56 research outputs found
BMAD: Benchmarks for Medical Anomaly Detection
Anomaly detection (AD) is a fundamental research problem in machine learning
and computer vision, with practical applications in industrial inspection,
video surveillance, and medical diagnosis. In medical imaging, AD is especially
vital for detecting and diagnosing anomalies that may indicate rare diseases or
conditions. However, there is a lack of a universal and fair benchmark for
evaluating AD methods on medical images, which hinders the development of more
generalized and robust AD methods in this specific domain. To bridge this gap,
we introduce a comprehensive evaluation benchmark for assessing anomaly
detection methods on medical images. This benchmark encompasses six reorganized
datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT,
chest X-ray, and digital histopathology) and three key evaluation metrics, and
includes a total of fourteen state-of-the-art AD algorithms. This standardized
and well-curated medical benchmark with the well-structured codebase enables
comprehensive comparisons among recently proposed anomaly detection methods. It
will facilitate the community to conduct a fair comparison and advance the
field of AD on medical imaging. More information on BMAD is available in our
GitHub repository: https://github.com/DorisBao/BMA
Robust Stereoscopic Crosstalk Prediction
We propose a new metric to predict perceived crosstalk using the original images rather than both the original and ghosted images. The proposed metrics are based on color information. First, we extract a disparity map, a color difference map, and a color contrast map from original image pairs. Then, we use those maps to construct two new metrics (Vdispc and Vdlogc). Metric Vdispc considers the effect of the disparity map and the color difference map, while Vdlogc addresses the influence of the color contrast map. The prediction performance is evaluated using various types of stereoscopic crosstalk images. By incorporating Vdispc and Vdlogc, the new metric Vpdlc is proposed to achieve a higher correlation with the perceived subject crosstalk scores. Experimental results show that the new metrics achieve better performance than previous methods, which indicate that color information is one key factor for crosstalk visible prediction. Furthermore, we construct a new data set to evaluate our new metrics
Ultra-small topological spin textures with size of 1.3nm at above room temperature in Fe78Si9B13 amorphous alloy
Topologically protected spin textures, such as skyrmions1,2 and vortices3,4,
are robust against perturbations, serving as the building blocks for a range of
topological devices5-9. In order to implement these topological devices, it is
necessary to find ultra-small topological spin textures at room temperature,
because small size implies the higher topological charge density, stronger
signal of topological transport10,11 and the higher memory density or
integration for topological quantum devices5-9. However, finding ultra-small
topological spin textures at high temperatures is still a great challenge up to
now. Here we find ultra-small topological spin textures in Fe78Si9B13 amorphous
alloy. We measured a large topological Hall effect (THE) up to above room
temperature, indicating the existence of highly densed and ultra-small
topological spin textures in the samples. Further measurements by small-angle
neutron scattering (SANS) reveal that the average size of ultra-small magnetic
texture is around 1.3nm. Our Monte Carlo simulations show that such ultra-small
spin texture is topologically equivalent to skyrmions, which originate from
competing frustration and Dzyaloshinskii-Moriya interaction12,13 coming from
amorphous structure14-17. Taking a single topological spin texture as one bit
and ignoring the distance between them, we evaluated the ideal memory density
of Fe78Si9B13, which reaches up to 4.44*104 gigabits (43.4 TB) per in2 and is 2
times of the value of GdRu2Si218 at 5K. More important, such high memory
density can be obtained at above room temperature, which is 4 orders of
magnitude larger than the value of other materials at the same temperature.
These findings provide a unique candidate for magnetic memory devices with
ultra-high density.Comment: 26 pages, 4 figure
A New Approach to Off-Line Robust Model Predictive Control for Polytopic Uncertain Models
Concerning the robust model predictive control (MPC) for constrained systems with polytopic model characterization, some approaches have already been given in the literature. One famous approach is an off-line MPC, which off-line finds a state-feedback law sequence with corresponding ellipsoidal domains of attraction. Originally, each law in the sequence was calculated by fixing the infinite horizon control moves as a single state feedback law. This paper optimizes the feedback law in the larger ellipsoid, foreseeing that, if it is applied at the current instant, then better feedback laws in the smaller ellipsoids will be applied at the following time. In this way, the new approach achieves a larger domain of attraction and better control performance. A simulation example shows the effectiveness of the new technique
nbs: a new representation for point surfaces based on genetic clustering algorithm: cad and graphics
b-spline surfaces of clustered point sets with normal maps
China Comp Federat, IEEE Beijing Sect, Peking Univ, Inst Comp Sci & Technol, Peking Univ, Sch EECS, Natl Nat Sci Fdn China, Microsoft Res Asia, Peking Univ, Natl Lab Machine Percept, Key Lab High Confidence Software Technologies, Minist EducIn this paper, we propose a novel method that represents the highly-complex point sets by clustering the points to normal-mapped B-spline surfaces (NBSs). The main idea is to construct elaborate normal maps on simple surfaces for the realisti
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