WFB: watermarking-based copyright protection framework for federated learning model via blockchain.

Federated learning (FL) enables users to train the global model cooperatively without exposing their private data across the engaged parties, which is widely used in privacy-sensitive business. However, during the life cycle of FL models, both adversaries' attacks and ownership generalization threaten the FL models' copyright and affect the models' reliability. To address these problems, existing model watermarking techniques can be used to verify FL model's ownership.

A triangular-trapezoidal dual-channel shaping algorithm for resistive anode readout systems and its FPGA implementation.

This paper introduces a novel digital triangular-trapezoidal double-channel shaping algorithm to enhance the counting rate of resistive anode detectors. The algorithm is based on the trapezoidal shaping algorithm and improves it. At the extreme counting rate, the trapezoidal shaping algorithm cannot alleviate the pulse pileup, so the counting rate cannot meet the requirements of a high performance detector.

Diversity matters: Cross-head mutual mean-teaching for semi-supervised medical image segmentation.

Semi-supervised medical image segmentation (SSMIS) has witnessed substantial advancements by leveraging limited labeled data and abundant unlabeled data. Nevertheless, existing state-of-the-art (SOTA) methods encounter challenges in accurately predicting labels for the unlabeled data, giving rise to disruptive noise during training and susceptibility to erroneous information overfitting. Moreover, applying perturbations to inaccurate predictions further impedes consistent learning.

Federated learning for network attack detection using attention-based graph neural networks.

Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-based Graph Neural Network for detecting cross-level and cross-department network attacks.

Supervisor's neuroticism and problematic Internet use among graduate students: the mediating role of supervisor-student relationship quality and the moderating role of fear of the supervisor's negative evaluation.

Background: Graduate students exhibit vulnerability to problematic Internet use, which can result in adverse physical, psychological, and social consequences. However, limited studies have addressed this issue among graduate students, and even fewer have explored the unique factors contributing to their problematic Internet use. Therefore, to address this gap, the current study aims to probe the relationship between supervisor's neuroticism and problematic Internet use among graduate students, the mediating effect of the supervisor-student relationship quality, as well as the moderating effect of fear of the supervisor's negative evaluation.

Innovation and challenges of artificial intelligence technology in personalized healthcare.

As the burgeoning field of Artificial Intelligence (AI) continues to permeate the fabric of healthcare, particularly in the realms of patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care. Through the introduction of innovative instruments such as virtual assistant chatbots, wearable monitoring devices, predictive analytic models, personalized treatment regimens, and automated appointment systems, AI is not only amplifying the quality of care but also empowering patients and fostering a more interactive dynamic between the patient and the healthcare provider.

Detecting cell types and densities in the tumor microenvironment improves prognostic risk assessment for breast cancer.

A comprehensive evaluation of the relationship between the densities of various cell types in the breast cancer tumor microenvironment and patient prognosis is currently lacking. Additionally, the absence of a large patch-level whole slide imaging (WSI) dataset of breast cancer with annotated cell types hinders the ability of artificial intelligence to evaluate cell density in breast cancer WSI. We first employed Lasso-Cox regression to build a breast cancer prognosis assessment model based on cell density in a population study.

SAMGAT: structure-aware multilevel graph attention networks for automatic rumor detection.

The rapid dissemination of unverified information through social platforms like Twitter poses considerable dangers to societal stability. Identifying real versus fake claims is challenging, and previous work on rumor detection methods often fails to effectively capture propagation structure features. These methods also often overlook the presence of comments irrelevant to the discussion topic of the source post.

Degradation prediction of fuel cell systems based on different operating conditions in dynamic cycling condition.

In this paper, the degradation of PEMFC under different operating conditions in dynamic cycle condition is studied. Firstly, according to the failure mechanism of PEMFC, various operating conditions in dynamic cycle condition are classified, and the health indexes are established. Simultaneously, the rates and degrees of the output voltage decline of the PEMFC under different operating conditions during the dynamic cycling process were compared.

Designing antiperovskite derivatives atomic-position splitting for photovoltaic applications.

Due to the success of halide perovskites in the photovoltaic field, halide perovskite-derived semiconductors have also been widely studied for optoelectronic applications. However, the photovoltaic performance of these perovskite derivatives still lags significantly behind their perovskite counterparts, mainly due to deficiencies at the B-site or X-site of the derivatives, which disrupt the connectivity of the key [BX] octahedra units. Herein, we developed a class of antiperovskite-derived materials with the formula , achieved by splitting the A anion, originally at the corner site of the cubic antiperovskite structure, into three edge-centered sites.

