本文提出了一个最先进的框架，时间是封闭式卷积神经网络（TGCNN），该框架利用时间信息和门控机制来解决农作物分类问题。此外，构建了几个植被指数以扩大输入数据的维度以利用光谱信息。TGCNN中都考虑了空间（频道）和时间（逐步）相关性。具体而言，我们的初步分析表明，在此数据集中，逐步信息更为重要。最后，门控机制有助于捕获高阶关系。我们的TGCNN解决方案分别达到$ 0.973 $ F1分数，$ 0.977 $ AUC ROC和$ 0.948 $ iou。此外，它在不同的本地任务（肯尼亚，巴西和多哥）中的其他三个基准优于其他三个基准。总体而言，我们的实验表明TGCNN在这个地球观察时间序列分类任务中是有利的。

The task of reconstructing 3D human motion has wideranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as they take single-view videos as inputs. Replacing the multi-view Mo- Cap systems with a monocular HMR method would break the current barriers to collecting accurate 3D motion thus making exciting applications like motion analysis and motiondriven animation accessible to the general public. However, performance of existing HMR methods degrade when the video contains challenging and dynamic motion that is not in existing MoCap datasets used for training. This reduces its appeal as dynamic motion is frequently the target in 3D motion recovery in the aforementioned applications. Our study aims to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action. Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection. To further validate NeMo using 3D metrics, we collected a small MoCap dataset mimicking actions in Penn Action,and show that NeMo achieves better 3D reconstruction compared to various baselines.

Learning with noisy label (LNL) is a classic problem that has been extensively studied for image tasks, but much less for video in the literature. A straightforward migration from images to videos without considering the properties of videos, such as computational cost and redundant information, is not a sound choice. In this paper, we propose two new strategies for video analysis with noisy labels: 1) A lightweight channel selection method dubbed as Channel Truncation for feature-based label noise detection. This method selects the most discriminative channels to split clean and noisy instances in each category; 2) A novel contrastive strategy dubbed as Noise Contrastive Learning, which constructs the relationship between clean and noisy instances to regularize model training. Experiments on three well-known benchmark datasets for video classification show that our proposed tru{\bf N}cat{\bf E}-split-contr{\bf A}s{\bf T} (NEAT) significantly outperforms the existing baselines. By reducing the dimension to 10\% of it, our method achieves over 0.4 noise detection F1-score and 5\% classification accuracy improvement on Mini-Kinetics dataset under severe noise (symmetric-80\%). Thanks to Noise Contrastive Learning, the average classification accuracy improvement on Mini-Kinetics and Sth-Sth-V1 is over 1.6\%.

When a large language model (LLM) performs complex reasoning by chain of thought (CoT), it can be highly sensitive to individual mistakes. We have had to train verifiers to address this issue. As we all know, after human inferring a conclusion, they often check it by re-verifying it, which can avoid some mistakes. We propose a new method called self-verification that uses the conclusion of the CoT as a condition to build a new sample and asks the LLM to re-predict the original conditions which be masked. We calculate an explainable verification score based on the accuracy. This method can improve the accuracy of multiple arithmetics and logical reasoning datasets when using few-shot learning. we have demonstrated that LLMs can conduct explainable self-verification of their own conclusions and achieve competitive reasoning performance. Extensive experimentals have demonstrated that our method can help multiple large language models with self-verification can avoid interference from incorrect CoT. Code is available at \url{https://github.com/WENGSYX/Self-Verification}

Machine Learning (ML) approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs). Recent work has achieved near-perfect performance by following binary- and multi-class network anomaly detection tasks. Such systems depend on the availability of both (benign and malicious) network data classes during the training phase. However, attack data samples are often challenging to collect in most organisations due to security controls preventing the penetration of known malicious traffic to their networks. Therefore, this paper proposes a Deep One-Class (DOC) classifier for network intrusion detection by only training on benign network data samples. The novel one-class classification architecture consists of a histogram-based deep feed-forward classifier to extract useful network data features and use efficient outlier detection. The DOC classifier has been extensively evaluated using two benchmark NIDS datasets. The results demonstrate its superiority over current state-of-the-art one-class classifiers in terms of detection and false positive rates.

