459 research outputs found
On the Existing Problems and Countermeasures of Practical Teaching Mode on China’s Public Security Academies
Practical teaching is a significant approach in the cultivation of applied talents in public security academies. Strengthening practical teaching and focusing on cultivating the practical ability of students are the basic requirements for the reform of public security education. This paper focuses on the existing problems in the practical teaching model of public security academies, analyzes the reasons and puts forward the constructive idea of practical teaching models based on the actual situation practice
ViCo: Engaging Video Comment Generation with Human Preference Rewards
Engaging video comments play an important role in video social media, as they
are the carrier of feelings, thoughts, or humor of the audience. Preliminary
works have made initial exploration for video comment generation by adopting
caption-style encoder-decoder models. However, comment generation presents some
unique challenges distinct from caption generation, which makes these methods
somewhat less effective at generating engaging comments. In contrast to the
objective and descriptive nature of captions, comments tend to be inherently
subjective, making it hard to quantify and evaluate the engagement of comments.
Furthermore, the scarcity of truly engaging comments brings difficulty to
collecting enough high-quality training examples. In this paper, we propose
ViCo with three novel designs to tackle the above challenges for generating
engaging Video Comments. Firstly, to quantify the engagement of comments, we
utilize the number of "likes" each comment receives as a proxy of human
preference after an appropriate debiasing procedure. Secondly, to automatically
evaluate the engagement of comments, we train a reward model to align its
judgment to the above proxy. Our user studies indicate that this reward model
effectively aligns with human judgments. Lastly, to alleviate the scarcity of
high-quality comments, an initial generator is trained on readily available but
noisy data to generate comments. Then the reward model is employed to offer
feedback on the generated comments, thus optimizing the initial generator. To
facilitate the research of video commenting, we collect a large video
comment-dataset (ViCo-20k) with rich metadata from a popular video website.
Experiments on ViCo-20k show that the comments generated by our ViCo model
exhibit the best performance in terms of both quantitative and qualitative
results, particularly when engagement is considered
Classification of External Vibration Sources through Data-Driven Models Using Hybrid CNNs and LSTMs
Discovery of new cellulases from the metagenome by a metagenomics-guided strategy.
Background Energy shortage has become a global problem. Production of biofuels from renewable biomass resources is an inevitable trend of sustainable development. Cellulose is the most abundant and renewable resource in nature. Lack of new cellulases with unique properties has become the bottleneck of the efficient utilization of cellulose. Environmental metagenomes are regarded as huge reservoirs for a variety of cellulases. However, new cellulases cannot be obtained easily by functional screening of metagenomic libraries. Results In this work, a metagenomics-guided strategy for obtaining new cellulases from the metagenome was proposed. Metagenomic sequences of DNA extracted from the anaerobic beer lees converting consortium enriched at thermophilic conditions were assembled, and 23 glycoside hydrolase (GH) sequences affiliated with the GH family 5 were identified. Among the 23 GH sequences, three target sequences (designated as cel7482, cel3623 and cel36) showing low identity with those known GHs were chosen as the putative cellulase genes to be functionally expressed in Escherichia coli after PCR cloning. The three cellulases were classified into endo-β-1,4-glucanases by product pattern analysis. The recombinant cellulases were more active at pH 5.5 and within a temperature range of 60–70 °C. Computer-assisted 3D structure modeling indicated that the active residues in the active site of the recombinant cellulases were more similar to each other compared with non-active site residues. The recombinant cel7482 was extremely tolerant to 2 M NaCl, suggesting that cel7482 may be a halotolerant cellulase. Moreover, the recombinant cel7482 was shown to have an ability to resist three ionic liquids (ILs), which are widely used for cellulose pretreatment. Furthermore, active cel7482 was secreted by the twin-arginine translocation (Tat) pathway of Bacillus subtilis 168 into the culture medium, which facilitates the subsequent purification and reduces the formation of inclusion body in the context of overexpression. Conclusions This study demonstrated a simple and efficient method for direct cloning of new cellulase genes from environmental metagenomes. In the future, the metagenomics-guided strategy may be applied to the high-throughput screening of new cellulases from environmental metagenomes.published_or_final_versio
AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation
We propose a novel framework for learning high-level cognitive capabilities
in robot manipulation tasks, such as making a smiley face using building
blocks. These tasks often involve complex multi-step reasoning, presenting
significant challenges due to the limited paired data connecting human
instructions (e.g., making a smiley face) and robot actions (e.g., end-effector
movement). Existing approaches relieve this challenge by adopting an open-loop
paradigm decomposing high-level instructions into simple sub-task plans, and
executing them step-by-step using low-level control models. However, these
approaches are short of instant observations in multi-step reasoning, leading
to sub-optimal results. To address this issue, we propose to automatically
collect a cognitive robot dataset by Large Language Models (LLMs). The
resulting dataset AlphaBlock consists of 35 comprehensive high-level tasks of
multi-step text plans and paired observation sequences. To enable efficient
data acquisition, we employ elaborated multi-round prompt designs that
effectively reduce the burden of extensive human involvement. We further
propose a closed-loop multi-modal embodied planning model that autoregressively
generates plans by taking image observations as input. To facilitate effective
learning, we leverage MiniGPT-4 with a frozen visual encoder and LLM, and
finetune additional vision adapter and Q-former to enable fine-grained spatial
perception for manipulation tasks. We conduct experiments to verify the
superiority over existing open and closed-loop methods, and achieve a
significant increase in success rate by 21.4% and 14.5% over ChatGPT and GPT-4
based robot tasks. Real-world demos are shown in
https://www.youtube.com/watch?v=ayAzID1_qQk
Pave the Way to Grasp Anything: Transferring Foundation Models for Universal Pick-Place Robots
Improving the generalization capabilities of general-purpose robotic agents
has long been a significant challenge actively pursued by research communities.
