On the Inversion of High Energy Proton

Inversion of the K-fold stochastic autoconvolution integral equation is an elementary nonlinear problem, yet there are no de facto methods to solve it with finite statistics. To fix this problem, we introduce a novel inverse algorithm based on a combination of minimization of relative entropy, the Fast Fourier Transform and a recursive version of Efron's bootstrap. This gives us power to obtain new perspectives on non-perturbative high energy QCD, such as probing the ab initio principles underlying the approximately negative binomial distributions of observed charged particle final state multiplicities, related to multiparton interactions, the fluctuating structure and profile of proton and diffraction. As a proof-of-concept, we apply the algorithm to ALICE proton-proton charged particle multiplicity measurements done at different center-of-mass energies and fiducial pseudorapidity intervals at the LHC, available on HEPData. A strong double peak structure emerges from the inversion, barely visible without it.Comment: 29 pages, 10 figures, v2: extended analysis (re-projection ratios, 2D

Spectral methods for multimodal data analysis

Spectral methods have proven themselves as an important and versatile tool in a wide range of problems in the fields of computer graphics, machine learning, pattern recognition, and computer vision, where many important problems boil down to constructing a Laplacian operator and finding a few of its eigenvalues and eigenfunctions. Classical examples include the computation of diffusion distances on manifolds in computer graphics, Laplacian eigenmaps, and spectral clustering in machine learning. In many cases, one has to deal with multiple data spaces simultaneously. For example, clustering multimedia data in machine learning applications involves various modalities or ``views'' (e.g., text and images), and finding correspondence between shapes in computer graphics problems is an operation performed between two or more modalities. In this thesis, we develop a generalization of spectral methods to deal with multiple data spaces and apply them to problems from the domains of computer graphics, machine learning, and image processing. Our main construction is based on simultaneous diagonalization of Laplacian operators. We present an efficient numerical technique for computing joint approximate eigenvectors of two or more Laplacians in challenging noisy scenarios, which also appears to be the first general non-smooth manifold optimization method. Finally, we use the relation between joint approximate diagonalizability and approximate commutativity of operators to define a structural similarity measure for images. We use this measure to perform structure-preserving color manipulations of a given image

Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection

Object recognition is a significant approach employed for recognizing suitable objects from the image. Various improvements, particularly in computer vision, are probable to diagnose highly difficult tasks with the assistance of local feature detection methodologies. Detecting multi-class objects is quite challenging, and many existing researches have worked to enhance the overall accuracy. But because of certain limitations like higher network loss, degraded training ability, improper consideration of features, less convergent and so on. The proposed research introduced a hybrid convolutional neural network (H-CNN) approach to overcome these drawbacks. The collected input images are pre-processed initially through Gaussian filtering to eradicate the noise and enhance the image quality. Followed by image pre-processing, the objects present in the images are localized using Grid Guided Localization (GGL). The effective features are extracted from the localized objects using the AlexNet model. Different objects are classified by replacing the concluding softmax layer of AlexNet with Support Vector Regression (SVR) model. The losses present in the network model are optimized using the Improved Grey Wolf (IGW) optimization procedure. The performances of the proposed model are analyzed using PYTHON. Various datasets are employed, including MIT-67, PASCAL VOC2010, Microsoft (MS)-COCO and MSRC. The performances are analyzed by varying the loss optimization algorithms like improved Particle Swarm Optimization (IPSO), improved Genetic Algorithm (IGA), and improved dragon fly algorithm (IDFA), improved simulated annealing algorithm (ISAA) and improved bacterial foraging algorithm (IBFA), to choose the best algorithm. The proposed accuracy outcomes are attained as PASCAL VOC2010 (95.04%), MIT-67 dataset (96.02%), MSRC (97.37%), and MS COCO (94.53%), respectively

