Minimizing Age of Collection for Multiple Access in Wireless Industrial Internet of Things

This paper investigates the information freshness of Industrial Internet of Things (IIoT) systems, where each IoT device makes a partial observation of a common target and transmits the information update to a central receiver to recover the complete observation. We consider the age of collection (AoC) performance as a measure of information freshness. Unlike the conventional age of information (AoI) metric, the instantaneous AoC decreases only when all cooperative packets for a common observation are successfully received. Hence, effectively allocating wireless time-frequency resources among IoT devices to achieve a low average AoC at the central receiver is paramount. Three multiple access schemes are considered in this paper: time-division multiple access (TDMA) without retransmission, TDMA with retransmission, and frequency-division multiple access (FDMA). First, our theoretical analysis indicates that TDMA with retransmission outperforms the other two schemes in terms of average AoC. Subsequently, we implement information update systems based on the three schemes on software-defined radios. Experimental results demonstrate that considering the medium access control (MAC) overhead in practice, FDMA achieves a lower average AoC than TDMA with or without retransmission in the high signal-to-noise ratio (SNR) regime. In contrast, TDMA with retransmission provides a stable and relatively low average AoC over a wide SNR range, which is favorable for IIoT applications. Overall, we present a theoretical-plus-experimental investigation of AoC in IIoT information update systems

ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering

We propose a novel attention based deep learning architecture for visual question answering task (VQA). Given an image and an image related natural language question, VQA generates the natural language answer for the question. Generating the correct answers requires the model's attention to focus on the regions corresponding to the question, because different questions inquire about the attributes of different image regions. We introduce an attention based configurable convolutional neural network (ABC-CNN) to learn such question-guided attention. ABC-CNN determines an attention map for an image-question pair by convolving the image feature map with configurable convolutional kernels derived from the question's semantics. We evaluate the ABC-CNN architecture on three benchmark VQA datasets: Toronto COCO-QA, DAQUAR, and VQA dataset. ABC-CNN model achieves significant improvements over state-of-the-art methods on these datasets. The question-guided attention generated by ABC-CNN is also shown to reflect the regions that are highly relevant to the questions

Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos

Multi-person 3D mesh recovery from videos is a critical first step towards automatic perception of group behavior in virtual reality, physical therapy and beyond. However, existing approaches rely on multi-stage paradigms, where the person detection and tracking stages are performed in a multi-person setting, while temporal dynamics are only modeled for one person at a time. Consequently, their performance is severely limited by the lack of inter-person interactions in the spatial-temporal mesh recovery, as well as by detection and tracking defects. To address these challenges, we propose the Coordinate transFormer (CoordFormer) that directly models multi-person spatial-temporal relations and simultaneously performs multi-mesh recovery in an end-to-end manner. Instead of partitioning the feature map into coarse-scale patch-wise tokens, CoordFormer leverages a novel Coordinate-Aware Attention to preserve pixel-level spatial-temporal coordinate information. Additionally, we propose a simple, yet effective Body Center Attention mechanism to fuse position information. Extensive experiments on the 3DPW dataset demonstrate that CoordFormer significantly improves the state-of-the-art, outperforming the previously best results by 4.2%, 8.8% and 4.7% according to the MPJPE, PAMPJPE, and PVE metrics, respectively, while being 40% faster than recent video-based approaches. The released code can be found at https://github.com/Li-Hao-yuan/CoordFormer.Comment: ICCV 202

Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADCs is particularly important since a good codebook design can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices $K$. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of $K$ is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for $K$ and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of $K$ are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of $K$ and the design of the ADC quantizer; 2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol.Comment: Submitted to IEEE for possible publicatio

Prospective comparison of 68Ga-FAPI-04 and 18F-FDG PET/CT for tumor staging in nasopharyngeal carcinoma

