MateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments

People with Visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-explored in the field of human-centered robotics. To fill this gap, in this work, a wearable vision-based robotic system, MateRobot, is established for PVI to recognize materials and object categories beforehand. To address the computational constraints of mobile platforms, we propose a lightweight yet accurate model MateViT to perform pixel-wise semantic segmentation, simultaneously recognizing both objects and materials. Our methods achieve respective 40.2% and 51.1% of mIoU on COCOStuff-10K and DMS datasets, surpassing the previous method with +5.7% and +7.0% gains. Moreover, on the field test with participants, our wearable system reaches a score of 28 in the NASA-Task Load Index, indicating low cognitive demands and ease of use. Our MateRobot demonstrates the feasibility of recognizing material property through visual cues and offers a promising step towards improving the functionality of wearable robots for PVI. The source code has been made publicly available at https://junweizheng93.github.io/publications/MATERobot/MATERobot.html.Comment: Accepted to ICRA 2024. The source code is publicly available at https://junweizheng93.github.io/publications/MATERobot/MATERobot.htm

S3^3-MonoDETR: Supervised Shape&Scale-perceptive Deformable Transformer for Monocular 3D Object Detection

Recently, transformer-based methods have shown exceptional performance in monocular 3D object detection, which can predict 3D attributes from a single 2D image. These methods typically use visual and depth representations to generate query points on objects, whose quality plays a decisive role in the detection accuracy. However, current unsupervised attention mechanisms without any geometry appearance awareness in transformers are susceptible to producing noisy features for query points, which severely limits the network performance and also makes the model have a poor ability to detect multi-category objects in a single training process. To tackle this problem, this paper proposes a novel "Supervised Shape&Scale-perceptive Deformable Attention" (S$^3$-DA) module for monocular 3D object detection. Concretely, S$^3$-DA utilizes visual and depth features to generate diverse local features with various shapes and scales and predict the corresponding matching distribution simultaneously to impose valuable shape&scale perception for each query. Benefiting from this, S$^3$-DA effectively estimates receptive fields for query points belonging to any category, enabling them to generate robust query features. Besides, we propose a Multi-classification-based Shape$\&$Scale Matching (MSM) loss to supervise the above process. Extensive experiments on KITTI and Waymo Open datasets demonstrate that S$^3$-DA significantly improves the detection accuracy, yielding state-of-the-art performance of single-category and multi-category 3D object detection in a single training process compared to the existing approaches. The source code will be made publicly available at https://github.com/mikasa3lili/S3-MonoDETR.Comment: The source code will be made publicly available at https://github.com/mikasa3lili/S3-MonoDET

Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision

Self-supervised representation learning for human action recognition has developed rapidly in recent years. Most of the existing works are based on skeleton data while using a multi-modality setup. These works overlooked the differences in performance among modalities, which led to the propagation of erroneous knowledge between modalities while only three fundamental modalities, i.e., joints, bones, and motions are used, hence no additional modalities are explored. In this work, we first propose an Implicit Knowledge Exchange Module (IKEM) which alleviates the propagation of erroneous knowledge between low-performance modalities. Then, we further propose three new modalities to enrich the complementary information between modalities. Finally, to maintain efficiency when introducing new modalities, we propose a novel teacher-student framework to distill the knowledge from the secondary modalities into the mandatory modalities considering the relationship constrained by anchors, positives, and negatives, named relational cross-modality knowledge distillation. The experimental results demonstrate the effectiveness of our approach, unlocking the efficient use of skeleton-based multi-modality data. Source code will be made publicly available at https://github.com/desehuileng0o0/IKEM.Comment: Accepted to ICASSP 2024. The source code will be made publicly available at https://github.com/desehuileng0o0/IKE

Efficacy and safety of pharmacotherapy for refractory or unexplained chronic cough: a systematic review and network meta-analysis

Background: Refractory chronic cough (RCC) has a significant impact on patient's health-related quality of life and represents a challenge in clinical management. However, the optimal treatment for RCC remains controversial. This study aimed to investigate and compare the efficacy and safety of the current pharmacological therapeutic options for RCC. Methods: A systematic review was performed by searching PubMed, Web of Science, Embase, and Ovid databases from January 1, 2008 to March 1, 2023. All randomised control trials (RCTs) reporting outcomes of efficacy or/and safety were included in the Bayesian network meta-analysis. Here, we compared the effects on Leicester Cough Questionnaire (LCQ), Visual Analogue Scale (VAS), and objective cough frequency of patients with RCC. Besides, we also compared the incidence of adverse events (AEs) for analysis of safety. PROSPERO registration: CRD42022345940. Findings: 19 eligible RCTs included 3326 patients and 7 medication categories: P2X3 antagonist, GABA modulator, Transient Receptor Potential (TRP) modulator, NK-1 agonist, opioid analgesic, macrolide, and sodium cromoglicate. Compared with placebo, mean difference (MD) of LCQ and 24 h cough frequency for P2X3 antagonist relief were 1.637 (95% CI: 0.887–2.387) and −11.042 (P = 0.035). Compared with placebo, effect sizes (MD for LCQ and cough severity VAS) for GABA modulator were 1.347 (P = 0.003) and −7.843 (P = 0.003). In the network meta-analysis, gefapixant is the most effective treatment for patients with RCC (The Surface Under the Cumulative Ranking Curves (SUCRA) is 0.711 in LCQ, 0.983 in 24 h cough frequency, and 0.786 in cough severity VAS). Lesogaberan had better efficacy than placebo (SUCRA: 0.632 vs. 0.472) in 24 h cough frequency. Eliapixant and lesogaberan had better efficacy than placebo in cough severity VAS. However, TRP modulator had worse efficacy than placebo. In the meta-analysis of AEs, the present study found P2X3 antagonist had a significant correlation to AEs (RR: 1.129, 95% CI: 1.012–1.259), especially taste-related AEs (RR: 6.216, P < 0.05). Interpretation: In this network meta-analysis, P2X3 antagonist showing advantages in terms of efficacy is currently the most promising medication for treatment of RCC. GABA modulator also showed potential efficacy for RCC but with AEs of the central system. Nevertheless, the role of TRP modulator needed to be revisited. Further research is needed to determine the potential beneficiary population for optimizing the pharmacological management of chronic cough. Funding: National Natural Science Foundation of China ( 81870079), Guangdong Science and Technology Project ( 2021A050520012), Incubation Program of National Science Foundation for Distinguished Young Scholars ( GMU2020-207)

DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000