Explore the Power of Dropout on Few-shot Learning

The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is a regularization technique used in traditional deep learning methods. In this paper, we explore the power of dropout on few-shot learning and provide some insights about how to use it. Extensive experiments on the few-shot object detection and few-shot image classification datasets, i.e., Pascal VOC, MS COCO, CUB, and mini-ImageNet, validate the effectiveness of our method.Comment: arXiv admin note: substantial text overlap with arXiv:2210.0640

An approach to multiple attribute decision making based on the induced Choquet integral with fuzzy number intuitionistic fuzzy information

In this paper, we investigate the multiple attribute decision making problems with fuzzy number intuitionistic fuzzy information. Firstly, some operational laws of fuzzy number intuitionistic fuzzy values, score function and accuracy function of fuzzy number intuitionistic fuzzy values are introduced. Then, we have developed two fuzzy number intuitionistic fuzzy Choquet integral aggregation operators: induced fuzzy number intuitionistic fuzzy choquet ordered averaging (IFNIFCOA) operator and induced fuzzy number intuitionistic fuzzy choquet ordered geometric (IFNIFCOG) operator. The prominent characteristic of the operators is that they can not only consider the importance of the elements or their ordered positions, but also reflect the correlation among the elements or their ordered positions. We have studied some desirable properties of the IFNIFCOA and IFNIFCOG operators, such as commutativity, idempotency and monotonicity, and applied the IFNIFCOA and IFNIFCOGM operators to multiple attribute decision making with fuzzy number intuitionistic fuzzy information. Finally an illustrative example has been given to show the developed method

DiffusionVMR: Diffusion Model for Video Moment Retrieval

Video moment retrieval is a fundamental visual-language task that aims to retrieve target moments from an untrimmed video based on a language query. Existing methods typically generate numerous proposals manually or via generative networks in advance as the support set for retrieval, which is not only inflexible but also time-consuming. Inspired by the success of diffusion models on object detection, this work aims at reformulating video moment retrieval as a denoising generation process to get rid of the inflexible and time-consuming proposal generation. To this end, we propose a novel proposal-free framework, namely DiffusionVMR, which directly samples random spans from noise as candidates and introduces denoising learning to ground target moments. During training, Gaussian noise is added to the real moments, and the model is trained to learn how to reverse this process. In inference, a set of time spans is progressively refined from the initial noise to the final output. Notably, the training and inference of DiffusionVMR are decoupled, and an arbitrary number of random spans can be used in inference without being consistent with the training phase. Extensive experiments conducted on three widely-used benchmarks (i.e., QVHighlight, Charades-STA, and TACoS) demonstrate the effectiveness of the proposed DiffusionVMR by comparing it with state-of-the-art methods

Decouple knowledge from paramters for plug-and-play language modeling

Pre-trained language models(PLM) have made impressive results in various NLP tasks. It has been revealed that one of the key factors to their success is the parameters of these models implicitly learn all kinds of knowledge during pre-training. However, encoding knowledge implicitly in the model parameters has two fundamental drawbacks. First, the knowledge is neither editable nor scalable once the model is trained, which is especially problematic in that knowledge is consistently evolving. Second, it lacks interpretability and prevents humans from understanding which knowledge PLM requires for a certain problem. In this paper, we introduce PlugLM, a pre-training model with differentiable plug-in memory(DPM). The key intuition is to decouple the knowledge storage from model parameters with an editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the DPM. To justify this design choice, we conduct evaluations in three settings including: (1) domain adaptation. PlugLM obtains 3.95 F1 improvements across four domains on average without any in-domain pre-training. (2) knowledge update. PlugLM could absorb new knowledge in a training-free way after pre-training is done. (3) in-task knowledge learning. PlugLM could be further improved by incorporating training samples into DPM with knowledge prompting.Comment: ACL2023 Finding

3-[(1H-Benzimidazol-2-yl)sulfanyl­methyl]benzonitrile

In the title compound, C15H11N3S, the dihedral angle between the benzimidazole ring system and the benzene ring is 51.8 (2)°. The crystal structure exhibits inter­molecular N—H⋯N hydrogen bonds which lead to the formation of C(4) chains along the [001] direction

Evolution of iron-rich intermetallics and its effect on the mechanical properties of Al–Cu–Mn–Fe–Si alloys after thermal exposure and high-temperature tensile testing

Si addition is commonly used to modify the iron-rich intermetallics in Al–Cu–Mn–Fe alloys, which is beneficial to increasing the use of recycled aluminum. Most of the available research has focused on the effect of Si content on the room-temperature mechanical properties of Al–Cu–Mn–Fe alloys. To expand the application of Al–Cu–Mn–Fe–Si alloys as light heat-resistant structural components in the automotive and aerospace industries, it is of great importance to investigate the evolution of iron-rich intermetallics and its effect on the fracture behavior of Al–Cu–Mn–Fe–Si alloys after thermal exposure and high-temperature tensile testing. In this work, the evolution of iron-rich intermetallics and the high-temperature mechanical properties of heat-treated Al-6.5Cu-0.6Mn-0.5Fe alloys with different Si contents after thermal exposure and high-temperature tensile testing were assessed by tensile tests, image analysis, scanning electron microscopy, X-Ray diffraction, transmission electron microscopy, and atomic probe tomography. The results indicate that the Al-6.5Cu-0.6Mn-0.5Fe alloys with 0.1Si and 0.5Si additions have excellent and stable high-temperature mechanical properties after long thermal exposure, which are better than those of most heat-resistant Al alloys. The high performance of the high-temperature mechanical properties is attributed to the high heat resistance of secondary intermetallics and precipitated particles. The addition of Si is detrimental to the strength of Al-6.5Cu-0.6Mn-0.5Fe alloys after long thermal exposure. This can be attributed to the solid-state phase transformation of iron-rich intermetallics from α-Fe to β-Fe, which results in the increase of needle-like Fe-rich phases and Si particles, the agglomeration of secondary intermetallics, and the consumption of Al$_{2}$Cu phases

Multi-Tenant Provisioning for Quantum Key Distribution Networks with Heuristics and Reinforcement Learning: A Comparative Study

Quantum key distribution (QKD) networks are potential to be widely deployed in the immediate future to provide long-term security for data communications. Given the high price and complexity, multi-tenancy has become a cost-effective pattern for QKD network operations. In this work, we concentrate on addressing the online multi-tenant provisioning (On-MTP) problem for QKD networks, where multiple tenant requests (TRs) arrive dynamically. On-MTP involves scheduling multiple TRs and assigning non-reusable secret keys derived from a QKD network to multiple TRs, where each TR can be regarded as a high-security-demand organization with the dedicated secret-key demand. The quantum key pools (QKPs) are constructed over QKD network infrastructure to improve management efficiency for secret keys. We model the secret-key resources for QKPs and the secret-key demands of TRs using distinct images. To realize efficient On-MTP, we perform a comparative study of heuristics and reinforcement learning (RL) based On-MTP solutions, where three heuristics (i.e., random, fit, and best-fit based On-MTP algorithms) are presented and a RL framework is introduced to realize automatic training of an On-MTP algorithm. The comparative results indicate that with sufficient training iterations the RL-based On-MTP algorithm significantly outperforms the presented heuristics in terms of tenant-request blocking probability and secret-key resource utilization