바이오메디컬 응용을 위한 질화갈륨 마이크로막대 발광 다이오드

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 물리학과, 2020. 8. 이규철.Bio-medical applications using nanomaterials are an emerging research field that takes advantage of the properties of high material quality and small size for applications at the interface of biological cells and devices. Researchers have been studying about bio-medical applications. For this applications, the formation of interface with living things and devices is important. Moreover, the direct interface with cells is very important because the cells are the basic unit of the living things. The size of materials or devices should be nano/micro meter size to form the proper interface with cells because the size of cell is around from few hundreds nanometer to few hundreds micrometer. Among these bio-medical applications using nanomaterials, the optogenetics is a recently emerging new method to study and manipulate the behavior of neuronal cells with light. Recently, many papers about optogenetics applications were reported using light-emitting diodes. Gallium nitride (GaN) is a promising material for fabrication of light-emitting diode because of its high free exciton binding energy, direct band gap property. Using this promising material advantage, GaN material can be used for this application. In particular, GaN is a well-known, non-toxic, biocompatible material with a high optical quality. In this reason, this GaN material is suitable for the recent trend for bio-medical applications. Here, this dissertation introduces bio-medical applications using GaN micro-/nanomaterials and GaN microrod LED fabrication for optogenetics application. First, I discuss on laser emission from GaN microrods that were introduced into mammalian cells and the application of these microrods for cell labeling. GaN microrods were grown on graphene-coated SiO2/Si substrates by metal-organic vapor-phase epitaxy. Microrods are easily detached from the substrates because of the weakness of the Van der Waals forces between GaN and graphene. The uptake of microrods into HeLa cells via endocytosis and viability after uptake was investigated. Normal cellular activities, including migration and division, were observed over two weeks in culture. Furthermore, photoluminescence spectra of the internalized microrods exhibited sharp laser emission peaks with a low lasing threshold of 270 kW/cm2. The following part demonstrate that the GaN microrod LEDs fabricated on the thin film LED for optogenerics application. The diameter of 200 nm nanorod LEDs also fabricated using dry and wet etching processes. The fabricated GaN microrod LEDs showed that enough output power for the optogenetics experiment. Moreover, the diameter of 200 nm nanorod LED showed higher power efficiency. The intracellular potential variations from the cells were also observed with patch clamp method after light illumination using GaN microrod LEDs.나노/마이크로 물질은 매우 작은 크기를 가지고 있으며, 이를 이용해 세포와 나노물질 사이의 바이오 인터페이스 형성에 유리다. 이러한 나노물질을 이용한 바이오메디컬 응용분야는 최근 각광을 받는 분야이며, 많은 연구자들에 의해 연구되어 왔다. 세포는 모든 생명체의 기본 단위이며 이러한 세포들의 크기는 수백 나노에서 수백 마이크로 사이에 있다. 이러한 이유로 바이오메디컬 응용을 하기 위해서는 만들어진 소자와 세포간의 직접적인 인터페이스 형성이 매우 중요하고 할 수 있다. 많은 바이오메디컬 응용분야 중에 광유전학 분야는 신경세포 연구에 널리 쓰이는 분야중 하나이다. 최근 많은 연구자들이 다양한 발광다이오드를 이용하여 광유전학 분야에 이용 가능한 소자를 연구 중이다. 높은 자유 엑시톤 결합에너지와 직접밴드갭을 갖는 질화갈륨은 발광다이오드 소자제작에 가장 적합한 물질중 하나이다. 특히 높은 광특성과 함께 무독성과 생체적합성을 갖는 질화갈륨 물질은 바이오메디컬 응용을 위한 적합한 소재라고 할 수 있다. 이를 이용한 바이오메디컬 응용분야는 많은 연구자들에 의하여 연구되고 있으며, 본 학위논문은 질화갈륨 나노/마이크로 소재와 질화갈륨 마이크로막대 발광다이오드를 이용한 바이오메디컬 응용분야에 대하여 다룬다. 첫 번째 주제로는 질화갈륨 마이크로막대를 이용한 세포내 레이저 실험에 관하여 다룬다. 질화갈륨 마이크로 막대의 레이징 특성을 이용한 세포 라벨링은 다수의 세포추적을 위해 이용될 수 있다. 이를 위해 질화갈륨은 금속화학기상증착법을 이용하여 그래핀 위에 성장이 되었다. 약한 반데르발스 결합을 갖는 그래핀 기판위에 성장시킨 질화갈륨 마이크로막대는 손쉽게 기판으로부터 분리가 가능하다. 또한 크기가 작은 질화갈륨 마이크로막대는 자발적인 세포내이입과정을 거쳐 손쉽게 세포내부로 유입시킬 수 있다. 