We propose an extended primal-dual algorithm framework for solving a general nonconvex optimization model. This work is motivated by image reconstruction problems in a class of nonlinear imaging, where the forward operator can be formulated as a nonl
inear convex function with respect to the reconstructed image. Using the proposed framework, we put forward six specific iterative schemes, and present their detailed mathematical explanation. We also establish the relationship to existing algorithms. Moreover, under proper assumptions, we analyze the convergence of the schemes for the general model when the optimal dual variable regarding the nonlinear operator is non-vanishing. As a representative, the image reconstruction for spectral computed tomography is used to demonstrate the effectiveness of the proposed algorithm framework. By special properties of the concrete problem, we further prove the convergence of these customized schemes when the optimal dual variable regarding the nonlinear operator is vanishing. Finally, the numerical experiments show that the proposed algorithm has good performance on image reconstruction for various data with non-standard scanning configuration.
Person re-identification (re-ID) has gained more and more attention due to its widespread applications in intelligent video surveillance. Unfortunately, the mainstream deep learning methods still need a large quantity of labeled data to train models,
and annotating data is an expensive work in real-world scenarios. In addition, due to domain gaps between different datasets, the performance is dramatically decreased when re-ID models pre-trained on label-rich datasets (source domain) are directly applied to other unlabeled datasets (target domain). In this paper, we attempt to remedy these problems from two aspects, namely data and methodology. Firstly, we develop a data collector to automatically generate synthetic re-ID samples in a computer game, and construct a data labeler to simultaneously annotate them, which free humans from heavy data collections and annotations. Based on them, we build two synthetic person re-ID datasets with different scales, GSPR and mini-GSPR datasets. Secondly, we propose a synthesis-based multi-domain collaborative refinement (SMCR) network, which contains a synthetic pretraining module and two collaborative-refinement modules to implement sufficient learning for the valuable knowledge from multiple domains. Extensive experiments show that our proposed framework obtains significant performance improvements over the state-of-the-art methods on multiple unsupervised domain adaptation tasks of person re-ID.
The prosperity of the cryptocurrency ecosystem drives the needs for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the cu
stody of their digital assets to the middlemen, thus eliminating the security and privacy issues of CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap exchanges and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the rug pull scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least $16 million from 40,165 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem.
Hawking radiation from primordial black holes (PBH) can ionize and heat up neutral gas during the cosmic dark ages, leaving imprints on the global 21cm signal of neutral hydrogen. We use the global 21cm signal to constrain the abundance of spinning P
BHs in mass range of $[2 times 10^{13}, 10^{18}]$ grams. We consider several extended PBH distribution models. Our results show that 21cm can set the most stringent PBH bounds in our mass window. Compared with constraints set by {it{Planck}} cosmic microwave background (CMB) data, 21-cm limits are more stringent by about two orders of magnitudes. PBHs with higher spin are typically more strongly constrained. Our 21cm constraints for the monochromatic mass distribution rule out spinless PBHs with initial mass below $1.4 times 10^{17} {rm{g}}$, whereas extreme Kerr PBHs with reduced initial spin of $a_0=0.999$ are excluded as the dominant dark matter component for masses below $6 times 10^{17} {rm{g}}$. We also derived limits for the log-normal, power-law and critical collapse distributions.
One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two
tasks. In this work, we propose a Task-aligned One-stage Object Detection (TOOD) that explicitly aligns the two tasks in a learning-based manner. First, we design a novel Task-aligned Head (T-Head) which offers a better balance between learning task-interactive and task-specific features, as well as a greater flexibility to learn the alignment via a task-aligned predictor. Second, we propose Task Alignment Learning (TAL) to explicitly pull closer (or even unify) the optimal anchors for the two tasks during training via a designed sample assignment scheme and a task-aligned loss. Extensive experiments are conducted on MS-COCO, where TOOD achieves a 51.1 AP at single-model single-scale testing. This surpasses the recent one-stage detectors by a large margin, such as ATSS (47.7 AP), GFL (48.2 AP), and PAA (49.0 AP), with fewer parameters and FLOPs. Qualitative results also demonstrate the effectiveness of TOOD for better aligning the tasks of object classification and localization. Code is available at https://github.com/fcjian/TOOD.
