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Se ha creado un espacio universitario donde, gracias a destacados especialistas en paz, desarrollo, seguridad y educación transformadora, se han aportado enfoques y prácticas para afrontar los desafíos de la convivencia social e impulsar la cultura de la paz, todo ello muy necesario para la formación inicial de maestros y maestras. Por otro lado, se ha facilitado y experimentado una propuesta didáctica orientada a promover el dialogo y respeto mutuo en el ámbito de la Didáctica de la Química. Finalmente, se ha llevado a cabo un estudio exploratorio sobre la actitud de los futuros docentes (estudiantes de la Facultad de Educación - Centro de Formación del Profesorado de la Universidad Complutense de Madrid) hacia la paz y la guerra.

Objective Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical dictionaries. We demonstrate the pipeline by curating a Unified Medical Language System (UMLS)-colloquial symptom dictionary from COVID-19-related tweets as proof of concept.Methods COVID-19-related tweets from February 1, 2020, to April 30, 2022 were used. The pipeline includes three modules: a named entity recognition module to detect symptoms in tweets; an entity normalization module to aggregate detected entities; and a mapping module that iteratively maps entities to Unified Medical Language System concepts. A random 500 entity samples were drawn from the final dictionary for accuracy validation. Additionally, we conducted a symptom frequency distribution analysis to compare our dictionary to a pre-defined lexicon from previous research.Results We identified 498 480 unique symptom entity expressions from the tweets. Pre-processing reduces the number to 18 226. The final dictionary contains 38 175 unique expressions of symptoms that can be mapped to 966 UMLS concepts (accuracy = 95%). Symptom distribution analysis found that our dictionary detects more symptoms and is effective at identifying psychiatric disorders like anxiety and depression, often missed by pre-defined lexicons.Conclusions This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data. The final lexicon's high accuracy, validated by medical professionals, underscores the potential of this methodology to reliably interpret, and categorize vast amounts of unstructured social media data into actionable medical insights across diverse linguistic and regional landscapes.

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Our extensive experiments, conducted across ten self-supervised training methods and six datasets, demonstrate that Gen-AF attains high testing accuracy and robust testing accuracy against state-of-the-art DAEs.

Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real clients with main task-related data, which is impractical. In the context of real-world industrial scenarios, even the simplest defense suffices to defend against the state-of-the-art attack, 3DFed. A practical FL backdoor attack remains in a nascent stage of development. To bridge this gap, we present DarkFed. Initially, we emulate a series of fake clients, thereby achieving the attacker proportion typical of academic research scenarios. Given that these emulated fake clients lack genuine training data, we further propose a data-free approach to backdoor FL. Specifically, we delve into the feasibility of injecting a backdoor using a shadow dataset. Our exploration reveals that impressive attack performance can be achieved, even when there is a substantial gap between the shadow dataset and the main task dataset. This holds true even when employing synthetic data devoid of any semantic information as the shadow dataset. Subsequently, we strategically construct a series of covert backdoor updates in an optimized manner, mimicking the properties of benign updates, to evade detection by defenses. A substantial body of empirical evidence validates the tangible effectiveness of DarkFed., Comment: This paper has been accepted by IJCAI 2024

As a unique form of rural-urban transition settlement in China’s rapid urbanization process, the “urban village” plays a very distinctive part in promoting urbanization, being a transit point for many rural people entering urban areas. The term “informal employment migrants” usually refers to those people, who have no formal employment status and work in informal, low-skilled, low-paying jobs in the city. These people often have no stable job security or social protection, facing problems with housing, education and healthcare, as well as a lack of social interaction and cultural and recreational activities, etc. They are the main force in the industry, promoting urban development and securing the service base, while having little power in terms of economic ability and social status. The rights and interests of the millions of these non-local residents living and working in urban villages, are very important aspects but are usually overlooked by current planning instruments. In the daily life of an urban village, such as Kangle village, not only do the profits from the rental of collective land and property village-owned enterprises benefit only the household residents of the village, but the organization and participation in any public activities within the village have nothing to do with the tenants. As a result, these migrant tenants, who have come to Guangzhou to make a living in the case of this project, are mere outsiders in terms of the distribution of benefits or the share of spiritual entertainment in the urban village. As outsiders, they are often the most affected by the regeneration, the most opposed to it and suffer most directly from its benefits, however, have the weakest voice in the planning progress. Therefore, in urban regeneration planning, we planners should pay more attention to the existence and living needs of these informal employment migrants, provide them with better living and working conditions, improve their quality of life, and promot

As a unique form of rural-urban transition settlement in China’s rapid urbanization process, the “urban village” plays a very distinctive part in promoting urbanization, being a transit point for many rural people entering urban areas. The term “informal employment migrants” usually refers to those people, who have no formal employment status and work in informal, low-skilled, low-paying jobs in the city. These people often have no stable job security or social protection, facing problems with housing, education and healthcare, as well as a lack of social interaction and cultural and recreational activities, etc. They are the main force in the industry, promoting urban development and securing the service base, while having little power in terms of economic ability and social status. The rights and interests of the millions of these non-local residents living and working in urban villages, are very important aspects but are usually overlooked by current planning instruments. In the daily life of an urban village, such as Kangle village, not only do the profits from the rental of collective land and property village-owned enterprises benefit only the household residents of the village, but the organization and participation in any public activities within the village have nothing to do with the tenants. As a result, these migrant tenants, who have come to Guangzhou to make a living in the case of this project, are mere outsiders in terms of the distribution of benefits or the share of spiritual entertainment in the urban village. As outsiders, they are often the most affected by the regeneration, the most opposed to it and suffer most directly from its benefits, however, have the weakest voice in the planning progress. Therefore, in urban regeneration planning, we planners should pay more attention to the existence and living needs of these informal employment migrants, provide them with better living and working conditions, improve their quality of life, and promot

