Central Asia: The Vanguard in Jointly Building the «Belt & Road» Community of Shared Future for Mankind

The Silk Road originated in China, while Central Asia served as the crossroads of the Eurasian region. In 140 BC, during the Han Dynasty, Zhang Qian embarked on a mission to the Western Regions, present-day Central Asia. He paved the way from the East to the West, completing a challenging journey. President Xi proposed constructing the Silk Road Economic Belt (SREB) in Kazakhstan, making Central Asia the starting point and the first western station of the Belt and Road Initiative (BRI). Central Asia has always been at the forefront of building the BRI, setting an example for constructing a community with a shared future for humanity

Decreased buffering capacity and increased recovery time for legacy phosphorus in a typical watershed in eastern China between 1960 and 2010

Legacy phosphorus (P) accumulated in watersheds from excessive historical P inputs is recognized as an important component of water pollution control and sustainable P management in watersheds worldwide. However, little is known about how watershed P buffering capacity responds to legacy P pressures over time and how long it takes for riverine P concentrations to recover to a target level, especially in developing countries. This study examined long-term (1960–2010) accumulated legacy P stock, P buffering capacity and riverine TP flux dynamics to predict riverine P-reduction recovery times in the Yongan watershed of eastern China. Due to a growing legacy P stock coupled with changes in land use and climate, estimated short- and long-term buffering metrics (i.e., watershed ability to retain current year and historically accumulated surplus P, respectively) decreased by 65% and 36%, respectively, resulting in a 15-fold increase of riverine P flux between 1980 and 2010. An empirical model incorporating accumulated legacy P stock and annual precipitation was developed (R2 = 0.99) and used to estimate a critical legacy P stock of 22.2 ton P km−2 (95% CI 19.4–25.3 ton P km−2) that would prevent exceedance of a target riverine TP concentration of 0.05 mg P L−1. Using an exponential decay model, the recovery time for depleting the estimated legacy P stock in 2010 (29.3 ton P km−2) to the critical level (22.2 ton P km−2) via riverine flux was 456 years (95% CI 353–560 years), 159 years (95% CI 57–262 years) and 318 years (95% CI 238–400 years) under scenarios of a 4% reduction in annual P inputs, total cessation of P inputs, and 4% reduction of annual P inputs with a 10% increase in average annual precipitation, respectively. Given the lower P buffering capacity and lengthening recovery time, strategies to reduce P inputs and utilize soil legacy P for crop production are necessary to effectively control riverine P pollution and conserve global rock P resources. A long-term perspective that incorporates both contemporary and historical information is required for developing sustainable P management strategies to optimize both agronomic and environmental benefits at the watershed scale

Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations

Large language models (LLMs) can generate fluent natural language texts when given relevant documents as background context. This ability has attracted considerable interest in developing industry applications of LLMs. However, LLMs are prone to generate hallucinations that are not supported by the provided sources. In this paper, we propose a hierarchical framework to detect and mitigate such ungrounded hallucination. Our framework uses Chain of Natural Language Inference (CoNLI) for hallucination detection and hallucination reduction via post-editing. Our approach achieves state-of-the-art performance on hallucination detection and enhances text quality through rewrite, using LLMs without any fine-tuning or domain-specific prompt engineering. We show that this simple plug-and-play framework can serve as an effective choice for hallucination detection and reduction, achieving competitive performance across various contexts.Comment: The source code is available at https://github.com/microsoft/CoNLI_hallucinatio

Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition

Few-shot visual recognition refers to recognize novel visual concepts from a few labeled instances. Many few-shot visual recognition methods adopt the metric-based meta-learning paradigm by comparing the query representation with class representations to predict the category of query instance. However, current metric-based methods generally treat all instances equally and consequently often obtain biased class representation, considering not all instances are equally significant when summarizing the instance-level representations for the class-level representation. For example, some instances may contain unrepresentative information, such as too much background and information of unrelated concepts, which skew the results. To address the above issues, we propose a novel metric-based meta-learning framework termed instance-adaptive class representation learning network (ICRL-Net) for few-shot visual recognition. Specifically, we develop an adaptive instance revaluing network with the capability to address the biased representation issue when generating the class representation, by learning and assigning adaptive weights for different instances according to their relative significance in the support set of corresponding class. Additionally, we design an improved bilinear instance representation and incorporate two novel structural losses, i.e., intra-class instance clustering loss and inter-class representation distinguishing loss, to further regulate the instance revaluation process and refine the class representation. We conduct extensive experiments on four commonly adopted few-shot benchmarks: miniImageNet, tieredImageNet, CIFAR-FS, and FC100 datasets. The experimental results compared with the state-of-the-art approaches demonstrate the superiority of our ICRL-Net

PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning

In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples. It is found that leveraging the textual space of a powerful pre-trained image-language model (such as CLIP) can be beneficial in learning visual features. Therefore, we develop a novel method termed PartSeg for few-shot part segmentation based on multimodal learning. Specifically, we design a part-aware prompt learning method to generate part-specific prompts that enable the CLIP model to better understand the concept of ``part'' and fully utilize its textual space. Furthermore, since the concept of the same part under different object categories is general, we establish relationships between these parts during the prompt learning process. We conduct extensive experiments on the PartImageNet and Pascal$\_$Part datasets, and the experimental results demonstrated that our proposed method achieves state-of-the-art performance

BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service

Online Food Ordering Service (OFOS) is a popular location-based service that helps people to order what you want. Compared with traditional e-commerce recommendation systems, users' interests may be diverse under different spatiotemporal contexts, leading to various spatiotemporal data distribution, which limits the fitting capacity of the model. However, numerous current works simply mix all samples to train a set of model parameters, which makes it difficult to capture the diversity in different spatiotemporal contexts. Therefore, we address this challenge by proposing a Bottom-up Adaptive Spatiotemporal Model(BASM) to adaptively fit the spatiotemporal data distribution, which further improve the fitting capability of the model. Specifically, a spatiotemporal-aware embedding layer performs weight adaptation on field granularity in feature embedding, to achieve the purpose of dynamically perceiving spatiotemporal contexts. Meanwhile, we propose a spatiotemporal semantic transformation layer to explicitly convert the concatenated input of the raw semantic to spatiotemporal semantic, which can further enhance the semantic representation under different spatiotemporal contexts. Furthermore, we introduce a novel spatiotemporal adaptive bias tower to capture diverse spatiotemporal bias, reducing the difficulty to model spatiotemporal distinction. To further verify the effectiveness of BASM, we also novelly propose two new metrics, Time-period-wise AUC (TAUC) and City-wise AUC (CAUC). Extensive offline evaluations on public and industrial datasets are conducted to demonstrate the effectiveness of our proposed modle. The online A/B experiment also further illustrates the practicability of the model online service. This proposed method has now been implemented on the Ele.me, a major online food ordering platform in China, serving more than 100 million online users