Galaxy Spin Classification I: Z-wise vs S-wise Spirals With Chirality Equivariant Residual Network

The angular momentum of galaxies (galaxy spin) contains rich information about the initial condition of the Universe, yet it is challenging to efficiently measure the spin direction for the tremendous amount of galaxies that are being mapped by the ongoing and forthcoming cosmological surveys. We present a machine learning based classifier for the Z-wise vs S-wise spirals, which can help to break the degeneracy in the galaxy spin direction measurement. The proposed Chirality Equivariant Residual Network (CE-ResNet) is manifestly equivariant under a reflection of the input image, which guarantees that there is no inherent asymmetry between the Z-wise and S-wise probability estimators. We train the model with Sloan Digital Sky Survey (SDSS) images, with the training labels given by the Galaxy Zoo 1 (GZ1) project. A combination of data augmentation tricks are used during the training, making the model more robust to be applied to other surveys. We find a $\sim\!30\%$ increase of both types of spirals when Dark Energy Spectroscopic Instrument (DESI) images are used for classification, due to the better imaging quality of DESI. We verify that the $\sim\!7\sigma$ difference between the numbers of Z-wise and S-wise spirals is due to human bias, since the discrepancy drops to $<\!1.8\sigma$ with our CE-ResNet classification results. We discuss the potential systematics that are relevant to the future cosmological applications.Comment: 13+4 pages, 11 figures, 2 tables, to be submitted to Ap

Traditional Chinese medicine combination therapy for patients with steroid-dependent ulcerative colitis: study protocol for a randomized controlled trial

SPIRIT 2013 Checklist: recommended items to address in a clinical trial protocol and related documents. (DOC 133 kb

Image Deblurring According to Facially Recognized Locations Within the Image

This publication describes techniques for image deblurring according to a facially recognized locations within the image. An algorithm may use facial detection and recognition to selectively sharpen aspects of faces within an image and the surrounding area associated with the facial detection. In one or more aspects, the selectivity of sharpening improves the computational load and other aspects of image provision to improve overall computer function, power consumption, and user experience. Individual faces within the image may be cropped or thumbnailed, providing portions of the image that include the faces. Counterpart images associated with the individual faces may be found within a database having a repository of sharp features associated with the counterpart images. As such, the features may be integrated with the blurred faces of the original image to sharpen an image output

Improving Code Generation by Dynamic Temperature Sampling

Recently, Large Language Models (LLMs) have shown impressive results in code generation. However, existing decoding strategies are designed for Natural Language (NL) generation, overlooking the differences between NL and programming languages (PL). Due to this oversight, a better decoding strategy for code generation remains an open question. In this paper, we conduct the first systematic study to explore a decoding strategy specialized in code generation. With an analysis of loss distributions of code tokens, we find that code tokens can be divided into two categories: challenging tokens that are difficult to predict and confident tokens that can be easily inferred. Among them, the challenging tokens mainly appear at the beginning of a code block. Inspired by the above findings, we propose a simple yet effective method: Adaptive Temperature (AdapT) sampling, which dynamically adjusts the temperature coefficient when decoding different tokens. We apply a larger temperature when sampling for challenging tokens, allowing LLMs to explore diverse choices. We employ a smaller temperature for confident tokens avoiding the influence of tail randomness noises. We apply AdapT sampling to LLMs with different sizes and conduct evaluations on two popular datasets. Results show that AdapT sampling significantly outperforms state-of-the-art decoding strategy

Thermal management of the hotspots in 3-D integrated circuits

Vertical integration for microelectronics possesses significant challenges due to its fast dissipation of heat generated in multiple device planes. This paper focuses on thermal management of a 3-D integrated circuit, and micro-channel cooling is adopted to deal with the 3-D integrated circuitthermal problems. In addition, thermal through-silicon vias are also used to improve the capacity of heat trans-mission. It is found that combination of microchannel cooling and thermal through-silicon vias can remarkably alleviate the hotspots. The results presented in this paper are expected to aid in the development of thermal design guidelines for 3-D integrated circuits