The Plio-Pleistocene evaporitic lacustrine succession in the Qaidam Basin, which was recovered by drill core SG-1, provides the material for understanding the maturation of dolomite since dolomite is present in the core from 7.6 to 870 m depth, spanning a time interval of 0.1–2.8 Ma. Twenty-two samples were collected at different burial depths. The samples were examined by thin-section petrography and SEM for textures, and analyzed for mineralogy (XRD), trace elements and carbon and oxygen isotopes. The main mineral compositions of each sample are dolomite, quartz, halite and gypsum. Dolomite is mostly scattered with spheroidal to ellipsoidal shapes, several microns in diameter, partially encapsulated in gypsum. Microbial micropores and calcified microorganisms are observed on and within the dolomite crystals and in the thin sections. The degree of ordering of the dolomite gradually increases with burial depth, which basically conforms to the first-order reaction function. The crystal constants of dolomite decrease with burial depth, indicating recrystallisation with increasing overburden. The trace elements of the dolomites are significantly different from those of hydrothermal dolomites, but close to lacustrine microbial carbonate. It is concluded that the dolomites in core SG-1 are mainly authigenic, with microbial processes a likely facilitator of dolomite precipitation. The dolomite formed and then evolved in crystal structure during burial, gradually approaching ordered stochiometric dolomite. This study gives clues to link microbial dolomite to the massive ideal dolomite rock encountered in the geological record.

$\textbf{Objectives}$: Large Language Models (LLMs) such as ChatGPT and Med-PaLM have excelled in various medical question-answering tasks. However, these English-centric models encounter challenges in non-English clinical settings, primarily due to limited clinical knowledge in respective languages, a consequence of imbalanced training corpora. We systematically evaluate LLMs in the Chinese medical context and develop a novel in-context learning framework to enhance their performance. $\textbf{Materials and Methods}$: The latest China National Medical Licensing Examination (CNMLE-2022) served as the benchmark. We collected 53 medical books and 381,149 medical questions to construct the medical knowledge base and question bank. The proposed Knowledge and Few-shot Enhancement In-context Learning (KFE) framework leverages the in-context learning ability of LLMs to integrate diverse external clinical knowledge sources. We evaluated KFE with ChatGPT(GPT3.5), GPT4, Baichuan2(BC2)-7B, and BC2-13B in CNMLE-2022 and investigated the effectiveness of different pathways for incorporating LLMs with medical knowledge from 7 perspectives. $\textbf{Results}$: Directly applying ChatGPT failed to qualify for the CNMLE-2022 at a score of 51. Cooperated with the KFE, the LLMs with varying sizes yielded consistent and significant improvements. The ChatGPT's performance surged to 70.04 and GPT-4 achieved the highest score of 82.59. This surpasses the qualification threshold (60) and exceeds the average human score of 68.70. It also enabled a smaller BC2-13B to pass the examination, showcasing the great potential in low-resource settings. $\textbf{Conclusion}$: By synergizing medical knowledge through in-context learning, LLM can extend clinical insight beyond language barriers, significantly reducing language-related disparities of LLM applications and ensuring global benefit in healthcare.

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo), a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability., Comment: Accepted by CVPR2023. Code is available at https://github.com/CGCL-codes/TeCo

We present the discovery of three pulsars in Globular Cluster M15 (NGC 7078) by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). In the three pulsars, PSR~J2129+1210J (M15J) is a millisecond pulsar with a spinning period of 11.84 ms and a dispersion measure of 66.68 pc cm$^{-3}$. Both PSR~J2129+1210K and L (M15K and L) are long period pulsars with spinning periods of 1928 ms and 3961 ms , respectively, while M15L is the GC pulsar with the longest spinning period till now. The discoveries of M15K and L support the theory that core-collapsed Globular Clusters may contain partially recycled long period pulsars. With the same dataset, the timing solutions of M15A to H were updated, and the timing parameter P1 of M15F is different from the previous results, which is approximately 0.027$\times 10^{-18} ss^{-1}$ from our work and $0.032 \times 10^{-18} ss^{-1}$ from Anderson's\citep{anderson-1993}. As predicted by Rodolfi et al. , the luminosity of M15C kept decreasing and the latest detection in our dataset is on December 20$^{\rm th}$, 2022. We also detected M15I for one more time. The different barycentric spin periods indicate that this pulsar should locate in a binary system, manifesting itself as the exceptional one in such a core-collapsing GC., Comment: 10 pages, 4 figures, 2 tables, submitted to ApJ Letter

\textit{Federated learning} (FL) and \textit{split learning} (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. \textit{Split federated learning} (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of robustness. In this paper, we present a novel poisoning attack known as MISA. It poisons both the top and bottom models, causing a \textbf{\underline{misa}}lignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention., Comment: This paper has been accepted by the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)

