Study kinematics of Ricci Solitons in various fluid spacetimes.
problem Understanding the motion of Ricci Solitons in different fluid spacetimes.
method Examined specific fluid spacetimes including string cloud, string fluid, etc., and analyzed kinematics.
result Obtained results and discussed physical aspects of these spacetimes.
FCN improves lidar cloud detection accuracy.
problem Segmenting lidar imagery into cloud locations.
method Semi-supervised learning with pre-training and fully supervised learning.
result FCN achieves higher cloud identification accuracy.
Generative model for 3D point clouds using invertible flows.
problem Generating realistic 3D point clouds.
method Invertible flow-based models for point cloud generation with parameter sharing and embedding vectors.
result The model generates high-quality 3D point clouds with good similarity.
Graph neural network constructs a sparse latent point cloud from dense point clouds.
problem Efficiently reconstructing and simulating point clouds with fine details.
method Irregular graph convolutional neural network with non-isotropic operations.
result The model can reconstruct dense point clouds from a sparse latent representation.
New method upsamples sparse, non-uniform point clouds more accurately.
problem Suboptimal results from existing point cloud upsampling methods.
method Imposes manifold distribution constraints using Gaussian functions.
result Generates higher-quality, more uniformly distributed dense point clouds.
In recent decades, the use of 3D point clouds has been widespread in computer industry. The development of techniques in analyzing point clouds is increasingly important. In particular, mapping of point clouds has been a challenging problem. In this paper, we develop a discrete analogue of the Teichmüller extremal mapp…
Framework optimizes cloud container sizing for ML tasks.
problem Challenges in cloud container configuration for ML services.
method Autonomous scaling using nested-loop Monte Carlo simulation.
result Reduces compute cost and accelerates ML algorithms.
Study compares cloud ML services for binary classification tasks.
problem Evaluate performance of major cloud ML services on binary classification.
method Constructed benchmark using Kaggle datasets; compared Azure and Amazon services.
result Identifies strengths and weaknesses of current cloud ML services.
Convolutional LSTMs classify clouds as noise, improving remote sensing accuracy.
problem Clouds hinder remote sensing accuracy; current methods are inadequate.
method Used a Convolutional LSTM network to classify clouds as noise.
result Network internalizes cloud-filtering mechanism without explicit training.
In D(13.1) (arXiv:1606.08529 [hep-th]), we introduced an admissible condition on differentiable maps φ:(XAz,E;∇)→Y from an Azumaya/matrix manifold XAz (with the fundamental module E) with a connection ∇ on E to a manifold Y in order to resolve a pull-push issue in …
A novel method compresses point cloud attributes by folding them onto a 2D grid.
problem Efficiently compressing point cloud attributes for storage and transmission.
method Interpreting point clouds as 2D manifolds, folding onto a grid, and mapping attributes to the grid using optimized methods.
result The proposed folding-based approach achieves performance comparable to state-of-the-art codecs.
Self-supervised method learns from unlabelled point clouds by reconstructing them.
problem Efficiently learning from large, unlabelled 3D point cloud datasets.
method Trains neural networks to reconstruct point clouds with randomly rearranged parts.
result Method learns semantic properties of point clouds and improves downstream object classification.
Efficiently synthesizes atmospheric cloud images using neural networks and Monte Carlo integration.
problem Rendering atmospheric clouds, especially their characteristic silverlining and whiteness, is challenging.
method Pre-learning the radiant flux distribution from cloud exemplars and using a deep neural network to predict radiance.
result The method synthesizes clouds nearly indistinguishable from reference solutions in seconds.
A novel method compares 3D point clouds using information geometry.
problem Comparing 3D point clouds in machine learning applications.
method Interprets point clouds as probability density functions on a statistical manifold, using GMM and Modified Symmetric KL divergence.
result Demonstrates effectiveness through various case studies.
Derives path integrals for perturbative strings on various backgrounds.
problem Calculating path integrals for strings on curved backgrounds.
method Derives path integrals from string geometry theory by considering fluctuations around string backgrounds.
result Derives path integrals of all order perturbative strings on various backgrounds.
Deep learning detects cloud changes due to human aerosols.
problem Uncertainty in the effect of anthropogenic aerosols on cloud properties and Earth's energy balance.
method Deep convolutional neural networks to analyze cloud images.
result Identified and characterized specific cloud perturbations due to human aerosols.
