models achieve adequate accuracy to some extent. However, memory requirements (large-scaled parameters) and the high computational cost of CNN-based PLD models are burning issues for the memory restricted mobile and IoT-based devices. Therefore, after tracing ROIs, three proposed depth-wise separable convolutional PLD (DSCPLD) models, such as segmented modified DSCPLD (S-modified MobileNet), segmented reduced DSCPLD (S-reduced MobileNet), and segmented extended DSCPLD (S-extended MobileNet), are utilized to represent the constructive trade-off among accuracy, model size, and computational latency. Moreover, we have compared our proposed DSCPLD recognition models with state-of-the-art models, such as MobileNet, VGG16, VGG19, and AlexNet. Among segmented-based DSCPLD models, S-modified MobileNet achieves the best accuracy of 99.55% and F1-sore of 97.07%. Besides, we have simulated our DSCPLD models using both full plant leaf images and segmented plant leaf images and conclude that, after using modified ACS, all models increase their accuracy and F1-score. Furthermore, a new plant leaf dataset containing 6580 images of eight plants was used to experiment with several depth-wise separable convolution models.

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Dual-Stream Guided-Learning via a Priori Optimization for Person Re-identification

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3447715 ◽

2021 ◽

Vol 17 (4) ◽

pp. 1-22

Author(s):

Junyi Wu ◽

Yan Huang ◽

Qiang Wu ◽

Zhipeng Gao ◽

Jianqiang Zhao ◽

...

Keyword(s):

Learning Strategy ◽

State Of The Art ◽

A Priori ◽

Background Information ◽

Stream Network ◽

Related Information ◽

Guided Learning ◽

Segmentation Algorithms ◽

Art Methods ◽

Background Clutter

The task of person re-identification (re-ID) is to find the same pedestrian across non-overlapping camera views. Generally, the performance of person re-ID can be affected by background clutter. However, existing segmentation algorithms cannot obtain perfect foreground masks to cover the background information clearly. In addition, if the background is completely removed, some discriminative ID-related cues (i.e., backpack or companion) may be lost. In this article, we design a dual-stream network consisting of a Provider Stream (P-Stream) and a Receiver Stream (R-Stream). The R-Stream performs an a priori optimization operation on foreground information. The P-Stream acts as a pusher to guide the R-Stream to concentrate on foreground information and some useful ID-related cues in the background. The proposed dual-stream network can make full use of the a priori optimization and guided-learning strategy to learn encouraging foreground information and some useful ID-related information in the background. Our method achieves Rank-1 accuracy of 95.4% on Market-1501, 89.0% on DukeMTMC-reID, 78.9% on CUHK03 (labeled), and 75.4% on CUHK03 (detected), outperforming state-of-the-art methods.

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Gaussian Elimination versus Greedy Methods for the Synthesis of Linear Reversible Circuits

ACM Transactions on Quantum Computing ◽

10.1145/3474226 ◽

2021 ◽

Vol 2 (3) ◽

pp. 1-26

Author(s):

Timothée Goubault De Brugière ◽

Marc Baboulin ◽

Benoît Valiron ◽

Simon Martiel ◽

Cyril Allouche

Keyword(s):

Quantum Computing ◽

State Of The Art ◽

Gaussian Elimination ◽

Random Operators ◽

Lu Factorization ◽

Large Problem ◽

Computational Costs ◽

Elimination Algorithm ◽

Reversible Circuits ◽

Greedy Methods

Linear reversible circuits represent a subclass of reversible circuits with many applications in quantum computing. These circuits can be efficiently simulated by classical computers and their size is polynomially bounded by the number of qubits, making them a good candidate to deploy efficient methods to reduce computational costs. We propose a new algorithm for synthesizing any linear reversible operator by using an optimized version of the Gaussian elimination algorithm coupled with a tuned LU factorization. We also improve the scalability of purely greedy methods. Overall, on random operators, our algorithms improve the state-of-the-art methods for specific ranges of problem sizes: The custom Gaussian elimination algorithm provides the best results for large problem sizes (n > 150), while the purely greedy methods provide quasi optimal results when n < 30. On a benchmark of reversible functions, we manage to significantly reduce the CNOT count and the depth of the circuit while keeping other metrics of importance (T-count, T-depth) as low as possible.

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Front-to-Front Heuristic Search for Satisficing Classical Planning

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/567 ◽

2020 ◽

Author(s):

Ryo Kuroiwa ◽

Alex Fukunaga

Keyword(s):

Heuristic Search ◽

Search Strategy ◽

State Of The Art ◽

Heuristic Evaluation ◽

Bidirectional Search

Although symbolic bidirectional search is successful in optimal classical planning, state-of-the-art satisficing planners do not use bidirectional search. Previous bidirectional search planners for satisficing planning behaved similarly to a trivial portfolio, which independently executes forward and backward search without the desired ``meet-in-the-middle'' behavior of bidirectional search where the forward and backward search frontiers intersect at some point relatively far from the forward and backward start states. In this paper, we propose Top-to-Top Bidirectional Search (TTBS), a new bidirectional search strategy with front-to-front heuristic evaluation. We show that TTBS strongly exhibits ``meet-in-the-middle'' behavior and can solve instances solved by neither forward nor backward search on a number of domains.

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Long-term target tracking combined with re-detection

10.21203/rs.3.rs-51036/v3 ◽

2020 ◽

Author(s):

Juanjuan Wang ◽

HaoRan Yang ◽

Ning Xu ◽

Chengqin Wu ◽

ZengShun Zhao ◽

...

Keyword(s):

Correlation Energy ◽

State Of The Art ◽

Tracking Algorithm ◽

Correlation Filters ◽

Short Term ◽

Tracking Method ◽

Tracking Tasks ◽

Svm Model ◽

Confidence Degree

Abstract The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Learning Adaptive Discriminative Correlation Filters (LADCF) tracking algorithm with a re-detection component based on the SVM model. The LADCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

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