Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition

Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamics for single-person action recognition due to its ability of modeling the temporal information in various ranges of dynamic contexts. However, existing RNN models only focus on capturing the temporal dynamics of the person-person interactions by naively combining the activity dynamics of individuals or modeling them as a whole. This neglects the inter-related dynamics of how person-person interactions change over time. To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people. Specifically, for each frame, two sub-memory units store individual motion information, while a concurrent LSTM unit selectively integrates and stores inter-related motion information between interacting people from these two sub-memory units via a new co-memory cell. Experimental results on the BIT and UT datasets show the superiority of Co-LSTSM compared with the state-of-the-art methods

Training-Free Instance Segmentation from Semantic Image Segmentation Masks

In recent years, the development of instance segmentation has garnered significant attention in a wide range of applications. However, the training of a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations. In contrast, weakly-supervised instance segmentation methods (i.e., with image-level class labels or point labels) struggle to satisfy the accuracy and recall requirements of practical scenarios. In this paper, we propose a novel paradigm for instance segmentation called training-free instance segmentation (TFISeg), which achieves instance segmentation results from image masks predicted using off-the-shelf semantic segmentation models. TFISeg does not require training a semantic or/and instance segmentation model and avoids the need for instance-level image annotations. Therefore, it is highly efficient. Specifically, we first obtain a semantic segmentation mask of the input image via a trained semantic segmentation model. Then, we calculate a displacement field vector for each pixel based on the segmentation mask, which can indicate representations belonging to the same class but different instances, i.e., obtaining the instance-level object information. Finally, instance segmentation results are obtained after being refined by a learnable category-agnostic object boundary branch. Extensive experimental results on two challenging datasets and representative semantic segmentation baselines (including CNNs and Transformers) demonstrate that TFISeg can achieve competitive results compared to the state-of-the-art fully-supervised instance segmentation methods without the need for additional human resources or increased computational costs. The code is available at: TFISegComment: 14 pages,5 figure

Persistent Josephson tunneling between Bi2_2Sr2_2CaCu2_2O8+x_{8+x} flakes twisted by 45∘^\circ across the superconducting dome

There is a heated debate on the Josephson effect in twisted Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ flakes. Recent experimental results suggest the presence of either anomalously isotropic pairing or exotic $d$+i$d$-wave pairing, in addition to the commonly believed $d$-wave one. Here, we address this controversy by fabricating ultraclean junctions with uncompromised crystalline quality and stoichiometry at the junction interfaces. In the optimally doped regime, we obtain prominent Josephson coupling (2-4 mV) in multiple junctions with the twist angle of 45$^\circ$, in sharp contrast to a recent report that shows two orders of magnitude suppression around 45$^\circ$ from the value at 0$^\circ$. We further extend this study to the previously unexplored overdoped regime and observe pronounced Josephson tunneling at 45$^\circ$ together with Josephson diode effect up to 50 K. Our work helps establish the persistent presence of an isotropic pairing component across the entire superconducting phase diagram.Comment: 6 pages, 5 figure

Specific frequency bands of amplitude low-frequency fluctuations in memory-related cognitive impairment: predicting Alzheimer’s disease

Resting-state functional magnetic resonance imaging was utilized to measure the amplitude low frequency fluctuations (ALFF) in human subjects with Alzheimer’s disease (AD) and normal control (NC). Two specific frequency bands (Slow5: 0.01-0.027Hz and Slow4: 0.027-0.073Hz) were analysed in the main cognitive control related four subregions of the right ventral lateral prefrontal cortex (VLPFC), i.e. IFJ, posterior-VLPFC, mid-VLPFC, and anterior-VLPFC. Differences in ALFF values between the AD and the NC group were found throughout the subregions of the right VLPFC. Compared to normal control group, decreased ALFF values were observed in AD patients in the IFJ (in two given frequency bands), and the mid-VLPFC (in Slow5). In contrast, increased ALFF valued were observed in AD patients in the posterior- and anterior-VLPFC (in both Slow5 and Slow4), and also in the mid-VLPFC in Slow4. Moreover, significant ALFF differences between the IFJ and three other subregions of the right VLPFC were found. Furthermore, ALFF values in the right VLPFC showed significant correlations with the time course of disease. Taken together, our findings suggest that AD patients have largely abnormalities in intrinsic neural oscillations which are in line with the AD pathophysiology, and further reveal that the abnormalities are dependent on specific frequency bands. Thus, frequency-domain analyses of the ALFF may provide a useful tool to investigate the AD pathophysiology