질화갈륨이 유입된 세포는 2주간의 세포배양 후에도 정상적인 세포이동과 세포분열을 나타내는 등 정상적인 세포활동을 보였다. 더욱이 세포내부로 유입된 질화갈륨 마이크로막대로부터 강한 레이징 신호가 측정됨을 확인 하였으며, 270 kW/cm2의 낮은 레이징 문턱전력밀도를 보임을 측정 하였다. 이어지는 연구에서는 질화갈륨 마이크로막대 발광다이오드 소자의 제작과 이를 이용한 광유전학 분야로의 응용에 대하여 다룬다. 이 연구에서는 200 nm 의 지름을 갖는 질화갈륨 마이크로막대 발광다이오드 소자가 건식식각공정과 습식식각공정을 이용하여 제조 되었다. 또한 제조된 발광다이오드 소자에서는 광유전학 분야에 사용되기 충분한 세기의 빛이 발광됨을 측정 하였다. 제조된 소자를 이용해 빛에 의해서 개폐가 조절되는 이온 채널이 발현된 세포를 자극하였으며, 세포내부에서의 전압변화가 패치 클램프 법을 이용해 측정 되었다.Chapter 1. Introduction 19 1.1. Motivation: Current research status in bio-medical applications using nano/micro inorganic materials 19 1.2. Objective and approach. 21 1.3. Outline. 22 Chapter 2. Literature Review 23 2.1. Intracellular lasers using micro-/nano materials. 23 2.1.1. Intracellular lasers using organic materials. 23 2.1.2. Intracellular lasers using inorganic materials 27 2.2. Micro LEDs for optogenetics application. 29 2.2.1. Organic micro LEDs for optogenetics application 29 2.2.2. Inorganic micro LEDs for optogenetics application. 33 Chapter 3. Experimental methods. 35 3.1. Metal-organic chemical vapor deposition system. 35 3.1.1. Gas delivery system 35 3.1.2. Growth chamber and substrate heating. 38 3.1.3. Low pressure pumping and exhaust system . 40 3.1.4. Gas and reactants. 40 3.2. Growth techniques 42 3.2.1. ZnO nanotube growth on graphene films42 3.3. Optical characterization 45 3.3.1. Confocal micro-photoluminescence measurement. 45 3.3.2. Photoluminescence measurement at high pumping density. 47 3.3.3. Electroluminescence measurement. 47 3.4. Light-emitting diodes fabrication 49 3.4.1. Thin film light-emitting diode fabrication. 49 3.4.2. Micro- and nanostructure light-emitting diodes fabrication. 53 Chapter 4. GaN microrod intracellular laser. 59 4.1. Introduction. 59 4.2. GaN microrod growth. 60 4.3. Sample preparation for intracellular laser experiment 63 4.4. Internalization of the GaN microrod into cells. 66 4.5. Biocompatibility of GaN microrod. 68 4.6. Lasing characteristics of intracellular GaN microrod laser 70 4.7. Summary. 75 Chapter 5. GaN thin film and microrod LED for optogenetics 76 5.1. Introduction 76 5.2. Fabrication of microrod LED. 76 5.3. EL characteristics of microrod LEDs. 77 5.3.1. EL peak shift in microrod LEDs 83 5.3.2. Power efficiency of microrod LEDs 86 5.4. Intracellular potential variation stimulated by GaN LEDs. 90 5.5. Summary. 93 Chapter 6. Concluding remarks and outlooks. 94 6.1. Summary. 94 6.2. Future work and outlook 95 Appendix A. Position and morphology controlled ZnO nanotube growth on CVD graphene films. 97 A.1. Introduction 97 A.2. Growth behavior of ZnO nanotube on graphene films 97 A.2.1. Effect of growth temperature on ZnO nanotube morphology. 98 A.2.2. Effect of gas and metal-organic source molecular flow rate on ZnO nanotube morphology. 100 A.2.3. Effect of pressure on ZnO nanotube morphology 102 A.2.4. Growth behavior of ZnO nanotube on graphene films. 104 A.3. Summary 106 Appendix B. Highly-sensitive, flexible pressure sensors using ZnO nanotube arrays grown on graphene films. 107 B.1. Introduction 107 B.2. fabrication of ZnO nanotube pressure sensor. 109 B.3. Current responses of ZnO nanotube pressure sensor 112 B.4. Pressure responses according to the different ZnO nanotube dimensions 115 B.5. Bio-medical applications using flexible ZnO nanotube pressure sensor. 119 B.6. summary. 122 Appendix C. Neuronal mRNP transport follows an aging Lvy walk. 123 C.1. Introduction 123 C.2. Active transport of individual endogenous mRNP in neurons. 126 C.3. Transport of mRNP is composed of discrete runs and rests. 130 C.4. Transport of mRNP is composed of discrete runs and rests. 135 C.5. Neuronal mRNP particles perform an aging Lvy walk 140 C.6. Summary 147 References 149 Abstract in Korean (국문초록) 154 Acknowledgment. 157 Curriculum Vitae 159Docto

