SemanticSLAM: Learning based Semantic Map Construction and Robust Camera Localization

Current techniques in Visual Simultaneous Localization and Mapping (VSLAM) estimate camera displacement by comparing image features of consecutive scenes. These algorithms depend on scene continuity, hence requires frequent camera inputs. However, processing images frequently can lead to significant memory usage and computation overhead. In this study, we introduce SemanticSLAM, an end-to-end visual-inertial odometry system that utilizes semantic features extracted from an RGB-D sensor. This approach enables the creation of a semantic map of the environment and ensures reliable camera localization. SemanticSLAM is scene-agnostic, which means it doesn't require retraining for different environments. It operates effectively in indoor settings, even with infrequent camera input, without prior knowledge. The strength of SemanticSLAM lies in its ability to gradually refine the semantic map and improve pose estimation. This is achieved by a convolutional long-short-term-memory (ConvLSTM) network, trained to correct errors during map construction. Compared to existing VSLAM algorithms, SemanticSLAM improves pose estimation by 17%. The resulting semantic map provides interpretable information about the environment and can be easily applied to various downstream tasks, such as path planning, obstacle avoidance, and robot navigation. The code will be publicly available at https://github.com/Leomingyangli/SemanticSLAMComment: 2023 IEEE Symposium Series on Computational Intelligence (SSCI) 6 page

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

A Full Characterization of Excess Risk via Empirical Risk Landscape

In this paper, we provide a unified analysis of the excess risk of the model trained by a proper algorithm with both smooth convex and non-convex loss functions. In contrast to the existing bounds in the literature that depends on iteration steps, our bounds to the excess risk do not diverge with the number of iterations. This underscores that, at least for smooth loss functions, the excess risk can be guaranteed after training. To get the bounds to excess risk, we develop a technique based on algorithmic stability and non-asymptotic characterization of the empirical risk landscape. The model obtained by a proper algorithm is proved to generalize with this technique. Specifically, for non-convex loss, the conclusion is obtained via the technique and analyzing the stability of a constructed auxiliary algorithm. Combining this with some properties of the empirical risk landscape, we derive converged upper bounds to the excess risk in both convex and non-convex regime with the help of some classical optimization results.Comment: 38page

Induction of Cellular Immune Response by DNA Vaccine Coexpressing E. acervulina 3-1E Gene and Mature CHIl-15 Gene

We previously reported that the chimeric DNA vaccine pcDNA-3-1E-linker-mChIL-15, fused through linking Eimeria acervulina 3-1E encoding gene and mature chicken IL-15 (mChIL-15) gene with four flexible amino acid SPGS, could significantly offer protection against homologous challenge. In the present study, the induction of cellular immune response induced by the chimeric DNA vaccine pcDNA-3-1E-linker-mChIL-15 was investigated. Spleen lymphocyte subpopulations were characterized by flow cytometric analysis. The spleen lymphocyte proliferation assays were measured by 3-[4,5-dimethylthiazol-2-y1]-2,5-diphenyltetrazolium bromide (MTT) method. The mRNA profiles of ChIL-2 and ChIFN-γ in spleen were characterized by means of real-time PCR. Chickens immunized with pcDNA-3-1E-linker-mChIL-15 exhibited significant upregulated level of ChIL-2 and ChIFN-γ transcripts in spleen following two immunizations compared with chickens in other groups (P < 0.01). In comparison with pcDNA3.1-immunized and control groups, lymphocyte proliferation, percentage of CD8α+ cell, and levels of ChIL-2 and ChIFN-γ transcripts in the group immunized with pcDNA-3-1E-linker-mChIL-15 were significantly increased on day 6 following challenge (P < 0.05, P < 0.01, and P < 0.01, resp.). Our data suggested that the fusion antigen 3-1E-linker-mChIL-15 could be a potential candidate for E. acervulina vaccine development

Towards Accelerating Training of Batch Normalization: A Manifold Perspective

Batch normalization (BN) has become a crucial component across diverse deep neural networks. The network with BN is invariant to positively linear re-scaling of weights, which makes there exist infinite functionally equivalent networks with various scales of weights. However, optimizing these equivalent networks with the first-order method such as stochastic gradient descent will converge to different local optima owing to different gradients across training. To alleviate this, we propose a quotient manifold \emph{PSI manifold}, in which all the equivalent weights of the network with BN are regarded as the same one element. Then, gradient descent and stochastic gradient descent on the PSI manifold are also constructed. The two algorithms guarantee that every group of equivalent weights (caused by positively re-scaling) converge to the equivalent optima. Besides that, we give the convergence rate of the proposed algorithms on PSI manifold and justify that they accelerate training compared with the algorithms on the Euclidean weight space. Empirical studies show that our algorithms can consistently achieve better performances over various experimental settings

CBA: Contextual Background Attack against Optical Aerial Detection in the Physical World

Patch-based physical attacks have increasingly aroused concerns. However, most existing methods focus on obscuring targets captured on the ground, and some of these methods are simply extended to deceive aerial detectors. They smear the targeted objects in the physical world with the elaborated adversarial patches, which can only slightly sway the aerial detectors' prediction and with weak attack transferability. To address the above issues, we propose to perform Contextual Background Attack (CBA), a novel physical attack framework against aerial detection, which can achieve strong attack efficacy and transferability in the physical world even without smudging the interested objects at all. Specifically, the targets of interest, i.e. the aircraft in aerial images, are adopted to mask adversarial patches. The pixels outside the mask area are optimized to make the generated adversarial patches closely cover the critical contextual background area for detection, which contributes to gifting adversarial patches with more robust and transferable attack potency in the real world. To further strengthen the attack performance, the adversarial patches are forced to be outside targets during training, by which the detected objects of interest, both on and outside patches, benefit the accumulation of attack efficacy. Consequently, the sophisticatedly designed patches are gifted with solid fooling efficacy against objects both on and outside the adversarial patches simultaneously. Extensive proportionally scaled experiments are performed in physical scenarios, demonstrating the superiority and potential of the proposed framework for physical attacks. We expect that the proposed physical attack method will serve as a benchmark for assessing the adversarial robustness of diverse aerial detectors and defense methods

Improved OOD Generalization via Conditional Invariant Regularizer

Recently, generalization on out-of-distribution (OOD) data with correlation shift has attracted great attention. The correlation shift is caused by the spurious attributes that correlate to the class label, as the correlation between them may vary in training and test data. For such a problem, we show that given the class label, the conditionally independent models of spurious attributes are OOD generalizable. Based on this, a metric Conditional Spurious Variation (CSV) which controls OOD generalization error, is proposed to measure such conditional independence. To improve the OOD generalization, we regularize the training process with the proposed CSV. Under mild assumptions, our training objective can be formulated as a nonconvex-concave mini-max problem. An algorithm with provable convergence rate is proposed to solve the problem. Extensive empirical results verify our algorithm's efficacy in improving OOD generalization