A Behavior-Based Approach To Securing Email Systems

The Malicious Email Tracking (MET) system, reported in a prior publication, is a behavior-based security system for email services. The Email Mining Toolkit (EMT) presented in this paper is an offline email archive data mining analysis system that is designed to assist computing models of malicious email behavior for deployment in an online MET system. EMT includes a variety of behavior models for email attachments, user accounts and groups of accounts. Each model computed is used to detect anomalous and errant email behaviors. We report on the set of features implemented in the current version of EMT, and describe tests of the system and our plans for extensions to the set of models

Direct Measurements of the Transport of Nonequilibrium Electrons in Gold Films with Different Crystal Structures

The transport of femtosecond-laser-excited nonequilibrium electrons across polycrystalline and single-crystalline gold films has been investigated through time-of-flight measurements. The thicknesses of the films range from 25 to 400 nm. Ballistic electrons as well as electrons interacting with other electrons and/or with the lattice have been observed. The ballistic component dominates the transport in the thinner films, whereas the interactive transport mechanism is dominant at the upper end of the thickness range. A slower effective velocity of the interactive component is observed in the polycrystalline samples, and is assumed to arise from the presence of grain boundaries. The reflection coefficient of excited electrons at the grain boundaries is extracted from the experiment and is estimated to be r=0.12. The experiment also suggests that thermal equilibrium among the excited electrons is not fully established in the first 500 fs after excitation. © 1993 The American Physical Society

Leerplanverkenning actief burgerschap: handreiking voor schoolontwikkeling

SLO hoopt met deze publicaties aan scholen een handreiking te bieden voor het formuleren van een eigen visie en het samenstellen een onderwijsprogramma dat tegemoet komt aan kenmerken van actief en verantwoordelijk burgerschap. Wij realiseren ons dat weten waar je voor staat als school en dat ook nog eens aantoonbaar waarmaken, niet alleen complex is maar ook raakt aan schoolontwikkeling en schoolidentiteit. En aan betrokkenheid van ouders en de omgeving. En dat vraagt om een effectieve maar hanteerbare samenwerking tussen praktijk, beleid en onderzoek

AI-based association analysis for medical imaging using latent-space geometric confounder correction

AI has greatly enhanced medical image analysis, yet its use in epidemiological population imaging studies remains limited due to visualization challenges in non-linear models and lack of confounder control. Addressing this, we introduce an AI method emphasizing semantic feature interpretation and resilience against multiple confounders. Our approach's merits are tested in three scenarios: extracting confounder-free features from a 2D synthetic dataset; examining the association between prenatal alcohol exposure and children's facial shapes using 3D mesh data; exploring the relationship between global cognition and brain images with a 3D MRI dataset. Results confirm our method effectively reduces confounder influences, establishing less confounded associations. Additionally, it provides a unique visual representation, highlighting specific image alterations due to identified correlations.Comment: 18 pages; 7 figure

Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI

Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning-based registration algorithms, including joint optimization approaches, currently suffer from bias due to selection of a fixed reference frame and only support pairwise transformations. We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations. We evaluate the proposed method on longitudinal analysis of a white matter tract in a brain MRI dataset with 2-3 time-points for 3249 individuals, i.e., 8045 images in total. The reproducibility of the method is evaluated on test-retest data from 97 individuals. The results confirm that the implicit reference image is an average of the input image. In addition, the proposed framework leads to consistent segmentations and significantly lower processing bias than that of a pair-wise fixed-reference approach. This processing bias is even smaller than those obtained when translating segmentations by only one voxel, which can be attributed to subtle numerical instabilities and interpolation. Therefore, we postulate that the proposed mean-space learning strategy could be widely applied to learning-based registration tasks. In addition, this group-wise framework introduces a novel way for learning-based longitudinal studies by direct construction of an unbiased within-subject template and allowing reliable and efficient analysis of spatio-temporal imaging biomarkers.Comment: SPIE Medical Imaging 2021 (oral

Using acoustic waves to induce high-frequency current oscillations in superlattices

We show that GHz acoustic waves in semiconductor superlattices can induce THz electron dynamics that depend critically on the wave amplitude. Below a threshold amplitude, the acoustic wave drags electrons through the superlattice with a peak drift velocity overshooting that produced by a static electric field. In this regime, single electrons perform drifting orbits with THz frequency components. When the wave amplitude exceeds the critical threshold, an abrupt onset of Bloch-like oscillations causes negative differential velocity. The acoustic wave also affects the collective behavior of the electrons by causing the formation of localised electron accumulation and depletion regions, which propagate through the superlattice, thereby producing self-sustained current oscillations even for very small wave amplitudes. We show that the underlying single-electron dynamics, in particular the transition between the acoustic wave dragging and Bloch oscillation regimes, strongly influence the spatial distribution of the electrons and the form of the current oscillations. In particular, the amplitude of the current oscillations depends non-monotonically on the strength of the acoustic wave, reflecting the variation of the single-electron drift velocity.Comment: 10 pages, 8 figure

Antagonism of the proinflammatory and pronociceptive actions of canonical and biased agonists of protease-activated receptor-2

Diverse proteases cleave protease-activated receptor-2 (PAR2) on primary sensory neurons and epithelial cells to evoke pain and inflammation. Trypsin and tryptase activate PAR2 by a canonical mechanism that entails cleavage within the extracellular N-terminus revealing a tethered ligand that activates the cleaved receptor. Cathepsin-S and elastase are biased agonists that cleave PAR2 at different sites to activate distinct signalling pathways. Although PAR2 is a therapeutic target for inflammatory and painful diseases, the divergent mechanisms of proteolytic activation complicate the development of therapeutically useful antagonists