Improving Sparse Representation-Based Classification Using Local Principal Component Analysis

Sparse representation-based classification (SRC), proposed by Wright et al., seeks the sparsest decomposition of a test sample over the dictionary of training samples, with classification to the most-contributing class. Because it assumes test samples can be written as linear combinations of their same-class training samples, the success of SRC depends on the size and representativeness of the training set. Our proposed classification algorithm enlarges the training set by using local principal component analysis to approximate the basis vectors of the tangent hyperplane of the class manifold at each training sample. The dictionary in SRC is replaced by a local dictionary that adapts to the test sample and includes training samples and their corresponding tangent basis vectors. We use a synthetic data set and three face databases to demonstrate that this method can achieve higher classification accuracy than SRC in cases of sparse sampling, nonlinear class manifolds, and stringent dimension reduction.Comment: Published in "Computational Intelligence for Pattern Recognition," editors Shyi-Ming Chen and Witold Pedrycz. The original publication is available at http://www.springerlink.co

Accelerated Li⁺ Desolvation for Diffusion Booster Enabling Low‐Temperature Sulfur Redox Kinetics via Electrocatalytic Carbon‐Grazfted‐CoP Porous Nanosheets

Lithium–sulfur (Li–S) batteries are famous for their high energy density and low cost, but prevented by sluggish redox kinetics of sulfur species due to depressive Li ion diffusion kinetics, especially under low-temperature environment. Herein, a combined strategy of electrocatalysis and pore sieving effect is put forward to dissociate the Li+ solvation structure to stimulate the free Li+ diffusion, further improving sulfur redox reaction kinetics. As a protocol, an electrocatalytic porous diffusion-boosted nitrogen-doped carbon-grafted-CoP nanosheet is designed via forming the NCoP active structure to release more free Li+ to react with sulfur species, as fully investigated by electrochemical tests, theoretical simulations and in situ/ex situ characterizations. As a result, the cells with diffusion booster achieve desirable lifespan of 800 cycles at 2 C and excellent rate capability (775 mAh g−1 at 3 C). Impressively, in a condition of high mass loading or low-temperature environment, the cell with 5.7 mg cm−2 stabilizes an areal capacity of 3.2 mAh cm−2 and the charming capacity of 647 mAh g−1 is obtained under 0 °C after 80 cycles, demonstrating a promising route of providing more free Li ions toward practical high-energy Li–S batteries

The 3rd Anti-UAV Workshop & Challenge: Methods and Results

The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released. There are two main differences between this year's competition and the previous two. First, we have expanded the existing dataset, and for the first time, released a training set so that participants can focus on improving their models. Second, we set up two tracks for the first time, i.e., Anti-UAV Tracking and Anti-UAV Detection & Tracking. Around 76 participating teams from the globe competed in the 3rd Anti-UAV Challenge. In this paper, we provide a brief summary of the 3rd Anti-UAV Workshop & Challenge including brief introductions to the top three methods in each track. The submission leaderboard will be reopened for researchers that are interested in the Anti-UAV challenge. The benchmark dataset and other information can be found at: https://anti-uav.github.io/.Comment: Technical report for 3rd Anti-UAV Workshop and Challenge. arXiv admin note: text overlap with arXiv:2108.0990