Unlearnable datasets lead to a drastic drop in the generalization performance of models trained on them by introducing elaborate and imperceptible perturbations into clean training sets. Many existing defenses, e.g., JPEG compression and adversarial training, effectively counter UDs based on norm-constrained additive noise. However, a fire-new type of convolution-based UDs have been proposed and render existing defenses all ineffective, presenting a greater challenge to defenders. To address this, we express the convolution-based unlearnable sample as the result of multiplying a matrix by a clean sample in a simplified scenario, and formalize the intra-class matrix inconsistency as $\Theta_{imi}$, inter-class matrix consistency as $\Theta_{imc}$ to investigate the working mechanism of the convolution-based UDs. We conjecture that increasing both of these metrics will mitigate the unlearnability effect. Through validation experiments that commendably support our hypothesis, we further design a random matrix to boost both $\Theta_{imi}$ and $\Theta_{imc}$, achieving a notable degree of defense effect. Hence, by building upon and extending these facts, we first propose a brand-new image COrruption that employs randomly multiplicative transformation via INterpolation operation to successfully defend against convolution-based UDs. Our approach leverages global pixel random interpolations, effectively suppressing the impact of multiplicative noise in convolution-based UDs. Additionally, we have also designed two new forms of convolution-based UDs, and find that our defense is the most effective against them.

Multimodal contrastive learning aims to train a general-purpose feature extractor, such as CLIP, on vast amounts of raw, unlabeled paired image-text data. This can greatly benefit various complex downstream tasks, including cross-modal image-text retrieval and image classification. Despite its promising prospect, the security issue of cross-modal pre-trained encoder has not been fully explored yet, especially when the pre-trained encoder is publicly available for commercial use. In this work, we propose AdvCLIP, the first attack framework for generating downstream-agnostic adversarial examples based on cross-modal pre-trained encoders. AdvCLIP aims to construct a universal adversarial patch for a set of natural images that can fool all the downstream tasks inheriting the victim cross-modal pre-trained encoder. To address the challenges of heterogeneity between different modalities and unknown downstream tasks, we first build a topological graph structure to capture the relevant positions between target samples and their neighbors. Then, we design a topology-deviation based generative adversarial network to generate a universal adversarial patch. By adding the patch to images, we minimize their embeddings similarity to different modality and perturb the sample distribution in the feature space, achieving unviersal non-targeted attacks. Our results demonstrate the excellent attack performance of AdvCLIP on two types of downstream tasks across eight datasets. We also tailor three popular defenses to mitigate AdvCLIP, highlighting the need for new defense mechanisms to defend cross-modal pre-trained encoders., Comment: This paper has been accepted by the ACM International Conference on Multimedia (ACM MM '23, October 29-November 3, 2023, Ottawa, ON, Canada)

\textit{Federated learning} (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious participants could independently or collusively upload well-crafted updates to deteriorate the performance of the global model. However, existing defenses could only mitigate part of Byzantine attacks, without providing an all-sided shield for FL. It is difficult to simply combine them as they rely on totally contradictory assumptions. In this paper, we propose FPD, a \underline{\textbf{f}}our-\underline{\textbf{p}}ronged \underline{\textbf{d}}efense against both non-colluding and colluding Byzantine attacks. Our main idea is to utilize absolute similarity to filter updates rather than relative similarity used in existingI works. To this end, we first propose a reliable client selection strategy to prevent the majority of threats in the bud. Then we design a simple but effective score-based detection method to mitigate colluding attacks. Third, we construct an enhanced spectral-based outlier detector to accurately discard abnormal updates when the training data is \textit{not independent and identically distributed} (non-IID). Finally, we design update denoising to rectify the direction of the slightly noisy but harmful updates. The four sequentially combined modules can effectively reconcile the contradiction in addressing non-colluding and colluding Byzantine attacks. Extensive experiments over three benchmark image classification datasets against four state-of-the-art Byzantine attacks demonstrate that FPD drastically outperforms existing defenses in IID and non-IID scenarios (with $30\%$ improvement on model accuracy)., Comment: This paper has been accepted by the 31st ACM International Conference on Multimedia (MM '23)

Adversarial examples (AEs) for DNNs have been shown to be transferable: AEs that successfully fool white-box surrogate models can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable AEs, many of these findings lack explanations and even lead to inconsistent advice. In this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing little robustness phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates, we attribute it to a trade-off between two predominant factors: model smoothness and gradient similarity. Our investigations focus on their joint effects, rather than their separate correlations with transferability. Through a series of theoretical and empirical analyses, we conjecture that the data distribution shift in adversarial training explains the degradation of gradient similarity. Building on these insights, we explore the impacts of data augmentation and gradient regularization on transferability and identify that the trade-off generally exists in the various training mechanisms, thus building a comprehensive blueprint for the regulation mechanism behind transferability. Finally, we provide a general route for constructing better surrogates to boost transferability which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other, and emphasize the crucial role of manipulating surrogate models., Comment: IEEE Symposium on Security and Privacy (Oakland) 2024; Extended version of camera-ready

