Rapid environmental changes in the Lake Qinghai basin during the late Holocene

The Lake Qinghai Basin is sensitive to global and regional climate change because of its unique geographical location. It is the hotspot for paleoclimate research in East Asia. In this study, we reconstructed the environmental evolution of the Lake Qinghai since ∼9 ka by using a high-resolution peat and fluvial-lacustrine record (Laoyinggou profile) obtained at the foot of Nanshan Mountain. Based on 8 AMS14C dates and lithology, loss on ignition (LOI), total organic matter (TOC), X-ray fluorescence (XRF) core-scanning measurements, ratio of total organic carbon to nitrogen (TOC/TN), and sediment particle sorting coefficients, we show that during the Middle Holocene (∼9–4.4 ka BP) this region was primarily dominated by the Asian summer monsoon, with a consistent, warm, and humid environment. By contrast, during the late Holocene (4.4 ka to present), the climatic context in this area fluctuated dramatically at the millennial scales. The low TOC content, lower TOC/TN ration and strong hydroclimatic indicate six rapid climate change events, which occurred at ∼4.0 ka, ∼3.6 ka, ∼3.2 ka, ∼2.8 ka, ∼2.1 ka, and ∼1.4 ka, all of which coincided to cold episodes in the North Atlantic Ocean

Multi-modal Queried Object Detection in the Wild

We introduce MQ-Det, an efficient architecture and pre-training strategy design to utilize both textual description with open-set generalization and visual exemplars with rich description granularity as category queries, namely, Multi-modal Queried object Detection, for real-world detection with both open-vocabulary categories and various granularity. MQ-Det incorporates vision queries into existing well-established language-queried-only detectors. A plug-and-play gated class-scalable perceiver module upon the frozen detector is proposed to augment category text with class-wise visual information. To address the learning inertia problem brought by the frozen detector, a vision conditioned masked language prediction strategy is proposed. MQ-Det's simple yet effective architecture and training strategy design is compatible with most language-queried object detectors, thus yielding versatile applications. Experimental results demonstrate that multi-modal queries largely boost open-world detection. For instance, MQ-Det significantly improves the state-of-the-art open-set detector GLIP by +7.8% zero-shot AP on the LVIS benchmark and averagely +6.3% AP on 13 few-shot downstream tasks, with merely 3% pre-training time required by GLIP. Code is available at https://github.com/YifanXu74/MQ-Det.Comment: Under revie