Cuckoo lévy flight with otsu for image segmentation in cancer detection

Detecting cancer cells from computed tomography (CT), magnetic resonance imaging (MRI) or mammogram scan images is a challenging task as the images are black and white and the neighbouring organs tend to be separated by edges with smooth varying intensity. On top of that, medical images segmentation is challenging due to the presence of weakly correlated and ambiguous multiple regions of interest. A few bio-inspired algorithms were developed to efficiently generate optimum threshold values for the process of segmenting such images. Their exhaustive search nature makes them computationally expensive when extended to multilevel thresholding, thus, this research is keen to solve the optimum threshold problems. This research propose an enhancement of image segmentation algorithms based on Otsu’s method by incorporating Cuckoo Search (CS) method for Lévy flight generation while simultaneously modifying and optimizing it to work on CT, MRI or mammogram image scanners, specifically to detect breast cancer. The performance of the proposed Otsu’s method with CS algorithm was compared with other bio-inspired algorithms such as Otsu with Particle Swarm Optimization (PSO) and Otsu with Darwinian Particle Swarm Optimization (DPSO). The experimental results were validated by measuring the peak signal-to-noise ratio (PNSR), mean squared error (MSE), feature similarity index (FSIM) and CPU running time for all cases investigated. The proposed Otsu’s method with CS algorithm experimental results achieved an average of 231.52 of MSE, 24.60 of PNSR, 0.93 of FSIM and 3.36 second of CPU running time. The method evolved to be more promising and computationally efficient for segmenting medical images. It is expected that the experiment results will benefit those in the areas of computer vision, remote sensing and image processing application

Field theoretic formulation and empirical tracking of spatial processes

Spatial processes are attacked on two fronts. On the one hand, tools from theoretical and statistical physics can be used to understand behaviour in complex, spatially-extended multi-body systems. On the other hand, computer vision and statistical analysis can be used to study 4D microscopy data to observe and understand real spatial processes in vivo. On the rst of these fronts, analytical models are developed for abstract processes, which can be simulated on graphs and lattices before considering real-world applications in elds such as biology, epidemiology or ecology. In the eld theoretic formulation of spatial processes, techniques originating in quantum eld theory such as canonical quantisation and the renormalization group are applied to reaction-di usion processes by analogy. These techniques are combined in the study of critical phenomena or critical dynamics. At this level, one is often interested in the scaling behaviour; how the correlation functions scale for di erent dimensions in geometric space. This can lead to a better understanding of how macroscopic patterns relate to microscopic interactions. In this vein, the trace of a branching random walk on various graphs is studied. In the thesis, a distinctly abstract approach is emphasised in order to support an algorithmic approach to parts of the formalism. A model of self-organised criticality, the Abelian sandpile model, is also considered. By exploiting a bijection between recurrent con gurations and spanning trees, an e cient Monte Carlo algorithm is developed to simulate sandpile processes on large lattices. On the second front, two case studies are considered; migratory patterns of leukaemia cells and mitotic events in Arabidopsis roots. In the rst case, tools from statistical physics are used to study the spatial dynamics of di erent leukaemia cell lineages before and after a treatment. One key result is that we can discriminate between migratory patterns in response to treatment, classifying cell motility in terms of sup/super/di usive regimes. For the second case study, a novel algorithm is developed to processes a 4D light-sheet microscopy dataset. The combination of transient uorescent markers and a poorly localised specimen in the eld of view leads to a challenging tracking problem. A fuzzy registration-tracking algorithm is developed to track mitotic events so as to understand their spatiotemporal dynamics under normal conditions and after tissue damage.Open Acces

Nonlinear Spectral Geometry Processing via the TV Transform

We introduce a novel computational framework for digital geometry processing, based upon the derivation of a nonlinear operator associated to the total variation functional. Such operator admits a generalized notion of spectral decomposition, yielding a sparse multiscale representation akin to Laplacian-based methods, while at the same time avoiding undesirable over-smoothing effects typical of such techniques. Our approach entails accurate, detail-preserving decomposition and manipulation of 3D shape geometry while taking an especially intuitive form: non-local semantic details are well separated into different bands, which can then be filtered and re-synthesized with a straightforward linear step. Our computational framework is flexible, can be applied to a variety of signals, and is easily adapted to different geometry representations, including triangle meshes and point clouds. We showcase our method throughout multiple applications in graphics, ranging from surface and signal denoising to detail transfer and cubic stylization.Comment: 16 pages, 20 figure