Discriminative and Generative Learning with Style Information

Conventional machine learning approaches usually assume that the patterns follow the identical and independent distribution (i.i.d.). However, in many empirical cases, such condition might be violated when data are equipped with diverse and inconsistent style information. The effectiveness of those traditional predictors may be limited due to the violation of the i.i.d. assumption brought by the existence of the style inconsistency. In this thesis, we investigate how the style information can be appropriately utilized for further lifting up the performance of machine learning models. It is fulfilled by not only introducing the style information into some state-of-the-art models, some new architectures, frameworks are also designed and implemented with specific purposes to make proper use of the style information. The main work is listed as the following summaries: First, the idea of the style averaging is initially introduced by an example of an image process based sunglasses recovery algorithm to perform robust one-shot facial expression recognition task. It is named as Style Elimination Transformation (SET). By recovering the pixels corrupted by the dark colors of the sunglasses brought by the proposed algorithm, the classification performance is promoted on several state-of-the-art machine learning classifiers even in a one-shot training setting. Then the investigation of the style normalization and style neutralization is investigated with both discriminative and generative machine learning approaches respectively. In discriminative learning models with style information, the style normalization transformation (SNT) is integrated into the support vector machines (SVM) for both classification and regression, named as the field support vector classification (F-SVC) and field support vector regression (F-SVR) respectively. The SNT can be represented with the nonlinearity by mapping the sufficiently complicated style information to the high-dimensional reproducing kernel Hilbert space. The learned SNT would normalize the inconsistent style information, producing i.i.d. examples, on which the SVM will be applied. Furthermore, a self-training based transductive framework will be introduced to incorporate with the unseen styles during training. The transductive SNT (T-SNT) is learned by transferring the trained styles to the unknown ones. Besides, in generative learning with style information, the style neutralization generative adversarial classifier (SN-GAC) is investigated to incorporate with the style information when performing the classification. As a neural network based framework, the SN-GAC enables the nonlinear mapping due to the nature of the nonlinearity of the neural network transformation with the generative manner. As a generalized and novel classification framework, it is capable of synthesizing style-neutralized high-quality humanunderstandable patterns given any style-inconsistent ones. Being learned with the adversarial training strategy in the first step, the final classification performance will be further promoted by fine-tuning the classifier when those style-neutralized examples can be well generated. Finally, the reversed task of the upon-mentioned style neutralization in the SN-GAC model, namely, the generation of arbitrary-style patterns, is also investigated in this thesis. By introducing the W-Net, a deep architecture upgraded from the famous U-Net model for image-to-image translation tasks, the few-shot (even the one-shot) arbitrary-style Chinese character generation task will be fulfilled. Same as the SN-GAC model, the W-Net is also trained with the adversarial training strategy proposed by the generative adversarial network. Such W-Net architecture is capable of generating any Chinese characters with the similar style as those given a few, or even one single, stylized examples. For all the proposed algorithms, frameworks, and models mentioned above for both the prediction and generation tasks, the inconsistent style information is taken into appropriate consideration. Inconsistent sunglasses information is eliminated by an image processing based sunglasses recovery algorithm in the SET, producing style-consistent patterns. The facial expression recognition is performed based on those transformed i.i.d. examples. The SNT is integrated into the SVM model, normalizing the inconsistent style information nonlinearly with the kernelized mapping. The T-SNT further enables the field prediction on those unseen styles during training. In the SN-GAC model, the style neutralization is performed by the neural network based upgraded U-Net architecture. Trained with separated steps with the adversarial optimization strategy included, it produces the high-quality style-neutralized i.i.d. patterns. The following classification is learned to produce superior performance with no additional computation involved. The W-Net architecture enables the free manipulation of the style data generation task with only a few, or even one single, style reference(s) available. It makes the Few-shot, or even the One-shot, Chinese Character Generation with the Arbitrary-style information task to be realized. Such appealing property is hardly seen in the literature

