scholarly journals Eventually dendric shift spaces

2020 ◽  
pp. 1-26
Author(s):  
FRANCESCO DOLCE ◽  
DOMINIQUE PERRIN
Keyword(s):  
Discrete Geometry ◽  
Block Coding ◽  
Shift Spaces ◽  
New Class ◽  
Fixed Length ◽  
Natural Transformations ◽  
Number Of Classes

We define a new class of shift spaces which contains a number of classes of interest, like Sturmian shifts used in discrete geometry. We show that this class is closed under two natural transformations. The first one is called conjugacy and is obtained by sliding block coding. The second one is called the complete bifix decoding, and typically includes codings by non-overlapping blocks of fixed length.

scholarly journals Precariat: from a proto-class to a new class. Review of the monograph of Zh.T. Toshchenko

2021 ◽  
Vol 27 (2) ◽  
pp. 218-224
Author(s):  
S. V. Kozin
Keyword(s):  
Social Class ◽  
Statistical Data ◽  
Publishing House ◽  
Russian Society ◽  
The Gaze ◽  
New Class ◽  
The Past ◽  
Clear Vision ◽  
Number Of Classes ◽  
Academy Of Sciences

As you know, the study of the past, present and such a vague future of society (and its number of classes) still attracts the gaze of many representatives of the scientific academic society (including colleagues in the sociological workshop). This article is a review of the monograph of corresponding member of the Russian Academy of Sciences Zh.T. Toshchenko “Precariat: from the proto-class to the new class”, published in 2018 by “Nauka” publishing house. The monograph allows the reader to feel how rapidly the socio-class structure of foreign, Soviet, and then Russian society was changing. In it, Zh.T. Toshchenko clearly reflects the historical aspects of the development and functioning of a new social class — the “precariat”. The conclusion is proved that the precariat does not have a clear vision of its future, confidence in the security of its personal life and the guarantee of a quiet old age at the end of employment. An impressive number of foreign and domestic statistical data further confirms the conclusions of reasonable Zh.T. Toshchenko. In the future, the author of the monograph explores the consequences of the existence and functioning of this new social-class phenomenon.

scholarly journals On generalized spiral-like analytic functions

Filomat ◽  
2014 ◽  
Vol 28 (7) ◽  
pp. 1493-1503 ◽  
Author(s):  
Khalida Noor ◽  
Nazar Khan ◽  
Muhammad Noor
Keyword(s):  
Analytic Functions ◽  
Unit Disc ◽  
Open Unit ◽  
Sufficient Condition ◽  
Open Unit Disc ◽  
New Class ◽  
Bounded Radius Rotation ◽  
Number Of Classes ◽  
Inclusion Results

In this paper, we use the concept of bounded Mocanu variation to introduce a new class of analytic functions, defined in the open unit disc, which unifies a number of classes previously studied such as those of functions with bounded radius rotation and bounded Mocanu variation. It also generalizes the concept of ?-spiral likeness in some sense. Some interesting properties of this class including inclusion results, arclength problems and a sufficient condition for univalency are studied.

Mixture Modeling with Pairwise, Instance-Level Class Constraints

2005 ◽  
Vol 17 (11) ◽  
pp. 2482-2507 ◽  
Author(s):  
Qi Zhao ◽  
David J. Miller
Keyword(s):  
Synthetic Data ◽  
Ground Truth ◽  
Mixture Modeling ◽  
Data Set ◽  
Ground Truth Data ◽  
New Class ◽  
Recent Approach ◽  
Semisupervised Clustering ◽  
The One ◽  
Number Of Classes

The goal of semisupervised clustering/mixture modeling is to learn the underlying groups comprising a given data set when there is also some form of instance-level supervision available, usually in the form of labels or pairwise sample constraints. Most prior work with constraints assumes the number of classes is known, with each learned cluster assumed to be a class and, hence, subject to the given class constraints. When the number of classes is unknown or when the one-cluster-per-class assumption is not valid, the use of constraints may actually be deleterious to learning the ground-truth data groups. We address this by (1) allowing allocation of multiple mixture components to individual classes and (2) estimating both the number of components and the number of classes. We also address new class discovery, with components void of constraints treated as putative unknown classes. For both real-world and synthetic data, our method is shown to accurately estimate the number of classes and to give favorable comparison with the recent approach of Shental, Bar-Hillel, Hertz, and Weinshall (2003).