Objective: Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical dictionaries. We demonstrate the pipeline by curating a UMLS-colloquial symptom dictionary from COVID-19-related tweets as proof of concept. Methods: COVID-19-related tweets from February 1, 2020, to April 30, 2022 were used. The pipeline includes three modules: a named entity recognition module to detect symptoms in tweets; an entity normalization module to aggregate detected entities; and a mapping module that iteratively maps entities to Unified Medical Language System concepts. A random 500 entity sample were drawn from the final dictionary for accuracy validation. Additionally, we conducted a symptom frequency distribution analysis to compare our dictionary to a pre-defined lexicon from previous research. Results: We identified 498,480 unique symptom entity expressions from the tweets. Pre-processing reduces the number to 18,226. The final dictionary contains 38,175 unique expressions of symptoms that can be mapped to 966 UMLS concepts (accuracy = 95%). Symptom distribution analysis found that our dictionary detects more symptoms and is effective at identifying psychiatric disorders like anxiety and depression, often missed by pre-defined lexicons. Conclusions: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data. The final lexicon's high accuracy, validated by medical professionals, underscores the potential of this methodology to reliably interpret and categorize vast amounts of unstructured social media data into actionable medical insights across diverse linguistic and regional landscapes., Comment: Updated full paper. Abstract presented at IEEE ICHI 2023 and AMIA Annual Symposium 2023

Over the last three centuries, childbirth has gradually become an issue of public concern worldwide, being linked increasingly to the welfare of populations and nations. During this process, medical improvements in obstetrics and midwifery have been introduced to daily childbirth practices by medical authorities and enforced by state governments in many countries, significantly contributing to the decline of childbirth-related mortality rates. This dissertation focuses on the transformation of childbirth practices in China during the Republican era (1912-1949) and its impacts on neonatal, infant, and maternal mortality rates. By using governmental archives, reports of hospitals and health stations, demographic and social surveys, and other historical sources, this dissertation first investigates how missionary and governmental initiatives pushed forward the institutionalization of childbirth in different parts of China from the late nineteenth to the early twentieth century. Then the dissertation presents case studies of Beijing (1926-1937) and Sichuan (1938-1949), as well as three comparisons regarding neonatal and maternal mortality rates in China and elsewhere in the world, through which it analyzes how biomedical approaches of the “new style midwifery” helped prevent neonatal, infant and maternal mortality by reducing infection-related diseases during and after childbirth. Combining qualitative and quantitative methods, this dissertation provides new insights into how childbirth became less risky across time and space.

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modifications for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause a performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data., Comment: Accepted by the 37th AAAI Conference on Artificial Intelligence (AAAI-23)

Federated learning is a newly emerging distributed learning framework that facilitates the collaborative training of a shared global model among distributed participants with their privacy preserved. However, federated learning systems are vulnerable to Byzantine attacks from malicious participants, who can upload carefully crafted local model updates to degrade the quality of the global model and even leave a backdoor. While this problem has received significant attention recently, current defensive schemes heavily rely on various assumptions, such as a fixed Byzantine model, availability of participants' local data, minority attackers, IID data distribution, etc. To relax those constraints, this paper presents Robust-FL, the first prediction-based Byzantine-robust federated learning scheme where none of the assumptions is leveraged. The core idea of the Robust-FL is exploiting historical global model to construct an estimator based on which the local models will be filtered through similarity detection. We then cluster local models to adaptively adjust the acceptable differences between the local models and the estimator such that Byzantine users can be identified. Extensive experiments over different datasets show that our approach achieves the following advantages simultaneously: (i) independence of participants' local data, (ii) tolerance of majority attackers, (iii) generalization to variable Byzantine model., Comment: This paper has been accepted by the 18th International Conference on Mobility, Sensing and Networking (MSN 2022)

While deep face recognition (FR) systems have shown amazing performance in identification and verification, they also arouse privacy concerns for their excessive surveillance on users, especially for public face images widely spread on social networks. Recently, some studies adopt adversarial examples to protect photos from being identified by unauthorized face recognition systems. However, existing methods of generating adversarial face images suffer from many limitations, such as awkward visual, white-box setting, weak transferability, making them difficult to be applied to protect face privacy in reality. In this paper, we propose adversarial makeup transfer GAN (AMT-GAN), a novel face protection method aiming at constructing adversarial face images that preserve stronger black-box transferability and better visual quality simultaneously. AMT-GAN leverages generative adversarial networks (GAN) to synthesize adversarial face images with makeup transferred from reference images. In particular, we introduce a new regularization module along with a joint training strategy to reconcile the conflicts between the adversarial noises and the cycle consistence loss in makeup transfer, achieving a desirable balance between the attack strength and visual changes. Extensive experiments verify that compared with state of the arts, AMT-GAN can not only preserve a comfortable visual quality, but also achieve a higher attack success rate over commercial FR APIs, including Face++, Aliyun, and Microsoft., Comment: Accepted by CVPR2022. Code is available at https://github.com/CGCL-codes/AMT-GAN

© 2020, The Author(s). Since the discovery of metal nanoparticles (NPs) in the 1960s, unknown toxicity, cost and the ethical hurdles of research in humans have hindered the translation of these NPs to clinical use. In this work, we demonstrate that Pt NPs with protein coronas are generated in vivo in human blood when a patient is treated with cisplatin. These self-assembled Pt NPs form rapidly, accumulate in tumors, and remain in the body for an extended period of time. Additionally, the Pt NPs are safe for use in humans and can act as anti-cancer agents to inhibit chemotherapy-resistant tumor growth by consuming intracellular glutathione and activating apoptosis. The tumor inhibitory activity is greatly amplified when the Pt NPs are loaded in vitro with the chemotherapeutic drug, daunorubicin, and the formulation is effective even in daunorubicin-resistant models. These in vivo-generated metal NPs represent a biocompatible drug delivery platform for chemotherapy resistant tumor treatment.

