Identification and Estimation of Causal Effects Using non-Gaussianity and Auxiliary Covariates

Assessing causal effects in the presence of unmeasured confounding is a challenging problem. Although auxiliary variables, such as instrumental variables, are commonly used to identify causal effects, they are often unavailable in practice due to stringent and untestable conditions. To address this issue, previous researches have utilized linear structural equation models to show that the causal effect can be identifiable when noise variables of the treatment and outcome are both non-Gaussian. In this paper, we investigate the problem of identifying the causal effect using auxiliary covariates and non-Gaussianity from the treatment. Our key idea is to characterize the impact of unmeasured confounders using an observed covariate, assuming they are all Gaussian. The auxiliary covariate can be an invalid instrument or an invalid proxy variable. We demonstrate that the causal effect can be identified using this measured covariate, even when the only source of non-Gaussianity comes from the treatment. We then extend the identification results to the multi-treatment setting and provide sufficient conditions for identification. Based on our identification results, we propose a simple and efficient procedure for calculating causal effects and show the $\sqrt{n}$-consistency of the proposed estimator. Finally, we evaluate the performance of our estimator through simulation studies and an application.Comment: 16 papges, 7 Figure

Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses

For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significantly eases communication resource pressure for vehicular metaverse applications to achieve this goal. SemCom enables high-quality and ultra-efficient vehicular communication, even with explosively increasing data traffic among vehicles. In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool. The global and local metaverses are brand-new concepts from the metaverse's distribution standpoint. Considering the QoS of users, this article explores the potential security vulnerabilities of the proposed framework. To that purpose, this study highlights a specific security risk to the framework's SemCom module and offers a viable defense solution, so encouraging community researchers to focus more on vehicular metaverse security. Finally, we provide an overview of the open issues of secure SemCom in the vehicular metaverses, notably pointing out potential future research directions

On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks

Generative Artificial Intelligence (GAI) shows remarkable productivity and creativity in Mobile Edge Networks, such as the metaverse and the Industrial Internet of Things. Federated learning is a promising technique for effectively training GAI models in mobile edge networks due to its data distribution. However, there is a notable issue with communication consumption when training large GAI models like generative diffusion models in mobile edge networks. Additionally, the substantial energy consumption associated with training diffusion-based models, along with the limited resources of edge devices and complexities of network environments, pose challenges for improving the training efficiency of GAI models. To address this challenge, we propose an on-demand quantized energy-efficient federated diffusion approach for mobile edge networks. Specifically, we first design a dynamic quantized federated diffusion training scheme considering various demands from the edge devices. Then, we study an energy efficiency problem based on specific quantization requirements. Numerical results show that our proposed method significantly reduces system energy consumption and transmitted model size compared to both baseline federated diffusion and fixed quantized federated diffusion methods while effectively maintaining reasonable quality and diversity of generated data

The microstructure network and thermoelectric properties of bulk (Bi,Sb)₂Te₃

We report small-angle neutron scattering studies on the microstructure network in bulk (Bi,Sb)(2)Te-3 synthesized by the melt-spinning (MS) and the spark-plasma-sintering (SPS) process. We find that rough interfaces of multiscale microstructures generated by the MS are responsible for the large reduction of both lattice thermal conductivity and electrical conductivity. Our study also finds that subsequent SPS forms a microstructure network of similar to 10 nm thick lamellae and smooth interfaces between them. This nanoscale microstructure network with smooth interfaces increases electrical conductivity while keeping a low thermal conductivity, making it an ideal microstructure for high thermoelectric efficiency

Label-Free Biomedical Imaging with High Sensitivity by Stimulated Raman Scattering Microscopy

Label-free chemical contrast is highly desirable in biomedical imaging. Spontaneous Raman microscopy provides specific vibrational signatures of chemical bonds, but is often hindered by low sensitivity. Here we report a three-dimensional multiphoton vibrational imaging technique based on stimulated Raman scattering (SRS). The sensitivity of SRS imaging is significantly greater than that of spontaneous Raman microscopy, which is achieved by implementing high-frequency (megahertz) phase-sensitive detection. SRS microscopy has a major advantage over previous coherent Raman techniques in that it offers background-free and readily interpretable chemical contrast. We show a variety of biomedical applications, such as differentiating distributions of omega-3 fatty acids and saturated lipids in living cells, imaging of brain and skin tissues based on intrinsic lipid contrast, and monitoring drug delivery through the epidermis.Chemistry and Chemical Biolog

