scholarly journals matOptimize: A parallel tree optimization method enables online phylogenetics for SARS-CoV-2

2022 ◽  
Author(s):  
Cheng Ye ◽  
Bryan Thornlow ◽  
Angie S Hinrichs ◽  
Devika Torvi ◽  
Robert Lanfear ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
State Of The Art ◽  
Optimization Method ◽  
Transmission Dynamics ◽  
Time Constraints ◽  
Precise Analysis ◽  
Tree Optimization ◽  
Transformative Approach ◽  
Magnitude Improvement ◽  
Memory Efficient

Phylogenetic tree optimization is necessary for precise analysis of evolutionary and transmission dynamics, but existing tools are inadequate for handling the scale and pace of data produced during the COVID-19 pandemic. One transformative approach, online phylogenetics, aims to incrementally add samples to an ever-growing phylogeny, but there are no previously-existing approaches that can efficiently optimize this vast phylogeny under the time constraints of the pandemic. Here, we present matOptimize, a fast and memory-efficient phylogenetic tree optimization tool based on parsimony that can be parallelized across multiple CPU threads and nodes, and provides orders of magnitude improvement in runtime and peak memory usage compared to existing state-of-the-art methods. We have developed this method particularly to address the pressing need during the COVID-19 pandemic for daily maintenance and optimization of a comprehensive SARS-CoV-2 phylogeny. Thus, our approach addresses an important need for daily maintenance and refinement of a comprehensive SARS-CoV-2 phylogeny.

scholarly journals Robust CNN Compression Framework for Security-Sensitive Embedded Systems

2021 ◽  
Vol 11 (3) ◽  
pp. 1093
Author(s):  
Jeonghyun Lee ◽  
Sangkyun Lee
Keyword(s):  
Embedded Systems ◽  
Optimization Problem ◽  
State Of The Art ◽  
Classification Problems ◽  
Proximal Gradient Method ◽  
Knowledge Distillation ◽  
New Type ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
Memory Efficient

Convolutional neural networks (CNNs) have achieved tremendous success in solving complex classification problems. Motivated by this success, there have been proposed various compression methods for downsizing the CNNs to deploy them on resource-constrained embedded systems. However, a new type of vulnerability of compressed CNNs known as the adversarial examples has been discovered recently, which is critical for security-sensitive systems because the adversarial examples can cause malfunction of CNNs and can be crafted easily in many cases. In this paper, we proposed a compression framework to produce compressed CNNs robust against such adversarial examples. To achieve the goal, our framework uses both pruning and knowledge distillation with adversarial training. We formulate our framework as an optimization problem and provide a solution algorithm based on the proximal gradient method, which is more memory-efficient than the popular ADMM-based compression approaches. In experiments, we show that our framework can improve the trade-off between adversarial robustness and compression rate compared to the existing state-of-the-art adversarial pruning approach.

scholarly journals Revealing the Transmission Dynamics of COVID-19: A Bayesian Framework for Rt Estimation

2021 ◽  
Author(s):  
Xian Yang ◽  
Shuo Wang ◽  
Yuting Xing ◽  
Ling Li ◽  
Richard Yi Da Xu ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Model Selection ◽  
Reproduction Number ◽  
State Of The Art ◽  
Transmission Dynamics ◽  
Selection Mechanism ◽  
Problem Of Time ◽  
Joint Inference ◽  
Abrupt Changes ◽  
Bayesian Smoothing

Abstract In epidemiological modelling, the instantaneous reproduction number, Rt, is important to understand the transmission dynamics of infectious diseases. Current Rt estimates often suffer from problems such as lagging, averaging and uncertainties demoting the usefulness of Rt. To address these problems, we propose a new method in the framework of sequential Bayesian inference where a Data Assimilation approach is taken for Rt estimation, resulting in the state-of-the-art ‘DARt’ system for Rt estimation. With DARt, the problem of time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is improved by instantaneous updating upon new observations and a model selection mechanism capturing abrupt changes caused by interventions; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt through simulations and demonstrate its power in revealing the transmission dynamics of COVID-19.

