Ground state degeneracy of the Ising cage-net model

The Ising cage-net model, first proposed in Phys. Rev. X 9, 021010 (2019), is a representative type I fracton model with nontrivial non-abelian features. In this paper, we calculate the ground state degeneracy of this model and find that, even though it follows a similar coupled layer structure as the X-cube model, the Ising cage-net model cannot be "foliated" in the same sense as X-cube as defined in Phys. Rev. X 8, 031051 (2018). A more generalized notion of "foliation'' is hence needed to understand the renormalization group transformation of the Ising cage-net model. The calculation is done using an operator algebra approach that we develop in this paper, and we demonstrate its validity through a series of examples

Thermally-actuated, phase change flow control for microfluidic systems

An easy to implement, thermally-actuated, noninvasive method for flow control in microfluidic devices is described. This technique takes advantage of the phase change of the working liquid itself—the freezing and melting of a portion of a liquid slug—to noninvasively close and open flow passages (referred to as a phase change valve). The valve was designed for use in a miniature diagnostic system for detecting pathogens in oral fluids at the point of care. The paper describes the modeling, construction, and characteristics of the valve. The experimental results favorably agree with theoretical predictions. In addition, the paper demonstrates the use of the phase change valves for flow control, sample metering and distribution into multiple analysis paths, sealing of a polymerase chain reaction (PCR) chamber, and sample introduction into and withdrawal from a closed loop. The phase change valve is electronically addressable, does not require any moving parts, introduces only minimal dead volume, is leakage and contamination free, and is biocompatible

Towards Open-Scenario Semi-supervised Medical Image Classification

Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution e.g., classes and domains. However, in practical scenarios, unlabeled data would be from unseen classes or unseen domains, and it is still challenging to exploit them by existing SSL methods. Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification. We first design a novel scoring mechanism, called dual-path outliers estimation, to identify samples from unseen classes. Meanwhile, to extract unseen-domain samples, we then apply an effective variational autoencoder (VAE) pre-training. After that, we conduct domain adaptation to fully exploit the value of the detected unseen-domain samples to boost semi-supervised training. We evaluated our proposed framework on dermatology and ophthalmology tasks. Extensive experiments demonstrate our model can achieve superior classification performance in various medical SSL scenarios

CSP and Takeout Genes Modulate the Switch between Attraction and Repulsion during Behavioral Phase Change in the Migratory Locust

Behavioral plasticity is the most striking trait in locust phase transition. However, the genetic basis for behavioral plasticity in locusts is largely unknown. To unravel the molecular mechanisms underlying the behavioral phase change in the migratory locust Locusta migratoria, the gene expression patterns over the time courses of solitarization and gregarization were compared by oligonucleotide microarray analysis. Data analysis revealed that several gene categories relevant to peripheral olfactory perception are strongly regulated in a total of 1,444 differentially expressed genes during both time courses. Among these candidate genes, several CSP (chemosensory protein) genes and one takeout gene, LmigTO1, showed higher expression in gregarious and solitarious locusts, respectively, and displayed opposite expression trends during solitarization and gregarization. qRT-PCR experiments revealed that most CSP members and LmigTO1 exhibited antenna-rich expressions. RNA interference combined with olfactory behavioral experiments confirmed that the CSP gene family and one takeout gene, LmigTO1, are involved in the shift from repulsion to attraction between individuals during gregarization and in the reverse transition during solitarization. These findings suggest that the response to locust-emitted olfactory cues regulated by CSP and takeout genes is involved in the behavioral phase change in the migratory locust and provide a previously undescribed molecular mechanism linked to the formation of locust aggregations

ZSTAD: Zero-Shot Temporal Activity Detection

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS14 and the Charades datasets show promising performance in terms of detecting unseen activities

TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution

The goal of scene text image super-resolution is to reconstruct high-resolution text-line images from unrecognizable low-resolution inputs. The existing methods relying on the optimization of pixel-level loss tend to yield text edges that exhibit a notable degree of blurring, thereby exerting a substantial impact on both the readability and recognizability of the text. To address these issues, we propose TextDiff, the first diffusion-based framework tailored for scene text image super-resolution. It contains two modules: the Text Enhancement Module (TEM) and the Mask-Guided Residual Diffusion Module (MRD). The TEM generates an initial deblurred text image and a mask that encodes the spatial location of the text. The MRD is responsible for effectively sharpening the text edge by modeling the residuals between the ground-truth images and the initial deblurred images. Extensive experiments demonstrate that our TextDiff achieves state-of-the-art (SOTA) performance on public benchmark datasets and can improve the readability of scene text images. Moreover, our proposed MRD module is plug-and-play that effectively sharpens the text edges produced by SOTA methods. This enhancement not only improves the readability and recognizability of the results generated by SOTA methods but also does not require any additional joint training. Available Codes:https://github.com/Lenubolim/TextDiff