Recent research demonstrates the effectiveness of using fine-tuned language models~(LM) for dense retrieval. However, dense retrievers are hard to train, typically requiring heavily engineered fine-tuning pipelines to realize their full potential. In
this paper, we identify and address two underlying problems of dense retrievers: i)~fragility to training data noise and ii)~requiring large batches to robustly learn the embedding space. We use the recently proposed Condenser pre-training architecture, which learns to condense information into the dense vector through LM pre-training. On top of it, we propose coCondenser, which adds an unsupervised corpus-level contrastive loss to warm up the passage embedding space. Retrieval experiments on MS-MARCO, Natural Question, and Trivia QA datasets show that coCondenser removes the need for heavy data engineering such as augmentation, synthesis, or filtering, as well as the need for large batch training. It shows comparable performance to RocketQA, a state-of-the-art, heavily engineered system, using simple small batch fine-tuning.
Manipulation of micro/nano particles has been well studied and demonstrated by optical, electromagnetic, and acoustic approaches, or their combinations. Manipulation of internal structure of droplet/particle is rarely explored and remains challenging
due to its complicated nature. Here we demonstrated the manipulation of internal structure of disk-in-sphere endoskeletal droplets using acoustic wave for the first time. We developed a model to investigate the physical mechanisms behind this novel phenomenon. Theoretical analysis of the acoustic interactions indicated that these assembly dynamics arise from a balance of the primary and secondary radiation forces. Additionally, the disk orientation was found to change with acoustic driving frequency, which allowed on-demand, reversible adjusting disk orientations with respect to the substrate. This novel dynamic behavior leads to unique reversible arrangements of the endoskeletal droplets and their internal architecture, which may provide a new avenue for directed assembly of novel hierarchical colloidal architectures and intracellular organelles or intra-organoid structures.
A novel photonics-based RF reception approach is proposed as a competitive solution to meet the current challenges of photonic-based approaches and to realize high performances at the same time. The proposed approach adopts the superheterodyne config
uration by a combination manner of electronic techniques and photonic techniques, including the ultrawideband generation of optical LO, the two-stage photonic superheterodyne frequency conversion and the real-time IF compensation. An engineering prototype has been developed and its performance has been evaluated in the laboratory environment. The experiment results preliminarily verify the feasibility of the proposed approach and its engineering potential. The typical performances are as follows: 0.1 GHz~ 45GHz operation spectrum range (>40 GHz), 900 MHz instantaneous bandwidth, 101 dBHz2/3 SFDR and 130 dBHz LDR, image rejections of ~80 dB for 1st frequency conversion and >90 dB for 2nd frequency conversion.
Crowd localization is a new computer vision task, evolved from crowd counting. Different from the latter, it provides more precise location information for each instance, not just counting numbers for the whole crowd scene, which brings greater chall
enges, especially in extremely congested crowd scenes. In this paper, we focus on how to achieve precise instance localization in high-density crowd scenes, and to alleviate the problem that the feature extraction ability of the traditional model is reduced due to the target occlusion, the image blur, etc. To this end, we propose a Dilated Convolutional Swin Transformer (DCST) for congested crowd scenes. Specifically, a window-based vision transformer is introduced into the crowd localization task, which effectively improves the capacity of representation learning. Then, the well-designed dilated convolutional module is inserted into some different stages of the transformer to enhance the large-range contextual information. Extensive experiments evidence the effectiveness of the proposed methods and achieve state-of-the-art performance on five popular datasets. Especially, the proposed model achieves F1-measure of 77.5% and MAE of 84.2 in terms of localization and counting performance, respectively.
We numerically demonstrate that a planar slab made of magnetic Weyl semimetal (a class of topological materials) can emit high-purity circularly polarized (CP) thermal radiation over a broad mid- and long-wave infrared wavelength range for a signific
ant portion of its emission solid angle. This effect fundamentally arises from the strong infrared gyrotropy or nonreciprocity of these materials which primarily depends on the momentum separation between Weyl nodes in the band structure. We clarify the dependence of this effect on the underlying physical parameters and highlight that the spectral bandwidth of CP thermal emission increases with increasing momentum separation between the Weyl nodes. We also demonstrate using recently developed thermal discrete dipole approximation (TDDA) computational method that finite-size bodies of magnetic Weyl semimetals can emit spectrally broadband CP thermal light, albeit over smaller portion of the emission solid angle compared to the planar slabs. Our work identifies unique fundamental and technological prospects of magnetic Weyl semimetals for engineering thermal radiation and designing efficient CP light sources.
