Shareable Driving Style Learning and Analysis with a Hierarchical Latent Model

Driving style is usually used to characterize driving behavior for a driver or a group of drivers. However, it remains unclear how one individual's driving style shares certain common grounds with other drivers. Our insight is that driving behavior is a sequence of responses to the weighted mixture of latent driving styles that are shareable within and between individuals. To this end, this paper develops a hierarchical latent model to learn the relationship between driving behavior and driving styles. We first propose a fragment-based approach to represent complex sequential driving behavior, allowing for sufficiently representing driving behavior in a low-dimension feature space. Then, we provide an analytical formulation for the interaction of driving behavior and shareable driving style with a hierarchical latent model by introducing the mechanism of Dirichlet allocation. Our developed model is finally validated and verified with 100 drivers in naturalistic driving settings with urban and highways. Experimental results reveal that individuals share driving styles within and between them. We also analyzed the influence of personalities (e.g., age, gender, and driving experience) on driving styles and found that a naturally aggressive driver would not always keep driving aggressively (i.e., could behave calmly sometimes) but with a higher proportion of aggressiveness than other types of drivers

Spikformer: When Spiking Neural Network Meets Transformer

We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the self-attention mechanism. The former offers an energy-efficient and event-driven paradigm for deep learning, while the latter has the ability to capture feature dependencies, enabling Transformer to achieve good performance. It is intuitively promising to explore the marriage between them. In this paper, we consider leveraging both self-attention capability and biological properties of SNNs, and propose a novel Spiking Self Attention (SSA) as well as a powerful framework, named Spiking Transformer (Spikformer). The SSA mechanism in Spikformer models the sparse visual feature by using spike-form Query, Key, and Value without softmax. Since its computation is sparse and avoids multiplication, SSA is efficient and has low computational energy consumption. It is shown that Spikformer with SSA can outperform the state-of-the-art SNNs-like frameworks in image classification on both neuromorphic and static datasets. Spikformer (66.3M parameters) with comparable size to SEW-ResNet-152 (60.2M,69.26%) can achieve 74.81% top1 accuracy on ImageNet using 4 time steps, which is the state-of-the-art in directly trained SNNs models

Simulation of tumor ablation in hyperthermia cancer treatment: A parametric study

A holistic simulation framework is established on magnetic hyperthermia modeling to solve the treatment process of tumor, which is surrounded by a healthy tissue block. The interstitial tissue fluid, MNP distribution, temperature profile, and nanofluids are involved in the simulation. Study evaluates the cancer treatment efficacy by cumulative-equivalent-minutes-at-43 centigrade (CEM43), a widely accepted thermal dose coming from the cell death curve. Results are separated into the conditions of with or without gravity effect in the computational domain, where two baseline case are investigated and compared. An optimal treatment time 46.55 min happens in the baseline case without gravity, but the situation deteriorates with gravity effect where the time for totally killing tumor cells prolongs 36.11% and meanwhile causing 21.32% ablation in healthy tissue. For the cases without gravity, parameter study of Lewis number and Heat source number are conducted and the variation of optimal treatment time are both fitting to the inverse functions. For the case considering the gravity, parameters Buoyancy ratio and Darcy ratio are investigated and their influence on totally killing tumor cells and the injury on healthy tissue are matching with the parabolic functions. The results are beneficial to the prediction of various conditions, and provides useful guide to the magnetic hyperthermia treatment

Modelling floppy iris syndrome and the impact of pupil size and ring devices on iris displacement

INTRODUCTION:The aim of this paper was to further develop a previously described finite element model which equates clinical iris billowing movements with mechanical buckling behaviour, simulating floppy iris syndrome. We wished to evaluate the impact of pupil dilation and mechanical devices on normal iris and floppy iris models. METHODS:Theoretical mathematical modelling and computer simulations were used to assess billowing/buckling patterns of the iris under loading pressures for the undilated and dilated normal iris, the undilated and dilated floppy iris, and additionally with a mechanical ring device. RESULTS:For the normal iris, billowing/buckling occurred at a critical pressure of 19.92 mmHg for 5 mm pupil size, which increased to 28.00 mmHg (40.56%) with a 7 mm pupil. The Malyugin ring device significantly increased critical initiating buckling pressures in the normal iris scenario, to 34.58 mmHg (73.59%) for 7 mm ring with boundary conditions I (BC I) and 34.51 mmHg (73.24%) with BC II. For the most floppy iris modelling (40% degradation), initiating buckling value was 18.04 mmHg (-9.44%), which increased to 28.39 mmHg (42.52%) with the 7 mm ring. These results were much greater than for normal undilated iris without restrictive mechanical expansion (19.92 mmHg). CONCLUSION:This simulation demonstrates that pupil expansion devices inhibit iris billowing even in the setting of floppy iris syndrome. Our work also provides a model to further investigate the impact of pupil size or pharmacological interventions on anterior segment conditions affected by iris position

