834 research outputs found
Generalized Gumbel-Softmax Gradient Estimator for Various Discrete Random Variables
Estimating the gradients of stochastic nodes is one of the crucial research
questions in the deep generative modeling community, which enables the gradient
descent optimization on neural network parameters. This estimation problem
becomes further complex when we regard the stochastic nodes to be discrete
because pathwise derivative techniques cannot be applied. Hence, the stochastic
gradient estimation of discrete distributions requires either a score function
method or continuous relaxation of the discrete random variables. This paper
proposes a general version of the Gumbel-Softmax estimator with continuous
relaxation, and this estimator is able to relax the discreteness of probability
distributions including more diverse types, other than categorical and
Bernoulli. In detail, we utilize the truncation of discrete random variables
and the Gumbel-Softmax trick with a linear transformation for the relaxed
reparameterization. The proposed approach enables the relaxed discrete random
variable to be reparameterized and to backpropagated through a large scale
stochastic computational graph. Our experiments consist of (1) synthetic data
analyses, which show the efficacy of our methods; and (2) applications on VAE
and topic model, which demonstrate the value of the proposed estimation in
practices
Improvisation of classification performance based on feature optimization for differentiation of Parkinson’s disease from other neurological diseases using gait characteristics
Most neurological disorders that include Parkinson’s disease (PD) as well as other neurological diseases such as Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD) have some common abnormalities regarding the movement, vocal, and cognitive behaviors of sufferers. Variations in the manifestation of these types of abnormality help distinguish one disorder from another. In this study, differentiation was performed based on the gait characteristics of patients afflicted by different neurological disorders. In the recent past, many researchers have applied different machine learning and feature selection techniques to the classification of different groups of patients based on common abnormalities. However, in an era of modernization where the focus is on timely low-cost automatization and pattern recognition, such techniques require improvisation to provide high performance. We attempted to improve the performance of such techniques using different feature optimization methods, such as a genetic algorithm (GA) and principal component analysis (PCA), and applying different classification approaches, i.e., linear, nonlinear, and probabilistic classifiers. In this study, gait dynamics data of patients suffering with PD, ALS, and HD were collated from a public database, and a binary classification approach was used by taking PD as one group and adopting ALS+HD as another group. Performance comparison was achieved using different classification techniques that incorporated optimized feature sets obtained from GA and PCA. In comparison with other classifiers using different feature sets, the highest accuracy (97.87%) was obtained using random forest combined with GA-based feature sets. The results provide evidence that could assist medical practitioners in differentiating PD from other neurological diseases using gait characteristics
Sequential Recommendation with Relation-Aware Kernelized Self-Attention
Recent studies identified that sequential Recommendation is improved by the
attention mechanism. By following this development, we propose Relation-Aware
Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the
Transformer with augmentation of a probabilistic model. The original
self-attention of Transformer is a deterministic measure without
relation-awareness. Therefore, we introduce a latent space to the
self-attention, and the latent space models the recommendation context from
relation as a multivariate skew-normal distribution with a kernelized
covariance matrix from co-occurrences, item characteristics, and user
information. This work merges the self-attention of the Transformer and the
sequential recommendation by adding a probabilistic model of the recommendation
task specifics. We experimented RKSA over the benchmark datasets, and RKSA
shows significant improvements compared to the recent baseline models. Also,
RKSA were able to produce a latent space model that answers the reasons for
recommendation.Comment: 8 pages, 5 figures, AAA
Utilizing ECG Waveform Features as New Biometric Authentication Method
In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%
Transcanal Endoscopic Ear Surgery for Congenital Cholesteatoma
Objectives As endoscopic instrumentation, techniques and knowledges have significantly improved recently, endoscopic ear surgery has become increasingly popular. Transcanal endoscopic ear surgery (TEES) can provide better visualization of hidden areas in the middle ear cavity during congenital cholesteatoma removal. We aimed to describe outcomes for TEES for congenital cholesteatoma in a pediatric population. Methods Twenty-five children (age, 17 months to 9 years) with congenital cholesteatoma confined to the middle ear underwent TEES by an experienced surgeon; 13 children had been classified as Potsic stage I, seven as stage II, and five as stage III. The mean follow-up period was 24 months. Recurrence of congenital cholesteatoma and surgical complication was observed. Results Congenital cholesteatoma can be removed successfully via transcanal endoscopic approach in all patients, and no surgical complications occurred; only one patient with a stage II cholesteatoma showed recurrence during the follow-up visit, and the patient underwent revision surgery. The other patients underwent one-stage operations and showed no cholesteatoma recurrence at their last visits. Two patients underwent second-stage ossicular reconstruction. Conclusion Although the follow-up period and number of patients were limited, pediatric congenital cholesteatoma limited to the middle ear cavity could be safely and effectively removed using TEES
Bidirectional two colored light emission from stress-activated ZnS-microparticles-embedded polydimethylsiloxane elastomer films
Bidirectional two-colored mechanoluminescent light emission has been demonstrated by unifying two polydimethylsiloxane elastomer layers functionalized with zinc sulfide doped with Cu (ZnS:Cu) or Cu and Mn (ZnS:Cu,Mn). The bilayered composite films are simply fabricated by dispensing uncured ZnS:Cu,Mn + PDMS onto previously spin-coated and ardened ZnS:Cu + PDMS film. The robust PDMS-PDMS bonding yields a ilm which can simultaneously emit light with color coordinates of (0.25, 0.56) and (0.50, 0.48), similar to the intrinsic colors of ZnS:Cu and ZnS:Cu,Mn, respectively. Composite films can emit light in upper and lower directions without fracture when it is stretched. © 2013 Optical Society of America.1
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder
The problem of fair classification can be mollified if we develop a method to
remove the embedded sensitive information from the classification features.
This line of separating the sensitive information is developed through the
causal inference, and the causal inference enables the counterfactual
generations to contrast the what-if case of the opposite sensitive attribute.
Along with this separation with the causality, a frequent assumption in the
deep latent causal model defines a single latent variable to absorb the entire
exogenous uncertainty of the causal graph. However, we claim that such
structure cannot distinguish the 1) information caused by the intervention
(i.e., sensitive variable) and 2) information correlated with the intervention
from the data. Therefore, this paper proposes Disentangled Causal Effect
Variational Autoencoder (DCEVAE) to resolve this limitation by disentangling
the exogenous uncertainty into two latent variables: either 1) independent to
interventions or 2) correlated to interventions without causality.
Particularly, our disentangling approach preserves the latent variable
correlated to interventions in generating counterfactual examples. We show that
our method estimates the total effect and the counterfactual effect without a
complete causal graph. By adding a fairness regularization, DCEVAE generates a
counterfactual fair dataset while losing less original information. Also,
DCEVAE generates natural counterfactual images by only flipping sensitive
information. Additionally, we theoretically show the differences in the
covariance structures of DCEVAE and prior works from the perspective of the
latent disentanglement
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