Develops a method for conformal parameterization of point clouds without fixed boundaries.
problem Desirable distortion in fixed-boundary parameterizations of point clouds.
method Free-boundary conformal parameterization method involving approximation of point cloud Laplacian and boundary treatment.
result High-quality point cloud meshing achieved through the proposed method.
Algorithm classifies point clouds using deep set linearized optimal transport.
problem Classifying point clouds efficiently and accurately.
method Deep Set Linearized Optimal Transport, ICNNs, and a discriminator network.
result Efficiently distinguishes between various classes of point clouds.
ARDEN improves deep learning performance on mobile devices by protecting privacy in the cloud.
problem Balancing privacy and performance in mobile deep learning with limited device capacity.
method ARDEN partitions DNN across mobile devices and cloud, using data transformation and noise addition for privacy, and noisy training for robustness.
result ARDEN enhances inference performance on cloud while maintaining strong privacy.
Topological string theory derived from string geometry for non-perturbative effects.
problem Deriving non-perturbative effects in string theory.
method Formulating topological string geometry theory and deriving the partition function from fluctuations around a classical solution.
result Perturbative partition function of topological string theory derived.
Cumulo dataset for cloud classification at 1km resolution.
problem Uncertainty in cloud modelling for climate projections.
method Benchmark dataset of 1km MODIS imagery and CloudSat labels.
result IResNet model discovers new cloud sub-classes.
Identifies all perturbative vacua in bosonic string theory.
problem Identifying all perturbative vacua in bosonic string theory.
method Completely identified perturbative vacua through string fluctuations.
result Derivation of path-integrals up to any order from fluctuations.
This paper analyzes CNNs for malware detection in cloud IaaS.
problem Malware vulnerability in cloud IaaS environments.
method Analysis of Convolutional Neural Networks (CNNs) for online malware detection using process-level performance metrics.
result State-of-the-art DenseNets and ResNets effectively detect malware in online cloud systems.
Estimating boundaries from point clouds with improved accuracy and rigorous error estimates.
problem Identifying the boundary of a domain from point cloud samples.
method Developed new estimators for normal vectors, distances, and boundary tests; provided error estimates.
result Efficient and accurate estimators for boundary properties on point clouds.
String geometry theory connects strings to space-time and finds string vacua.
problem Identify and find the global minimum of the string vacuum.
method Identify perturbative vacua, derive path-integrals, and solve the global minimum using analytical and numerical methods.
result The global minimum of the effective potential is the string vacuum.
Paper uses tensor regression to analyze point clouds for process optimization.
problem Challenges in modeling and analyzing high-dimensional point cloud data.
method Utilizes multilinear algebra and tensor regression techniques.
result Successfully models and links point cloud variational patterns to process variables.
Derives path-integrals for superstrings on curved backgrounds using string geometry theory.
problem Calculating path-integrals for superstrings on curved backgrounds.
method Derives path-integrals from string geometry theory by considering fluctuations around string backgrounds.
result Derives path-integrals for perturbative superstrings on all string backgrounds.
This paper classifies virtual string links up to cobordism using group theory.
problem Classifying virtual string links up to cobordism.
method Using group theory, specifically the group Zn(n−1). result Virtual string links up to cobordism are classified by elements of Zn(n−1). The article discusses how to create a special type of triangle mesh for surfaces in 3D space.
problem Creating a special type of triangle mesh for surfaces in 3D space.
method Using sufficient conditions and the diagonal switch algorithm to find an embedded Delaunay triangulation.
result The diagonal switch algorithm can find an embedded Delaunay triangulation for a point cloud on an embedded surface in R3. CloudLSTM forecasts geospatial point-cloud data streams accurately.
problem Forecasting over geospatial point-cloud data streams.
method Introduces CloudLSTM, a recurrent neural model with a Dynamic Point-cloud Convolution (DConv) operator.
result CloudLSTM outperforms competitor models in long-term predictions for point-cloud data.
Point cloud is the most fundamental representation of 3D geometric objects. Analyzing and processing point cloud surfaces is important in computer graphics and computer vision. However, most of the existing algorithms for surface analysis require connectivity information. Therefore, it is desirable to develop a mesh st…
SPINN optimizes neural network inference on devices and cloud.
problem Inference on mobile devices is challenging due to high computational demands and dynamic connectivity.
method Synergistic progressive inference with a novel scheduler.
result SPINN achieves up to 2x higher throughput and reduces server cost by up to 6.8x.
