Tabu Search: A Meta Heuristic for Netlist Partitioning

The main goal of the paper is to explore the effectiveness of a new method called Tabu Search [1] on partitioning and compare it with two techniques widely used in CAD tools for circuit partitioning i.e., Sanchis Interchange method and Simulated Annealing, in terms of the running time and quality of solution. The proposed method integrates the well known iterative multi-way interchange method with Tabu Search and leads to a very powerful network partitioning heuristic. It is characterized by an ability to escape local optima which usually cause simple descent algorithms to terminate by using a short term memory of recent solutions. Moreover, Tabu Search permits backtracking to previous solutions, which explore different directions and generates better partitions.The quality of the test results on MCNC benchmark circuits are very promising in most cases. Tabu Search yields netlist partitions that contain 20%–67% fewer cut nets and are generated 2/3 to (1/2) times faster than the best netlist partitions obtained by using an interchange method. Comparable partitions to those obtained by Simulated Annealing are obtained 5 to 20 times faster.

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De-noising of transient electromagnetic data based on the long short-term memory-autoencoder

Geophysical Journal International ◽

10.1093/gji/ggaa424 ◽

2020 ◽

Vol 224 (1) ◽

pp. 669-681

Author(s):

Sihong Wu ◽

Qinghua Huang ◽

Li Zhao

Keyword(s):

Field Data ◽

Short Term Memory ◽

Noise Removal ◽

Data Sets ◽

Late Time ◽

Short Term ◽

Term Memory ◽

Transient Electromagnetic ◽

Long Short Term Memory

SUMMARY Late-time transient electromagnetic (TEM) data contain deep subsurface information and are important for resolving deeper electrical structures. However, due to their relatively small signal amplitudes, TEM responses later in time are often dominated by ambient noises. Therefore, noise removal is critical to the application of TEM data in imaging electrical structures at depth. De-noising techniques for TEM data have been developed rapidly in recent years. Although strong efforts have been made to improving the quality of the TEM responses, it is still a challenge to effectively extract the signals due to unpredictable and irregular noises. In this study, we develop a new type of neural network architecture by combining the long short-term memory (LSTM) network with the autoencoder structure to suppress noise in TEM signals. The resulting LSTM-autoencoders yield excellent performance on synthetic data sets including horizontal components of the electric field and vertical component of the magnetic field generated by different sources such as dipole, loop and grounded line sources. The relative errors between the de-noised data sets and the corresponding noise-free transients are below 1% for most of the sampling points. Notable improvement in the resistivity structure inversion result is achieved using the TEM data de-noised by the LSTM-autoencoder in comparison with several widely-used neural networks, especially for later-arriving signals that are important for constraining deeper structures. We demonstrate the effectiveness and general applicability of the LSTM-autoencoder by de-noising experiments using synthetic 1-D and 3-D TEM signals as well as field data sets. The field data from a fixed loop survey using multiple receivers are greatly improved after de-noising by the LSTM-autoencoder, resulting in more consistent inversion models with significantly increased exploration depth. The LSTM-autoencoder is capable of enhancing the quality of the TEM signals at later times, which enables us to better resolve deeper electrical structures.

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Nature rejuvenation: Long-term (1989–2016) vs short-term memory approach based appraisal of water quality of the upper part of Ganga River, India

Environmental Technology & Innovation ◽

10.1016/j.eti.2020.101164 ◽

2020 ◽

Vol 20 ◽

pp. 101164 ◽

Cited By ~ 1

Author(s):

Amit Kumar ◽

Saurabh Mishra ◽

A.K. Taxak ◽

Rajiv Pandey ◽

Zhi-Guo Yu

Keyword(s):

Water Quality ◽

Short Term Memory ◽

Ganga River ◽

Short Term ◽

Term Memory

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Association of Quality of Sleep and Decreased Short Term Memory Function in Medical Students of Pelita Harapan University

Medicinus ◽

10.19166/med.v7i7.2760 ◽

2020 ◽

Vol 7 (7) ◽

pp. 216

Author(s):

Stevanie Budianto ◽

Yusak M.T Siahaan

Keyword(s):

