PENGARUH PARTISIPASI PEMAKAI DAN KETIDAKPASTIAN TUGAS TERHADAP SISTEM INFORMASI AKUNTANSI DENGAN UKURAN ORGANISASI SEBAGAI VARIABEL MODERASI

The purpose of this research is to obtain empirical evidence of the effect of user participation and assignment uncertainty on the performance of accounting information systemi by measuring organization as moderating variable. The research was carried out on the LPD in the Ubudi sub-district. The selection of the samples in this research is using purposive sampling method, with a total of 75 respondents consisting of the Head of the LPD, Administration and the Treasurer. Data collection was carried out by distributing questionnaires. The analysis technique used is multiple line regression analysis and Moderating Regression Analysis (MRA). The results of this research show that: 1) user participation has a positive influence on the performance of SIAi in the Ubudi Sub-district LPD, 2) assignment uncertainty has a negative effect on SIAi performance in Ubudi Sub-district LPD, 3) organizational size has a negative effect on SIAi performance in Ubudi Sub-district LPD, 4) size of organization is able to moderate the influence of user participation on the performance of SIAi in the LPD of Ubudi District, 5) size of organization does not moderate the effect of uncertainty of assignment on the performance of SIAi in the Ubudi sub-district LP

Deformable Image Registration with Learning

As a fundamental task in medical image analysis, deformable image registration (DIR) is the process of estimating the deformation vector fields (DVFs) to images. In classic optimization-based DIR method, DVF is solved by optimizing a cost function consisting of image dissimilarity and DVF regularity, which typically involves time-consuming iterative processes. Deep-learning (DL)-based DIR has been developed in recent years, which offers a much faster alternative and the benefit from data-driven regularizing behaviors. This dissertation aims to develop accurate and robust DIR methods and address the lingering challenges in DL-DIR. First, we propose a DIR network that is conscious of and self-adaptive to deformation of various scales to improve accuracy. Second, we propose supervised and unsupervised approaches to incorporate learned implicit feasibility prior into DIR. Third, we propose a domain adaptation method to address the potential domain shift in DIR and improve accuracy and robustness on new data. Finally, we propose a DIR approach to synthesize continuous 4D motion from 3D image pair. Experiments with lung and cardiac images showed that the proposed techniques yielded significant performance improvement. We demonstrate the strength of combining physical-driven rationales and DL techniques in DIR

Imposing implicit feasibility constraints on deformable image registration using a statistical generative model

Purpose: Deformable registration problems are conventionally posed in a regularized optimization framework, where balance between fidelity and prescribed regularization usually needs to be tuned for each case. Even so, using a single weight to control regularization strength may be insufficient to reflect spatially variant tissue properties and limit registration performance. In this study, we proposed to incorporate a spatially variant deformation prior into image registration framework using a statistical generative model. Approach: A generator network is trained in an unsupervised setting to maximize the likelihood of observing the moving and fixed image pairs, using an alternating back-propagation approach. The trained model imposes constraints on deformation and serves as an effective low-dimensional deformation parametrization. During registration, optimization is performed over this learned parametrization, eliminating the need for explicit regularization and tuning. The proposed method was tested against SimpleElastix, DIRNet, and Voxelmorph. Results: Experiments with synthetic images and simulated CTs showed that our method yielded registration errors significantly lower than SimpleElastix and DIRNet. Experiments with cardiac magnetic resonance images showed that the method encouraged physical and physiological feasibility of deformation. Evaluation with left ventricle contours showed that our method achieved a dice of ( 0.93±0.03 ) with significant improvement over all SimpleElastix options, DIRNet, and VoxelMorph. Mean average surface distance was on millimeter level, comparable to the best SimpleElastix setting. The average 3D registration time was 12.78 s, faster than 24.70 s in SimpleElastix. Conclusions: The learned implicit parametrization could be an efficacious alternative to regularized B-spline model, more flexible in admitting spatial heterogeneity

MRA-free intracranial vessel localization on MR vessel wall images.