With the prosperity of wireless and mobile communications, the allocation of wireless resources, e.g., spectrum channels, femtocell access permissions, and resource blocks of D2D connections, has become a matter of great concern, which leads to the emergence and popularity of electronic auction. However, the lack of privacy protection and fairness guarantee in the existing market has posed great obstacles to user participation in electronic auctions. Without privacy protection, users will be worried about the unauthorized exposure of their commercial secrets, which may hurt their payoffs in the long run. Without fairness guarantee, selfish/malicious users may prematurely abort the auction to avoid unsatisfactory payment, thus causing loss to honest users and wasting system resources. Furthermore, the lack of fairness guarantee will incur an exchange dilemma, where mutually distrusting parties may refuse to initiate the goods/money exchange, hindering the market-clearing of auctions. Although extensive efforts have been made to develop privacy-preserving auction mechanisms for wireless markets, the fairness issue, to the best of our knowledge, has never been taken into account in the context of auctions. Moreover, a truly practical framework that can be applied to most mainstream auction formats is also missing in the literature. In this paper, we present the first general framework, named SAFE, for representative auctions in (but not limited to) wireless markets. SAFE achieves a wide range of security goals with much higher efficiency than existing solutions. Furthermore, SAFE ensures auction fairness in a trust-free manner by developing a full set of carefully-designed modular protocols that can be easily adapted to any auction format. We implement the SAFE framework over a simulated Ethereum network. Extensive experimental results confirm that SAFE can indeed achieve high economic efficiency in terms of social welfare and participation satisfaction at very low com

Longlong Hu,1 Huihui Bao,1,2 Xiao Huang,1 Wei Zhou,1,2 Tao Wang,1,2 Lingjuan Zhu,1,2 Xi Liu,3 Minghui Li,1,3 Xiaoshu Cheng1,2 1Department of Cardiovascular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China; 2Center for Prevention and Treatment of Cardiovascular Diseases, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China; 3Center of Cardiovascular Medicine, Inner Mongolia People’s Hospital, Hohhot, Inner Mongolia, People’s Republic of ChinaCorrespondence: Minghui LiCenter of Cardiovascular Medicine, Inner Mongolia People’s Hospital, No. 20 Zhaowuda Road, Hohhot, Inner Mongolia, 010017, People’s Republic of China, Tel +8615389819973, Fax +86-791-86262262, Email 15389819973@163.comXiaoshu ChengDepartment of Cardiovascular Medicine, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, 330006, People’s Republic of China, Tel +8613607089128, Fax +86-791-86262262, Email xiaoshumenfan126@163.comBackground: Several recent studies have shown the relationship between the triglyceride glucose (TyG) index and the risk of stroke in the general population and in a few patient cohorts; however, the role of the TyG index on stroke risk in elderly hypertensive patients has not been determined. Thus, we aimed to investigate the association of the TyG index with first stroke and first ischemic stroke in elderly individuals with hypertension.Methods: We included 8487 elderly subjects with hypertension from the China H-type Hypertension Registry Study for the current analysis. The TyG index was calculated as ln (fasting triglyceride [mg/dL] × fasting glucose [mg/dL]/2). Outcomes were the first stroke and first ischemic stroke.Results: During a median follow-up of 1.72 years, the first stroke was diagnosed in 82 patients (0.97%), and the first ischemic stroke was diagnosed in 48 patients (0.57%). Multivariable Cox propo

© 2020, The Author(s). Since the discovery of metal nanoparticles (NPs) in the 1960s, unknown toxicity, cost and the ethical hurdles of research in humans have hindered the translation of these NPs to clinical use. In this work, we demonstrate that Pt NPs with protein coronas are generated in vivo in human blood when a patient is treated with cisplatin. These self-assembled Pt NPs form rapidly, accumulate in tumors, and remain in the body for an extended period of time. Additionally, the Pt NPs are safe for use in humans and can act as anti-cancer agents to inhibit chemotherapy-resistant tumor growth by consuming intracellular glutathione and activating apoptosis. The tumor inhibitory activity is greatly amplified when the Pt NPs are loaded in vitro with the chemotherapeutic drug, daunorubicin, and the formulation is effective even in daunorubicin-resistant models. These in vivo-generated metal NPs represent a biocompatible drug delivery platform for chemotherapy resistant tumor treatment.