PurposeTo explore the difference in the effectiveness of gallium-68 fibroblast activation protein inhibitor (68Ga-FAPI-04) PET/CT and fluorine-18 fluorodeoxyglucose (18F-FDG) PET/CT for the initial staging of patients with nasopharyngeal carcinoma (NPC).MethodsThe Affiliated Hospital of Southwest Medical University hosted this single-center prospective investigation (Clinical Trials registration No.ChiCTR2100044131) between March 2020 and September 2021. Within a week, all subjects underwent MR scans, 68Ga-FAPI-04 PET/CT, and 18F-FDG PET/CT in order. The effectiveness of medical staging employing 68Ga-FAPI-04 and 18F-FDG PET/CT was compared.ResultsTwenty-eight patients with primary NPC were evaluated (mean age53 ± 11 years). 68Ga-FAPI-04 PET/CT indicated an elevated recognition rate for diagnosing primary tumors (28/28 [100%] vs. 27/28 [96%]) and lymph node metastases (263/285 [92%] vs. 228/285 [80%]), but a lower detection rate for distant metastases (5/7 [71%] vs. 7/7 [100%]) compared with 18F-FDG PET/CT. A significant association between the maximum standard uptake value (SUVmax) of 18F-FDG PET and 68Ga-FAPI-04 PET was found in the primary cancers (r = 0.691, p < 0.001). In comparison to 18F-FDG PET/CT, 68Ga-FAPI-04 PET/CT upstaged the T stage in five patients while downstaging the N stage in seven patients. 68Ga-FAPI-04 PET/CT corrected the overall staging of five patients on18F-FDG PET/CT.Conclusion68Ga-FAPI-04 PET/CT is preferable to 18F-FDG PET/CT for NPC staging in terms of the detection efficiency for primary tumors and lymph node metastasis. This is especially true when evaluating the primary cancer and any spread to contiguous tissues. It is possible to improve the staging assessment of NPC by using 68Ga-FAPI-04 PET/CT in conjunction with 18F-FDG PET/CT

Metabolism and Pharmacokinetics of Novel Selective Vascular Endothelial Growth Factor Receptor-2 Inhibitor Apatinib in Humans

ABSTRACT Apatinib is a new oral antiangiogenic molecule that inhibits vascular endothelial growth factor receptor-2. The present study aimed to determine the metabolism, pharmacokinetics, and excretion of apatinib in humans and to identify the enzymes responsible for its metabolism. The primary routes of apatinib biotransformation included E-and Z-cyclopentyl-3-hydroxylation, N-dealkylation, pyridyl-25-N-oxidation, 16-hydroxylation, dioxygenation, and O-glucuronidation after 3-hydroxylation. Nine major metabolites were confirmed by comparison with reference standards. The total recovery of the administered dose was 76.8% within 96 hours postdose, with 69.8 and 7.02% of the administered dose excreted in feces and urine, respectively. About 59.0% of the administered dose was excreted unchanged via feces. Unchanged apatinib was detected in negligible quantities in urine, indicating that systemically available apatinib was extensively metabolized. The major circulating metabolite was the pharmacologically inactive E-3-hydroxy-apatinib-O-glucuronide (M9-2), the steady-state exposure of which was 125% that of the apatinib. The steady-state exposures of E-3-hydroxy-apatinib (M1-1), Z-3-hydroxy-apatinib (M1-2), and apatinib-25-N-oxide (M1-6) were 56, 22, and 32% of parent drug exposure, respectively. Calculated as pharmacological activity index values, the contribution of M1-1 to the pharmacology of the drug was 5.42 to 19.3% that of the parent drug. The contribution of M1-2 and M1-6 to the pharmacology of the drug was less than 1%. Therefore, apatinib was a major contributor to the overall pharmacological activity in humans. Apatinib was metabolized primarily by CYP3A4/ 5 and, to a lesser extent, by CYP2D6, CYP2C9, and CYP2E1. UGT2B7 was the main enzyme responsible for M9-2 formation. Both UGT1A4 and UGT2B7 were responsible for Z-3-hydroxyapatinib-O-glucuronide (M9-1) formation

Understanding the Electron Beam Resilience of Two-Dimensional Conjugated Metal–Organic Frameworks

Knowledge of the atomic structure of layer-stacked two-dimensional conjugated metal–organic frameworks (2D c-MOFs) is an essential prerequisite for establishing their structure–property correlation. For this, atomic resolution imaging is often the method of choice. In this paper, we gain a better understanding of the main properties contributing to the electron beam resilience and the achievable resolution in the high-resolution TEM images of 2D c-MOFs, which include chemical composition, density, and conductivity of the c-MOF structures. As a result, sub-angstrom resolution of 0.95 Å has been achieved for the most stable 2D c-MOF of the considered structures, Cu3(BHT) (BHT = benzenehexathiol), at an accelerating voltage of 80 kV in a spherical and chromatic aberration-corrected TEM. Complex damage mechanisms induced in Cu3(BHT) by the elastic interactions with the e-beam have been explained using detailed ab initio molecular dynamics calculations. Experimental and calculated knock-on damage thresholds are in good agreement