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A Finite State Model for Time Travel [article]

Hwee Kuan Lee
2011 arXiv   pre-print
A time machine that sends information back to the past may, in principle, be built using closed time-like curves. However, the realization of a time machine must be congruent with apparent paradoxes that arise from traveling back in time. Using a simple model to analyze the consequences of time travel, we show that several paradoxes, including the grandfather paradox and Deutsch's unproven theorem paradox, are precluded by basic axioms of probability. However, our model does not prohibit
more » ... ng back in time to affect past events in a self-consistent manner.
arXiv:1101.1927v1 fatcat:3uuuz36qbbefzahbqyloxf6q44

Transition Matrices and Time Travel

Hwee Kuan Lee
2011 Physics Procedia  
It has been proven by Lee [1] that the grandfather paradox and Deutsch's unproven paradox are precluded for twoand three-state graphical models.  ...  Following the formulation of Lee [1] , suppose a signal is send back at time t = k to the past at time t = i.  ...  Recently, Lee [1] has shown for the first time that the grandfather paradox and Deutsch's unproven theorem paradox are related to the basic axioms of probabilities.  ... 
doi:10.1016/j.phpro.2011.05.060 fatcat:ittjk4wlqndq7d2djyvtyomqme

Weakly-supervised learning on Schrodinger equation [article]

Kenta Shiina, Hwee Kuan Lee, Yutaka Okabe, Hiroyuki Mori
2021 arXiv   pre-print
We propose a machine learning method to solve Schrodinger equations for a Hamiltonian that consists of an unperturbed Hamiltonian and a perturbation. We focus on the cases where the unperturbed Hamiltonian can be solved analytically or solved numerically with some fast way. Given a potential function as input, our deep learning model predicts wave functions and energies using a weakly-supervised method. Information of first-order perturbation calculation for randomly chosen perturbations is
more » ... to train the model. In other words, no label (or exact solution) is necessary for the training, which is why the method is called weakly-supervised, not supervised. The trained model can be applied to calculation of wave functions and energies of Hamiltonian containing arbitrary perturbation. As an example, we calculated wave functions and energies of a harmonic oscillator with a perturbation and results were in good agreement with those obtained from exact diagonalization.
arXiv:2106.12094v1 fatcat:wn3kde7ve5aehex6kkrnnyoje4

Fence GAN: Towards Better Anomaly Detection [article]

Cuong Phuc Ngo, Amadeus Aristo Winarto, Connie Kou Khor Li, Sojeong Park, Farhan Akram, Hwee Kuan Lee
2019 arXiv   pre-print
Anomaly detection is a classical problem where the aim is to detect anomalous data that do not belong to the normal data distribution. Current state-of-the-art methods for anomaly detection on complex high-dimensional data are based on the generative adversarial network (GAN). However, the traditional GAN loss is not directly aligned with the anomaly detection objective: it encourages the distribution of the generated samples to overlap with the real data and so the resulting discriminator has
more » ... een found to be ineffective as an anomaly detector. In this paper, we propose simple modifications to the GAN loss such that the generated samples lie at the boundary of the real data distribution. With our modified GAN loss, our anomaly detection method, called Fence GAN (FGAN), directly uses the discriminator score as an anomaly threshold. Our experimental results using the MNIST, CIFAR10 and KDD99 datasets show that Fence GAN yields the best anomaly classification accuracy compared to state-of-the-art methods.
arXiv:1904.01209v1 fatcat:d445h7p675bxdofxif24iu3mia

Accelerated spin dynamics using deep learning corrections

Sojeong Park, Wooseop Kwak, Hwee Kuan Lee
2020 Scientific Reports  
Theoretical models capture very precisely the behaviour of magnetic materials at the microscopic level. This makes computer simulations of magnetic materials, such as spin dynamics simulations, accurately mimic experimental results. New approaches to efficient spin dynamics simulations are limited by integration time step barrier to solving the equations-of-motions of many-body problems. Using a short time step leads to an accurate but inefficient simulation regime whereas using a large time
more » ... p leads to accumulation of numerical errors that render the whole simulation useless. In this paper, we use a Deep Learning method to compute the numerical errors of each large time step and use these computed errors to make corrections to achieve higher accuracy in our spin dynamics. We validate our method on the 3D Ferromagnetic Heisenberg cubic lattice over a range of temperatures. Here we show that the Deep Learning method can accelerate the simulation speed by 10 times while maintaining simulation accuracy and overcome the limitations of requiring small time steps in spin dynamic simulations.
doi:10.1038/s41598-020-70558-1 pmid:32792674 fatcat:4cm4vlrlcjdgjdn26v5v5khb3y

