GaitFormer: Revisiting Intrinsic Periodicity for Gait Recognition

Gait recognition aims to distinguish different walking patterns by analyzing video-level human silhouettes, rather than relying on appearance information. Previous research on gait recognition has primarily focused on extracting local or global spatial-temporal representations, while overlooking the intrinsic periodic features of gait sequences, which, when fully utilized, can significantly enhance performance. In this work, we propose a plug-and-play strategy, called Temporal Periodic Alignment (TPA), which leverages the periodic nature and fine-grained temporal dependencies of gait patterns. The TPA strategy comprises two key components. The first component is Adaptive Fourier-transform Position Encoding (AFPE), which adaptively converts features and discrete-time signals into embeddings that are sensitive to periodic walking patterns. The second component is the Temporal Aggregation Module (TAM), which separates embeddings into trend and seasonal components, and extracts meaningful temporal correlations to identify primary components, while filtering out random noise. We present a simple and effective baseline method for gait recognition, based on the TPA strategy. Extensive experiments conducted on three popular public datasets (CASIA-B, OU-MVLP, and GREW) demonstrate that our proposed method achieves state-of-the-art performance on multiple benchmark tests

Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images

Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections. Existing medical image segmentation methods are almost uni-modal methods based on image. However, these image-only methods tend to produce inaccurate results unless trained with large amounts of annotated data. To overcome this challenge, we propose a language-driven segmentation method that uses text prompt to improve to the segmentation result. Experiments on the QaTa-COV19 dataset indicate that our method improves the Dice score by 6.09% at least compared to the uni-modal methods. Besides, our extended study reveals the flexibility of multi-modal methods in terms of the information granularity of text and demonstrates that multi-modal methods have a significant advantage over image-only methods in terms of the size of training data required.Comment: Provisional Acceptance by MICCAI 202

MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation

Domain shift has been a long-standing issue for medical image segmentation. Recently, unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentation performance by distilling knowledge from a label-rich source domain to a target domain without labels. In this work, we propose a multi-scale self-ensembling based UDA framework for automatic segmentation of two key brain structures i.e., Vestibular Schwannoma (VS) and Cochlea on high-resolution T2 images. First, a segmentation-enhanced contrastive unpaired image translation module is designed for image-level domain adaptation from source T1 to target T2. Next, multi-scale deep supervision and consistency regularization are introduced to a mean teacher network for self-ensemble learning to further close the domain gap. Furthermore, self-training and intensity augmentation techniques are utilized to mitigate label scarcity and boost cross-modality segmentation performance. Our method demonstrates promising segmentation performance with a mean Dice score of 83.8% and 81.4% and an average asymmetric surface distance (ASSD) of 0.55 mm and 0.26 mm for the VS and Cochlea, respectively in the validation phase of the crossMoDA 2022 challenge.Comment: Accepted by BrainLes MICCAI proceedings (5th solution for MICCAI 2022 Cross-Modality Domain Adaptation (crossMoDA) Challenge

Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks

In this paper, we propose a novel layer-adaptive weight-pruning approach for Deep Neural Networks (DNNs) that addresses the challenge of optimizing the output distortion minimization while adhering to a target pruning ratio constraint. Our approach takes into account the collective influence of all layers to design a layer-adaptive pruning scheme. We discover and utilize a very important additivity property of output distortion caused by pruning weights on multiple layers. This property enables us to formulate the pruning as a combinatorial optimization problem and efficiently solve it through dynamic programming. By decomposing the problem into sub-problems, we achieve linear time complexity, making our optimization algorithm fast and feasible to run on CPUs. Our extensive experiments demonstrate the superiority of our approach over existing methods on the ImageNet and CIFAR-10 datasets. On CIFAR-10, our method achieves remarkable improvements, outperforming others by up to 1.0% for ResNet-32, 0.5% for VGG-16, and 0.7% for DenseNet-121 in terms of top-1 accuracy. On ImageNet, we achieve up to 4.7% and 4.6% higher top-1 accuracy compared to other methods for VGG-16 and ResNet-50, respectively. These results highlight the effectiveness and practicality of our approach for enhancing DNN performance through layer-adaptive weight pruning. Code will be available on https://github.com/Akimoto-Cris/RD_VIT_PRUNE

Ruthenium nanoclusters modified by zinc species towards enhanced electrochemical hydrogen evolution reaction