Sleep Quality ◽

Short Term Memory ◽

Memory Function ◽

Poor Sleep ◽

Quality Of Sleep ◽

Poor Sleep Quality ◽

Short Term ◽

Chi Square ◽

Term Memory

Background: Memory is a place where information is stored from the learning process or experience. There are several types of memory , one of them is short term memory. Declining sleep quality is directly proportional to the decrease in short-term memory. Poor sleep quality is often associated with medical student due to exams or vast amount of tasks. Therefore, researcher wants to see whether there is significant correlation between sleep quality and short-term memory function in students.Aim: To assess the association of the quality of sleep towards short term memory function of medical student of Pelita Harapan University.Methods: This study was conducted with a cross-sectional method, with taking sample using the method of a simple random sample. A total of 90 respondents at University of Pelita Harapan were taken. Data collected sorted out according to the inclusion and exclusion criteria. Quality of sleep assessed with PSQI questionnaire while short-term memory assessed by Digit span backward test. Results processed with SPSS version 24 and tested with Chi Square.Results: Data analyzed by Chi square test showed there are 33 students (58.9%) have poor sleep quality and short term memory function. There are also significant association between the quality of sleep and short term memory function (p value = 0.026)Conclusion: There is significant association between the quality of sleep and short term memory function of medical students of Pelita Harapan University.

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Implementation of Long-Short Term Memory Neural Network (LSTM) for Predicting The Water Quality Parameters in Sungai Selangor

Journal of Computing Research and Innovation ◽

10.24191/jcrinn.v6i4.243 ◽

2021 ◽

Vol 6 (4) ◽

pp. 40-49

Author(s):

Nur Natasya Mohd Anuar ◽

Nur Fatihah Fauzi ◽

Huda Zuhrah Ab Halim ◽

Nur Izzati Khairudin ◽

Nurizatul Syarfinas Ahmad Bakhtiar ◽

...

Keyword(s):

Water Quality ◽

Short Term Memory ◽

Oxygen Demand ◽

Quality Parameters ◽

Water Quality Parameters ◽

Short Term ◽

Term Memory ◽

Quality Of Water ◽

Long Short Term Memory

Predictions of future events must be factored into decision-making. Predictions of water quality are critical to assist authorities in making operational, management, and strategic decisions to keep the quality of water supply monitored under specific criteria. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, the purpose of this paper is to develop and train a Long-Short Term Memory (LSTM) Neural Network to predict water quality parameters in the Selangor River. The primary goal of this study is to predict five (5) water quality parameters in the Selangor River, namely Biochemical Oxygen Demand (BOD), Ammonia Nitrogen (NH3-N), Chemical Oxygen Demand (COD), pH, and Dissolved Oxygen (DO), using secondary data from different monitoring stations along the river basin. The accuracy of this method was then measured using RMSE as the forecast measure. The results show that by using the Power of Hydrogen (pH), the dataset yielded the lowest RMSE value, with a minimum of 0.2106 at station 004 and a maximum of 1.2587 at station 001. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and revealed the future developing trend of water quality parameters, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of water parameters.

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The developmental trajectory of nonword repetition

Applied Psycholinguistics ◽

10.1017/s014271640623039x ◽

2006 ◽

Vol 27 (4) ◽

pp. 552-556 ◽

Cited By ~ 14

Author(s):

Shula Chiat

Keyword(s):

Short Term Memory ◽

Developmental Trajectory ◽

Nonword Repetition ◽

Limited Capacity ◽

Short Term ◽

Phonological Representations ◽

Term Memory ◽

Temporary Storage ◽

Original Presentation

In line with the original presentation of nonword repetition as a measure of phonological short-term memory (Gathercole & Baddeley, 1989), the theoretical account Gathercole (2006) puts forward in her Keynote Article focuses on phonological storage as the key capacity common to nonword repetition and vocabulary acquisition. However, evidence that nonword repetition is influenced by a variety of factors other than item length has led Gathercole to qualify this account. In line with arguments put forward by Snowling, Chiat, and Hulme (1991), one of Gathercole's current claims is that nonword repetition and word learning are constrained by “the quality of temporary storage of phonological representations, and this quality is multiply determined.” Phonological storage is not just a quantity-limited capacity.