Semi-automated quantitative Drosophila wings measurements

Sheng Yang Michael Loh, Yoshitaka Ogawa, Sara Kawana, Koichiro Tamura, Hwee Kuan Lee
2017 BMC Bioinformatics  
Drosophila melanogaster is an important organism used in many fields of biological research such as genetics and developmental biology. Drosophila wings have been widely used to study the genetics of development, morphometrics and evolution. Therefore there is much interest in quantifying wing structures of Drosophila. Advancement in technology has increased the ease in which images of Drosophila can be acquired. However such studies have been limited by the slow and tedious process of
more » ... phenotypic data. Results: We have developed a system that automatically detects and measures key points and vein segments on a Drosophila wing. Key points are detected by performing image transformations and template matching on Drosophila wing images while vein segments are detected using an Active Contour algorithm. The accuracy of our key point detection was compared against key point annotations of users. We also performed key point detection using different training data sets of Drosophila wing images. We compared our software with an existing automated image analysis system for Drosophila wings and showed that our system performs better than the state of the art. Vein segments were manually measured and compared against the measurements obtained from our system. Conclusion: Our system was able to detect specific key points and vein segments from Drosophila wing images with high accuracy.
doi:10.1186/s12859-017-1720-y pmid:28659123 pmcid:PMC5490177 fatcat:mghfvrqwafdqzcq62njn2l42ca

Super-resolution of spin configurations based on flow-based generative models [article]

Kenta Shiina, Lee Hwee Kuan, Hiroyuki Mori, Yutaka Okabe, Yusuke Tomita
2021 arXiv   pre-print
We present a super-resolution method for spin systems using a flow-based generative model that is a deep generative model with reversible neural network architecture. Starting from spin configurations on a two-dimensional square lattice, our model generates spin configurations of a larger lattice. As a flow-based generative model precisely estimates the distribution of the generated configurations, it can be combined with Monte Carlo simulation to generate large lattice configurations according
more » ... to the Boltzmann distribution. Hence, the long-range correlation on a large configuration is reduced into the shorter one through the flow-based generative model. This alleviates the critical slowing down near the critical temperature. We demonstrated 8 times increased lattice size in the linear dimensions using our super-resolution scheme repeatedly. We numerically show that by performing simulations for 16× 16 configurations, our model can sample lattice configurations at 128× 128 on which the thermal average of physical quantities has good agreement with the one evaluated by the traditional Metropolis-Hasting Monte Carlo simulation.
arXiv:2108.11494v1 fatcat:u5q6g4cylrfbxbyoj57q6obzki

Weakly Supervised Clustering by Exploiting Unique Class Count [article]

Mustafa Umit Oner, Hwee Kuan Lee, Wing-Kin Sung
2020 arXiv   pre-print
A weakly supervised learning based clustering framework is proposed in this paper. As the core of this framework, we introduce a novel multiple instance learning task based on a bag level label called unique class count (ucc), which is the number of unique classes among all instances inside the bag. In this task, no annotations on individual instances inside the bag are needed during training of the models. We mathematically prove that with a perfect ucc classifier, perfect clustering of
more » ... ual instances inside the bags is possible even when no annotations on individual instances are given during training. We have constructed a neural network based ucc classifier and experimentally shown that the clustering performance of our framework with our weakly supervised ucc classifier is comparable to that of fully supervised learning models where labels for all instances are known. Furthermore, we have tested the applicability of our framework to a real world task of semantic segmentation of breast cancer metastases in histological lymph node sections and shown that the performance of our weakly supervised framework is comparable to the performance of a fully supervised Unet model.
arXiv:1906.07647v2 fatcat:5shpivry7vdj5ituiq6y74fx6m

Studying The Effect of MIL Pooling Filters on MIL Tasks [article]

Mustafa Umit Oner, Jared Marc Song Kye-Jet, Hwee Kuan Lee, Wing-Kin Sung
2020 arXiv   pre-print
There are different multiple instance learning (MIL) pooling filters used in MIL models. In this paper, we study the effect of different MIL pooling filters on the performance of MIL models in real world MIL tasks. We designed a neural network based MIL framework with 5 different MIL pooling filters: 'max', 'mean', 'attention', 'distribution' and 'distribution with attention'. We also formulated 5 different MIL tasks on a real world lymph node metastases dataset. We found that the performance
more » ... our framework in a task is different for different filters. We also observed that the performances of the five pooling filters are also different from task to task. Hence, the selection of a correct MIL pooling filter for each MIL task is crucial for better performance. Furthermore, we noticed that models with 'distribution' and 'distribution with attention' pooling filters consistently perform well in almost all of the tasks. We attribute this phenomena to the amount of information captured by 'distribution' based pooling filters. While point estimate based pooling filters, like 'max' and 'mean', produce point estimates of distributions, 'distribution' based pooling filters capture the full information in distributions. Lastly, we compared the performance of our neural network model with 'distribution' pooling filter with the performance of the best MIL methods in the literature on classical MIL datasets and our model outperformed the others.
arXiv:2006.01561v1 fatcat:nie6htj6oncyxamfttms2q6p3q

