Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations

Zero-shot learning (ZSL) aims to recognize classes that do not have samples in the training set. One representative solution is to directly learn an embedding function associating visual features with corresponding class semantics for recognizing new classes. Many methods extend upon this solution, and recent ones are especially keen on extracting rich features from images, e.g. attribute features. These attribute features are normally extracted within each individual image; however, the common traits for features across images yet belonging to the same attribute are not emphasized. In this paper, we propose a new framework to boost ZSL by explicitly learning attribute prototypes beyond images and contrastively optimizing them with attribute-level features within images. Besides the novel architecture, two elements are highlighted for attribute representations: a new prototype generation module is designed to generate attribute prototypes from attribute semantics; a hard example-based contrastive optimization scheme is introduced to reinforce attribute-level features in the embedding space. We explore two alternative backbones, CNN-based and transformer-based, to build our framework and conduct experiments on three standard benchmarks, CUB, SUN, AwA2. Results on these benchmarks demonstrate that our method improves the state of the art by a considerable margin. Our codes will be available at https://github.com/dyabel/CoAR-ZSL.gitComment: Accepted to TNNL

Distributed adaptive leader–follower and leaderless consensus control of a class of strict-feedback nonlinear systems : a unified approach

Author's accepted manuscript.Available from 16/05/2022.acceptedVersio

Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model

Recently, vision-language pre-training shows great potential in open-vocabulary object detection, where detectors trained on base classes are devised for detecting new classes. The class text embedding is firstly generated by feeding prompts to the text encoder of a pre-trained vision-language model. It is then used as the region classifier to supervise the training of a detector. The key element that leads to the success of this model is the proper prompt, which requires careful words tuning and ingenious design. To avoid laborious prompt engineering, there are some prompt representation learning methods being proposed for the image classification task, which however can only be sub-optimal solutions when applied to the detection task. In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model. Different from the previous classification-oriented methods, DetPro has two highlights: 1) a background interpretation scheme to include the proposals in image background into the prompt training; 2) a context grading scheme to separate proposals in image foreground for tailored prompt training. We assemble DetPro with ViLD, a recent state-of-the-art open-world object detector, and conduct experiments on the LVIS as well as transfer learning on the Pascal VOC, COCO, Objects365 datasets. Experimental results show that our DetPro outperforms the baseline ViLD in all settings, e.g., +3.4 APbox and +3.0 APmask improvements on the novel classes of LVIS. Code and models are available at https://github.com/dyabel/detpro.Comment: Accepted by CVPR 202

Combining Paleomagnetic and Re‐Os Isotope Data to Date Hydrocarbon Generation and Accumulation Processes

Unraveling the complex relationship between orogenesis and hydrocarbon formation and accumulation is challenging and is often hampered by physical and chemical overprints of younger events. The Permian reservoir in the Longmen Shan orogen, South China, is such an example, and its evolution has been hotly debated. In this study, we use a new combination of paleomagnetic dating analysis and Re–Os isotope dating to try to resolve this. Paleomagnetic dating of the hydrocarbon-host carbonate indicates two remagnetization events during: (a) the Late Triassic, and (b) the Middle Jurassic–Cretaceous. These two remagnetization events are shown to represent two distinct stages of hydrocarbon accumulation. The paleomagnetic estimates are supported by Re–Os dating of bitumen (∼264 Ma) and oil (∼94 Ma). The two different Re–Os ages are associated with two periods of oil generation. We interpret these data in terms of known geological processes: (a) the ∼260 Ma Dongwu large igneous province caused oil generation, and the Indosinian tectonic event caused the migration and accumulation; and (b) the Late Cretaceous Yanshan orogenic events promoted another generation and entrapment of oil in the same reservoir. This combined approach reliably tracks the sequence of oil generation and accumulation, even when the source rock is uncertain, and multi-phase accumulation and complex tectonism has occurred. Given that paleomagnetic and Re–Os dating are independent methods which can constrain multiple geological processes, when used together they have the potential to be universally applied

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated $11\times$, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.Comment: Accepted in Science Advances (https://www.science.org/doi/10.1126/sciadv.adi1480

Healthy dietary patterns are associated with exposure to environmental chemicals in a pregnancy cohort

Healthy dietary patterns, such as the alternate Mediterranean diet and alternate Healthy Eating Index, benefit cardiometabolic health. However, several food components of these dietary patterns are primary sources of environmental chemicals. Here, using data from a racially and ethnically diverse US cohort, we show that healthy dietary pattern scores were positively associated with plasma chemical exposure in pregnancy, particularly for the alternate Mediterranean diet and alternate Healthy Eating Index with polychlorinated biphenyls and per- and poly-fluoroalkyl substances. The associations appeared stronger among Asian and Pacific Islanders. These findings suggest that optimizing the benefits of a healthy diet requires concerted regulatory efforts aimed at lowering environmental chemical exposure

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Neuromorphic electronics, an emerging field that aims for building electronic mimics of the biological brain, holds promise for reshaping the frontiers of information technology and enabling a more intelligent and efficient computing paradigm. As their biological brain counterpart, the neuromorphic electronic systems are complex, having multiple levels of organization. Inspired by David Marr's famous three-level analytical framework developed for neuroscience, the advances in neuromorphic electronic systems are selectively surveyed and given significance to these research endeavors as appropriate from the computational level, algorithmic level, or implementation level. Under this framework, the problem of how to build a neuromorphic electronic system is defined in a tractable way. In conclusion, the development of neuromorphic electronic systems confronts a similar challenge to the one neuroscience confronts, that is, the limited constructability of the low-level knowledge (implementations and algorithms) to achieve high-level brain-like (human-level) computational functions. An opportunity arises from the communication among different levels and their codesign. Neuroscience lab-on-neuromorphic chip platforms offer additional opportunity for mutual benefit between the two disciplines

Detrital zircon U–Pb geochronology from the Upper Carboniferous sediments of Benxi Formation in the North China Craton: implications for tectonic-sedimentary evolution

The provenance of the Upper Carboniferous Benxi Formation in North China Craton (NCC) has been considered as the northern margin of the NCC, not the North Qinling Orogenic Belt. To understand the provenance and the tectonic-sedimentary evolution during the sedimentary period of the Benxi Formation, the zircon U–Pb geochronology analysis was conducted on eleven clastic sandstone samples. The south of the NCC received clastic sediments from the North Qinling Orogenic Belt. The orogenic movements around the NCC in the Late Carboniferous period had significant impacts on the changes in paleotopography. During the early sedimentary period of the Hutian member of the Benxi Formation, the north of the Qinling Orogenic Belt was rapidly uplifted, and paleotopography was south-uplifting and north-dipping; thus, the clastic source was the North Qinling Orogenic Belt. From the late sedimentary period of the Benxi Formation Hutian member to the sedimentary period of the Jinci member, paleotopography was reversed. The northern margin of the NCC quickly uplifted, and paleotopography was north-uplifting and south-dipping. Two distinct provenances were present in the sediments of the Benxi Formation. The sediments were supplied predominately by the provenance in the north