Wireless Off-body Channel Analysis and Sparse Modeling

University of Technology Sydney. Faculty of Engineering and Information Technology.The successful application of very rapidly growing wearable devices relies on the research on the propagation characteristics of off-body channels which plays a key role in connecting the wireless body area network and cellular network, WiFi and other local area networks. This thesis concentrates on the bottleneck problems of the measurement, analysis and modeling of the off-body propagation characteristics. A large number of measurement investigations have been carried out to solve the thorny problem of complicated and changeable scenes of off-body channel and heavy fading caused by adjacent humans. These activities include different transmission schemes, different influence factors, and typical changeable configurations. Then, in this study, the systematic analysis of the measured big channel datasets are conducted based on traditional large/small scale propagation analysis methods and compressive sensing based sparse channel analysis methods. The first part of the thesis discusses the measurement and analysis of typical off-body channel types including single input single output (SISO), diversity reception and multiple input multiple output (MIMO). A two-factor integrated path loss model with variable body worn locations and variable access point (AP) height is proposed to improve the power management and link budgeting ability in off-body scenarios. A highly robust circularly polarized spatial diversity off-body scheme is made up and validated to tackle the heavy fading problem. In addition, the influences of humans including both hand-held effect and body obstruction effect on off-body transmission angular spectrum and capacity are estimated. In the second part of the thesis, the novel compressive sensing based sparse channel analysis methods are proposed to deal with the modeling problems of off-body temporal channels with complex multipath components. The channel impulse response (CIR) models of SISO and MIMO channels based on single measurement vector (SMV) and multi-measurement vector (MMV-CS) compressive sensing methods respectively are established. Finally, according to the off-body link types, the propagation characteristics, sparse analysis and modeling methods are integrated into several channel simulators with friendly GUI interface, whose source codes are shared on gitHub. Those models and simulators are expected to be used in theoretical analysis and engineering practice for the coverage planning, link simulation, algorithm design, and performance validation

Superiority of Multi-Head Attention in In-Context Linear Regression

We present a theoretical analysis of the performance of transformer with softmax attention in in-context learning with linear regression tasks. While the existing literature predominantly focuses on the convergence of transformers with single-/multi-head attention, our research centers on comparing their performance. We conduct an exact theoretical analysis to demonstrate that multi-head attention with a substantial embedding dimension performs better than single-head attention. When the number of in-context examples D increases, the prediction loss using single-/multi-head attention is in O(1/D), and the one for multi-head attention has a smaller multiplicative constant. In addition to the simplest data distribution setting, we consider more scenarios, e.g., noisy labels, local examples, correlated features, and prior knowledge. We observe that, in general, multi-head attention is preferred over single-head attention. Our results verify the effectiveness of the design of multi-head attention in the transformer architecture

DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Models

Recently, Generative Diffusion Models (GDMs) have showcased their remarkable capabilities in learning and generating images. A large community of GDMs has naturally emerged, further promoting the diversified applications of GDMs in various fields. However, this unrestricted proliferation has raised serious concerns about copyright protection. For example, artists including painters and photographers are becoming increasingly concerned that GDMs could effortlessly replicate their unique creative works without authorization. In response to these challenges, we introduce a novel watermarking scheme, DiffusionShield, tailored for GDMs. DiffusionShield protects images from copyright infringement by GDMs through encoding the ownership information into an imperceptible watermark and injecting it into the images. Its watermark can be easily learned by GDMs and will be reproduced in their generated images. By detecting the watermark from generated images, copyright infringement can be exposed with evidence. Benefiting from the uniformity of the watermarks and the joint optimization method, DiffusionShield ensures low distortion of the original image, high watermark detection performance, and the ability to embed lengthy messages. We conduct rigorous and comprehensive experiments to show the effectiveness of DiffusionShield in defending against infringement by GDMs and its superiority over traditional watermarking methods

RDKG: A Reinforcement Learning Framework for Disease Diagnosis on Knowledge Graph

