Physical-aware Cross-modal Adversarial Network for Wearable Sensor-based Human Action Recognition

Wearable sensor-based Human Action Recognition (HAR) has made significant strides in recent times. However, the accuracy performance of wearable sensor-based HAR is currently still lagging behind that of visual modalities-based systems, such as RGB video and depth data. Although diverse input modalities can provide complementary cues and improve the accuracy performance of HAR, wearable devices can only capture limited kinds of non-visual time series input, such as accelerometers and gyroscopes. This limitation hinders the deployment of multimodal simultaneously using visual and non-visual modality data in parallel on current wearable devices. To address this issue, we propose a novel Physical-aware Cross-modal Adversarial (PCA) framework that utilizes only time-series accelerometer data from four inertial sensors for the wearable sensor-based HAR problem. Specifically, we propose an effective IMU2SKELETON network to produce corresponding synthetic skeleton joints from accelerometer data. Subsequently, we imposed additional constraints on the synthetic skeleton data from a physical perspective, as accelerometer data can be regarded as the second derivative of the skeleton sequence coordinates. After that, the original accelerometer as well as the constrained skeleton sequence were fused together to make the final classification. In this way, when individuals wear wearable devices, the devices can not only capture accelerometer data, but can also generate synthetic skeleton sequences for real-time wearable sensor-based HAR applications that need to be conducted anytime and anywhere. To demonstrate the effectiveness of our proposed PCA framework, we conduct extensive experiments on Berkeley-MHAD, UTD-MHAD, and MMAct datasets. The results confirm that the proposed PCA approach has competitive performance compared to the previous methods on the mono sensor-based HAR classification problem.Comment: First IMU2SKELETON GANs approach for wearable HAR problem. arXiv admin note: text overlap with arXiv:2208.0809

One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation

Knowledge distillation~(KD) has proven to be a highly effective approach for enhancing model performance through a teacher-student training scheme. However, most existing distillation methods are designed under the assumption that the teacher and student models belong to the same model family, particularly the hint-based approaches. By using centered kernel alignment (CKA) to compare the learned features between heterogeneous teacher and student models, we observe significant feature divergence. This divergence illustrates the ineffectiveness of previous hint-based methods in cross-architecture distillation. To tackle the challenge in distilling heterogeneous models, we propose a simple yet effective one-for-all KD framework called OFA-KD, which significantly improves the distillation performance between heterogeneous architectures. Specifically, we project intermediate features into an aligned latent space such as the logits space, where architecture-specific information is discarded. Additionally, we introduce an adaptive target enhancement scheme to prevent the student from being disturbed by irrelevant information. Extensive experiments with various architectures, including CNN, Transformer, and MLP, demonstrate the superiority of our OFA-KD framework in enabling distillation between heterogeneous architectures. Specifically, when equipped with our OFA-KD, the student models achieve notable performance improvements, with a maximum gain of 8.0% on the CIFAR-100 dataset and 0.7% on the ImageNet-1K dataset. PyTorch code and checkpoints can be found at https://github.com/Hao840/OFAKD

Synthesis and characterization of biodegradable poly(ester anhydride) based on -caprolactone and adipic anhydride initiated by potassium poly(ethylene glycol)ate

Novel biodegradable poly(ester anhydride) block copolymers based on -caprolactone (-CL) and adipic anhydride (AA) were prepared by sequential polymerization. -CL was first initiated by potassium poly(ethylene glycol)ate and polymerized into active chains (PCL-O-K+), which were then used to initiate the ring-opening polymerization of AA. The effects of the AA feed ratio, solvent polarity, monomer concentration, and temperature on sequential polymerization were investigated. The copolymers, obtained under different conditions, were characterized by Fourier transform infrared, 1H NMR, gel permeation chromatography (GPC), and differential scanning calorimetry (DSC). The GPC results showed that the weight-average molecular weights of the block copolymers were approximately 6.0 × 104. The DSC results indicated the immiscibility of the two component

Popularity-driven coordinated caching in named data networking

ABSTRACT The built-in caching capability of future Named Data Networking (NDN) promises to enable effective content distribution at a global scale without requiring special infrastructure. The aim of this work is to design efficient caching schemes in NDN to achieve better performance at both the network layer and application layer. With the specific objective of minimizing the inter-ISP (Internet Service Provider) traffic and average access latency, we first formulate the optimization problems for different objectives and then solve them to obtain the optimal replica placement. Then we develop popularity-driven caching schemes which dynamically place the replicas in the caches on the en-route path in a coordination fashion. Simulation results show that the performances of our caching algorithms are much closer to the optimum and outperform the widely used schemes in terms of the inter-ISP traffic and the average number of access hops. Finally, we thoroughly evaluate the impact of several important design issues such as network topology, cache size, access pattern and content popularity on the caching performance and demonstrate that the proposed schemes are effective, stable, scalable and with reasonably light overhead

Double Input Capacitively Coupled Contactless Conductivity Detector with Phase Shift

A double input capacitively coupled contactless conductivity detector (DIC⁴D) device which gets higher sensitivity has been described in this paper. The detector consists of two input electrodes and one output electrode. When two alternating current (AC) voltages with the same amplitude and different phases are imposed on each input electrode, the equivalent resistance of the output electrode is reduced because of the interference of the two signals with different phase angles. For a capacitively coupled contactless conductivity detector (C⁴D), the ratio of the response of KCl solution to that of distilled water is 1.6. However, for DIC⁴D, the ratio is 1.55 at a phase difference of 0° and increases to 1.8 at the phase difference of 170°, respectively. For C⁴D, the response of KCl solution is a linear function of the logarithm of concentrations from 10^–5 M to 10^–2 M, and the slope is 5.58. However, the slope of the response increases to 7.13 in DIC⁴D, and the limit of detection (LOD) of DIC⁴D is estimated to be 5 × 10^–8 M. The slope of the three-way DIC⁴D is increased to 69.78. A flow injection device is employed for the evaluation of the applicability of DIC⁴D with the same range, and good reproducibility is confirmed through flow injection of the same solution 10 times. The relative standard deviation (RSD) is 0.7%, which demonstrates a promising application to capillary electrophoresis (CE)

SIRT7 Deacetylates STRAP to Regulate p53 Activity and Stability

Serine-threonine kinase receptor-associated protein (STRAP) functions as a regulator of both TGF-β and p53 signaling that participates in the regulation of cell proliferation and cell death in response to various stresses. Here, we demonstrate that STRAP acetylation plays an important role in p53-mediated cell cycle arrest and apoptosis. STRAP is acetylated at lysines 147, 148, and 156 by the acetyltransferases CREB-binding protein (CBP) and that the acetylation is reversed by the deacetylase sirtuin7 (SIRT7). Hypo- or hyperacetylation mutations of STRAP at lysines 147, 148, and 156 (3KR or 3KQ) influence its activation and stabilization of p53. Moreover, following 5-fluorouracil (5-FU) treatment, STRAP is mobilized from the cytoplasm to the nucleus and promotes STRAP acetylation. Our finding on the regulation of STRAP links p53 with SIRT7 influencing p53 activity and stability