scholarly journals Cortical representation variability aligns with in-class variances and can help one-shot learning

2021 ◽  
Author(s):  
Jiaqi Shang ◽  
Eric Shea-Brown ◽  
Stefan Mihalas
Keyword(s):  
Hierarchical Classification ◽  
High Dimensional ◽  
Single Unit Activity ◽  
Recurrent Networks ◽  
New Class ◽  
Mouse Cortex ◽  
Multiple Frames ◽  
High Level ◽  
Number Of Classes

AbstractLearning invariance across a set of transformations is an important step in mapping high-dimensional inputs to a limited number of classes. After understanding the set of\ invariances, can a new class be learned from one element? We propose a representation which can facilitate such learning: if the variability in representing individual elements across trials aligns with the variability among different elements in a class, then class boundaries learned from the variable representations of one element should be representative of the entire class. In this study, we test whether such a representation occurs in mouse visual systems. We use Neuropixels probes recording single unit activity in mice observing 200 repeats of natural movies taken from a set of 9 continuous clips. We observe that the trial-by-trial variability in the representation of individual frames is well aligned to the variability in representation of multiple frames from the same clip, but not well aligned to the variability among frames from different clips. Thus, the variable representations of images in the mouse cortex can be efficiently used to classify images into their clips. We compare these representations to those in artificial neural networks. We find that, when introducing noise in networks trained for classification (both feed-forward and recurrent networks), the variability in the representation of elements aligns with the in-class variance. The networks which best reproduce the in-vivo observed directions of variability were those trained on a hierarchical classification task. Taken together, these results point to a solution which the cortex can use for one-shot learning of a class: by using noise as a mechanism for generalization. This is a potential computational explanation for the high level of noise observed in the cortex.

In Situ microscopy of the anodic etching of silicon

1995 ◽  
Vol 53 ◽  
pp. 232-233
Author(s):  
Frances M. Ross ◽  
Peter C. Searson
Keyword(s):  
Composite Structures ◽  
High Surface ◽  
High Surface Area ◽  
Chemical Properties ◽  
Selective Etching ◽  
Silicon On Insulator ◽  
Second Phase ◽  
Porous Layers ◽  
New Class ◽  
Porous Semiconductors

Porous semiconductors represent a relatively new class of materials formed by the selective etching of a single or polycrystalline substrate. Although porous silicon has received considerable attention due to its novel optical properties1, porous layers can be formed in other semiconductors such as GaAs and GaP. These materials are characterised by very high surface area and by electrical, optical and chemical properties that may differ considerably from bulk. The properties depend on the pore morphology, which can be controlled by adjusting the processing conditions and the dopant concentration. A number of novel structures can be fabricated using selective etching. For example, self-supporting membranes can be made by growing pores through a wafer, films with modulated pore structure can be fabricated by varying the applied potential during growth, composite structures can be prepared by depositing a second phase into the pores and silicon-on-insulator structures can be formed by oxidising a buried porous layer. In all these applications the ability to grow nanostructures controllably is critical.

The microtubule associated protein (MAP) tau forms a new class of triple-stranded left-hand helical fibrous protein polymer

1992 ◽  
Vol 50 (1) ◽  
pp. 540-541
Author(s):  
G. C. Ruben ◽  
K. Iqbal ◽  
I. Grundke-Iqbal ◽  
H. Wisniewski ◽  
T. L. Ciardelli ◽  
...  
Keyword(s):  
Axial Ratio ◽  
Normal Structure ◽  
Paired Helical Filaments ◽  
Left Hand ◽  
New Class ◽  
Protein Polymer ◽  
Fibrous Protein ◽  
Protein Map ◽  
Neurotransmitter Transport ◽  
Alzheimer Neurofibrillary Tangles

In neurons, the microtubule associated protein, tau, is found in the axons. Tau stabilizes the microtubules required for neurotransmitter transport to the axonal terminal. Since tau has been found in both Alzheimer neurofibrillary tangles (NFT) and in paired helical filaments (PHF), the study of tau's normal structure had to preceed TEM studies of NFT and PHF. The structure of tau was first studied by ultracentrifugation. This work suggested that it was a rod shaped molecule with an axial ratio of 20:1. More recently, paraciystals of phosphorylated and nonphosphoiylated tau have been reported. Phosphorylated tau was 90-95 nm in length and 3-6 nm in diameter where as nonphosphorylated tau was 69-75 nm in length. A shorter length of 30 nm was reported for undamaged tau indicating that it is an extremely flexible molecule. Tau was also studied in relation to microtubules, and its length was found to be 56.1±14.1 nm.