Generalized Hartmann-Shack array of dielectric metalens sub-arrays for polarimetric beam profiling

To define and characterize optical systems, obtaining information on the amplitude, phase, and polarization profile of optical beams is of utmost importance. Polarimetry using bulk optics is well established to characterize the polarization state. Recently, metasurfaces and metalenses have successfully been introduced as compact optical components. Here, we take the metasurface concept to the system level by realizing arrays of metalens 2*3 sub-arrays, allowing to determine the polarization profile of an optical beam. We use silicon-based metalenses with a numerical aperture of 0.32 and a mean measured diffraction efficiency in transmission mode of 28% at 1550 nm wavelength. Together with a standard camera recording the array foci, our system is extremely compact and allows for real-time beam diagnostics by inspecting the foci amplitudes. By further analyzing the foci displacements in the spirit of a Hartmann-Shack wavefront sensor, we can simultaneously detect phase-gradient profiles. As application examples, we diagnose the polarization profiles of a radially polarized beam, an azimuthally polarized beam, and of a vortex beam.Comment: 20 pages, 6 figures

Compositionally Complex Perovskite Oxides as a New Class of Li-Ion Solid Electrolytes

Compositionally complex ceramics (CCCs), including high-entropy ceramics (HECs) as a subclass, offer new opportunities of materials discovery beyond the traditional methodology of searching new stoichiometric compounds. Herein, we establish new strategies of tailoring CCCs via a seamless combination of (1) non-equimolar compositional designs and (2) controlling microstructures and interfaces. Using oxide solid electrolytes for all-solid-state batteries as an exemplar, we validate these new strategies via discovering a new class of compositionally complex perovskite oxides (CCPOs) to show the possibility of improving ionic conductivities beyond the limit of conventional doping. As an example (amongst the 28 CCPOs examined), we demonstrate that the ionic conductivity can be improved by >60% in (Li0.375Sr0.4375)(Ta0.375Nb0.375Zr0.125Hf0.125)O3-{\delta}, in comparison with the state-of-art (Li0.375Sr0.4375)(Ta0.75Zr0.25)O3-{\delta} (LSTZ) baseline, via maintaining comparable electrochemical stability. Furthermore, the ionic conductivity can be improved by another >70% via grain boundary (GB) engineering, achieving >270% of the LSTZ baseline. This work suggests transformative new strategies for designing and tailoring HECs and CCCs, thereby opening a new window for discovering materials for energy storage and many other applications

Evidence based on Mendelian randomization and colocalization analysis strengthens causal relationships between structural changes in specific brain regions and risk of amyotrophic lateral sclerosis

BackgroundAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the degeneration of motor neurons in the brain and spinal cord with a poor prognosis. Previous studies have observed cognitive decline and changes in brain morphometry in ALS patients. However, it remains unclear whether the brain structural alterations contribute to the risk of ALS. In this study, we conducted a bidirectional two-sample Mendelian randomization (MR) and colocalization analysis to investigate this causal relationship.MethodsSummary data of genome-wide association study were obtained for ALS and the brain structures, including surface area (SA), thickness and volume of subcortical structures. Inverse-variance weighted (IVW) method was used as the main estimate approach. Sensitivity analysis was conducted detect heterogeneity and pleiotropy. Colocalization analysis was performed to calculate the posterior probability of causal variation and identify the common genes.ResultsIn the forward MR analysis, we found positive associations between the SA in four cortical regions (lingual, parahippocampal, pericalcarine, and middle temporal) and the risk of ALS. Additionally, decreased thickness in nine cortical regions (caudal anterior cingulate, frontal pole, fusiform, inferior temporal, lateral occipital, lateral orbitofrontal, pars orbitalis, pars triangularis, and pericalcarine) was significantly associated with a higher risk of ALS. In the reverse MR analysis, genetically predicted ALS was associated with reduced thickness in the bankssts and increased thickness in the caudal middle frontal, inferior parietal, medial orbitofrontal, and superior temporal regions. Colocalization analysis revealed the presence of shared causal variants between the two traits.ConclusionOur results suggest that altered brain morphometry in individuals with high ALS risk may be genetically mediated. The causal associations of widespread multifocal extra-motor atrophy in frontal and temporal lobes with ALS risk support the notion of a continuum between ALS and frontotemporal dementia. These findings enhance our understanding of the cortical structural patterns in ALS and shed light on potentially viable therapeutic targets