Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning

Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security standards, such as product recommendation and user behavior modeling. Under these scenarios, exploiting GNN's vulnerabilities and further downgrading its performance become extremely incentive for adversaries. Previous attackers mainly focus on structural perturbations or node injections to the existing graphs, guided by gradients from the surrogate models. Although they deliver promising results, several limitations still exist. For the structural perturbation attack, to launch a proposed attack, adversaries need to manipulate the existing graph topology, which is impractical in most circumstances. Whereas for the node injection attack, though being more practical, current approaches require training surrogate models to simulate a white-box setting, which results in significant performance downgrade when the surrogate architecture diverges from the actual victim model. To bridge these gaps, in this paper, we study the problem of black-box node injection attack, without training a potentially misleading surrogate model. Specifically, we model the node injection attack as a Markov decision process and propose Gradient-free Graph Advantage Actor Critic, namely G2A2C, a reinforcement learning framework in the fashion of advantage actor critic. By directly querying the victim model, G2A2C learns to inject highly malicious nodes with extremely limited attacking budgets, while maintaining a similar node feature distribution. Through our comprehensive experiments over eight acknowledged benchmark datasets with different characteristics, we demonstrate the superior performance of our proposed G2A2C over the existing state-of-the-art attackers. Source code is publicly available at: https://github.com/jumxglhf/G2A2C}.Comment: AAAI 2023. v2: update acknowledgement section. arXiv admin note: substantial text overlap with arXiv:2202.0938

VERTICES: Efficient Two-Party Vertical Federated Linear Model with TTP-aided Secret Sharing

Vertical Federated Learning (VFL) has emerged as one of the most predominant approaches for secure collaborative machine learning where the training data is partitioned by features among multiple parties. Most VFL algorithms primarily rely on two fundamental privacy-preserving techniques: Homomorphic Encryption (HE) and secure Multi-Party Computation (MPC). Though generally considered with stronger privacy guarantees, existing general-purpose MPC frameworks suffer from expensive computation and communication overhead and are inefficient especially under VFL settings. This study centers around MPC-based VFL algorithms and presents a novel approach for two-party vertical federated linear models via an efficient secret sharing (SS) scheme with a trusted coordinator. Our approach can achieve significant acceleration of the training procedure in vertical federated linear models of between 2.5x and 6.6x than other existing MPC frameworks under the same security setting

Identification and analysis of the secretome of plant pathogenic fungi reveals lifestyle adaptation

The secretory proteome plays an important role in the pathogenesis of phytopathogenic fungi. However, the relationship between the large-scale secretome of phytopathogenic fungi and their lifestyle is not fully understood. In the present study, the secretomes of 150 plant pathogenic fungi were predicted and the characteristics associated with different lifestyles were investigated. In total, 94,974 secreted proteins (SPs) were predicted from these fungi. The number of the SPs ranged from 64 to 1,662. Among these fungi, hemibiotrophic fungi had the highest number (average of 970) and proportion (7.1%) of SPs. Functional annotation showed that hemibiotrophic and necrotroph fungi, differ from biotrophic and symbiotic fungi, contained much more carbohydrate enzymes, especially polysaccharide lyases and carbohydrate esterases. Furthermore, the core and lifestyle-specific SPs orthogroups were identified. Twenty-seven core orthogroups contained 16% of the total SPs and their motif function annotation was represented by serine carboxypeptidase, carboxylesterase and asparaginase. In contrast, 97 lifestyle-specific orthogroups contained only 1% of the total SPs, with diverse functions such as PAN_AP in hemibiotroph-specific and flavin monooxygenases in necrotroph-specific. Moreover, obligate biotrophic fungi had the largest number of effectors (average of 150), followed by hemibiotrophic fungi (average of 120). Among these effectors, 4,155 had known functional annotation and pectin lyase had the highest proportion in the functionally annotated effectors. In addition, 32 sets of RNA-Seq data on pathogen-host interactions were collected and the expression levels of SPs were higher than that of non-SPs, and the expression level of effector genes was higher in biotrophic and hemibiotrophic fungi than in necrotrophic fungi, while secretase genes were highly expressed in necrotrophic fungi. Finally, the secretory activity of five predicted SPs from Setosphearia turcica was experimentally verified. In conclusion, our results provide a foundation for the study of pathogen-host interaction and help us to understand the fungal lifestyle adaptation