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A New Scatter Search Design for Multiobjective Combinatorial Optimization with an Application to Facility Location

INFORMS Journal on Computing ◽

10.1287/ijoc.2020.0966 ◽

2020 ◽

Author(s):

A. D. López-Sánchez ◽

J. Sánchez-Oro ◽

M. Laguna

Keyword(s):

Tabu Search ◽

Multiobjective Optimization ◽

Operations Research ◽

Short Term Memory ◽

Scatter Search ◽

Combination Method ◽

Heuristic Optimization ◽

Short Term ◽

Term Memory ◽

Search Design

Metaheuristic optimization is at the heart of the intersection between computer science and operations research. The INFORMS Journal of Computing has been fundamental in advancing the ideas behind metaheuristic methodologies. Fred Glover’s “Tabu Search—Part I” was published more than 30 years ago in the first volume of the then ORSA Journal on Computing. This article, one of the most cited in the area of heuristic optimization, paved the way for many contributions to the methodology and practice of operations research. As a continuation of this stream of research, we describe a new scatter search design for multiobjective optimization. The design includes a short-term memory tabu search and a path relinking combination method. We show how the strategies and mechanisms within scatter search and tabu search can be combined to produce a highly effective approach to multiobjective optimization.

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Algoritma LSTM-CNN untuk Binary Klasifikasi dengan Word2vec pada Media Online

Creative Information Technology Journal ◽

10.24076/citec.2021v8i1.264 ◽

2021 ◽

Vol 8 (1) ◽

pp. 64

Author(s):

Dedi Tri Hermanto ◽

Arief Setyanto ◽

Emha Taufiq Luthfi

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Direct Impact ◽

Test Results ◽

Short Term ◽

Term Memory ◽

Traditional Markets ◽

Interesting Topic ◽

Long Short Term Memory

Media online banyak menghasilkan berbagai macam berita, baik ekonomi, politik, kesehatan, olahraga atau ilmu pengetahuan. Di antara itu semua, ekonomi adalah salah satu topik menarik untuk dibahas. Ekonomi memiliki dampak langsung kepada warga negara, perusahaan, bahkan pasar tradisional tergantung pada kondisi ekonomi di suatu negara. Sentimen yang terkandung dalam berita dapat mempengaruhi pandangan masyarakat terhadap suatu hal atau kebijakan pemerintah. Topik ekonomi adalah bahasan yang menarik untuk dilakukan penelitian karena memiliki dampak langsung kepada masyarakat Indonesia. Namun, masih sedikit penelitian yang menerapkan metode deep learning yaitu Long Short-Term Memory dan CNN untuk analisis sentimen pada artikel finance di Indonesia. Penelitian ini bertujuan untuk melakukan pengklasifikasian judul berita berbahasa Indonesia berdasarkan sentimen positif, negatif dengan menggunakan metode LSTM, LSTM-CNN, CNN-LSTM. Dataset yang digunakan adalah data judul artikel berbahasa Indonesia yang diambil dari situs Detik Finance. Berdasarkan hasil pengujian memperlihatkan bahwa metode LSTM, LSTM-CNN, CNN-LSTM memiliki hasil akurasi sebesar, 62%, 65% dan 74%.Kata Kunci — LSTM, sentiment analysis, CNNOnline media produce a lot of various kinds of news, be it economics, politics, health, sports or science. Among them, economics is one interesting topic to discuss. The economy has a direct impact on citizens, companies, and even traditional markets depending on the economic conditions in a country. The sentiment contained in the news can influence people's views on a matter or government policy. The topic of economics is an interesting topic for research because it has a direct impact on Indonesian society. However, there are still few studies that apply deep learning methods, namely Long Short-Term Memory and CNN for sentiment analysis on finance articles in Indonesia. This study aims to classify Indonesian news headlines based on positive and negative sentiments using the LSTM, LSTM-CNN, CNN-LSTM methods. The dataset used is data on Indonesian language article titles taken from the Detik Finance website. Based on the test results, it shows that the LSTM, LSTM-CNN, CNN-LSTM methods have an accuracy of, 62%, 65% and 74%.Keywords — LSTM, sentiment analysis, CNN

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An Improved Ant Colony Optimized Tabu Search Algorithm for Makespan Improvement in Job Shop

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b6695.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 670-674

Keyword(s):

Tabu Search ◽

Completion Time ◽

Job Shop ◽

Short Term Memory ◽

Search Algorithm ◽

Ant Colony ◽

Rollback Recovery ◽

Short Term ◽

Term Memory ◽

Recovery Technique

In industries, the completion time of job problems is increased drastically in the production unit. In many existing kinds of research, the completion time i.e. makespan of the job is minimized using straight paths which is time-consuming. In this paper, we addressed this problem using an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm by identifying the fault occurrence position exactly to rollback. Also, we used a short term memory-based rollback recovery technique to roll back to its own short term memory to reduce the completion time of the job. Short term memory is used to visit the recent movements in Tabu search. Our proposed ACOTS-Cmax approach is efficient and consumed less completion time compared to the ACO algorithm

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