Effective and Efficient Query-aware Snippet Extraction for Web Search

Query-aware webpage snippet extraction is widely used in search engines to help users better understand the content of the returned webpages before clicking. Although important, it is very rarely studied. In this paper, we propose an effective query-aware webpage snippet extraction method named DeepQSE, aiming to select a few sentences which can best summarize the webpage content in the context of input query. DeepQSE first learns query-aware sentence representations for each sentence to capture the fine-grained relevance between query and sentence, and then learns document-aware query-sentence relevance representations for snippet extraction. Since the query and each sentence are jointly modeled in DeepQSE, its online inference may be slow. Thus, we further propose an efficient version of DeepQSE, named Efficient-DeepQSE, which can significantly improve the inference speed of DeepQSE without affecting its performance. The core idea of Efficient-DeepQSE is to decompose the query-aware snippet extraction task into two stages, i.e., a coarse-grained candidate sentence selection stage where sentence representations can be cached, and a fine-grained relevance modeling stage. Experiments on two real-world datasets validate the effectiveness and efficiency of our methods.Comment: Accepted by EMNLP202

Differentially Private Learning with Per-Sample Adaptive Clipping

Privacy in AI remains a topic that draws attention from researchers and the general public in recent years. As one way to implement privacy-preserving AI, differentially private learning is a framework that enables AI models to use differential privacy (DP). To achieve DP in the learning process, existing algorithms typically limit the magnitude of gradients with a constant clipping, which requires carefully tuned due to its significant impact on model performance. As a solution to this issue, latest works NSGD and Auto-S innovatively propose to use normalization instead of clipping to avoid hyperparameter tuning. However, normalization-based approaches like NSGD and Auto-S rely on a monotonic weight function, which imposes excessive weight on small gradient samples and introduces extra deviation to the update. In this paper, we propose a Differentially Private Per-Sample Adaptive Clipping (DP-PSAC) algorithm based on a non-monotonic adaptive weight function, which guarantees privacy without the typical hyperparameter tuning process of using a constant clipping while significantly reducing the deviation between the update and true batch-averaged gradient. We provide a rigorous theoretical convergence analysis and show that with convergence rate at the same order, the proposed algorithm achieves a lower non-vanishing bound, which is maintained over training iterations, compared with NSGD/Auto-S. In addition, through extensive experimental evaluation, we show that DP-PSAC outperforms or matches the state-of-the-art methods on multiple main-stream vision and language tasks.Comment: To appear in AAAI 2023, Revised acknowledgments and citation

Visualizing Drug Release from a Stimuli-Responsive Soft Material Based on Amine-Thiol Displacement

In this research, we developed a photoluminescent platform using amine-coupled fluorophores, generated from a single conjugate acceptor containing bis-vinylogous thioesters. Based on the experimental and computational results, the fluorescence turn-on mechanism was proposed to be charge separated induced energy radiative transition for the amine-coupled fluorophore, while the sulfur-containing precursor was not fluorescent since the energy internal conversion occurred through vibrational 2RS- (R represents alkyl groups) as energy acceptor(s). Further utilizing the conjugate acceptor, we establish a new fluorogenic approach via a highly cross-linked soft material to selectively detect cysteine under neutral aqueous conditions. Turn-on fluorescence emission and macroscopic degradation occurred in the presence of cysteine as the stimuli, which can be visually tracked due to the generation of an optical indicator and the cleavage of linkers within the matrix. Furthermore, a novel drug delivery system was constructed, achieving controlled release of sulfhydryl drug (6-mercaptopurine) which was tracked by photoluminescence and high-performance liquid chromatography. The photoluminescent molecules developed herein are suitable for visualizing polymeric degradation, making them suitable for additional “smart” material applications.</p

Visualizing Drug Release from a Stimuli-Responsive Soft Material Based on Amine-Thiol Displacement

In this research, we developed a photoluminescent platform using amine-coupled fluorophores, generated from a single conjugate acceptor containing bis-vinylogous thioesters. Based on the experimental and computational results, the fluorescence turn-on mechanism was proposed to be charge separated induced energy radiative transition for the amine-coupled fluorophore, while the sulfur-containing precursor was not fluorescent since the energy internal conversion occurred through vibrational 2RS- (R represents alkyl groups) as energy acceptor(s). Further utilizing the conjugate acceptor, we establish a new fluorogenic approach via a highly cross-linked soft material to selectively detect cysteine under neutral aqueous conditions. Turn-on fluorescence emission and macroscopic degradation occurred in the presence of cysteine as the stimuli, which can be visually tracked due to the generation of an optical indicator and the cleavage of linkers within the matrix. Furthermore, a novel drug delivery system was constructed, achieving controlled release of sulfhydryl drug (6-mercaptopurine) which was tracked by photoluminescence and high-performance liquid chromatography. The photoluminescent molecules developed herein are suitable for visualizing polymeric degradation, making them suitable for additional “smart” material applications.</p

UAV-Assisted Content Caching for Human-Centric Consumer Applications in IoV

With various consumer electronics deployed in Internet of Vehicles (IoV), human-centric consumer in-vehicle applications (e.g., driver assistance, path planning, and healthcare system) can supply high-quality driving experience and enhance travel safety within a short time. In addition, Unmanned Aerial Vehicles (UAV) are expected to be critical to assist terrestrial vehicular networks in delivering delay-sensitive contents of services. However, due to the mutual coupling of trajectory planning of UAVs, serving the same task requests repeatedly in the same area results in wasted resources. Hence, it is challenging to supply high-quality services while ensuring energy-efficient content caching. To solve this dilemma, a content Caching scheme with Trajectory design through differential evolution and Deep Reinforcement learning (CTDR) is introduced. Specifically, a content caching scheme based on differential evolution (DE) is first proposed. Next, a trajectory design optimization based on multi-agent proximal policy optimization (MAPPO) is designed to minimize system energy consumption. Eventually, the superiority of CTDR is demonstrated through various simulated experiments

A novel switchgear state assessment framework based on improved fuzzy C-means clustering method with deep belief network

Due to the problems such as fuzzy state assessment grading boundaries, the recognition accuracy is low when using traditional fuzzy techniques to grade the switchgear state. To address this problem, this paper proposes a switchgear state assessment and grading method based on deep belief network (DBN) and improved fuzzy C-means clustering (IFCM). Firstly, the switchgear state information data are processed by normalization method; then the feature parameters are extracted from the switchgear state information data by using DBN, and finally the extracted feature parameters are categorised according to the condition of switchgear equipment through clustering using IFCM. The experimental results show that the accuracy of the method in assessing the switchgear state under small sample conditions reaches 94, which exceeds the accuracy of other switchgear state assessment grading methods currently in use