Application and research progress of antibody drug conjugates in HER2 positive advanced gastric cancer

Gastric cancer is a malignant tumor with high heterogeneity and invasiveness. Its incidence rate ranks fifth in the world, and its mortality ranks third in the world. Most patients are in a state that cancer cannot be removed by surgery when symptoms appear. At present, systemic treatment is the main treatment for advanced gastric cancer, and human epidermal growth factor receptor 2 (HER2) is one of the important treatment targets for HER2 positive gastric cancer patients. With the continuous optimization of chemotherapy regimen and targeted drugs, the prognosis of some HER2 positive gastric cancer patients has improved significantly. However, the high incidence of drug resistance and high toxicity and side effects are still the bottlenecks limiting the application of HER2 targeted drugs. Therefore, the development of new anti-tumor drugs is of great significance to improve the long-term survival of HER2 positive gastric cancer patients. Antibody drug conjugate (ADC) is a new and efficient anti-tumor drug, which is composed of specific targeted monoclonal antibody, chemical connector and small molecular cytotoxic payload. Its main advantages are strong therapeutic effect and moderate tissue toxicity. In recent years, ADC has set off a huge upsurge in the targeted treatment of HER2 positive advanced gastric cancer. First, after years of development, a variety of ADC including DS-8201 and RC48 have been used in the second- and second-line treatment of gastric cancer. Secondly, with the progress of ADC bioengineering technology, including high proportion of drug antibodies, cleavable linkers, toxic loads that can trigger bystander effect, the new type of ADC can play a more significant therapeutic role in the treatment of specific target tumors, and some of them also have multiple targets and can have anti-tumor effect on multiple specific targets. At the same time, the research and development process of ADC has reached the third stage. The new generation of ADC, through site-specific coupling technology, has higher homogeneity and uniformity, more effective cytotoxic molecules, higher accuracy and lower non-targeted toxicity. In addition, the "targeted immunotherapy" composed of ADC and immune checkpoint inhibitor (ICI) may be a promising treatment strategy for advanced gastric cancer. This article briefly reviewed the application and the latest research progress of ADC in HER2 positive advanced gastric cancer patients in the era of targeted therapy, and discussed the treatment prospects and challenges of ADC combined with ICI in HER2 positive advanced gastric cancer

Stateless Deterministic Multi-Party EdDSA Signatures with Low Communication

EdDSA, standardized by both IRTF and NIST, is a variant of the well-known Schnorr signature based on Edwards curves, and enjoys the benefit of statelessly and deterministically deriving nonces (i.e., it does not require reliable source of randomness or state continuity). Recently, NIST calls for multi-party threshold EdDSA signatures in one mode of deriving nonce statelessly and deterministically and verifying such derivation via zero-knowledge (ZK) proofs. Multi-party full-threshold EdDSA signatures in the dishonest-majority malicious setting have the advantage of strong security guarantee, and specially cover the two-party case. However, it is challenging to translate the stateless and deterministic benefit of EdDSA to the multi-party setting, as no fresh randomness is available for the protocol execution. We present the notion of information-theoretic message authenticated codes (IT-MACs) over groups in the multi-verifier setting, and adopt the recent pseudorandom correlation function (PCF) to generate IT-MACs statelessly and deterministically. Furthermore, we generalize the two-party IT-MACs-based ZK protocol by Baum et al. (Crypto\u2721) into the multi-verifier setting, which may be of independent interest. Together with multi-verifier extended doubly-authenticated bits (mv-edabits) with errors, we design a multi-verifier zero-knowledge (MVZK) protocol to derive nonces statelessly and deterministically. Building upon the MVZK protocol, we propose a stateless deterministic multi-party EdDSA signature, tolerating all-but-one malicious corruptions. Compared to the state-of-the-art multi-party EdDSA signature by Garillot et al. (Crypto\u2721), we improve communication cost by a factor of $61\times$, at the cost of increasing computation cost by about $2.25\times$ and requiring three extra rounds

WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models

This paper introduces WordArt Designer, a user-driven framework for artistic typography synthesis, relying on the Large Language Model (LLM). The system incorporates four key modules: the LLM Engine, SemTypo, StyTypo, and TexTypo modules. 1) The LLM Engine, empowered by the LLM (e.g., GPT-3.5), interprets user inputs and generates actionable prompts for the other modules, thereby transforming abstract concepts into tangible designs. 2) The SemTypo module optimizes font designs using semantic concepts, striking a balance between artistic transformation and readability. 3) Building on the semantic layout provided by the SemTypo module, the StyTypo module creates smooth, refined images. 4) The TexTypo module further enhances the design's aesthetics through texture rendering, enabling the generation of inventive textured fonts. Notably, WordArt Designer highlights the fusion of generative AI with artistic typography. Experience its capabilities on ModelScope: https://www.modelscope.cn/studios/WordArt/WordArt.Comment: Accepted by EMNLP 2023, 10 pages, 11 figures, 1 table, the system is at https://www.modelscope.cn/studios/WordArt/WordAr

Efficacy and safety of traditional Chinese medicine external washing in the treatment of postoperative wound of diabetes complicated with anal fistula: Study protocol of a randomized, double-blind, placebo-controlled, multi-center clinical trial

Introduction: Anal fistula is one of the commonest ailments seen by anorectal surgeons as surgery is currently the preferred treatment for it. Diabetes mellitus is a risk factor that can lead to slow wound healing after anal fistula surgery. Because of the large postoperative wound surface of anal fistula, patients with diabetes can have an increased probability of wound infection, which makes it hard to heal. There is an extensive clinical experience for wound healing in traditional Chinese medicine (TCM). The Jiedu Shengji decoction (JSD) is a widely used external washing decoction in clinical practice. However, the current evidence on it is still insufficient. Therefore, we report this carefully designed clinical trial to assess the efficacy and safety of JSD in the treatment of postoperative wounds in diabetic patients with anal fistula.Methods and analysis: This study was designed to be a randomized, double-blind, placebo-controlled, multi-center clinical trial. There were 60 eligible participants who were randomized at a 1:1 ratio to the intervention and placebo groups. Both groups received the same standard treatment. The intervention group was given external washing decoction of TCM (JSD), while the placebo group was given the placebo made of excipients and flavoring agents. The main outcome measures include wound healing, distribution of wound pathogens, levels of inflammatory mediators, and blood glucose. The secondary outcome measures included lipids, the quality of the life evaluation scale (Short-Form Health Survey 36). Assessments were performed before the start of the study, at 1st, 2nd, 3rd, and 4th weeks after the intervention, and at 8th, 12th, and 16th follow-up weeks.Discussion: The clinical study we proposed will be the first randomized, double-blind, placebo-controlled, multi-center clinical trial study to assess the efficacy and safety of TCM external washing (JSD) in the treatment of postoperative wounds in diabetic patients with anal fistula.Ethics and dissemination: The Medical Ethics Committee of Hospital of Chengdu University of Traditional Chinese Medicine has reviewed this study protocol and gave its approval and consent on 17 March, 2022 (Ethical Review Number: 2022KL-018)

WordArt Designer API: User-Driven Artistic Typography Synthesis with Large Language Models on ModelScope

This paper introduces the WordArt Designer API, a novel framework for user-driven artistic typography synthesis utilizing Large Language Models (LLMs) on ModelScope. We address the challenge of simplifying artistic typography for non-professionals by offering a dynamic, adaptive, and computationally efficient alternative to traditional rigid templates. Our approach leverages the power of LLMs to understand and interpret user input, facilitating a more intuitive design process. We demonstrate through various case studies how users can articulate their aesthetic preferences and functional requirements, which the system then translates into unique and creative typographic designs. Our evaluations indicate significant improvements in user satisfaction, design flexibility, and creative expression over existing systems. The WordArt Designer API not only democratizes the art of typography but also opens up new possibilities for personalized digital communication and design.Comment: Spotlight Paper at the Workshop on Machine Learning for Creativity and Design, 37th Conference on Neural Information Processing Systems (NeurIPS 2023). 5 pages, 5 figure