Phase Space Spin-Entropy.

Quantum physics is intrinsically probabilistic, where the Born rule yields the probabilities associated with a state that deterministically evolves. The entropy of a quantum state quantifies the amount of randomness (or information loss) of such a state. The degrees of freedom of a quantum state are position and spin.

Quantum Knowledge in Phase Space.

Quantum physics through the lens of Bayesian statistics considers probability to be a degree of belief and subjective. A Bayesian derivation of the probability density function in phase space is presented. Then, a Kullback-Liebler divergence in phase space is introduced to define interference and entanglement.

ABC-Norm Regularization for Fine-Grained and Long-Tailed Image Classification.

Image classification for real-world applications often involves complicated data distributions such as fine-grained and long-tailed. To address the two challenging issues simultaneously, we propose a new regularization technique that yields an adversarial loss to strengthen the model learning. Specifically, for each training batch, we construct an adaptive batch prediction (ABP) matrix and establish its corresponding adaptive batch confusion norm (ABC-Norm).

On Quantum Entropy.

Quantum physics, despite its intrinsically probabilistic nature, lacks a definition of entropy fully accounting for the randomness of a quantum state. For example, von Neumann entropy quantifies only the incomplete specification of a quantum state and does not quantify the probabilistic distribution of its observables; it trivially vanishes for pure quantum states. We propose a quantum entropy that quantifies the randomness of a pure quantum state via a conjugate pair of observables/operators forming the quantum phase space.

Spin Entropy.

Two types of randomness are associated with a mixed quantum state: the uncertainty in the probability coefficients of the constituent pure states and the uncertainty in the value of each observable captured by the Born's rule probabilities. Entropy is a quantification of randomness, and we propose a spin-entropy for the observables of spin pure states based on the phase space of a spin as described by the geometric quantization method, and we also expand it to mixed quantum states. This proposed entropy overcomes the limitations of previously-proposed entropies such as von Neumann entropy which only quantifies the randomness of specifying the quantum state.

Label free cell-tracking and division detection based on 2D time-lapse images for lineage analysis of early embryo development.

In this paper we report a database and a series of techniques related to the problem of tracking cells, and detecting their divisions, in time-lapse movies of mammalian embryos. Our contributions are (1) a method for counting embryos in a well, and cropping each individual embryo across frames, to create individual movies for cell tracking; (2) a semi-automated method for cell tracking that works up to the 8-cell stage, along with a software implementation available to the public (this software was used to build the reported database); (3) an algorithm for automatic tracking up to the 4-cell stage, based on histograms of mirror symmetry coefficients captured using wavelets; (4) a cell-tracking database containing 100 annotated examples of mammalian embryos up to the 8-cell stage; and (5) statistical analysis of various timing distributions obtained from those examples.

DevStaR: high-throughput quantification of C. elegans developmental stages.

We present DevStaR, an automated computer vision and machine learning system that provides rapid, accurate, and quantitative measurements of C. elegans embryonic viability in high-throughput (HTP) applications. A leading genetic model organism for the study of animal development and behavior, C.

Rapid and accurate developmental stage recognition of C. elegans from high-throughput image data.

We present a hierarchical principle for object recognition and its application to automatically classify developmental stages of C. elegans animals from a population of mixed stages. The object recognition machine consists of four hierarchical layers, each composed of units upon which evaluation functions output a label score, followed by a grouping mechanism that resolves ambiguities in the score by imposing local consistency constraints.

Illusory volumes in human stereo perception.

Any complete theory of human stereopsis must model not only how the correspondences between locations in the two views are determined and the depths are recovered from their disparity, but also how the ambiguity arising from such factors as noise, periodicity, and large regions of constant intensity are resolved and missing data are interpolated. In investigating this process of recovering surface structure from sparse disparity information, using stereo pairs with sparse identifiable features, we made an observation that contradicts all extant models. It suggests the inadequacy of retinotopic representation in modeling surface perception in this stage.

Illusory surface perception and visual organization.

Illusory contours occur in a wide variety of circumstances in nature. A striking man- made example is the Kanizsa triangle. A common factor in all such figures is the perception of a surface occluding part of a background, i.