In this paper, we propose and analyze a first-order and a second-order time-stepping schemes for the anisotropic phase-field dendritic crystal growth model. The proposed schemes are based on an auxiliary variable approach for the Allen-Cahn equation and delicate treatment of the terms coupling the Allen-Cahn equation and temperature equation. The idea of the former is to introduce suitable auxiliary variables to facilitate construction of high order stable schemes for a large class of gradient flows. We propose a new technique to treat the coupling terms involved in the crystal growth model and introduce suitable stabilization terms to result in totally decoupled schemes, which satisfy a discrete energy law without affecting the convergence order. A delicate implementation demonstrates that the proposed schemes can be realized in a very efficient way. That is, it only requires solving four linear elliptic equations and a simple algebraic equation at each time step. A detailed comparison with existing schemes is given, and the advantage of the new schemes are emphasized. As far as we know this is the first second-order scheme that is totally decoupled, linear, unconditionally stable for the dendritic crystal growth model with variable mobility parameter., Comment: 21 pages, 33 figures, 40 references

Hui Zhang,1 Minghui Li,1 Jing Zhang,1 Yanbing Shen,2 Qi Gui2 1College of Medical, Huanghuai University, Zhumadian, Henan, People’s Republic of China; 2Department of Urology, The Central Hospital of Zhumadian, Zhumadian, Henan, People’s Republic of ChinaCorrespondence: Hui ZhangCollege of Medical, Huanghuai University, No. 2628, Mountain Panlong Road, Zhumdian, 463000, Henan, People’s Republic of ChinaTel +86-0396-2853503Email cecile98@163.comBackground: Exosomal circular RNAs (circRNAs) are involved in the pathogenesis of prostate cancer (PCa) and chemotherapy resistance. This research aimed to explore the function and molecular mechanism of circRNA X-linked inhibitor of apoptosis (circ-XIAP) in docetaxel (DTX) resistance of PCa.Methods: The expression of circ-XIAP, microRNA-1182 (miR-1182), tumor protein D52 (TPD52) was measured by quantitative real-time polymerase chain reaction (qRT-PCR). Exosomes were detected with transmission electron microscopy (TEM). Cluster of differentiation 63 (CD63), cluster of differentiation 9 (CD9) and TPD52 protein levels were detected by Western blot (WB). FIfty percent inhibitory concentration (IC50) of DTX and cell viability were determined using Cell Counting Kit-8 (CCK-8) assay. Colony formation assay was applied to assess colony-forming ability. Cell cycle distribution and apoptosis were analyzed by flow cytometry. Transwell assay was used for measuring cell migration and invasion. Dual-reporter luciferase assay was performed to confirm the interaction between miR-1182 and circ-XIAP or TPD52. The role of circ-XIAP in vivo was confirmed via the mice xenograft model.Results: Circ-XIAP and TPD52 were upregulated and miR-1182 was downregulated in DTX-resistant PCa tissue specimens and cell lines. Circ-XIAP was also overexpressed in exosomes from DTX-resistant cells and could be transmitted via exosomes. Circ-XIAP knockdown enhanced DTX sensitivity by suppressing DTX-resistant cell proliferation, migrat

With the prosperity of wireless and mobile communications, the allocation of wireless resources, e.g., spectrum channels, femtocell access permissions, and resource blocks of D2D connections, has become a matter of great concern, which leads to the emergence and popularity of electronic auction. However, the lack of privacy protection and fairness guarantee in the existing market has posed great obstacles to user participation in electronic auctions. Without privacy protection, users will be worried about the unauthorized exposure of their commercial secrets, which may hurt their payoffs in the long run. Without fairness guarantee, selfish/malicious users may prematurely abort the auction to avoid unsatisfactory payment, thus causing loss to honest users and wasting system resources. Furthermore, the lack of fairness guarantee will incur an exchange dilemma, where mutually distrusting parties may refuse to initiate the goods/money exchange, hindering the market-clearing of auctions. Although extensive efforts have been made to develop privacy-preserving auction mechanisms for wireless markets, the fairness issue, to the best of our knowledge, has never been taken into account in the context of auctions. Moreover, a truly practical framework that can be applied to most mainstream auction formats is also missing in the literature. In this paper, we present the first general framework, named SAFE, for representative auctions in (but not limited to) wireless markets. SAFE achieves a wide range of security goals with much higher efficiency than existing solutions. Furthermore, SAFE ensures auction fairness in a trust-free manner by developing a full set of carefully-designed modular protocols that can be easily adapted to any auction format. We implement the SAFE framework over a simulated Ethereum network. Extensive experimental results confirm that SAFE can indeed achieve high economic efficiency in terms of social welfare and participation satisfaction at very low com

With the prosperity of wireless and mobile communications, the allocation of wireless resources, e.g., spectrum channels, femtocell access permissions, and resource blocks of D2D connections, has become a matter of great concern, which leads to the emergence and popularity of electronic auction. However, the lack of privacy protection and fairness guarantee in the existing market has posed great obstacles to user participation in electronic auctions. Without privacy protection, users will be worried about the unauthorized exposure of their commercial secrets, which may hurt their payoffs in the long run. Without fairness guarantee, selfish/malicious users may prematurely abort the auction to avoid unsatisfactory payment, thus causing loss to honest users and wasting system resources. Furthermore, the lack of fairness guarantee will incur an exchange dilemma, where mutually distrusting parties may refuse to initiate the goods/money exchange, hindering the market-clearing of auctions. Although extensive efforts have been made to develop privacy-preserving auction mechanisms for wireless markets, the fairness issue, to the best of our knowledge, has never been taken into account in the context of auctions. Moreover, a truly practical framework that can be applied to most mainstream auction formats is also missing in the literature. In this paper, we present the first general framework, named SAFE, for representative auctions in (but not limited to) wireless markets. SAFE achieves a wide range of security goals with much higher efficiency than existing solutions. Furthermore, SAFE ensures auction fairness in a trust-free manner by developing a full set of carefully-designed modular protocols that can be easily adapted to any auction format. We implement the SAFE framework over a simulated Ethereum network. Extensive experimental results confirm that SAFE can indeed achieve high economic efficiency in terms of social welfare and participation satisfaction at very low com

