Finite quotients of the multiplicative group of a finite dimensional division algebra are solvable

We prove that finite quotients of the multiplicative group of a finite dimensional division algebra are solvable. Let be a finite dimensional division algebra having center , and let be a normal subgroup of finite index. Suppose is not solvable. Then we may assume that is a minimal nonsolvable group (MNS group for short), i.e. a nonsolvable group all of whose proper quotients are solvable. Our proof now has two main ingredients. One ingredient is to show that the commuting graph of a finite MNS group satisfies a certain property which we denote Property . This property includes the requirement that the diameter of the commuting graph should be , but is, in fact, stronger. Another ingredient is to show that if the commuting graph of has Property , then is open with respect to a nontrivial height one valuation of (assuming without loss of generality, as we may, that is finitely generated). After establishing the openness of (when is an MNS group) we apply the Nonexistence Theorem whose proof uses induction on the transcendence degree of over its prime subfield to eliminate as a possible quotient of , thereby obtaining a contradiction and proving our main result.

     

Looking for Difference Sets in Groups with Dihedral Images

We prove four theorems about groups with a dihedral (or cyclic) image containing a difference set. For the first two, suppose G, a group of order 2p with p an odd prime, contains a nontrivial (v, k, ) difference set D with order n = k – prime to p and self-conjugate modulo p. If G has an image of order p, then 0 2a + 2 for a unique choice of = ±1, and for a = (k – )/2p. If G has an image of order 2p, then and ( – 1)/( – 1). There are further constraints on n, a and . We give examples in which these theorems imply no difference set can exist in a group of a specified order, including filling in some entries in Smith's extension to nonabelian groups of Lander's tables. A similar theorem covers the case when p|n. Finally, we show that if G contains a nontrivial (v, k, ) difference set D and has a dihedral image D _2m with either (n, m) = 1 or m = p ^t for p an odd prime dividing n, then one of the C ₂ intersection numbers of D is divisible by m. Again, this gives some non-existence results.     

Zur quantitativen Auswertung von Mikro-Autoradiogrammen mit dem Densitron

Die vorliegende Arbeit beschāftigt sich mit der Answertung von Autoradiogrammen inhomogen markierter mikroskopischer Strukturen am Densitron. Dazu fertigten wir von herkömmlichen Stripping-Filmen (z. B. ORW O K 106) Mikro-Stufengraukeile an und bestimmten die Schwärzung S_ijeder Stufe photometrisch. Graukeile geeigneten Schwärzungsumfangs dienten dann zur Einstellung der Farbtrigger des Densitrons, so daß sich die bei dieser Einstellung gemessenen Farbflächen F_i des jeweiligen Objektes bestimmten Schwärzungswerten zuordnen ließen.

Als Modellobjekte verwendelen wir erstens Autoradiogramme von ³H-Thymidin-markierten Tumorzellen. Vergleichsmessungen an unterschiedlich lange exponierten, sonst aber identischen Präparaten ergaben eine gute Reproduzierbarkeit der Meßergebnisse. Als zweites Testobjekt dienlen Autoradiogramme einer nur schwach markierten Struktur im Gehirn von Fröschen. Die an diesem Grenzfall erhaltenen Ergebnisse werden mit Meßwerten verglichen, die vom gleichen Objekt mittels Scanning-Photometrie an einem. Mikroskop-Photometer SMP 01 (OPTON, Oberkoches, BRD) gewonnen wurden.     

An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis

The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.     

高频信息矢量匹配实现异源图像配准

介绍了一种通过提取红外与可见图像高频信息形成矢量模型的方法,该模型可用于可见和红外两种不同波段相同场景图像的配准。分析了红外与可见图像的共同特性,即边缘高频特性,并在作为模板的图像中提取这类高频信息。利用人工干预的方法形成矢量模型,该矢量模型可通过实时姿态信息进行实时三维变换,并重新投影为二维图形,据此可在另一图像中进行特征搜索,达到两者匹配的目的。利用实际数据进行了实验分析,结果显示,利用本文算法提取的矢量模型,经变换后能可靠地实现异源目标自动识别;实验证明此方法有效可行,并可推广应用于各种异源图像间的自动目标识别和配准处理。     

