ترغب بنشر مسار تعليمي؟ اضغط هنا

Distance bounds for high dimensional consistent digital rays and 2-D partially-consistent digital rays

146   0   0.0 ( 0 )
 نشر من قبل Man-Kwun Chiu
 تاريخ النشر 2020
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

We consider the problem of digitalizing Euclidean segments. Specifically, we look for a constructive method to connect any two points in $mathbb{Z}^d$. The construction must be {em consistent} (that is, satisfy the natural extension of the Euclidean axioms) while resembling them as much as possible. Previous work has shown asymptotically tight results in two dimensions with $Theta(log N)$ error, where resemblance between segments is measured with the Hausdorff distance, and $N$ is the $L_1$ distance between the two points. This construction was considered tight because of a $Omega(log N)$ lower bound that applies to any consistent construction in $mathbb{Z}^2$. In this paper we observe that the lower bound does not directly extend to higher dimensions. We give an alternative argument showing that any consistent construction in $d$ dimensions must have $Omega(log^{1/(d-1)} N)$ error. We tie the error of a consistent construction in high dimensions to the error of similar {em weak} constructions in two dimensions (constructions for which some points need not satisfy all the axioms). This not only opens the possibility for having constructions with $o(log N)$ error in high dimensions, but also opens up an interesting line of research in the tradeoff between the number of axiom violations and the error of the construction. In order to show our lower bound, we also consider a colored variation of the concept of discrepancy of a set of points that we find of independent interest.



قيم البحث

اقرأ أيضاً

In standard rounding, we want to map each value $X$ in a large continuous space (e.g., $R$) to a nearby point $P$ from a discrete subset (e.g., $Z$). This process seems to be inherently discontinuous in the sense that two consecutive noisy measuremen ts $X_1$ and $X_2$ of the same value may be extremely close to each other and yet they can be rounded to different points $P_1 e P_2$, which is undesirable in many applications. In this paper we show how to make the rounding process perfectly continuous in the sense that it maps any pair of sufficiently close measurements to the same point. We call such a process consistent rounding, and make it possible by allowing a small amount of information about the first measurement $X_1$ to be unidirectionally communicated to and used by the rounding process of $X_2$. The fault tolerance of a consistent rounding scheme is defined by the maximum distance between pairs of measurements which guarantees that they are always rounded to the same point, and our goal is to study the possible tradeoffs between the amount of information provided and the achievable fault tolerance for various types of spaces. When the measurements $X_i$ are arbitrary vectors in $R^d$, we show that communicating $log_2(d+1)$ bits of information is both sufficient and necessary (in the worst case) in order to achieve consistent rounding for some positive fault tolerance, and when d=3 we obtain a tight upper and lower asymptotic bound of $(0.561+o(1))k^{1/3}$ on the achievable fault tolerance when we reveal $log_2(k)$ bits of information about how $X_1$ was rounded. We analyze the problem by considering the possible colored tilings of the space with $k$ available colors, and obtain our upper and lower bounds with a variety of mathematical techniques including isoperimetric inequalities, the Brunn-Minkowski theorem, sphere packing bounds, and v{C}ech cohomology.
Discrimination of the detection of fast neutrons and gamma rays in a liquid scintillator detector has been investigated using digital pulse-processing techniques. An experimental setup with a 252Cf source, a BC-501 liquid scintillator detector, and a BaF2 detector was used to collect waveforms with a 100 Ms/s, 14 bit sampling ADC. Three identical ADCs were combined to increase the sampling frequency to 300 Ms/s. Four different digital pulse-shape analysis algorithms were developed and compared to each other and to data obtained with an analogue neutron-gamma discrimination unit. Two of the digital algorithms were based on the charge comparison method, while the analogue unit and the other two digital algorithms were based on the zero-crossover method. Two different figure-of-merit parameters, which quantify the neutron-gamma discrimination properties, were evaluated for all four digital algorithms and for the analogue data set. All of the digital algorithms gave similar or better figure-of-merit values than what was obtained with the analogue setup. A detailed study of the discrimination properties as a function of sampling frequency and bit resolution of the ADC was performed. It was shown that a sampling ADC with a bit resolution of 12 bits and a sampling frequency of 100 Ms/s is adequate for achieving an optimal neutron-gamma discrimination for pulses having a dynamic range for deposited neutron energies of 0.3-12 MeV. An investigation of the influence of the sampling frequency on the time resolution was made. A FWHM of 1.7 ns was obtained at 100 Ms/s.
Selectivity estimation aims at estimating the number of database objects that satisfy a selection criterion. Answering this problem accurately and efficiently is essential to many applications, such as density estimation, outlier detection, query opt imization, and data integration. The estimation problem is especially challenging for large-scale high-dimensional data due to the curse of dimensionality, the large variance of selectivity across different queries, and the need to make the estimator consistent (i.e., the selectivity is non-decreasing in the threshold). We propose a new deep learning-based model that learns a query-dependent piecewise linear function as selectivity estimator, which is flexible to fit the selectivity curve of any distance function and query object, while guaranteeing that the output is non-decreasing in the threshold. To improve the accuracy for large datasets, we propose to partition the dataset into multiple disjoint subsets and build a local model on each of them. We perform experiments on real datasets and show that the proposed model consistently outperforms state-of-the-art models in accuracy in an efficient way and is useful for real applications.
High dimensional data analysis for exploration and discovery includes three fundamental tasks: dimensionality reduction, clustering, and visualization. When the three associated tasks are done separately, as is often the case thus far, inconsistencie s can occur among the tasks in terms of data geometry and others. This can lead to confusing or misleading data interpretation. In this paper, we propose a novel neural network-based method, called Consistent Representation Learning (CRL), to accomplish the three associated tasks end-to-end and improve the consistencies. The CRL network consists of two nonlinear dimensionality reduction (NLDR) transformations: (1) one from the input data space to the latent feature space for clustering, and (2) the other from the clustering space to the final 2D or 3D space for visualization. Importantly, the two NLDR transformations are performed to best satisfy local geometry preserving (LGP) constraints across the spaces or network layers, to improve data consistencies along with the processing flow. Also, we propose a novel metric, clustering-visualization inconsistency (CVI), for evaluating the inconsistencies. Extensive comparative results show that the proposed CRL neural network method outperforms the popular t-SNE and UMAP-based and other contemporary clustering and visualization algorithms in terms of evaluation metrics and visualization.
141 - P.-A. Soderstrom 2008
The basic principles of detection of fast neutrons with liquid scintillator detectors are reviewed, together with a real example in the form of the Neutron Wall array. Two of the challenges in neutron detection, discrimination of neutrons and gamma r ays and identification of cross talk between detectors due to neutron scattering, are briefly discussed, as well as possible solutions to these problems. The possibilities of using digital techniques for pulse-shape discrimination are examined. Results from a digital and anal
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا