Person Search with Natural Language Description

Searching persons in large-scale image databases with the query of natural language description has important applications in video surveillance. Existing methods mainly focused on searching persons with image-based or attribute-based queries, which have major limitations for a practical usage. In this paper, we study the problem of person search with natural language description. Given the textual description of a person, the algorithm of the person search is required to rank all the samples in the person database then retrieve the most relevant sample corresponding to the queried description. Since there is no person dataset or benchmark with textual description available, we collect a large-scale person description dataset with detailed natural language annotations and person samples from various sources, termed as CUHK Person Description Dataset (CUHK-PEDES). A wide range of possible models and baselines have been evaluated and compared on the person search benchmark. An Recurrent Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to establish the state-of-the art performance on person search

Topic-based integrator matching for pull request

Pull Request (PR) is the main method for code contributions from the external contributors in GitHub. PR review is an essential part of open source software developments to maintain the quality of software. Matching a new PR for an appropriate integrator will make the PR reviewing more effective. However, PR and integrator matching are now organized manually in GitHub. To make this process more efficient, we propose a Topic-based Integrator Matching Algorithm (TIMA) to predict highly relevant collaborators(the core developers) as the integrator to incoming PRs . TIMA takes full advantage of the textual semantics of PRs. To define the relationships between topics and collaborators, TIMA builds a relation matrix about topic and collaborators. According to the relevance between topics and collaborators, TIMA matches the suitable collaborators as the PR integrator

Collateral Pathways from the Ventromedial Hypothalamus Mediate Defensive Behaviors

SummaryThe ventromedial hypothalamus (VMH) was thought to be essential for coping with threat, although its circuit mechanism remains unclear. To investigate this, we optogenetically activated steroidogenic factor 1 (SF1)-expressing neurons in the dorsomedial and central parts of the VMH (VMHdm/c), and observed a range of context-dependent somatomotor and autonomic responses resembling animals’ natural defensive behaviors. By activating independent pathways emanating from the VMHdm/c, we demonstrated that VMHdm/c projection to the dorsolateral periaqueductal gray (dlPAG) induces inflexible immobility, while the VMHdm/c to anterior hypothalamic nucleus (AHN) pathway promotes avoidance. Consistent with the behavior changes induced by VMH to AHN pathway activation, direct activation of the AHN elicited avoidance and escape jumping, but not immobility. Retrograde tracing studies revealed that nearly 50% of PAG-projecting VMHdm/c neurons send collateral projection to the AHN and vice versa. Thus, VMHdm/c neurons employ a one-to-many wiring configuration to orchestrate multiple aspects of defensive behaviors

Dual-channel Prototype Network for few-shot Classification of Pathological Images

In pathology, the rarity of certain diseases and the complexity in annotating pathological images significantly hinder the creation of extensive, high-quality datasets. This limitation impedes the progress of deep learning-assisted diagnostic systems in pathology. Consequently, it becomes imperative to devise a technology that can discern new disease categories from a minimal number of annotated examples. Such a technology would substantially advance deep learning models for rare diseases. Addressing this need, we introduce the Dual-channel Prototype Network (DCPN), rooted in the few-shot learning paradigm, to tackle the challenge of classifying pathological images with limited samples. DCPN augments the Pyramid Vision Transformer (PVT) framework for few-shot classification via self-supervised learning and integrates it with convolutional neural networks. This combination forms a dual-channel architecture that extracts multi-scale, highly precise pathological features. The approach enhances the versatility of prototype representations and elevates the efficacy of prototype networks in few-shot pathological image classification tasks. We evaluated DCPN using three publicly available pathological datasets, configuring small-sample classification tasks that mirror varying degrees of clinical scenario domain shifts. Our experimental findings robustly affirm DCPN's superiority in few-shot pathological image classification, particularly in tasks within the same domain, where it achieves the benchmarks of supervised learning

Design of multifunctional color routers with Kerker switching using generative adversarial networks

To achieve optoelectronic devices with high resolution and efficiency, there is a pressing need for optical structural units that possess an ultrasmall footprint yet exhibit strong controllability in both the frequency and spatial domains. For dielectric nanoparticles, the overlap of electric and magnetic dipole moments can scatter light completely forward or backward, which is called Kerker theory. This effect can expand to any multipoles and any directions, re-named as generalized Kerker effect, and realize controllable light manipulation at full space and full spectrum using well-designed dielectric structures. However, the complex situations of multipole couplings make it difficult to achieve structural design. Here, generative artificial intelligence (AI) is utilized to facilitate multi-objective-oriented structural design, wherein we leverage the concept of "combined spectra" that consider both spectra and direction ratios as labels. The proposed generative adversarial network (GAN) is named as DDGAN (double-discriminator GAN) which discriminates both images and spectral labels. Using trained networks, we achieve the simultaneous design for scattering color and directivities, RGB color routers, as well as narrowband light routers. Notably, all generated structures possess a footprint less than 600x600 nm indicating their potential applications in optoelectronic devices with ultrahigh resolution

Evaluation of Tung Oil (Vernicia fordii (Hemsl.)) for Controlling Termites

In worldwide, the use of chemical pesticides to protect wood has been greatly restricted. In recent years, a large number of researchers devoted to the search for natural, safe and non-polluting bioactive chemical compounds from plants as an alternative to synthetic organic chemical preservative. In Chinese folk, tung oil can be used as paint for wooden furniture to protect them from pests. This study aimed to evaluate the chemical compositions of raw and heated tung oil and their activity against termite. In choice bioassays, weight loss of wood treated with 5% raw or heated tung oil after 4 weeks was significantly less than that of the control group. In no-choice bioassays, there was a significant difference in termite survival and wood weight loss on raw and heated tung oil-treated wood. When tung oil-treatment concentrations increased to 5%, wood weight loss was less than 10%. There was no significant difference in termite survival and wood weight loss between raw and heated tung oil-treated wood. Survival of termites in both tung oil wood treatments was significantly lower than that in the starvation control after 4 weeks. Raw and heated tung oil significantly improved the resistance of pine wood to termites, and have the potential for the development of natural wood preservatives

Vanadium-Based Superconductivity in a Breathing Kagome Compound Ta2V3.1Si0.9

Superconductivity in V-based kagome metals has recently raised great interest as they exhibit the competing ground states associated with the flat bands and topological electronic structures. Here we report the discovery of superconductivity in Ta2V3.1Si0.9 with a superconducting transition temperature Tc of 7.5 K, much higher than those in previously reported kagome metals at ambient pressure. While the V ions form a two-dimensional breathing kagome structure, the length difference between two different V-V bonds is just 0.04, making it very close to the perfect kagome structure. Our results show that Ta2V3.1Si0.9 is a moderate-coupled superconductor with a large upper critical field that is close to the Pauli limit. DFT calculations give a van-Hove-singularity band located at Fermi energy, which may explain the relatively high Tc observed in this material.Comment: 19 pages, 5 figure