Ruthenium (Ru) has been considered a promising electrocatalyst for electrochemical hydrogen evolution reaction (HER) while its performance is limited due to the problems of particle aggregation and competitive adsorption of the reaction intermediates. Herein, we reported the synthesis of a zinc (Zn) modified Ru nanocluster electrocatalyst anchored on multiwalled carbon nanotubes (Ru-Zn/MWCNTs). The Ru-Zn catalysts were found to be highly dispersed on the MWCNTs substrate. Moreover, the Ru-Zn/MWCNTs exhibited low overpotentials of 26 and 119 mV for achieving current intensities of 10 and 100 mA cm−2 under alkaline conditions, respectively, surpassing Ru/MWCNTs with the same Ru loading and the commercial 5 wt% Pt/C (47 and 270 mV). Moreover, the Ru-Zn/MWCNTs showed greatly enhanced stability compared to Ru/MWCNTs with no significant decay after 10,000 cycles of CV sweeps and long-term operation for 90 h. The incorporation of Zn species was found to modify the electronic structure of the Ru active species and thus modulate the adsorption energy of the Had and OHad intermediates, which could be the main reason for the enhanced HER performance. This study provides a strategy to develop efficient and stable electrocatalysts towards the clean energy conversion field

InstructCoder: Empowering Language Models for Code Editing

Code editing encompasses a variety of pragmatic tasks that developers deal with daily. Despite its relevance and practical usefulness, automatic code editing remains an underexplored area in the evolution of deep learning models, partly due to data scarcity. In this work, we explore the use of large language models (LLMs) to edit code based on user instructions, covering a broad range of implicit tasks such as comment insertion, code optimization, and code refactoring. To facilitate this, we introduce InstructCoder, the first dataset designed to adapt LLMs for general-purpose code editing, containing highdiversity code-editing tasks. It consists of over 114,000 instruction-input-output triplets and covers multiple distinct code editing scenarios. The dataset is systematically expanded through an iterative process that commences with code editing data sourced from GitHub commits as seed tasks. Seed and generated tasks are used subsequently to prompt ChatGPT for more task data. Our experiments demonstrate that open-source LLMs fine-tuned on InstructCoder can edit code correctly based on users' instructions most of the time, exhibiting unprecedented code-editing performance levels. Such results suggest that proficient instruction-finetuning can lead to significant amelioration in code editing abilities. The dataset and the source code are available at https://github.com/qishenghu/CodeInstruct

Effectiveness of acupuncture as auxiliary combined with Western medicine for epilepsy: a systematic review and meta-analysis

BackgroundAlthough more and more clinical studies have shown that acupuncture as an auxiliary combined with Western medicine is effective in the treatment of patients with epilepsy, no systematic reviews of acupuncture as a treatment for epilepsy have been published. Hence, we conducted this meta-analysis to evaluate the effect of acupuncture treatment on patients with epilepsy.MethodsThis study retrieved randomized controlled trials (RCTs) of acupuncture treatment for epilepsy from various electronic databases including PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Chinese BioMedical Literature Database, and Wangfang database. These studies evaluated the effectiveness of acupuncture as an auxiliary treatment combined with Western medicine for patients with epilepsy. The methodological quality of the studies was assessed using the Cochrane Handbook for Systematic Reviews of Interventions.ResultsA total of 17 RCTs involving a total of 1,389 participants were included. The results showed that acupuncture combined with Western medicine improved the effective rates of treatment (OR: 4.28; 95% CI: 3.04–6.02; p < 0.001), and reduced the seizure frequency of patients (SMD: −3.29; 95% CI: −3.51 to −3.07; p < 0.001) and the EEG discharge frequency (SMD: −5.58; 95% CI: −7.02 to −4.14; p < 0.001). Regarding the quality of life and adverse events, the acupuncture group was superior to the control group in improving the overall quality of life of patients with epilepsy (SMD: 14.41; 95% CI: 12.51–16.32; p < 0.001) and decreased adverse events (OR: 0.38; 95% CI: 0.23–0.63, p < 0.001).ConclusionThe results of the analysis suggested that acupuncture combined with Western medicine is probably helpful in patients with epilepsy, but strong supportive data are not yet available. Given that this study is based on a low to moderate evidence-based analysis, the conclusions should be viewed with caution.Systematic review registrationPROSPERO, identifier no. CRD42023409923