scholarly journals Learning Compact Model for Large-Scale Multi-Label Data

2019 ◽  
Vol 33 ◽  
pp. 5385-5392 ◽  
Author(s):  
Tong Wei ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Performance Metrics ◽  
State Of The Art ◽  
Optimization Method ◽  
Model Parameters ◽  
Generalization Capability ◽  
Constrained Optimization Problem ◽  
Unseen Data ◽  
Model Size

Large-scale multi-label learning (LMLL) aims to annotate relevant labels from a large number of candidates for unseen data. Due to the high dimensionality in both feature and label spaces in LMLL, the storage overheads of LMLL models are often costly. This paper proposes a POP (joint label and feature Parameter OPtimization) method. It tries to filter out redundant model parameters to facilitate compact models. Our key insights are as follows. First, we investigate labels that have little impact on the commonly used LMLL performance metrics and only preserve a small number of dominant parameters for these labels. Second, for the remaining influential labels, we reduce spurious feature parameters that have little contribution to the generalization capability of models, and preserve parameters for only discriminative features. The overall problem is formulated as a constrained optimization problem pursuing minimal model size. In order to solve the resultant difficult optimization, we show that a relaxation of the optimization can be efficiently solved using binary search and greedy strategies. Experiments verify that the proposed method clearly reduces the model size compared to state-of-the-art LMLL approaches, in addition, achieves highly competitive performance.

scholarly journals Beyond equi-joins

2021 ◽  
Vol 14 (11) ◽  
pp. 2599-2612
Author(s):  
Nikolaos Tziavelis ◽  
Wolfgang Gatterbauer ◽  
Mirek Riedewald
Keyword(s):  
Experimental Study ◽  
State Of The Art ◽  
Database Systems ◽  
Ranking Function ◽  
Space Complexity ◽  
Time And Space ◽  
Running Time ◽  
Join Queries ◽  
Time And Space Complexity ◽  
Memory Efficient

We study theta-joins in general and join predicates with conjunctions and disjunctions of inequalities in particular, focusing on ranked enumeration where the answers are returned incrementally in an order dictated by a given ranking function. Our approach achieves strong time and space complexity properties: with n denoting the number of tuples in the database, we guarantee for acyclic full join queries with inequality conditions that for every value of k , the k top-ranked answers are returned in O ( n polylog n + k log k ) time. This is within a polylogarithmic factor of O ( n + k log k ), i.e., the best known complexity for equi-joins, and even of O ( n + k ), i.e., the time it takes to look at the input and return k answers in any order. Our guarantees extend to join queries with selections and many types of projections (namely those called "free-connex" queries and those that use bag semantics). Remarkably, they hold even when the number of join results is n ℓ for a join of ℓ relations. The key ingredient is a novel O ( n polylog n )-size factorized representation of the query output , which is constructed on-the-fly for a given query and database. In addition to providing the first nontrivial theoretical guarantees beyond equi-joins, we show in an experimental study that our ranked-enumeration approach is also memory-efficient and fast in practice, beating the running time of state-of-the-art database systems by orders of magnitude.

scholarly journals Improved N-Best Extraction with an Evaluation on Language Data

2021 ◽  
pp. 1-35
Author(s):  
Johanna Björklund ◽  
Frank Drewes ◽  
Anna Jonsson
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Search Space ◽  
Data Sets ◽  
Weighted Tree ◽  
Original Algorithm ◽  
Software Toolkit ◽  
Minimal Weight ◽  
Language Data ◽  
Memory Efficient

Abstract We show that a previously proposed algorithm for the N-best trees problem can be made more efficient by changing how it arranges and explores the search space. Given an integer N and a weighted tree automaton (wta) M over the tropical semiring, the algorithm computes N trees of minimal weight with respect to M. Compared to the original algorithm, the modifications increase the laziness of the evaluation strategy, which makes the new algorithm asymptotically more efficient than its predecessor. The algorithm is implemented in the software Betty, and compared to the state-of-the-art algorithm for extracting the N best runs, implemented in the software toolkit Tiburon. The data sets used in the experiments are wtas resulting from real-world natural language processing tasks, as well as artificially created wtas with varying degrees of nondeterminism. We find that Betty outperforms Tiburon on all tested data sets with respect to running time, while Tiburon seems to be the more memory-efficient choice.