Existing approaches often rely on collecting large-scale real-world robotic
data, such as the RT-1 dataset. However, these approaches typically suffer from
low efficiency, limiting their capability in open-domain scenarios with new
objects, and diverse backgrounds. In this paper, we propose a novel paradigm
that effectively leverages language-grounded segmentation masks generated by
state-of-the-art foundation models, to address a wide range of pick-and-place
robot manipulation tasks in everyday scenarios. By integrating precise
semantics and geometries conveyed from masks into our multi-view policy model,
our approach can perceive accurate object poses and enable sample-efficient
learning. Besides, such design facilitates effective generalization for
grasping new objects with similar shapes observed during training. Our approach
consists of two distinct steps. First, we introduce a series of foundation
models to accurately ground natural language demands across multiple tasks.
Second, we develop a Multi-modal Multi-view Policy Model that incorporates
inputs such as RGB images, semantic masks, and robot proprioception states to
jointly predict precise and executable robot actions. Extensive real-world
experiments conducted on a Franka Emika robot arm validate the effectiveness of
our proposed paradigm. Real-world demos are shown in YouTube
(https://www.youtube.com/watch?v=1m9wNzfp_4E ) and Bilibili
(https://www.bilibili.com/video/BV178411Z7H2/ )
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Localized kernel-based approximation for pricing financial options under regime switching jump diffusion model
In this paper, we consider European and American option pricing problems under regime switching jump diffusion models which are formulated as a system of partial integro-differential equations (PIDEs) with fixed and free boundaries. For free boundary problem arising in pricing American option, we use operator splitting method to deal with early exercise feature of American option. For developing a numerical technique we employ localized radial basis function generated finite difference (RBF-FD) approximation to overcome the ill-conditioning and high density issues of discretized matrices. The proposed method leads to linear systems with tridiagonal and diagonal dominant matrices. Also, in this paper the convergence and consistency of the proposed method are discussed. Numerical examples presented in the last section illustrate the robustness and practical performance of the proposed algorithm for pricing European and American options. Published by Elsevier B.V. on behalf of IMACS
CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment
The pre-trained image-text models, like CLIP, have demonstrated the strong
power of vision-language representation learned from a large scale of
web-collected image-text data. In light of the well-learned visual features,
some existing works transfer image representation to video domain and achieve
good results. However, how to utilize image-language pre-trained model (e.g.,
CLIP) for video-language pre-training (post-pretraining) is still under
explored. In this paper, we investigate two questions: 1) what are the factors
hindering post-pretraining CLIP to further improve the performance on
video-language tasks? and 2) how to mitigate the impact of these factors?
Through a series of comparative experiments and analyses, we find that the data
scale and domain gap between language sources have great impacts. Motivated by
these, we propose a Omnisource Cross-modal Learning method equipped with a
Video Proxy mechanism on the basis of CLIP, namely CLIP-ViP. Extensive results
show that our approach improves the performance of CLIP on video-text retrieval
by a large margin. Our model also achieves SOTA results on a variety of
datasets, including MSR-VTT, DiDeMo, LSMDC, and ActivityNet. We will release
our code and pre-trained CLIP-ViP models at
https://github.com/microsoft/XPretrain/tree/main/CLIP-ViP
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