scholarly journals Sample Compression Schemes for VC Classes

2016 ◽  
Vol 63 (3) ◽  
pp. 1-10 ◽  
Author(s):  
Shay Moran ◽  
Amir Yehudayoff
Keyword(s):  
Sample Compression

scholarly journals Sample compression, learnability, and the Vapnik-Chervonenkis dimension

1995 ◽  
Vol 21 (3) ◽  
pp. 269-304 ◽  
Author(s):  
Sally Floyd ◽  
Manfred Warmuth
Keyword(s):  
Sample Compression

scholarly journals THE COMPARISON OF SSD ALGORITHM WITH OTHER ECG SAMPLING ALGORITHMS

2006 ◽  
Vol 18 (03) ◽  
pp. 124-127
Author(s):  
HSIAO-HSUAN CHOU ◽  
YU-CHIEN SHIAU ◽  
TE-SON KUO
Keyword(s):  
Sampling Rate ◽  
Polygonal Approximation ◽  
Compression Performance ◽  
Uniform Sampling ◽  
Ecg Signals ◽  
Sample Compression ◽  
Sampling Algorithms ◽  
Two Measures ◽  
Test Equation ◽  
Electrocardiogram Ecg

We had proposed a novel and fast Electrocardiogram (ECG) signal compression algorithm for non-uniform sampling in time domain [1]. It meets the real-time requirement for clinical application. Moreover, the compression performance is stable and uniform even for abnormal ECG signals. A criterion called sum square difference (SSD) is defined as an error test equation. The algorithm using SSD to calculate error tolerance is applied to the records in MIT-BIH database (with 11-bit resolution and 360 Hz sampling rate). It belongs to the threshold-limited algorithm but [1] does not mention much about this kind of algorithm. In this paper we provide more comparisons among SSD, Fan, scan-along polygonal approximation (SAPA), maximum enclosed area (MEA), and optimization algorithm (OPT) using the two measures called sample compression ratio (SCR) and percent root mean squared difference (PRD) with proper mean offset that [1] does not adopt. The results show SSD outperforms the mentioned algorithms with the same computational complexity O(n). Moreover, the comparison with the best but time-consuming coder OPT (O (n3)) shows how much the algorithm can be improved.

scholarly journals THE INFLUENCE OF TECHNOLOGICAL FACTORS ON FOAM-GASEOUS SILICATE FORMATION MIXTURES AND PRODUCT PROPERTIES/TECHNOLOGINŲ VEIKSNIŲ ĮTAKA PUTŲ-DUJŲ SILIKATBETONIO FORMAMMO MIŠINŲ IR PRODUKTO SAVYBĖMS

1998 ◽  
Vol 4 (1) ◽  
pp. 49-55
Author(s):  
Antanas Laukaitis
Keyword(s):  
Compression Strength ◽  
Surface Active Agent ◽  
Active Agent ◽  
Intermediate Position ◽  
Concrete Mixture ◽  
Active Materials ◽  
Aluminium Powder ◽  
Sample Compression ◽  
Surface Active ◽  
Porous Silicate