Paper proposes DPN for encrypted speech recognition, maintaining privacy and security.
problem Privacy and security issues in cloud-based speech recognition.
method Deep Polynomial Network (DPN) for encrypted speech, cloud-local joint decoding.
result DPN can make frame-level predictions over encrypted speech and return them in encrypted form.
The paper transforms a convex hull into a concave surface around a point cloud.
problem Creating a concave surface that encloses all points in a point cloud.
method Iterative facet replacement and expansion of the convex hull.
result A method to evolve a convex hull into a concave surface that fits the point cloud.
Chiral string integrands simplify to ambitwistor string integrands in the tensionless limit.
problem Understanding the relationship between chiral and ambitwistor string integrands.
method Analyzing the tensionless limit of chiral superstring integrands.
result Chiral superstring integrands reduce to ambitwistor string integrands in the tensionless limit.
Paper proposes a hierarchical approach to malware detection in cloud environments.
problem Malware threat in cloud computing environments.
method Machine learning on graphs, hypergraphs, and natural language for malware detection and analysis.
result Federated learning for malware detection in multicloud environments.
Deep learning identifies precipitation clouds from all-sky camera data.
problem Automating cloud warning systems for observatories.
method Deep learning using EfficientNet network.
result Average accuracy of 99% in identifying rainfall potential and 96% in cloud coverage.
Cloud-native simulator studies global trading latencies.
problem Understanding latencies in planetary-scale automated trading systems.
method Cloud-native, open-source simulator with distributed clients.
result Demonstrates latencies in real-world trading systems.
3D point cloud attacks examine how neural networks can be fooled.
problem Understanding how 3D neural networks can be exploited by attackers.
method Examined two categories of attacks: distributional and shape attacks.
result Some shape attacks can fool 3D point cloud classification models even after preprocessing.
Method certifies edge predictions with cloud-level reliability.
problem Ensuring reliability of edge intelligence models.
method Conformal alignment-based cascading mechanism.
result Certifies conditional coverage with user control over risk level.
A virtual string can be defined as an equivalence class of planar diagrams under certain kinds of diagrammatic moves. Virtual strings are related to virtual knots in that a simple operation on a virtual knot diagram produces a diagram for a virtual string. In this paper we consider three operations on a virtual string …
A virtual string is a scheme of self-intersections of a closed curve on a surface. We study algebraic invariants of strings as well as two equivalence relations on the set of strings: homotopy and cobordism. We show that the homotopy invariants of strings form an infinite dimensional Lie group. We also discuss connecti…
The study proves strong cosmic censorship violation for spherically symmetric dust clouds.
problem Violation of strong cosmic censorship for spherically symmetric dust clouds.
method Derived an ordinary differential equation for light rays and used it to prove strong cosmic censorship violation.
result Generic violation of strong cosmic censorship for spherically symmetric dust clouds.
Method computes harmonic and conformal maps from point clouds.
problem Computing maps from irregular point cloud data.
method Meshless method using cubic lattice approximations.
result Harmonic and conformal maps computed accurately.
Generative model creates high-quality meshes from point clouds.
problem Creating accurate meshes from point cloud data.
method Modeling point cloud generation as sphere deformation through deep neural networks.
result The model efficiently generates high-quality meshes from point clouds.
Self-supervised learning improves few-shot classification and segmentation on point clouds.
problem Efficiently learn from limited labeled data in point cloud applications.
method Hierarchical cover-tree partitioning for self-supervised pre-training; restricted to support set for few-shot learning.
result Self-supervised learning significantly improves downstream classification and segmentation accuracy.
Research extends Cahn's results on virtual strings to connected non-parallel strings.
problem Understanding the homotopy of virtual strings and their minimal representatives.
method Generalization of Cahn's results using stabilizations, destabilizations, and homotopies.
result Kadokami's statement about virtual strings holds for connected non-parallel strings but not for all strings.
New formulas link string bordism to integers.
problem Understanding the third string bordism group.
method Geometric string structures and 3d TQFT.
result Integral formulas realize the string bordism isomorphism.