scholarly journals ON PROJECTIVE MANIFOLDS SWEPT OUT BY CUBIC VARIETIES

2012 ◽  
Vol 23 (07) ◽  
pp. 1250058 ◽  
Author(s):  
KIWAMU WATANABE
Keyword(s):  
High Dimensional ◽  
Projective Manifold ◽  
Projective Bundle ◽  
Projective Manifolds ◽  
Cubic Hypersurfaces

We study structures of embedded projective manifolds swept out by cubic varieties. We show if an embedded projective manifold is swept out by high-dimensional smooth cubic hypersurfaces, then it admits an extremal contraction which is a linear projective bundle or a cubic fibration. As an application, we give a characterization of smooth cubic hypersurfaces. We also classify embedded projective manifolds of dimension at most five swept out by copies of the Segre threefold ℙ1 × ℙ2. In the course of the proof, we classify projective manifolds of dimension five swept out by planes.

scholarly journals Deformations of the tangent bundle of projective manifolds

2020 ◽  
Vol 31 (11) ◽  
pp. 2050087
Author(s):  
Thomas Peternell
Keyword(s):  
Projective Space ◽  
Tangent Bundle ◽  
Complex Projective Space ◽  
Projective Manifold ◽  
First Order ◽  
Projective Manifolds ◽  
Complex Projective

We investigate when the tangent bundle of a projective manifold has a nontrivial first-order (or positive-dimensional) deformation. This leads to a new conjectural characterization of the complex projective space.

scholarly journals CHARACTERIZATION OF PROJECTIVE SPACES AND -BUNDLES AS AMPLE DIVISORS

2017 ◽  
Vol 233 ◽  
pp. 155-169 ◽  
Author(s):  
JIE LIU
Keyword(s):  
Recent Work ◽  
Projective Spaces ◽  
Ample Divisor ◽  
Projective Manifold ◽  
Projective Manifolds ◽  
Ample Divisors

Let $X$ be a projective manifold of dimension $n$. Suppose that $T_{X}$ contains an ample subsheaf. We show that $X$ is isomorphic to $\mathbb{P}^{n}$. As an application, we derive the classification of projective manifolds containing a $\mathbb{P}^{r}$-bundle as an ample divisor by the recent work of Litt.

HIGH DIMENSIONAL KNOT GROUPS AND HNN EXTENSIONS OF THE FIBONACCI GROUPS

1998 ◽  
Vol 07 (04) ◽  
pp. 503-508 ◽  
Author(s):  
ANDRZEJ SZCZEPAŃSKI
Keyword(s):  
High Dimensional ◽  
New Class ◽  
Hnn Extensions

We shall present a new class of examples of high dimensional knot groups. All of them are HNN extensions of the Fibonacci groups. We give also some characterization of these groups.

Characterization of particleboard made from oil heat-treated rubberwood particles at different mixing ratios

BioResources ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. 6795-6810
Author(s):  
Nurul Fatiha Osman ◽  
Paimon Bawon ◽  
Seng Hua Lee ◽  
Pakhriazad Hassan Zaki ◽  
Syeed SaifulAzry Osman Al-Eldrus ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Oil Content ◽  
Urea Formaldehyde ◽  
Physical And Mechanical Properties ◽  
Thermal Characterization ◽  
High Dimensional ◽  
Mixing Ratios ◽  
Uf Resin ◽  
Heat Treated

Particleboard was produced by mixing oil heat-treated rubberwood particles at different ratios, with the goal of achieving high dimensional stability. Rubberwood particles were soaked in palm oil for 2 h and heat treated at 200 °C for 2 h. The treated particles were soaked in boiling water for 30 min to remove oil and were tested for chemical alteration and thermal characterization via Fourier-transform infrared spectroscopy and thermogravimetric analysis. Particleboard was fabricated by mixing treated rubberwood particles (30%, 50%, and 70%) with untreated particles (70%, 50%, and 30%, respective to previous percentages) and bonded with urea-formaldehyde (UF) resin. The results revealed that oil-heat treated particles had greater thermal stability than the untreated particles. The addition of oil heat treated particles improved the physical properties of the particleboard with no significant reduction in mechanical strength. However, this was only valid for ratios of 70% untreated to 30% treated and 50% untreated to 50% treated. When a ratio of 70% oil heat treated particles was used, both the physical and mechanical properties were reduced drastically, due to bonding interference caused by excessive oil content. Particleboard made with a ratio of 5:5 (treated to untreated) exhibited the best physical and mechanical properties.

Engineering and characterization of high-dimensional states

2021 ◽  
Author(s):  
Alessia Suprano ◽  
Taira Giordani ◽  
Emanuele Polino ◽  
Danilo Zia ◽  
Sabrina Emiliani ◽  
...  
Keyword(s):  
High Dimensional

scholarly journals CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

F1000Research ◽  
2019 ◽  
Vol 6 ◽  
pp. 748 ◽  
Author(s):  
Malgorzata Nowicka ◽  
Carsten Krieg ◽  
Helena L. Crowell ◽  
Lukas M. Weber ◽  
Felix J. Hartmann ◽  
...  
Keyword(s):  
Cell Population ◽  
High Throughput ◽  
Mixed Models ◽  
Exploratory Data Analysis ◽  
Linear Mixed Models ◽  
High Dimensional ◽  
Cell Populations ◽  
Dimensional Scaling ◽  
Exploratory Data

High-dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high-throughput interrogation and characterization of cell populations. Here, we present an updated R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signalling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models or linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g., multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g., plots of aggregated signals).

A Combinatorial Optimization Problem for High Order PODs with Few Sensors

2006 ◽  
Vol 129 (2) ◽  
pp. 252-255
Author(s):  
Atanu K. Mohanty ◽  
Kanad Chakraborty ◽  
Anindya Chatterjee
Keyword(s):  
Combinatorial Optimization ◽  
Dynamic Systems ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
High Dimensional ◽  
Experimental Characterization ◽  
State Behavior ◽  
Simultaneous Measurements ◽  
Proper Orthogonal

Experimental characterization of high dimensional dynamic systems sometimes uses the proper orthogonal decomposition (POD). If there are many measurement locations and relatively fewer sensors, then steady-state behavior can still be studied by sequentially taking several sets of simultaneous measurements. The number required of such sets of measurements can be minimized if we solve a combinatorial optimization problem. We aim to bring this problem to the attention of engineering audiences, summarize some known mathematical results about this problem, and present a heuristic (suboptimal) calculation that gives reasonable, if not stellar, results.

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