scholarly journals FruitVegCNN: Power- and Memory-Efficient Classification of Fruits & Vegetables Using CNN in Mobile MPSoC

2020 ◽  
Author(s):  
Somdip Dey ◽  
Suman Saha ◽  
Amit Singh ◽  
Klaus D. Mcdonald-Maier
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Power Efficiency ◽  
Fruits And Vegetables ◽  
State Of The Art ◽  
Agricultural Industry ◽  
Power Efficient ◽  
System On A Chip ◽  
Memory Efficient

<div><div><div><p>Fruit and vegetable classification using Convolutional Neural Networks (CNNs) has become a popular application in the agricultural industry, however, to the best of our knowledge no previously recorded study has designed and evaluated such an application on a mobile platform. In this paper, we propose a power-efficient CNN model, FruitVegCNN, to perform classification of fruits and vegetables in a mobile multi-processor system-on-a-chip (MPSoC). We also evaluated the efficacy of FruitVegCNN compared to popular state-of-the-art CNN models in real mobile plat- forms (Huawei P20 Lite and Samsung Galaxy Note 9) and experimental results show the efficacy and power efficiency of our proposed CNN architecture.</p></div></div></div>

TripRec

2015 ◽  
Vol 11 (1) ◽  
pp. 45-65 ◽  
Author(s):  
Heli Sun ◽  
Jianbin Huang ◽  
Xinwei She ◽  
Zhou Yang ◽  
Jiao Liu ◽  
...  
Keyword(s):  
Real World ◽  
Efficient Algorithm ◽  
State Of The Art ◽  
Synthetic Data ◽  
Time Constraints ◽  
Data Sets ◽  
Generation Process ◽  
Time Requirement ◽  
Trip Planning ◽  
Frequent Item Sets

The problem of trip planning with time constraints aims to find the optimal routes satisfying the maximum time requirement and possessing the highest attraction score. In this paper, a more efficient algorithm TripRec is proposed to solve this problem. Based on the principle of the Aprior algorithm for mining frequent item sets, our method constructs candidate attraction sets containing k attractions by using the join rule on valid sets consisting of k-1 attractions. After all the valid routes from the valid k-1 attraction sets have been obtained, all of the candidate routes for the candidate k-sets can be acquired through a route extension approach. This method exhibits manifest improvement of the efficiency in the valid routes generation process. Then, by determining whether there exists at least one valid route, the paper prunes some candidate attraction sets to gain all the valid sets. The process will continue until no more valid attraction sets can be obtained. In addition, several optimization strategies are employed to greatly enhance the performance of the algorithm. Experimental results on both real-world and synthetic data sets show that our algorithm has the better pruning rate and efficiency compared with the state-of-the-art method.

Novel Memory Efficient Hardware Architecture for 5/3 Lifting-Based 2D Inverse DWT

2019 ◽  
Vol 28 (07) ◽  
pp. 1950118 ◽  
Author(s):  
Goran Savić ◽  
Vladimir Rajović
Keyword(s):  
State Of The Art ◽  
Computing Time ◽  
Hardware Architecture ◽  
Discrete Wavelet ◽  
Jpeg 2000 ◽  
One Dimensional ◽  
Reversible Transformation ◽  
Memory Size ◽  
Multiple Levels ◽  
Memory Efficient

This paper presents a novel memory efficient hardware architecture for 5/3 lifting-based two-dimensional (2D) inverse discrete wavelet transform (IDWT). The proposed architecture processes multiple levels of composition simultaneously using only one one-dimensional (1D) 5/3 lifting-based inverse vertical filter and only one 1D 5/3 lifting-based inverse horizontal filter. In case of [Formula: see text] levels of composition for [Formula: see text] image, the proposed 5/3 2D IDWT architecture requires the total memory of size less than [Formula: see text], which is lower memory size than memory size required in any other previously published architecture. In terms of total number of adders, total number of multipliers (shifters), total computing time and output latency, presented solution is comparable with other state-of-the-art solutions. Proposed hardware architecture is suitable for implementation in JPEG 2000 decoder, since default inverse filter for reversible transformation in JPEG 2000 standard is 5/3 IDWT filter.

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