Related papers: A density-based clustering algorithm for the CYGNO…
Spatial resolution of less than 200 um is challenging for thermal neutron detection. A novel readout scheme based on the time-projection-chamber (TPC) concept is used in a gaseous electron multiplier (GEM) detector. Thermal neutrons are…
Image clustering is a crucial but challenging task in multimedia machine learning. Recently the combination of clustering with deep learning has achieved promising performance against conventional methods on high-dimensional image data.…
The combination of Markov state modeling (MSM) and molecular dynamics (MD) simulations has been shown in recent years to be a valuable approach to unravel the slow processes of molecular systems with increasing complexity. While the…
The appearance of direct electron detectors marked a new era for electron diffraction. Their high sensitivity and low noise opens the possibility to extend electron diffraction from transmission electron microscopes (TEM) to lower energies…
Smart grids extremely rely on Information and Communications Technology (ICT) and smart meters to control and manage numerous parameters of the network. However, using these infrastructures make smart grids more vulnerable to cyber threats…
The TOTEM experiment at LHC has chosen the triple Gas Electron Multiplier (GEM) technology for its T2 telescope which will provide charged track reconstruction in the rapidity range 5.3<|eta|<6.5 and a fully inclusive trigger for…
This contribution investigates a prototype of a TPC readout with a highly pixelated CMOS ASIC, which is an option for charged particles tracking of the ILC. A triple GEM stack was joined with a TimePix and MediPix2 chip (pixel size of…
Electron Multiplying CCDs (EMCCDs) are used much less often than they might be because of the challenges they offer camera designers more comfortable with the design of slow-scan detector systems. However they offer an entirely new range of…
Image clustering is one of the crucial techniques in multimedia analytics and knowledge discovery. Recently, the Deep clustering method (DC), characterized by its ability to perform feature learning and cluster assignment jointly, surpasses…
Data scarcity hinders deep learning for medical imaging. We propose a framework for breast cancer classification in thermograms that addresses this using a Diffusion Probabilistic Model (DPM) for data augmentation. Our DPM-based…
We discuss recent advances in the development of cryogenic gaseous photomultipliers (GPM), for possible use in dark matter and other rare-event searches using noble-liquid targets. We present results from a 10 cm diameter GPM coupled to a…
We consider the problem of clustering noisy finite-length observations of stationary ergodic random processes according to their generative models without prior knowledge of the model statistics and the number of generative models. Two…
For the International Large Detector (ILD) at the planned International Linear Collider (ILC) a Time Projection Chamber (TPC) is foreseen as the main tracking detector. To achieve the required point resolution, Micro Pattern Gaseous…
Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieve an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly…
Image steganography plays a vital role in securing secret data by embedding it in the cover images. Usually, these images are communicated in a compressed format. Existing techniques achieve this but have low embedding capacity. Enhancing…
DBSCAN is one of the most important non-parametric unsupervised data analysis tools. By applying DBSCAN to a dataset, two key analytical results can be obtained: (1) clustering data points based on density distribution and (2) identifying…
Clustering algorithms are often used to find subpopulations in exploratory data analysis workflows. Not only the clusters themselves, but also their shape can represent meaningful subpopulations. In this paper, we present FLASC, an…
Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a…
Clustering consists of a popular set of techniques used to separate data into interesting groups for further analysis. Many data sources on which clustering is performed are well-known to contain random and systematic measurement errors.…
The dual-phase xenon time projection chamber (TPC) is a powerful technology to detect rare interactions such as scatters of dark matter particles on nuclei. In particular, the built-in gain of ionization signals in a dual-phase TPC makes it…