Automatic disease diagnosis from symptoms has attracted much attention in medical practices. It can assist doctors and medical practitioners in narrowing down disease candidates, reducing testing costs, improving diagnosis efficiency, and more importantly, saving human lives. Existing research has made significant progress in diagnosing disease but was limited by the gap between interpretability and accuracy. To fill this gap, in this paper, we propose a method called Reinforced Disease Diagnosis on Knowlege Graph (RDKG). Specifically, we first construct a knowledge graph containing all information from electronic medical records. To capture informative embeddings, we propose an enhanced knowledge graph embedding method that can embed information outside the knowledge graph into entity embedding. Then we transform the automatic disease diagnosis task into a Markov decision process on the knowledge graph. After that, we design a reinforcement learning method with a soft reward mechanism and a pruning strategy to solve the Markov decision process. We accomplish automated disease diagnosis by finding a path from symptoms to disease. The experimental results show that our model can effectively utilize heterogeneous information in the knowledge graph to complete the automatic disease diagnosis. Besides, our model demonstrates supreme performance in both accuracy and interpretability

Sharpness-Aware Data Poisoning Attack

Recent research has highlighted the vulnerability of Deep Neural Networks (DNNs) against data poisoning attacks. These attacks aim to inject poisoning samples into the models' training dataset such that the trained models have inference failures. While previous studies have executed different types of attacks, one major challenge that greatly limits their effectiveness is the uncertainty of the re-training process after the injection of poisoning samples, including the re-training initialization or algorithms. To address this challenge, we propose a novel attack method called ''Sharpness-Aware Data Poisoning Attack (SAPA)''. In particular, it leverages the concept of DNNs' loss landscape sharpness to optimize the poisoning effect on the worst re-trained model. It helps enhance the preservation of the poisoning effect, regardless of the specific retraining procedure employed. Extensive experiments demonstrate that SAPA offers a general and principled strategy that significantly enhances various types of poisoning attacks

PB2 segment promotes high-pathogenicity of H5N1 avian influenza viruses in mice

H5N1 influenza viruses with high lethality are a continuing threat to humans and poultry. Recently, H5N1 high-pathogenicity avian influenza virus (HPAIV) has been shown to transmit through aerosols between ferrets in lab experiments by acquiring some mutation. This is another deeply aggravated threat of H5N1 HPAIV to humans. To further explore the molecular determinant of H5N1 HPAIV virulence in a mammalian model, we compared the virulence of A/Duck/Guangdong/212/2004 (DK212) and A/Quail/Guangdong/90/2004 (QL90). Though they were genetically similar, they had different pathogenicity in mice, as well as their 16 reassortants. The results indicated that a swap of the PB2 gene could dramatically decrease the virulence of rgDK212 in mice (1896-fold) but increase the virulence of rgQL90 in mice (60-fold). Furthermore, the polymerase activity assays showed that swapping PB2 genes between these two viruses significantly changed the activity of polymerase complexes in 293T cells. The mutation Ser715Asn in PB2 sharply attenuated the virulence of rgDK212 in mice (2710-fold). PB2 segment promotes high-pathogenicity of H5N1 avian influenza viruses in mice and 715 Ser in PB2 plays an important role in determing high virulence of DK212 in mice

Bridging multiscale interfaces for developing ionically conductive high-voltage iron sulfate-containing sodium-based battery positive electrodes

Non-aqueous sodium-ion batteries (SiBs) are a viable electrochemical energy storage system for grid storage. However, the practical development of SiBs is hindered mainly by the sluggish kinetics and interfacial instability of positive-electrode active materials, such as polyanion-type iron-based sulfates, at high voltage. Here, to circumvent these issues, we proposed the multiscale interface engineering of Na$_{2.26}$Fe$_{1.87}$(SO$_4$)$_3$, where bulk heterostructure and exposed crystal plane were tuned to improve the Na-ion storage performance. Physicochemical characterizations and theoretical calculations suggested that the heterostructure of Na$_6$Fe(SO$_4$)$_4$ phase facilitated ionic kinetics by densifying Na-ion migration channels and lowering energy barriers. The (11-2) plane of Na$_{2.26}$Fe$_{1.87}$(SO$_4$)$_3$ promoted the adsorption of the electrolyte solution ClO4− anions and fluoroethylene carbonate molecules, which formed an inorganic-rich Na-ion conductive interphase at the positive electrode. When tested in combination with a presodiated FeS/carbon-based negative electrode in laboratory- scale single-layer pouch cell configuration, the Na$_{2.26}$Fe$_{1.87}$(SO$_4$)$_3$-based positive electrode enables an initial discharge capacity of about 83.9 mAh g$^{−1}$, an average cell discharge voltage of 2.35 V and a specific capacity retention of around 97% after 40 cycles at 24 mA g$^{−1}$ and 25 °C