As the deep learning rapidly promote, the artificial texts created by generative models are commonly used in news and social media. However, such models can be abused to generate product reviews, fake news, and even fake political content. The paper proposes a solution for the Russian Artificial Text Detection in the Dialogue shared task 2022 (RuATD 2022) to distinguish which model within the list is used to generate this text. We introduce the DeBERTa pre-trained language model with multiple training strategies for this shared task. Extensive experiments conducted on the RuATD dataset validate the effectiveness of our proposed method. Moreover, our submission ranked second place in the evaluation phase for RuATD 2022 (Multi-Class).

The weakly supervised instance segmentation is a challenging task. The existing methods typically use bounding boxes as supervision and optimize the network with a regularization loss term such as pairwise color affinity loss for instance segmentation. Through systematic analysis, we found that the commonly used pairwise affinity loss has two limitations: (1) it works with color affinity but leads to inferior performance with other modalities such as depth gradient, (2)the original affinity loss does not prevent trivial predictions as intended but actually accelerates this process due to the affinity loss term being symmetric. To overcome these two limitations, in this paper, we propose a novel asymmetric affinity loss which provides the penalty against the trivial prediction and generalizes well with affinity loss from different modalities. With the proposed asymmetric affinity loss, our method outperforms the state-of-the-art methods on the Cityscapes dataset and outperforms our baseline method by 3.5% in mask AP.

We consider the straggler problem in decentralized learning over a logical ring while preserving user data privacy. Especially, we extend the recently proposed framework of differential privacy (DP) amplification by decentralization by Cyffers and Bellet to include overall training latency--comprising both computation and communication latency. Analytical results on both the convergence speed and the DP level are derived for both a skipping scheme (which ignores the stragglers after a timeout) and a baseline scheme that waits for each node to finish before the training continues. A trade-off between overall training latency, accuracy, and privacy, parameterized by the timeout of the skipping scheme, is identified and empirically validated for logistic regression on a real-world dataset.

In deep learning, neural networks serve as noisy channels between input data and its representation. This perspective naturally relates deep learning with the pursuit of constructing channels with optimal performance in information transmission and representation. While considerable efforts are concentrated on realizing optimal channel properties during network optimization, we study a frequently overlooked possibility that neural networks can be initialized toward optimal channels. Our theory, consistent with experimental validation, identifies primary mechanics underlying this unknown possibility and suggests intrinsic connections between statistical physics and deep learning. Unlike the conventional theories that characterize neural networks applying the classic mean-filed approximation, we offer analytic proof that this extensively applied simplification scheme is not valid in studying neural networks as information channels. To fill this gap, we develop a corrected mean-field framework applicable for characterizing the limiting behaviors of information propagation in neural networks without strong assumptions on inputs. Based on it, we propose an analytic theory to prove that mutual information maximization is realized between inputs and propagated signals when neural networks are initialized at dynamic isometry, a case where information transmits via norm-preserving mappings. These theoretical predictions are validated by experiments on real neural networks, suggesting the robustness of our theory against finite-size effects. Finally, we analyze our findings with information bottleneck theory to confirm the precise relations among dynamic isometry, mutual information maximization, and optimal channel properties in deep learning.

This is a theoretical paper, as a companion paper of the plenary talk for the same conference ISAIC 2022. In contrast to conscious learning, which develops a single network for a normal life and is the main topic of the plenary talk, it is necessary to address the currently widespread approach, so-called "Deep Learning". Although "Deep Learning" may use different learning modes, including supervised, reinforcement and adversarial modes, almost all "Deep Learning" projects apparently suffer from the same misconduct, called "data deletion" and "test on training data". Consequently, Deep Learning almost always was not tested at all. Why? The so-called "test set" was used in the Post-Selection step of the training stage. This paper establishes a theorem that a simple method called Pure-Guess Nearest Neighbor (PGNN) reaches any required errors on validation set and test set, including zero-error requirements, through the "Deep Learning" misconduct, as long as the test set is in the possession of the author and both the amount of storage space and the time of training are finite but unbounded. However, Deep Learning methods, like the PGNN method, apparently are not generalizable since they have never been tested at all by a valid test set.