This study investigates demographic differences in cybersecurity culture in a large global organisation supporting safety critical and critical infrastructure sectors to target CSC improvement. A global survey was administered to all internal and external employees of a total of 21148 employees, with 6502 responses. The questionnaire evaluates nine CSC dimensions such as Password Management, Governance, Email Use. Anonymous survey responses were analysed using Kruskal-Wallis tests and Dunns post hoc comparisons to identify differences across demographic variables including employment, recruitment paths, managerial role, gender, age, tenure, and work base. CSC was broadly consistent across the organisation, with statistically significant but small to moderate demographic effects. CSC variations were observed across employment, age, recruitment paths, and line managerial role. In general, fulltime, internal, permanent, older employees, Merge and Acquisition recruits, and line managers consistently scored higher across multiple CSC dimensions. Parttime, younger, external employees, and those with 6 to 20 years of tenure in general scored lower. These patterns highlight higher-scoring
This paper aims to build a model that can Segment Anything in 3D medical images, driven by medical terminologies as Text prompts, termed as SAT. Our main contributions are three-fold: (i) We construct the first multimodal knowledge tree on human anatomy, including 6502 anatomical terminologies; Then, we build the largest and most comprehensive segmentation dataset for training, collecting over 22K 3D scans from 72 datasets, across 497 classes, with careful standardization on both image and label space; (ii) We propose to inject medical knowledge into a text encoder via contrastive learning and formulate a large-vocabulary segmentation model that can be prompted by medical terminologies in text form; (iii) We train SAT-Nano (110M parameters) and SAT-Pro (447M parameters). SAT-Pro achieves comparable performance to 72 nnU-Nets -- the strongest specialist models trained on each dataset (over 2.2B parameters combined) -- over 497 categories. Compared with the interactive approach MedSAM, SAT-Pro consistently outperforms across all 7 human body regions with +7.1% average Dice Similarity Coefficient (DSC) improvement, while showing enhanced scalability and robustness. On 2 external (cros
We present FUSE observations of the hot white dwarfs in the post-common envelope binaries Feige 24, EUVE J0720-317, BPM 6502, and EUVE J2013+400. The spectra show numerous photospheric absorption lines which trace the white dwarf orbital motion. We report the detection of C III, O VI, P V, and Si IV in the spectra of Feige 24, EUVE J0720-317 and EUVE J2013+400, and the detection of C III, N II, Si III, Si IV, and Fe III in the spectra of BPM 6502. Abundance measurements support the possibility that white dwarfs in post-common envelope binaries accrete material from the secondary star wind. The FUSE observations of BPM 6502 and EUVE J2013+400 cover a complete binary orbit. We used the FUSE spectra to measure the radial velocities traced by the white dwarf in the four binaries, where the zero-point velocity were fixed using the ISM velocities in the line of sight of the stellar systems. For BPM 6502 we determined a white dwarf velocity semi-amplitude of K_WD = 18.6+/-0.5km/s, and with the velocity semi-amplitude of the red dwarf companion (K_RD = 75.2+/-3.1 km/s), we estimate the mass ratio to be q = 0.25+/-0.01. Adopting a spectroscopic mass determination for the white dwarf, we inf
Spatiotemporal traffic data imputation is of great significance in intelligent transportation systems and data-driven decision-making processes. To perform efficient learning and accurate reconstruction from partially observed traffic data, we assert the importance of characterizing both global and local trends in time series. In the literature, substantial works have demonstrated the effectiveness of utilizing the low-rank property of traffic data by matrix/tensor completion models. In this study, we first introduce a Laplacian kernel to temporal regularization for characterizing local trends in traffic time series, which can be formulated as a circular convolution. Then, we develop a low-rank Laplacian convolutional representation (LCR) model by putting the circulant matrix nuclear norm and the Laplacian kernelized temporal regularization together, which is proved to meet a unified framework that has a fast Fourier transform (FFT) solution in log-linear time complexity. Through extensive experiments on several traffic datasets, we demonstrate the superiority of LCR over several baseline models for imputing traffic time series of various time series behaviors (e.g., data noises an
Causal discovery (CD) from time-varying data is important in neuroscience, medicine, and machine learning. Techniques for CD encompass randomized experiments, which are generally unbiased but expensive, and algorithms such as Granger causality, conditional-independence-based, structural-equation-based, and score-based methods that are only accurate under strong assumptions made by human designers. However, as demonstrated in other areas of machine learning, human expertise is often not entirely accurate and tends to be outperformed in domains with abundant data. In this study, we examine whether we can enhance domain-specific causal discovery for time series using a data-driven approach. Our findings indicate that this procedure significantly outperforms human-designed, domain-agnostic causal discovery methods, such as Mutual Information, VAR-LiNGAM, and Granger Causality on the MOS 6502 microprocessor, the NetSim fMRI dataset, and the Dream3 gene dataset. We argue that, when feasible, the causality field should consider a supervised approach in which domain-specific CD procedures are learned from extensive datasets with known causal relationships, rather than being designed by hum
A quantum problem once described as impossible for classical computers has now been solved using relatively modest hardware。 Researchers used tensor networks to compress the overwhelming wave function created by hundreds of entangled qubits, allowing some calculations to run on a laptop。 Their results matched both theoretical predictions and simula
NASA's Perseverance rover has reached an impressive new milestone on Mars, completing the equivalent of a full marathon by driving 26。2 miles (42。195 kilometers) across the Red Planet
A new book claims AI has been built on a flawed assumption dating back to Alan Turing's famous 1950 paper。 Denning argues that the most important parts of human intelligence, including common sense, intuition, culture, and practical know-how, cannot be encoded into computers。 He believes this makes true human-level AI impossible, regardless of how
China’s Chang’e-6 samples have uncovered a surprising difference between the Moon’s two hemispheres。 Solar wind particles penetrated deeper into the far-side soil because Earth’s magnetosphere slows the particles that reach the near side。 Noble gases locked inside the lunar regolith preserved evidence of this uneven bombardment
Dark matter may be far more complicated than scientists once believed。 A new study suggests it could consist of at least two different kinds of particles that slowly separate over time, with heavier particles sinking toward the centers of galaxies and lighter ones drifting outward。 This simple idea could explain several puzzling cosmic observations
A new particle detector called PLATON could replace millions of tiny detector components with a single block of light-producing material。 Using a light-field camera, highly sensitive photon sensors, and AI, it reconstructs particle paths in fast, detailed 3D。 Simulations suggest it could match or surpass today’s best detectors while being far easie
Scientists have created a programmable optical chip that can slow light on demand, giving engineers far greater control over how optical signals propagate through a circuit。 The technology could provide the delays, synchronization, and buffering functions needed to make light-based computing more practical。 A single chip could eventually perform se
Researchers have created self-destructing living plastic that uses engineered bacteria to completely break itself down when activated。 The material degrades in just six days without creating microplastics, offering a potential new solution for single-use plastic waste
The asteroid that wiped out the dinosaurs was likely an exceptionally rare CO chondrite from a distant region of the solar system。 Its unusual chemistry suggests that planet-cooling dust and debris, rather than sulfur inside the asteroid, may have delivered the deadliest blow
Scientists at Nanyang Technological University in Singapore have discovered a surprisingly simple way to create exotic light structures called optical skyrmions using a 200-year-old optical effect known as the Poisson spot。 Instead of relying on expensive, highly engineered materials, they simply shine a laser at a tiny circular disc, producing sta