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Detecting small scene text instances in the wild is particularly challenging, where the influence of irregular positions and nonideal lighting often leads to detection errors. We present MixNet, a hybrid architecture that combines the strengths of CNNs and Transformers, capable of accurately detecting small text from challenging natural scenes, regardless of the orientations, styles, and lighting conditions. MixNet incorporates two key modules: (1) the Feature Shuffle Network (FSNet) to serve as the backbone and (2) the Central Transformer Block (CTBlock) to exploit the 1D manifold constraint of the scene text. We first introduce a novel feature shuffling strategy in FSNet to facilitate the exchange of features across multiple scales, generating high-resolution features superior to popular ResNet and HRNet. The FSNet backbone has achieved significant improvements over many existing text detection methods, including PAN, DB, and FAST. Then we design a complementary CTBlock to leverage center line based features similar to the medial axis of text regions and show that it can outperform contour-based approaches in challenging cases when small scene texts appear closely. Extensive experimental results show that MixNet, which mixes FSNet with CTBlock, achieves state-of-the-art results on multiple scene text detection datasets.

Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our approach stands out for its uniqueness, as it relies solely on a single image coming from one patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical physicians to utilize their expertise, a geometry-based rendering of a single lesion image to generate the training set (the \emph{biggest} novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC data-set created by ourselves and achieved a mean Dice score of 0.888, which represents a significant advance toward clinical applications.

Recent advances in deep generative models have led to the development of methods capable of synthesizing high-quality, realistic images. These models pose threats to society due to their potential misuse. Prior research attempted to mitigate these threats by detecting generated images, but the varying traces left by different generative models make it challenging to create a universal detector capable of generalizing to new, unseen generative models. In this paper, we propose to inject a universal adversarial signature into an arbitrary pre-trained generative model, in order to make its generated contents more detectable and traceable. First, the imperceptible optimal signature for each image can be found by a signature injector through adversarial training. Subsequently, the signature can be incorporated into an arbitrary generator by fine-tuning it with the images processed by the signature injector. In this way, the detector corresponding to the signature can be reused for any fine-tuned generator for tracking the generator identity. The proposed method is validated on the FFHQ and ImageNet datasets with various state-of-the-art generative models, consistently showing a promising detection rate. Code will be made publicly available at \url{https://github.com/zengxianyu/genwm}.

Effective light cones, characterized by Lieb-Robinson bounds, emerge in non-relativistic local quantum systems. Here, we present several analytical results derived from logarithmic light cones (LLCs), which can arise in the one-dimensional XXZ model with random fields and in a phenomenological model of many-body localization (MBL). In the LLC regime, we prove that the entanglement growth is upper-bounded by logarithmic time with an additional subleading double-logarithmic term stemming from a real asymptotic solution of the \emph{Lambert W} function. In the context of the XXZ model, recent numerical results suggest that the double-logarithmic term correlates with number entropy. We also show that information scrambling is logarithmically slow in the LLC regime. Furthermore, we demonstrate that the LLC supports long-lived quantum memories under unitary time evolution as quantum codes with macroscopic code distance and an exponentially scaling lifetime. Our analytical results provide benchmarks for future numerical explorations of the MBL regime on large time scales., Comment: 7+3 pages, 2 figures. In version 3, the proof of Theorem 1 was refined and we demonstrated that the logarithmic light cone implies that entanglement grows at most logarithmically with time, with an additional double-logarithmic correction

The function $\mathrm{P}_{\mathbf{v}}(G)$, measuring the proportion of the elements of a finite group $G$ that are zeros of irreducible characters of $G$, takes (as proved in [12]) only values $\frac{m-1}{m}$, for $1 \leq m \leq 6$, in the interval $[0, \mathrm{P}_{\mathbf{v}}(A_7))$.In this paper, we give a complete classification of the finite groups $G$ such that $\mathrm{P}_{\mathbf{v}}(G)=\frac{m-1}{m}$ for $m=1,2,\cdots ,6$., Comment: arXiv admin note: text overlap with arXiv:2201.00550