HTsort: Enabling Fast and Accurate Spike Sorting on Multi-Electrode Arrays

Spike sorting is used to classify the spikes (action potentials acquired by physiological electrodes), aiming to identify their respective firing units. Now it has been developed to classify the spikes recorded by multi-electrode arrays (MEAs), with the improvement of micro-electrode technology. However, how to improve classification accuracy and maintain low time complexity simultaneously becomes a difficulty. A fast and accurate spike sorting approach named HTsort is proposed for high-density multi-electrode arrays in this paper. Several improvements have been introduced to the traditional pipeline that is composed of threshold detection and clustering method. First, the divide-and-conquer method is employed to utilize electrode spatial information to achieve pre-clustering. Second, the clustering method HDBSCAN (hierarchical density-based spatial clustering of applications with noise) is used to classify spikes and detect overlapping events (multiple spikes firing simultaneously). Third, the template merging method is used to merge redundant exported templates according to the template similarity and the spatial distribution of electrodes. Finally, the template matching method is used to resolve overlapping events. Our approach is validated on simulation data constructed by ourselves and publicly available data and compared to other state-of-the-art spike sorters. We found that the proposed HTsort has a more favorable trade-off between accuracy and time consumption. Compared with MountainSort and SpykingCircus, the time consumption is reduced by at least 40% when the number of electrodes is 64 and below. Compared with HerdingSpikes, the classification accuracy can typically improve by more than 10%. Meanwhile, HTsort exhibits stronger robustness against background noise than other sorters. Our more sophisticated spike sorter would facilitate neurophysiologists to complete spike sorting more quickly and accurately

MyoPS A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/)

Effects of B-site Co2O3 doping on microstructure and electrical properties of Na0.25K0.25Bi2.5Nb2O9 ceramics

The effects of cobalt addition on the properties of Na0.25K0.25Bi2.5Nb2O9 (NKBN) ebased ceramics have been investigated in details. It was found that the ceramics possess a pure phase of bismuth oxide layer etype structure. The Curie temperature T-c gradually increases from 653 degrees C to 662 degrees C with increasing the Co modification. The electrical properties of NKBNebased ceramics are improved significantly by the addition of Co. The piezoelectric constant d33, dielectric loss tan d, mechanical quality factor Q(m) and remanent polarization Pr for the NKBN ceramics with 0.20 wt% Co2O3 modification were found to be 23 pC/N, 0.35%, 3028, 12.03 mC/cm(2), respectively. Thermal annealing studies indicated that the cobalt emodified NKBN ceramics system possesses stable piezoelectric properties, demonstrating that the cobaltemodified NKBNebased ceramics are the promising candidates for highetemperature applications. (C) 2015 Elsevier B.V. All rights reserved

Microstructure and optical characteristics of Ce:Gd-3(Ga,Al)(5)O-12 ceramic for scintillator application

Cerium doped Gd-3(Al,Ga)(5)O-12 (GGAG) ceramics with good performances have been fabricated in oxygen atmosphere. The microstructure, luminescence properties, and scintillation characteristics of the GGAG ceramics have been investigated with a Bi4Ge3O12 (BGO) single crystal as the reference sample. The photoluminescence emission of the GGAG ceramics peaked at about 558 nm and matched well with the sensitivity of Si-based photodiodes. The decay time and light output of the GGAG ceramics were about 53 ns and 34400 +/- 1032 ph/MeV, respectively. Both properties are superior to that of commercial BGO single crystals. (C) 2014 Elsevier Ltd and Techna Group S.r.l. All rights reserved

Pedro Páramo y the Sound and the Fury:

The elastic properties of Cu2GeSe3, including bulk modulus, shear modulus, Young's modulus, Possion's ratio, and their anisotropic properties, have been investigated by using first-principles calculations. The calculated lattice parameters are in good agreement with previous calculations and experimental measurements. The result of bulk modulus by fitting the Birch-Murnaghan 3rd-order equation of state is well consistent with that calculated from the elastic constants. The ductile nature of Cu2GeSe3 is characterized according to Pugh's rule. The Debye temperature calculated from fitting heat capacity data is consistent with that obtained from sound velocity. Additionally, the elastic anisotropy is depicted in detail by plotting the directional dependence of the bulk and Young's moduli

YAG phosphor with spatially separated luminescence centers

A micron size YAG:Ce/YAG:Cr core-shell structure was designed and accomplished via the urea homogeneous precipitation method using the YAG:Ce spherical core as the introduced second phase. A well dispersed gel like encapsulation structure can be achieved before the formation of YAG:Ce/YAG:Cr core-shell particles via a calcination process. As prepared YAG:Ce/YAG:Cr particles can emit a broad range of photons from 500 to 750 nm with excitation light of 433 nm. A schematic illustration showing the mechanism of excitation-emission of the core-shell particles is presented. The integral spectra are composed of three parts: emission photons of YAG:Cr, YAG:Ce, and emission light of YAG:Cr excited by the emission photons of the YAG:Ce core according to the proposed mechanism. The method accomplished in this work can significantly improve the exploration of full spectrum luminescent powder synthesis and spectra designation