Nanoscale phase management of the 2D/3D heterostructure toward efficient perovskite solar cells.

The stabilization of the formamidinium lead iodide (FAPbI) structure is pivotal for the development of efficient photovoltaic devices. Employing two-dimensional (2D) layers to passivate the three-dimensional (3D) perovskite is essential for maintaining the α-phase of FAPbI and enhancing the power conversion efficiency (PCE) of perovskite solar cells (PSCs). However, the role of bulky ligands in the phase management of 2D perovskites, crucial for the stabilization of FAPbI, has not yet been elucidated.

Graphene-Based Tunable Polarization Conversion Metasurface for Array Antenna Radar Cross-Section Reduction.

A graphene-based tunable polarization conversion metasurface (PCM) was designed and analyzed for the purpose of reducing the radar cross-section (RCS) of array antennas. The metasurface comprises periodic shuttle-shaped metal patches, square-patterned graphene, and inclined grating-patterned graphene. By adjusting the Fermi energy levels of the upper () and lower () graphene layers, different states were achieved.

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (SCNN) for detecting sleep posture.

A hybrid quantum-classical classification model based on branching multi-scale entanglement renormalization ansatz.

Tensor networks are emerging architectures for implementing quantum classification models. The branching multi-scale entanglement renormalization ansatz (BMERA) is a tensor network known for its enhanced entanglement properties. This paper introduces a hybrid quantum-classical classification model based on BMERA and explores the correlation between circuit layout, expressiveness, and classification accuracy.

Robust Edge States of Quasi-1D Material TaNiSe and Applications in Saturable Absorbers.

The helical edge states (ESs) protected by underlying topology in two-dimensional topological insulators (TIs) arouse upsurges in saturable absorptions thanks to the strong photon-electron coupling in ESs. However, limited TIs demonstrate clear signatures of topological ESs at liquid nitrogen temperatures, hindering the applications of such exotic quantum states. Here, we demonstrate the existence of one-dimensional (1D) ESs at the step edge of the quasi-1D material TaNiSe at 78 K by scanning tunneling microscopy.

SRS-Net: a universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by physics-informed deep learning.

As a crucial nonlinear phenomenon, stimulated Raman scattering (SRS) plays multifaceted roles involved in forward and inverse problems. In fibre-optic systems, these roles range from detrimental interference that impairs optical performance to beneficial effects that enables various devices such as Raman amplifier. To obtain solutions of SRS, various numerical methods customized for different scenarios have been proposed.

Light People: Prof. Donal D C Bradley (FRS).

The invention of organic light emitting diodes (LEDs) led to enormous excitement in both academe and industry in the late 1980's. Flexibility, large area solution processability, roll-to-roll printing, low cost, and environmentally friendly are some of the advantages of organic semiconductor materials, which brought a new horizon for optoelectronics. Together with the achievement of organic solar cells, transistors, lasers, and amplifiers, this has demonstrated potential applications of organic semiconductors in displays, lighting, solar energy generation, electronics, sensing and imaging, and many aspects of photonics.

Unveiling the Mechanism of Spontaneous Nanoscroll Formation from Janus Transition Metal Dichalcogenide Nanoribbons.

Due to the atomic asymmetry, Janus transition metal dichalcogenide monolayers possess spontaneous curling and can even form one-dimensional nanoscrolls. Unveiling this spontaneous formation mechanism of nanoscrolls is of great importance for precise structural control. In this paper, we successfully simulate the process of Janus MoSSe nanoscroll formation from flat nanoribbons, based on molecular dynamics (MD) simulations with hybrid potentials.

Quasi-Planar Core Based Spiro-Type Hole-Transporting Material for Dopant-Free Perovskite Solar Cells.

Hole-transporting material (HTMs) are crucial for obtaining the stability and high efficiency of perovskite solar cells (PSCs). However, the current state-of-the-art n-i-p PSCs relied on the use of 2,2',7,7'-tetrakis(N,N-di-p-methoxyphenylamine)-9,9'-spirobifluorene (spiro-OMeTAD) exhibit inferior intrinsic and ambient stability due to the p-dopant and hydrophilic Li-TFSI additive. In this study, a new spiro-type HTM with a critical quasi-planar core (Z-W-03) is developed to improve both the thermal and ambient stability of PSCs.

Geometric Amplitude Accompanying Local Responses: Spinor Phase Information from the Amplitudes of Spin-Polarized STM Measurements.

Solving the Hamiltonian of a system yields the energy dispersion and eigenstates. The geometric phase of the eigenstates generates many novel effects and potential applications. However, the geometric properties of the energy dispersion go unheeded.