Agent Roles, Qua Individuals and the Counting Problem

2006 ◽  
pp. 143-160 ◽  
Author(s):  
Giancarlo Guizzardi
Keyword(s):  
Counting Problem

The Counting Problem

1988 ◽  
pp. 219-224
Author(s):  
John Maynard Smith
Keyword(s):  
Counting Problem

Newton’s Substance Monism, Distant Action, and the Nature of Newton’s Empiricism

2021 ◽  
pp. 30-44
Author(s):  
Eric Schliesser
Keyword(s):  
Counting Problem ◽  
Critical Response ◽  
Distant Action ◽  
Nature Of Matter

This chapter is a critical response to Hylarie Kochiras’ (2009) “Gravity and Newton’s substance counting problem.” First, it argues that Kochiras conflates substances and beings; it proceeds to show that Newton is a substance monist. Second, it argues against the claim that Newton is committed to two speculative doctrines attributed to him by Kochiras and, earlier, Andrew Janiak—namely the passivity of matter and the principle of local causation. Third, the paper argues that while Kochiras’ (and Janiak’s) arguments about Newton’s metaphysical commitments are mistaken, it qualifies the characterization of Newton as an extreme empiricist as defended by Howard Stein and Rob DiSalle. In particular, the paper shows that Newton’s empiricism was an intellectual and developmental achievement that built on nontrivial speculative commitments about the nature of matter and space.

Autonomous Human Flow Counting Service With Deep Neural Network

2018 ◽  
Vol 8 (3) ◽  
pp. 18-36
Author(s):  
Shing Hwang Doong
Keyword(s):  
Neural Network ◽  
Time Window ◽  
Classification Algorithms ◽  
Channel State ◽  
Counting Problem ◽  
State Information ◽  
Space Management ◽  
Final Layer ◽  
The Mean ◽  
Human Flow

Human flow counting has many applications in space management. This study applied channel state information (CSI) available in IEEE 802.11n networks to characterize the flow count. Raw features including the mean, standard deviation and five-number summary were extracted from CSI magnitudes in a time window. Due to the large number of raw features, stacked denoising autoencoders were used to extract higher level features and a final layer of softmax regression was used to classify the flow count. The resulting neural network beat many popular classification algorithms in predicting the correct flow size. In addition to CSI magnitudes, this study also explored the feasibility of using CSI phase-based features. It is found that the magnitude neural network provided a better prediction result than the phase neural network, and combining both networks yielded an even better solution to the flow counting problem.

scholarly journals A High-Density Crowd Counting Method Based on Convolutional Feature Fusion

2018 ◽  
Vol 8 (12) ◽  
pp. 2367 ◽  
Author(s):  
Hongling Luo ◽  
Jun Sang ◽  
Weiqun Wu ◽  
Hong Xiang ◽  
Zhili Xiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Feature Fusion ◽  
High Density ◽  
Counting Problem ◽  
Crowd Counting ◽  
Low Level ◽  
Density Map ◽  
High Level

In recent years, the trampling events due to overcrowding have occurred frequently, which leads to the demand for crowd counting under a high-density environment. At present, there are few studies on monitoring crowds in a large-scale crowded environment, while there exists technology drawbacks and a lack of mature systems. Aiming to solve the crowd counting problem with high-density under complex environments, a feature fusion-based deep convolutional neural network method FF-CNN (Feature Fusion of Convolutional Neural Network) was proposed in this paper. The proposed FF-CNN mapped the crowd image to its crowd density map, and then obtained the head count by integration. The geometry adaptive kernels were adopted to generate high-quality density maps which were used as ground truths for network training. The deconvolution technique was used to achieve the fusion of high-level and low-level features to get richer features, and two loss functions, i.e., density map loss and absolute count loss, were used for joint optimization. In order to increase the sample diversity, the original images were cropped with a random cropping method for each iteration. The experimental results of FF-CNN on the ShanghaiTech public dataset showed that the fusion of low-level and high-level features can extract richer features to improve the precision of density map estimation, and further improve the accuracy of crowd counting.

scholarly journals On The Improvement of Combinatorial Mathematics Teaching From Generating Function

2017 ◽  
Vol 10 (2) ◽  
pp. 104
Author(s):  
Cheng Ji
Keyword(s):  
Generating Function ◽  
Mathematics Teaching ◽  
Teaching Improvement ◽  
Counting Problem ◽  
Combination Number ◽  
Combinatorial Counting

Generating function is an effective method to solve combinatorial counting problem, but it most likely to be neglected in combinatorial mathematics teaching. In this paper, we provides a demonstration for combinatorial mathematics teaching improvement by using the generating function solving combination number and the sum of preceding  terms among sequence of numbers.

scholarly journals Quiver matrix model of ADHM type and BPS state counting in diverse dimensions

2020 ◽  
Vol 2020 (11) ◽  
Author(s):  
Hiroaki Kanno
Keyword(s):  
Partition Function ◽  
Matrix Model ◽  
Characteristic Property ◽  
Vacuum Expectation ◽  
Vacuum Expectation Value ◽  
Torus Action ◽  
Localization Theorem ◽  
Counting Problem ◽  
Expectation Value ◽  
Four Dimensions

Abstract We review the problem of Bogomol’nyi–Prasad–Sommerfield (BPS) state counting described by the generalized quiver matrix model of Atiyah–Drinfield–Hitchin–Manin type. In four dimensions the generating function of the counting gives the Nekrasov partition function, and we obtain a generalization in higher dimensions. By the localization theorem, the partition function is given by the sum of contributions from the fixed points of the torus action, which are labeled by partitions, plane partitions and solid partitions. The measure or the Boltzmann weight of the path integral can take the form of the plethystic exponential. Remarkably, after integration the partition function or the vacuum expectation value is again expressed in plethystic form. We regard it as a characteristic property of the BPS state counting problem, which is closely related to the integrability.

PARALLEL ALGORITHMS FOR SOME DOMINANCE PROBLEMS BASED ON THE PRAM MODEL

1993 ◽  
Vol 03 (04) ◽  
pp. 367-382
Author(s):  
I.W. CHAN ◽  
D.K. FRIESEN
Keyword(s):  
Computational Model ◽  
Single Point ◽  
Random Access ◽  
Geometric Algorithms ◽  
Counting Problem ◽  
Erew Pram ◽  
Pram Model ◽  
Input Size ◽  
Parallel Random Access Machine ◽  
Direct Dominance

Two parallel geometric algorithms based on the idea of point domination are presented. The first algorithm solves the d-dimensional isothetic rectangles intersection counting problem of input size N/2d, where d>1 and N is a multiple of 2d, in O( log d−1 N) time and O(N log N) space. The second algorithm solves the direct dominance reporting problem for a set of N points in the plane in O( log N+J) time and O(N log N) space, where J denotes the maximum of the number of direct dominances reported by any single point in the set. Both algorithms make use of the EREW PRAM (Exclusive Read Exclusive Write Parallel Random Access Machine) consisting of O(N) processors as the computational model.

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