Currently, how to deal with the massive garbage produced by various human activities is a hot topic all around the world. In this paper, a preliminary and essential step is to classify the garbage into different categories. However, the mainstream waste classification mode relies heavily on manual work, which consumes a lot of labor and is very inefficient. With the rapid development of deep learning, convolutional neural networks (CNN) have been successfully applied to various application fields. Therefore, some researchers have directly adopted CNNs to classify garbage through their images. However, compared with other images, the garbage images have their own characteristics (such as inter-class similarity, intra-class variance and complex background). Thus, neglecting these characteristics would impair the classification accuracy of CNN. To overcome the limitations of existing garbage image classification methods, a Depth-wise Separable Convolution Attention Module (DSCAM) is proposed in this paper. In DSCAM, the inherent relationships of channels and spatial positions in garbage image features are captured by two attention modules with depth-wise separable convolutions, so that our method could only focus on important information and ignore the interference. Moreover, we also adopt a residual network as the backbone of DSCAM to enhance its discriminative ability. We conduct the experiments on five garbage datasets. The experimental results demonstrate that the proposed method could effectively classify the garbage images and that it outperforms some classical methods.

YOLOv10, known for its efficiency in object detection methods, quickly and accurately detects objects in images. However, when detecting small objects in remote sensing imagery, traditional algorithms often encounter challenges like background noise, missing information, and complex multi-object interactions, which can affect detection performance. To address these issues, we propose an enhanced algorithm for detecting small objects, named SOD-YOLOv10. We design the Multi-Dimensional Information Interaction for Transformer Backbone Network (TransBone), which enhances global perception capabilities and effectively integrates both local and global information, thereby improving the detection of small object features. We also propose a feature fusion technology using an attention mechanism, called Aggregated Attention in Gated Feature Pyramid Network (AA-GFPN). This technology uses an efficient feature aggregation network and re-parameterization techniques to optimize information interaction between feature maps of different scales. Additionally, by incorporating the Aggregated Attention (AA) mechanism, it accurately identifies essential features of small objects. Moreover, we propose the Adaptive Focal Powerful IoU (AFP-IoU) loss function, which not only prevents excessive expansion of the anchor box area but also significantly accelerates model convergence. To evaluate our method, we conduct thorough tests on the RSOD, NWPU VHR-10, VisDrone2019 and AI-TOD datasets. The findings indicate that our SOD-YOLOv10 model attains 95.90%, 92.46%, 55.61% and 59.47% for mAP@0.5, and 73.42%, 66.84%, 39.03% and 42.67% for mAP@0.5:0.95.

In this work, we propose a new memristor SPICE model that accounts for the typical synaptic characteristics that have been previously demonstrated with practical memristive devices. We show that this model could account for both volatile and non-volatile memristance changes under distinct stimuli. We then demonstrate that our model is capable of supporting typical STDP with simple non-overlapping digital pulse pairs. Finally, we investigate the capability of our model to simulate the activity dependence dynamics of synaptic modification and present simulated results that are in excellent agreement with biological results.

Volatile programmable metallization cell is a promising threshold switching selector with excellent characteristics and simple structures. However, the large variation of threshold voltage is a major problem for practical application. In this work, we propose a dual-layer structure to increase selectivity and improve the threshold voltage variation. Compared to single-layer devices, this dual-layer device exhibits higher selectivity (>10⁷) and better threshold voltage uniformity with less than 5% fluctuation during 200 DC switching. The improvement is attributed to good control on the location of the filament formation and rupture after introducing a HfO₂ layer. It is deduced that a major factor consists of the difference of Ag ions mobility between SiTe and HfO₂ due to the grain boundary quantity.

Metal oxide-based electrochemical metallization memory (ECM) shows promising performance for next generation non-volatile memory. The negative-SET behavior has been observed in various oxide-based ECM devices. But the underlying mechanism of this behavior remains unaddressed and the role of the metal cation and oxygen vacancy in this behavior is unclear. In this work, we have observed two kinds of negative-SET (labeled as N-SET1 and N-SET2) behaviors in our Cu/ZrO2/Pt devices. Both the two behaviors can result in hard breakdown due to the high compliance current in reset process. The I-V characteristic shows that the two negative-SET behaviors have an obvious difference in operation voltage. Using four-probe resistance measurement method, the resistance-temperature characteristics of the ON-state after various negative-SET behaviors have been studied. The temperature dependence results demonstrate that the N-SET1 behavior is dominated by Cu conductive filament (CF) reformation caused by the Cu CF overgrowth phenomenon while the N-SET2 is related to the formation of oxygen vacancy CF. This work may provide a comprehensive understanding of the switching mechanism in oxide-based ECM devices.

Temporal action localization (TAL), which aims to identify and localize actions in long untrimmed videos, is a challenging task in video understanding. Recent studies have shown that the Transformer and its variants are effective at improving the performance of TAL. The success of the Transformer can be attributed to the use of multi-head self-attention (MHSA) as a token mixer to capture long-term temporal dependencies within the video sequence. However, in the existing Transformer architecture, the features obtained by multiple token mixing (i.e., self-attention) heads are treated equally, which neglects the distinct characteristics of different heads and hampers the exploitation of discriminative information. To this end, we present a new method called the adaptive dual selective Transformer (ADSFormer) for TAL in this paper. The key component in ADSFormer is the dual selective multihead token mixer (DSMHTM), which integrates multiple feature representations from different token mixing heads by adaptively selecting important features across both the head and channel dimensions. Moreover, we also incorporate our ADSFormer into a pyramid structure so that the multi-scale features obtained can be effectively combined to improve TAL performance. Benefiting from the dual selective multi-head token mixer (DSMHTM) and pyramid feature combination, ADSFormer outperforms several state-of-the-art methods on four challenging benchmark datasets: THUMOS14, MultiTHUMOS, EPIC-KITCHENS-100 and ActivityNet-1.3. We publicly released our codes and pretrained models at: https://github.com/LiQiang0307/ADSFormer.