Ising (Zeeman-type) spin-orbit coupling (SOC) generated by in-plane inverse asymmetry has attracted considerable attention, especially in Ising superconductors and spin-valley coupling physics. However, many unconventional observations and emerging physical phenomena remain to be elucidated. Here, we theoretically study the spin texture of {\sigma}_z (spin angular momentum projection along z) induced by Ising SOC in 1Td WTe2, and propose an unconventional spin-to-charge conversion named inverse Ising effect, in which the directions of the spin current, spin polarization and charge current are not orthogonal. In particular, we predict the Ising magnetoresistance, whose resistance depends on the out-of-plane magnetic momentum in WTe2/ferromagnetic heterostructure. The Ising magnetoresistance is believed to be an interesting counterpart to the well studied spin Hall magnetoresistance. Our predictions provide promising way to spin-momentum locking and spin-charge conversion based on emerging Ising SOC.

We propose a new framework for conditional image synthesis from semantic layouts of any precision levels, ranging from pure text to a 2D semantic canvas with precise shapes. More specifically, the input layout consists of one or more semantic regions with free-form text descriptions and adjustable precision levels, which can be set based on the desired controllability. The framework naturally reduces to text-to-image (T2I) at the lowest level with no shape information, and it becomes segmentation-to-image (S2I) at the highest level. By supporting the levels in-between, our framework is flexible in assisting users of different drawing expertise and at different stages of their creative workflow. We introduce several novel techniques to address the challenges coming with this new setup, including a pipeline for collecting training data; a precision-encoded mask pyramid and a text feature map representation to jointly encode precision level, semantics, and composition information; and a multi-scale guided diffusion model to synthesize images. To evaluate the proposed method, we collect a test dataset containing user-drawn layouts with diverse scenes and styles. Experimental results show that the proposed method can generate high-quality images following the layout at given precision, and compares favorably against existing methods. Project page \url{https://zengxianyu.github.io/scenec/}

Due to the Weyl anomaly, an axial vector field produces novel anomalous chiral currents in spacetime with boundaries. Remarkably, the chiral current is not invariant under the gauge transformation of axial vector fields. As a result, more potential terms appear, and the chiral current becomes larger than the electric current near the boundary. This paper investigates the anomalous chiral current in AdS/BCFT. We find that the minimal holographic model of the axial vector field cannot produce the expected chiral current. To resolve this problem, we propose adding a suitable boundary action. Furthermore, we notice a similar situation for holographic condensation. Finally, we obtain the shape dependence of holographic chiral current and verify that it agrees with the field-theoretical result., Comment: 12 pages, 0 figure, revision accepted for publication in PLB

The ground state degeneracy of topologically ordered gapped Hamiltonians is the bedrock for self-correcting quantum memories, which are unfortunately not stable away from equilibrium even at zero temperature. This plague precludes practical robust self-correction since stability at zero temperature is a prerequisite for finite-temperature robustness. In this work, we show that the emergence of a bounded light cone renders the unitary time evolution a quasi-adiabatic continuation that preserves topological order, with the initial ground space retaining its macroscopic distance at all times as a quantum code. We also show how bounded light cones can emerge through suitable perturbations in Kitaev's toric code and honeycomb model. Our results suggest that topological orders and self-correcting quantum memories can be dynamically robust at zero temperature., Comment: 7+5 pages, 4+3 figures. the Supplemental Material is updated. In version 4, we show the emergence of a bounded light cone in the disordered Kitaev's honeycomb model

For a character $\chi$ of a finite group $G$, the number cod$(\chi):=|G:\mathrm{ker}(\chi)|/\chi(1)$ is called the codegree of $\chi$.In this paper, we give a solvability criterion for a finite group $G$ depending on the minimum of the ratio $\chi(1)^2 /\mathrm{cod}(\chi )$, when $\chi $ varies among the irreducible characters of $G$.

For an irreducible character $\chi$ of a finite group $G$, the codegree of $\chi$ is defined as $|G:\ker(\chi)|/\chi(1)$. In this paper, we determine finite nonsolvable groups with exactly three nonlinear irreducible character codegrees, and they are $\mathrm{L}_2(2^f)$ for $f\ge 2$, $\mathrm{PGL}_2(q)$ for odd $q\ge 5$ or $\mathrm{M}_{10}$.

We classify the finite groups $G$ which satisfies the condition that every complex irreducible character,whose degree's square doesn't divide the index of its kernel in $G$, lies in the same Galois conjugacy class.