Error estimation in measuring strain fields with DIC on planar sheet metal specimens with a non-perpendicular camera alignment

The determination of strain fields based on displacement components obtained via 2D-DIC is subject to several errors that originate from various sources. In this contribution, we study the impact of a non-perpendicular camera alignment to a planar sheet metal specimen's surface. The errors are estimated in a numerical experiment. To this purpose, deformed images - that were obtained by imposing finite element (FE) displacement fields on an undeformed image - are numerically rotated for various Euler angles. It is shown that a 3D-DIC stereo configuration induces a substantial compensation for the introduced image-plane displacement gradients. However, higher strain accuracy and precision are obtained - up to the level of a perfect perpendicular alignment - in a proposed “rectified” 2D-DIC setup. This compensating technique gains benefit from both 2D-DIC (single camera view, basic amount of correlation runs, no cross-camera matching nor triangulation) and 3D-DIC (oblique angle compensation). Our conclusions are validated in a real experiment on SS304.     

Defocus and binocular vision based stereo particle pairing method for 3D particle tracking velocimetry

We propose a new method, based on the depth-from-defocus technique and binocular vision, for solving the stereo particle pairing problem in 3D particle tracking velocimetry (PTV). Firstly, the apparent particle depth is measured with a single camera, using the depth-from-defocus technique. Secondly, a strict mathematical model of the particle-to-particle correspondence relationship between the left and right images, taking into account the refractions at the interfaces in the optical path, is presented, with the assumption that the apparent particle depth is measured. Thirdly, based on the apparent particle depth and particle-to-particle correspondence relationship, the epipoplar line is truncated into a short line segment by cutting off, where the apparent particle depth extends beyond its estimated range. For the first time, the range of the blur circle radius is employed as an additional stereo particle pairing constraint. Finally, the optimal pairing particle is selected by applying the epipolar line segment and blur circle radius constraints. The experimental results show that the rate of correct pairing is significantly improved compared with the epipolar line nearest neighbor analysis, especially when the particle density is increasing.     

Middle-school students’ concept images of geometric translations

This study explored sixth grade students’ concept images of geometric translations and the possible sources of their conceptions in a non-technological environment. The data were gathered through a written instrument, student and teacher interviews and document analyses. Analyses of student responses revealed two major concept images of geometric translations: (a) translation as translational motion, and (b) translation as both translational and rotational motion. Students who held these conceptions showed various levels of understanding, such as conceiving translations as undefined motion, partially-defined motion, and defined-motion of a single geometric figure on the plane. The findings of the study suggested, in general, consistencies between students’ concept images and their concept definitions. However, most of the students’ concept definitions were inconsistent with the formal concept definition of geometric translations.Data analyses also revealed five interpretations of a translation vector: (a) vector as a reference line, (b) vector as a symmetry line, (c) vector as a direction indicator, (d) vector as a parameter, and (e) vector as an abstract tool. Furthermore, classroom instruction, mathematics and science textbooks, real-life examples and everyday language were the major sources of students’ concept images of geometric translations.     

一种改进的模糊阈值选取方法

提出了一种改进的模糊阈值选取方法。针对传统的模糊阈值法隶属函数窗宽不容易确定的出发点，结合传统的易于实现的阈值选取方法，巧妙的绕过了确定窗宽的问题，能够结合模糊阈值法和该传统阈值法各自所能达到的优点，并且方法非常简单，明了。实践证明，这种方法是行之有效的，并具有较强的场景适应能力。     

分割图像插值的一种局部算法

对于医学序列切片，图像上像素之间的间隔常常小于切片之间的距离，而在图像处理中，常常需要它们有一致的分辨率。对于已分割的切片图像，插入的切片图像也应是分割的图像。本文提出了一种图像插值的局部算法，该算法在产生新插入图像每点的分割信息与颜色信息时，计算只与相邻切片图像的局部像素值有关。因此既保持了相邻切片的物体形态过渡自然，又具有良好的数值稳定性。应用该算法于医学序列切片进行插值时，可达到良好的视觉效果。算法已成功应用于医学数据的三维重建系统。