The aim of this work was to investigate 300 kg/m3 density foam-gaseous silicate concrete production parameters and properties. The optimal mentioned density product formation parameter determination was conducted in a wide density interval. The raw material chemical composition is given in Table 1. Sand slime and porous silicate concrete mixture formation was performed in a laboratory mixer at 750 RPM. Surface active agents sulphanol and OP-10 (ethylphenyl ethylene glycol ether) was used for this purpose. An additional blowing agent-aluminium powder hydrophilizated with sulfanol (20 g/kg) was used. Formation mixture plasticity strength was calculated according to equation 1. Low-density porous silicate concrete sample compression strength depends not only on raw material fineness, binder amount, but also on its structure. Cast silicate concrete samples (without aluminium powder) were formed to determine the milled sand fineness needed for the optimal mixture activity. Their compression strength at 1100 kg/m3 density was calculated using equation 2. The sample compression strength dependency on mixture activity and sand fineness is given in Fig 1. The cast silicate concrete mixture technological parameters are given in Table 2. The mixtures activity is 20%, when the sand fineness approaches 130 m2/kg and 27%—340, 31%—500. Surface active materials amount (0,1—0,2%) lowers the silicate concrete samples compression strength insignificantly (Fig 2). The formation mixture envolves the surrounding air during sand slime and surface active agent mixing and partly swells. The amount of entrained air depends on the mixing time (Fig 3). However the main result is reached in 5 min. The slime density decreases from 1.7 to 0.8 kg/1, ie by 2.1 times. The mixing of surface active materials with all the mixture components is more effective, then whipping the slime separately with surface active agent and then adding lime and mixing again with the blown sand slime (Fig 4). This is explained by the fact, that when lime is added to the blown sand slime, its structure is partly destroyed. The surface active additives lower the foam silicate concrete formation mixture fluidity (Fig 5), due to the absorbed air during mixing. Sulphanol is a more effective surface active agent, than OP—10 (Fig 5). It is impossible to reach a sample density lower than 400 kg/m3, when surface active agents are mixed with silicate concrete mixture. That is why experiments were conducted where aluminium powder was added, ie a foam-gaseous silicate concrete was produced. Its density depends an V/S ratio and aluminium powder amount (Fig 6). The investigation of 300 kg/m3 density porous silicate concrete mass plasticity strength showed that it is the highest for gaseous silicate concrete and the lowest for foam silicate concrete. Foam-gaseous silicate concrete mass plasticity strength occupies an intermediate position (Fig 7). The porous silicate concrete mixtures highest temperature also depends on the porous silicate type. A gaseous silicate concrete mixture reaches 88 °C already in 30 min. Foam silicate concrete temperature increases more slowly and reaches 60 °C in 60 min. Foam-gaseous silicate concrete mixture temperature occupies an intermediate position and reaches 69 °C after 36 min (Fig 8). The sample compression strength is the highest for foam silicate concrete and the lowest for gaseous silicate concrete. Foam-gaseous silicate concrete sample compression strength occupies an intermediate position and depends directly on pores produced by whipping sand slime with surface active materials and mixture mixing with Al powder, ratio (Fig 9). This is predetermined by the different pore origin and pore structure formed during different degrees of mass warm-up. The latter was discussed in our earlier publications [8,13,15].

scholarly journals Two-Dimensional Partial Cubes

10.37236/8934 ◽  
2020 ◽  
Vol 27 (3) ◽  
Author(s):  
Victor Chepoi ◽  
Kolja Knauer ◽  
Manon Philibert
Keyword(s):  
Cell Structure ◽  
Low Rank ◽  
Complete Graphs ◽  
Two Dimensional ◽  
Vc Dimension ◽  
Set Systems ◽  
Sample Compression ◽  
Vertex Set ◽  
Dimension 2 ◽  
Pseudoline Arrangements

We investigate the structure of two-dimensional partial cubes, i.e., of isometric subgraphs of hypercubes whose vertex set defines a set family of VC-dimension at most 2. Equivalently, those are the partial cubes which are not contractible to the 3-cube $Q_3$ (here contraction means contracting the edges corresponding to the same coordinate of the hypercube). We show that our graphs can be obtained from two types of combinatorial cells (gated cycles and gated full subdivisions of complete graphs) via amalgams. The cell structure of two-dimensional partial cubes enables us to establish a variety of results. In particular, we prove that all partial cubes of VC-dimension 2 can be extended to ample aka lopsided partial cubes of VC-dimension 2, yielding that the set families defined by such graphs satisfy the sample compression conjecture by Littlestone and Warmuth (1986) in a strong sense. The latter is a central conjecture of the area of computational machine learning, that is far from being solved even for general set systems of VC-dimension 2. Moreover, we point out relations to tope graphs of COMs of low rank and region graphs of pseudoline arrangements.

Multi-sample Compression of Iris Images Using High Efficiency Video Coding

2018 ◽  
Author(s):  
Christian Rathgeb ◽  
Torsten Schlett ◽  
Nicolas Buchmann ◽  
Harald Baier ◽  
Christoph Busch
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
High Efficiency Video Coding ◽  
Sample Compression
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