Study of segregation in La1.8Sr0.2CuO4 superconductor by STEM-EDS microanalysis

1987 ◽  
Vol 45 ◽  
pp. 54-55
Author(s):  
T. F. Kelly ◽  
P. J. Lee ◽  
E. E. Hellstrom ◽  
D. C. Larbalestier
Keyword(s):  
Rare Earth ◽  
High Field ◽  
Transport Current ◽  
Total Thickness ◽  
New Class ◽  
Ceramic Superconductors ◽  
Current Densities ◽  
Group Ii ◽  
Eds Microanalysis

Recently there has been much excitement over a new class of high Tc (>30 K) ceramic superconductors of the form A1-xBxCuO4-x, where A is a rare earth and B is from Group II. Unfortunately these materials have only been able to support small transport current densities 1-10 A/cm2. It is very desirable to increase these values by 2 to 3 orders of magnitude for useful high field applications. The reason for these small transport currents is as yet unknown. Evidence has, however, been presented for superconducting clusters on a 50-100 nm scale and on a 1-3 μm scale. We therefore planned a detailed TEM and STEM microanalysis study in order to see whether any evidence for the clusters could be seen.A La1.8Sr0.2Cu04 pellet was cut into 1 mm thick slices from which 3 mm discs were cut. The discs were subsequently mechanically ground to 100 μm total thickness and dimpled to 20 μm thickness at the center.

Electronic structure studies of conducting polymers by electron energy-loss spectroscopy

1996 ◽  
Vol 54 ◽  
pp. 160-161
Author(s):  
J. Fink
Keyword(s):  
Electronic Structure ◽  
Conducting Polymers ◽  
Conjugated Polymers ◽  
Electron Acceptors ◽  
Electron Donors ◽  
Light Emitting ◽  
One Dimensional ◽  
New Class ◽  
Electrical Conductivities ◽  
Electron Energy Loss

Conducting polymers comprises a new class of materials achieving electrical conductivities which rival those of the best metals. The parent compounds (conjugated polymers) are quasi-one-dimensional semiconductors. These polymers can be doped by electron acceptors or electron donors. The prototype of these materials is polyacetylene (PA). There are various other conjugated polymers such as polyparaphenylene, polyphenylenevinylene, polypoyrrole or polythiophene. The doped systems, i.e. the conducting polymers, have intersting potential technological applications such as replacement of conventional metals in electronic shielding and antistatic equipment, rechargable batteries, and flexible light emitting diodes.Although these systems have been investigated almost 20 years, the electronic structure of the doped metallic systems is not clear and even the reason for the gap in undoped semiconducting systems is under discussion.

Mechanochemical changes on cyclometalated Ir(iii) acyclic carbene complexes – design and tuning of luminescent mechanochromic transition metal complexes

2020 ◽  
Vol 7 (3) ◽  
pp. 786-794 ◽  
Author(s):  
Jingqi Han ◽  
Kin-Man Tang ◽  
Shun-Cheung Cheng ◽  
Chi-On Ng ◽  
Yuen-Kiu Chun ◽  
...  
Keyword(s):  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Carbene Complexes ◽  
New Class

A new class of luminescent cyclometalated Ir(iii) complexes with readily tunable mechanochromic properties derived from the mechanically induced trans-to-cis isomerization have been developed.

Amino-functional polyethers: versatile, stimuli-responsive polymers

2020 ◽  
Vol 11 (24) ◽  
pp. 3940-3950 ◽  
Author(s):  
Patrick Verkoyen ◽  
Holger Frey
Keyword(s):  
Ring Opening ◽  
Review Article ◽  
Stimuli Responsive ◽  
New Class ◽  
Stimuli Responsive Polymers ◽  
Responsive Polymers

Amino-functional polyethers have emerged as a new class of “smart”, i.e. pH- and thermoresponsive materials. This review article summarizes the synthesis and applications of these materials, obtained from ring-opening of suitable epoxide monomers.

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