Over the last three centuries, childbirth has gradually become an issue of public concern worldwide, being linked increasingly to the welfare of populations and nations. During this process, medical improvements in obstetrics and midwifery have been introduced to daily childbirth practices by medical authorities and enforced by state governments in many countries, significantly contributing to the decline of childbirth-related mortality rates. This dissertation focuses on the transformation of childbirth practices in China during the Republican era (1912-1949) and its impacts on neonatal, infant, and maternal mortality rates. By using governmental archives, reports of hospitals and health stations, demographic and social surveys, and other historical sources, this dissertation first investigates how missionary and governmental initiatives pushed forward the institutionalization of childbirth in different parts of China from the late nineteenth to the early twentieth century. Then the dissertation presents case studies of Beijing (1926-1937) and Sichuan (1938-1949), as well as three comparisons regarding neonatal and maternal mortality rates in China and elsewhere in the world, through which it analyzes how biomedical approaches of the “new style midwifery” helped prevent neonatal, infant and maternal mortality by reducing infection-related diseases during and after childbirth. Combining qualitative and quantitative methods, this dissertation provides new insights into how childbirth became less risky across time and space.

Convolutional neural network is a machine-learning model widely applied in various prediction tasks, such as computer vision and medical image analysis. Their great predictive power requires extensive computation, which encourages model owners to host the prediction service in a cloud platform. Recent researches focus on the privacy of the query and results, but they do not provide model privacy against the model-hosting server and may leak partial information about the results. Some of them further require frequent interactions with the querier or heavy computation overheads, which discourages querier from using the prediction service. This paper proposes a new scheme for privacy-preserving neural network prediction in the outsourced setting, i.e., the server cannot learn the query, (intermediate) results, and the model. Similar to SecureML (S&P'17), a representative work that provides model privacy, we leverage two non-colluding servers with secret sharing and triplet generation to minimize the usage of heavyweight cryptography. Further, we adopt asynchronous computation to improve the throughput, and design garbled circuits for the non-polynomial activation function to keep the same accuracy as the underlying network (instead of approximating it). Our experiments on MNIST dataset show that our scheme achieves an average of 122x, 14.63x, and 36.69x reduction in latency compared to SecureML, MiniONN (CCS'17), and EzPC (EuroS&P'19), respectively. For the communication costs, our scheme outperforms SecureML by 1.09x, MiniONN by 36.69x, and EzPC by 31.32x on average. On the CIFAR dataset, our scheme achieves a lower latency by a factor of 7.14x and 3.48x compared to MiniONN and EzPC, respectively. Our scheme also provides 13.88x and 77.46x lower communication costs than MiniONN and EzPC on the CIFAR dataset.

Chronic lung diseases, such as chronic obstructive pulmonary disease (COPD) and pulmonary fibrosis, are a major cause of mortality worldwide. With the increasing incidence with ageing, the full impact of these diseases is yet to be realised. For most chronic lung diseases there are limited treatments options, with the existing approaches mainly addressing symptom relief. Little progress has been made, in recent years, in the development of new therapeutic strategies for managing these burdensome pathologies. There is an urgent need to increase our understanding of the mechanisms underlying these diseases. Endogenous progenitor cells (stem cells) have been recognised in many organs, including the lungs where they are suggested to maintain a population of cells that are able to facilitate the endogenous repair processes. Emerging knowledge of how these repair processes are disrupted in chronic lung diseases and the potential to capitalise upon the regenerative capacity of stem cell populations raise the hopes of the field worldwide for innovative treatment approaches for these devastating diseases in the future. This chapter outlines the series of diseases that may benefit from these emerging new therapeutic outlooks.

Searchable symmetric encryption (SSE) allows the data owner to outsource an encrypted database to a remote server in a private manner while maintaining the ability for selectively search. So far, most existing solutions focus on an honest-but-curious server, while security designs against a malicious server have not drawn enough attention. A few recent works have attempted to construct verifiable SSE that enables the data owner to verify the integrity of search results. Nevertheless, these verification mechanisms are highly dependent on specific SSE schemes, and fail to support complex queries. A general verification mechanism is desired that can be applied to all SSE schemes. In this work, instead of concentrating on a central server, we explore the potential of the smart contract, an emerging blockchain-based decentralized technology, and construct decentralized SSE schemes where the data owner can receive correct search results with assurance without worrying about potential wrongdoings of a malicious server. We study both public and private blockchain environments and propose two designs with a trade-off between security and efficiency. To better support practical applications, the multi-user setting of SSE is further investigated where the data owner allows authenticated users to search keywords in shared documents. We implement prototypes of our two designs and present experiments and evaluations to demonstrate the practicability of our decentralized SSE schemes.