Light sheet fluorescence microscopy (LSFM): past, present and future

John Lim, Hwee Kuan Lee, Weimiao Yu, Sohail Ahmed
2014 The Analyst  
Light sheet fluorescence microscopy (LSFM) has emerged as an important imaging modality to follow biology in live 3D samples over time with reduced phototoxicity and photobleaching.
doi:10.1039/c4an00624k pmid:25118817 fatcat:5wnsm4lfdrgxze3jvrlpqc5ohi

Enhancing Transformation-based Defenses using a Distribution Classifier [article]

Connie Kou, Hwee Kuan Lee, Ee-Chien Chang, Teck Khim Ng
2020 arXiv   pre-print
Adversarial attacks on convolutional neural networks (CNN) have gained significant attention and there have been active research efforts on defense mechanisms. Stochastic input transformation methods have been proposed, where the idea is to recover the image from adversarial attack by random transformation, and to take the majority vote as consensus among the random samples. However, the transformation improves the accuracy on adversarial images at the expense of the accuracy on clean images.
more » ... ile it is intuitive that the accuracy on clean images would deteriorate, the exact mechanism in which how this occurs is unclear. In this paper, we study the distribution of softmax induced by stochastic transformations. We observe that with random transformations on the clean images, although the mass of the softmax distribution could shift to the wrong class, the resulting distribution of softmax could be used to correct the prediction. Furthermore, on the adversarial counterparts, with the image transformation, the resulting shapes of the distribution of softmax are similar to the distributions from the clean images. With these observations, we propose a method to improve existing transformation-based defenses. We train a separate lightweight distribution classifier to recognize distinct features in the distributions of softmax outputs of transformed images. Our empirical studies show that our distribution classifier, by training on distributions obtained from clean images only, outperforms majority voting for both clean and adversarial images. Our method is generic and can be integrated with existing transformation-based defenses.
arXiv:1906.00258v2 fatcat:cbn3qnrdungodl74tvedgtloz4

Machine-Learning Studies on Spin Models

Kenta Shiina, Hiroyuki Mori, Yutaka Okabe, Hwee Kuan Lee
2020 Scientific Reports  
Kuan Lee model.  ...  to the A*STAR Research Attachment Programme (ARAP) of Singapore for financial support.Machine-Learning Studies on Spin Models: Supplementary informationKenta Shiina, Hiroyuki Mori, Yutaka Okabe, and Hwee  ... 
doi:10.1038/s41598-020-58263-5 pmid:32034178 pmcid:PMC7005704 fatcat:y3aumomo2ze4jahgh2wccnqnwi

Exchange bias with interacting random antiferromagnetic grains

Hwee Kuan Lee, Yutaka Okabe
2006 Physical Review B  
A model consisting of random interacting anti-ferromagnetic (AF) grains coupled to a ferromagnetic (FM) layer is developed to study the exchange bias phenomenon. This simple model is able to describe several exchange bias behavior observed in real materials. Shifts in hysteresis loops are observed as a function of cooling field and average grain size. We establish a direct relationship between cooling field dependence of exchange bias, coercivity and magnetization state on the AF-FM interface.
more » ... e also verify that the exchange bias field is inversely proportional to the grain size, and this behavior is independent of the inter-grain interactions, AF/FM coupling and cooling field.
doi:10.1103/physrevb.73.140403 fatcat:gm5iivmqb5agdf5xo5meajx66y

Machine-Learning Study using Improved Correlation Configuration and Application to Quantum Monte Carlo Simulation [article]

Yusuke Tomita, Kenta Shiina, Yutaka Okabe, Hwee Kuan Lee
2020 arXiv   pre-print
We use the Fortuin-Kasteleyn representation based improved estimator of the correlation configuration as an alternative to the ordinary correlation configuration in the machine-learning study of the phase classification of spin models. The phases of classical spin models are classified using the improved estimators, and the method is also applied to the quantum Monte Carlo simulation using the loop algorithm. We analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition of the spin 1/2
more » ... XY model on the square lattice. We classify the BKT phase and the paramagnetic phase of the quantum XY model using the machine-learning approach. We show that the classification of the quantum XY model can be performed by using the training data of the classical XY model.
arXiv:2007.15477v1 fatcat:uby5luherzbkxejtpfawzdspkm

Flipped-Adversarial AutoEncoders [article]

Jiyi Zhang and Hung Dang and Hwee Kuan Lee and Ee-Chien Chang
2018 arXiv   pre-print
We propose a flipped-Adversarial AutoEncoder (FAAE) that simultaneously trains a generative model G that maps an arbitrary latent code distribution to a data distribution and an encoder E that embodies an "inverse mapping" that encodes a data sample into a latent code vector. Unlike previous hybrid approaches that leverage adversarial training criterion in constructing autoencoders, FAAE minimizes re-encoding errors in the latent space and exploits adversarial criterion in the data space.
more » ... mental evaluations demonstrate that the proposed framework produces sharper reconstructed images while at the same time enabling inference that captures rich semantic representation of data.
arXiv:1802.04504v5 fatcat:vknrs727jrcpnhg5tqb336hzti
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