One-class classification (OCC) deals with the classification problem in which the training data has data points belonging to target class only. In this paper, we present a one-class classification algorithm;

# Self-adaption grey DBSCAN clustering. (arXiv:1912.11477v1 [cs.LG])

Clustering analysis, a classical issue in data mining, is widely used in various research areas. This article aims at proposing a self-adaption grey DBSCAN clustering (SAG-DBSCAN) algorithm. First, the grey

# CProp: Adaptive Learning Rate Scaling from Past Gradient Conformity. (arXiv:1912.11493v1 [cs.LG])

Most optimizers including stochastic gradient descent (SGD) and its adaptive gradient derivatives face the same problem where an effective learning rate during the training is vastly different. A learning rate

# An Analysis of the Expressiveness of Deep Neural Network Architectures Based on Their Lipschitz Constants. (arXiv:1912.11511v1 [cs.LG])

Deep neural networks (DNNs) have emerged as a popular mathematical tool for function approximation due to their capability of modelling highly nonlinear functions. Their applications range from image classification and

# Spatio-Temporal Random Partition Models. (arXiv:1912.11542v1 [stat.ME])

The number of scientific fields that regularly collect data that are spatio-temporal continues to grow. An intuitive feature of this type of data is that measurements taken on experimental units

# Learning Transferable Features for Speech Emotion Recognition. (arXiv:1912.11547v1 [eess.AS])

Emotion recognition from speech is one of the key steps towards emotional intelligence in advanced human-machine interaction. Identifying emotions in human speech requires learning features that are robust and discriminative

# A Framework for Simulating Gauge Theories with Dipolar Spin Systems. (arXiv:1912.11488v1 [quant-ph])

Gauge theories appear broadly in physics, ranging from the Standard Model of particle physics to long-wavelength descriptions of topological systems in condensed matter. However, systems with sign problems are largely

# Efficient compression of quantum braided circuits. (arXiv:1912.11503v1 [quant-ph])

Mapping a quantum algorithm to any practical large-scale quantum computer will require a sequence of compilations and optimizations. At the level of fault-tolerant encoding, one likely requirement of this process

# Simultaneously determining the frequency sum and time difference of two photons through sum frequency generation. (arXiv:1912.11505v1 [quant-ph])

We propose, analyze and evaluate a technique for the joint measurement of time-frequency entanglement between two photons. In particular, we show that the frequency sum and time difference of two

# Evaluation of the spectrum of a quantum system using machine learning based on incomplete information about the wavefunctions. (arXiv:1912.11509v1 [physics.comp-ph])

We propose an effective approach to rapid estimation of the energy spectrum of quantum systems with the use of machine learning (ML) algorithm. In the ML approach (back propagation), the