Analysis of vessel morphology is important in assessing intracranial atherosclerosis disease (ICAD). Recently, magnetic resonance (MR) vessel wall imaging (VWI) has been introduced to image ICAD and characterize morphology for atherosclerotic lesions. In order to automatically perform quantitative analysis on VWI data, MR angiography (MRA) acquired in the same imaging session is typically used to localize the vessel segments of interest. However, MRA may be unavailable caused by the lack or failure of the sequence in a VWI protocol. This study aims to investigate the feasibility to infer the vessel location directly from VWI. We propose to synergize an atlas-based method to preserve general vessel structure topology with a deep learning network in the motion field domain to correct the residual geometric error. Performance is quantified by examining the agreement between the extracted vessel structures from the pair-acquired and alignment-corrected angiogram, and the estimated output using a cross-validation scheme. Our proposed pipeline yields clinically feasible performance in localizing intracranial vessels, demonstrating the promise of performing vessel morphology analysis using VWI alone

Beetle-Inspired Bidirectional, Asymmetric Interlocking Using Geometry-Tunable Nanohairs

We present bidirectional, asymmetric interlocking behaviors between tilted micro- and nanohair arrays inspired from the actual wing locking device of beetles. The measured shear adhesion force between two identical tilted microhair arrays (1.5 μm radius, 30 μm height) turned out to be higher in the reverse direction than that in the angled direction, suggesting that the directionality of beetle’s microtrichia may play a critical role in preventing the elytra from shifting along the middle of insect body. Furthermore, we observed dramatic enhancement of shear adhesion using asymmetric interlocking of various nanohair arrays (tilting angle, δ < 40°). A maximum shear locking force of ∼60 N/cm² was measured for the nanohair arrays of 50 nm radius and 1 μm height with a hysteresis as high as ∼3. A simple theoretical model was developed to describe the measured asymmetric adhesion forces and hysteresis, in good agreement with the experimental data

Electrical switching of Ising-superconducting nonreciprocity for quantum neuronal transistor

Abstract Nonreciprocal quantum transport effect is mainly governed by the symmetry breaking of the material systems and is gaining extensive attention in condensed matter physics. Realizing electrical switching of the polarity of the nonreciprocal transport without external magnetic field is essential to the development of nonreciprocal quantum devices. However, electrical switching of superconducting nonreciprocity remains yet to be achieved. Here, we report the observation of field-free electrical switching of nonreciprocal Ising superconductivity in Fe3GeTe2/NbSe2 van der Waals (vdW) heterostructure. By taking advantage of this electrically switchable superconducting nonreciprocity, we demonstrate a proof-of-concept nonreciprocal quantum neuronal transistor, which allows for implementing the XOR logic gate and faithfully emulating biological functionality of a cortical neuron in the brain. Our work provides a promising pathway to realize field-free and electrically switchable nonreciprocity of quantum transport and demonstrate its potential in exploring neuromorphic quantum devices with both functionality and performance beyond the traditional devices

Interfacial magnetic spin Hall effect in van der Waals Fe3GeTe2/MoTe2 heterostructure

Abstract The spin Hall effect (SHE) allows efficient generation of spin polarization or spin current through charge current and plays a crucial role in the development of spintronics. While SHE typically occurs in non-magnetic materials and is time-reversal even, exploring time-reversal-odd (T-odd) SHE, which couples SHE to magnetization in ferromagnetic materials, offers a new charge-spin conversion mechanism with new functionalities. Here, we report the observation of giant T-odd SHE in Fe3GeTe2/MoTe2 van der Waals heterostructure, representing a previously unidentified interfacial magnetic spin Hall effect (interfacial-MSHE). Through rigorous symmetry analysis and theoretical calculations, we attribute the interfacial-MSHE to a symmetry-breaking induced spin current dipole at the vdW interface. Furthermore, we show that this linear effect can be used for implementing multiply-accumulate operations and binary convolutional neural networks with cascaded multi-terminal devices. Our findings uncover an interfacial T-odd charge-spin conversion mechanism with promising potential for energy-efficient in-memory computing

The 2022 applied physics by pioneering women: a roadmap

International audienceAbstract Women have made significant contributions to applied physics research and development, and their participation is vital to continued progress. Recognizing these contributions is important for encouraging increased involvement and creating an equitable environment in which women can thrive. This Roadmap on Women in Applied Physics, written by women scientists and engineers, is intended to celebrate women’s accomplishments, highlight established and early career researchers enlarging the boundaries in their respective fields, and promote increased visibility for the impact women have on applied physics research. Perspectives cover the topics of plasma materials processing and propulsion, super-resolution microscopy, bioelectronics, spintronics, superconducting quantum interference device technology, quantum materials, 2D materials, catalysis and surface science, fuel cells, batteries, photovoltaics, neuromorphic computing and devices, nanophotonics and nanophononics, and nanomagnetism. Our intent is to inspire more women to enter these fields and encourage an atmosphere of inclusion within the scientific community