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called LARSEN-ELM for overcoming this problem. In our paper, we would like to show two key steps in LARSEN-ELM. In the first step, preprocessing, we select the input variables highly related to the output using least angle regression (LARS). In the second step, training, we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In the experiments, we apply a sum of two sines and four datasets from UCI repository to verify the robustness of our approach. The experimental results show that compared with original ELM and other methods such as OP-ELM, GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance while keeping a relatively high speed., Comment: Accepted for publication in Neurocomputing, 01/19/2014

Surface-enhanced Raman scattering of single-walled carbon nanotubes (SWNTs) on silver films deposited on various substrates, including sapphire, quartz and glass, has been studied systematically. The characteristic features of G-band and D-band have been analyzed and compared with arc discharged and laser ablation samples. D-band is much more sensitive than G-band. The position and the intensity of the G-band in SERS spectra depend on substrates. The peak shift and absolute intensity of G-band on sapphire is obviously larger than that on glass. The contribution of high frequency vibrations to the D-band is also deendent on substrates. Compared to the SWNTs samples with a high semiconducting tube concentration, the sample with high metallic tube concentration has a stronger interaction with silver films., In Chinese with English summary.

Studies have been carried out of the development of deformation and permeability of coals under true triaxial stress conditions. The results indicate that the volumetric strain and bedding jointly affect permeability. An analytical stress-strain model was developed in which the deformation and permeability of the bedding and non-bedding are separated through bedding ratio and bedding elastic modulus. The bedding ratio is the ratio of the total length of bedding to the total length of coal normal to bedding planes, and is closely related to the porosity. The model is used to calculate four elastic moduli: a bedding elastic modulus and a non-bedding elastic modulus in the direction normal to bedding planes, and two elastic moduli in the direction parallel to bedding planes. The bedding elastic modulus is relatively small, indicating that the bedding effect cannot be ignored. The permeability is highly sensitive to the bedding of coal with low stress in the direction normal to the bedding. The ratio of the bedding permeability to the total permeability is different due to different stresses in the direction normal to bedding planes, which is linked with the compression of bedding. A permeability model which can reflect the different effects of beddings and non-beddings to the total permeability is proposed and verified., Studies have been carried out of the development of deformation and permeability of coals under true triaxial stress conditions. The results indicate that the volumetric strain and bedding jointly affect permeability. An analytical stress-strain model was developed in which the deformation and permeability of the bedding and non-bedding are separated through bedding ratio and bedding elastic modulus. The bedding ratio is the ratio of the total length of bedding to the total length of coal normal to bedding planes, and is closely related to the porosity. The model is used to calculate four elastic moduli: a bedding elastic modulus and a non-bedding elastic modulus in the direction normal to bedding planes, and two elastic moduli in the direction parallel to bedding planes. The bedding elastic modulus is relatively small, indicating that the bedding effect cannot be ignored. The permeability is highly sensitive to the bedding of coal with low stress in the direction normal to the bedding. The ratio of the bedding permeability to the total permeability is different due to different stresses in the direction normal to bedding planes, which is linked with the compression of bedding. A permeability model which can reflect the different effects of beddings and non-beddings to the total permeability is proposed and verified.

Investigations of the directional permeability evolution of intact and fractured coal have been conducted under different simulated geological conditions to study the effects of fracture geometry, water adsorption and stress conditions as a function of stress. The results indicate that permeability anisotropy is more pronounced in fractured coal than in intact coal. The permeability order was maintained following the introduction of macrofractures into coal. The fracture compressibility in the butt cleat plane flow direction was higher than that in the other two flow directions for both intact and fractured coal. The presence of water in coal could reduce the permeability by up to one order of magnitude, and a more significant permeability decrease was observed in coal specimens containing rough macrofractures. Permeability hysteresis for both intact and fractured coal was found to be somewhat dependent on the stress condition. The hysteresis effect of coal was more significant under triaxial stress conditions and less pronounced under true-triaxial stress conditions. It is concluded that when conducting hydraulic stimulation in coal seams, special attention should be given to the natural pre-existing fracture properties., Investigations of the directional permeability evolution of intact and fractured coal have been conducted under different simulated geological conditions to study the effects of fracture geometry, water adsorption and stress conditions as a function of stress. The results indicate that permeability anisotropy is more pronounced in fractured coal than in intact coal. The permeability order was maintained following the introduction of macrofractures into coal. The fracture compressibility in the butt cleat plane flow direction was higher than that in the other two flow directions for both intact and fractured coal. The presence of water in coal could reduce the permeability by up to one order of magnitude, and a more significant permeability decrease was observed in coal specimens containing rough macrofractures. Permeability hysteresis for both intact and fractured coal was found to be somewhat dependent on the stress condition. The hysteresis effect of coal was more significant under triaxial stress conditions and less pronounced under true-triaxial stress conditions. It is concluded that when conducting hydraulic stimulation in coal seams, special attention should be given to the natural pre-existing fracture properties.

Este proyecto describe una propuesta didáctica orientada a la enseñanza de la ciencia a través del uso de noticias de los medios de comunicación. El objetivo principal es mejorar la comprensión de los estudiantes sobre conceptos científicos al mismo tiempo que fomenta un enfoque crítico y responsable hacia los temas que se tratan en dichas noticias. Al incorporar ejemplos del mundo real y eventos actuales, el proyecto busca involucrar a los estudiantes y promover una comprensión más profunda de los temas científicos., This project describes a teaching proposal of science education through the utilization of news from the media. The primary goal is to enhance students' comprehension of scientific concepts while fostering a critical and responsible approach to the issues discussed in these news articles. By incorporating real-world examples and current events, the project seeks to engage students and promote a deeper understanding of scientific topics.