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper

Dynamic study of the piezo-driven pipettes in intracytoplasmic sperm injection

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on June 19, 2009)Vita.Thesis (Ph. D.) University of Missouri-Columbia 2008.[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Intracytoplasmic Sperm Injection (ICSI) is regarded as a critical assisted reproductive technology (ART) procedure in which a single spermatozoon is mechanically injected into the cytoplasm of an oocyte using an injection pipette. During ICSI, one piezo-driven actuator (PDA) connected to the pipette is routinely utilized. Importantly, the pipettes driven by PDA are typically filled with the mercury plug. The presence of mercury is associated with serious potential toxicity effects, both related to personnel safety and to the developmental competence of the manipulated oocytes. Therefore, this research is developed to analyze the effects of mercury on the dynamic characteristics of the piezo-driven pipette, and to achieve the improved method to replace mercury. The lateral vibration of the injection pipette is first analyzed, the finite element model including the effects of the fluids both inside and outside the pipette is built up, and the transient responses of the pipette are obtained by the numerical simulations. Based on the dynamic model of PDA connected to the pipette, the longitudinal vibration of the pipette is studied as well. The role of the small momentum diffusivity of mercury is specifically investigated in facilitating the ICSI procedures. "Scissor theory" is proposed to explain the function of mercury in details. Based on the simulation results, our PDA system is developed. Numerous preliminary experiments are performed with the commercial Piezo-driven systems and our system. Our system is more efficient than the present commercial systems with the presence of mercury plug; furthermore, our system does work successfully in penetrating mouse oocytes without mercury plug.Includes bibliographical reference

MRFA-Net: Multi-Scale Receptive Feature Aggregation Network for Cloud and Shadow Detection

The effective segmentation of clouds and cloud shadows is crucial for surface feature extraction, climate monitoring, and atmospheric correction, but it remains a critical challenge in remote sensing image processing. Cloud features are intricate, with varied distributions and unclear boundaries, making accurate extraction difficult, with only a few networks addressing this challenge. To tackle these issues, we introduce a multi-scale receptive field aggregation network (MRFA-Net). The MRFA-Net comprises an MRFA-Encoder and MRFA-Decoder. Within the encoder, the net includes the asymmetric feature extractor module (AFEM) and multi-scale attention, which capture diverse local features and enhance contextual semantic understanding, respectively. The MRFA-Decoder includes the multi-path decoder module (MDM) for blending features and the global feature refinement module (GFRM) for optimizing information via learnable matrix decomposition. Experimental results demonstrate that our model excelled in generalization and segmentation performance when addressing various complex backgrounds and different category detections, exhibiting advantages in terms of parameter efficiency and computational complexity, with the MRFA-Net achieving a mean intersection over union (MIoU) of 94.12% on our custom Cloud and Shadow dataset, and 87.54% on the open-source HRC_WHU dataset, outperforming other models by at least 0.53% and 0.62%. The proposed model demonstrates applicability in practical scenarios where features are difficult to distinguish

Rags to riches? Entrepreneurs' social classes, resourceful time allocation, and venture performance

Social classes shape entrepreneurial pursuits in that entrepreneurs from lower social class groups face more resource deficiencies compared to those from higher social class groups. In this study, we theorize that being resourceful with a particular resource—time—helps ventures run by lower-class entrepreneurs achieve better performance. However, we further argue that the extent to which entrepreneurs use time resourcefully is affected by the cognitive schemas stamped on them by their social class backgrounds. Our empirical analysis of 8663 Chinese private entrepreneurs between 2006 and 2010 lends robust support to these arguments. By revealing both material and cognitive constraints stemming from entrepreneurs' social classes, our study contributes to research on social classes and entrepreneurial resourcefulness and has important implications for understanding the persistence of inequality in entrepreneurship

Application of ^99mTc-Labeled WL12 Peptides as a Tumor PD-L1-Targeted SPECT Imaging Agent: Kit Formulation, Preclinical Evaluation, and Study on the Influence of Coligands

With the development of PD-1/PD-L1 immune checkpoint inhibitor therapy, the ability to monitor PD-L1 expression in the tumor microenvironment is important for guiding therapy. This study was performed to develop a novel radiotracer with optimal pharmacokinetic properties to reflect PD-L1 expression in vivo via single-photon emission computed tomography (SPECT) imaging. [99mTc]Tc-HYNIC-WL12-tricine/M (M = TPPTS, PDA, ISONIC, 4-PSA) complexes with high radiochemical purity (>97%) and suitable molar activity (from 100.5 GBq/μmol to 300 GBq/μmol) were prepared through a kit preparation process. All 99mTc-labeled HYNIC-WL12 radiotracers displayed good in vitro stability for 4 h. The affinity and specificity of the four radiotracers for PD-L1 were demonstrated both in vitro and in vivo. The results of biodistribution studies displayed that the pharmacokinetics of the 99mTc-HYNIC-conjugated radiotracers were significantly influenced by the coligands of the radiotracers. Among them, [99mTc]Tc-HYNIC-WL12-tricine/ISONIC exhibited the optimal pharmacokinetic properties (t1/2α = 8.55 min, t1/2β = 54.05 min), including the fastest clearance in nontarget tissues, highest tumor-to-background contrast (e.g., tumor-to-muscle ratio, tumor-to-blood ratio: 40.42 ± 1.59, 14.72 ± 2.77 at 4 h p.i., respectively), and the lowest estimated radiation absorbed dose, highlighting its potential as a clinical SPECT imaging probe for tumor PD-L1 detection