Researchers successfully implemented quantum autoencoders using quantum adders in a cloud quantum computer.
problem Reducing resource usage in quantum computations.
method Experimental implementation of quantum autoencoders using approximate quantum adders in a cloud quantum computer.
result Experimental fidelities are in good agreement with theoretical predictions, proving the feasibility of quantum autoencoders via quantum adders.
Quantum computers can optimize foreign exchange reserves management.
problem Optimizing foreign exchange reserves management using quantum computing.
method Demonstrated through quantum Monte Carlo risk measurement and quantum algorithms for portfolio optimization.
result Quantum computers can theoretically optimize FX reserves management in the future.
Quantum reservoir computing tackles noisy quantum computers for temporal tasks.
problem Efficiently process input sequences on noisy quantum computers.
method Quantum reservoir computing using dissipative quantum dynamics.
result Small and noisy quantum reservoirs can handle high-order nonlinear temporal tasks.
Study finds no significant difference in neural network weights with quantum random numbers.
problem Effects of biased quantum random numbers on neural network initialization.
method Empirical study using quantum hardware and classical pseudo-random numbers.
result No statistically significant difference found between quantum random numbers and other types.
Quantum GNNs outperform classical GNNs in jet tagging.
problem Classifying partons initiating jets from high-energy particle collisions.
method Comparison of classical and quantum GNNs and their equivariant counterparts.
result Quantum GNNs outperformed classical GNNs in binary classification tasks.
Quantum circuits explained using Shapley values for better understanding.
problem Improving the explainability of quantum machine learning circuits.
method Applying Shapley values to quantify gate importance in quantum circuits.
result Quantum circuits can be explained by their gate importance, enhancing understanding and interpretability.
High-fidelity quantum simulations demonstrated on short-coherence hardware.
problem Short coherence times limit the depth of quantum algorithms.
method Fixed State Variational Fast Forwarding (fsVFF) algorithm.
result Simulations of 600 time steps possible, 150x longer than previous methods.
Quantum circuits optimize financial portfolios faster than classical methods.
problem Dynamic portfolio optimization in financial markets.
method Variational Quantum Circuits for reinforcement learning.
result Quantum agents outperform classical RL models in risk-adjusted performance.
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.
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…
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.
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.
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.
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.
CHOPT optimizes hyperparameters in cloud ML platforms efficiently.
problem Efficient hyperparameter optimization in cloud-based machine learning platforms.
method Cloud-based HyperOpt framework with web interfaces and analysis tools.
result CHOPT finds hyperparameter configurations competitive with previous work.
RL enhances cloud autoscaling by learning optimal resource allocation.
problem Optimizing resource allocation in dynamic, uncertain cloud environments.
method Reinforcement Learning for dynamic, transparent, and adaptable resource management policies.
result RL enables transparent, dynamic, and adaptable resource management policies.
Predicts and classifies computational jobs for efficient resource allocation in cloud centers.
problem Efficiently scheduling and assigning resources to computational jobs in cloud centers.
method Applied LSTM neural network for job arrival prediction and BIRCH clustering for job classification.
result Improved accuracy in predicting and classifying computational jobs compared to existing methods.
Serverless cloud computing speeds up double machine learning model estimation.
problem Efficiently estimating double machine learning models with minimal cloud resource management.
method Serverless computing with AWS Lambda for repeated cross-fitting.
result Demonstrates significant reduction in estimation times and costs.
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…
This study uses cloud telemetry to detect DoS attacks with machine learning.
problem Detecting DoS attacks in cloud environments is challenging.
method Use of machine learning algorithms on cloud telemetry data.
result k-Nearest Neighbors (kNN) and decision tree (CART) accurately identify DoS attacks.
A new method detects and ranks anomalies in cloud computing platforms using multi-view learning.
problem High false-alarm rate in traditional anomaly detection methods.
method Online model using machine learning theory, ELM for efficiency, and multi-view feature fusion.
result Improved accuracy and efficiency in anomaly detection and ranking.
Simple policy outperforms complex ones in cloud auto-scaling.
problem Predicting resource scaling for large-scale cloud applications with limited deployment throughput.
method Probabilistic workload forecast for auto-scaling decisions based on risk aversion.
result The proposed policy outperforms sophisticated and simple benchmark policies in real-world and synthetic data.
Blockchain as a Service offers a secure, decentralized computing solution.
problem Lack of transparency, security, and privacy in cloud computing.
method Decentralized cooperative computing process using blockchain, homomorphic encryption, and SDN.
result Performance evaluated via different scenarios in simulations.
Extracts geometric information from point-clouds for multiclass classification.
problem Multiclass Classification with labeled point-clouds.
method Stochastic partial orderings and label embedding trees.
result Computes multiscale geometries for explainable prediction and error-free labeling.
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.
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.
This study uses LSTM and SARIMA models to forecast CPU usage in cloud computing.
problem Predicting future CPU usage in cloud computing centers.
method Used LSTM and SARIMA models to forecast CPU usage over short and long time scales.
result SARIMA model performed better for long-term predictions but LSTM was more robust.
A hybrid neural network optimizes AI deployment on edge and cloud for energy efficiency.
problem Energy and resource constraints in edge devices for deep learning models.
method Conditionally deep hybrid neural network with quantized layers at edge and full-precision layers at cloud.
result Early classification at the edge reduces energy consumption by 5.5x on CIFAR-10 dataset.
Novel neural network layer improves long-range interactions in point clouds.
problem Efficiently treating long-range interactions in point clouds.
method Long-range convolutional (LRC)-layer that incorporates Fourier transforms.
result Global all-to-all convolution operation performed in nearly-linear time.
Computes bounds on reach and r-convexity from point cloud data.
problem Computing geometric properties of sets from sparse data.
method Computes upper bounds on reach and r-convexity from point cloud data.
result Bounds converge to true values as point cloud density increases.
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.
A new method for computing shape barycenters from point clouds using Procrustes-Wasserstein distance.
problem Computing representative shapes from point clouds with precise alignment and shape preservation.
method Developed a new distance metric (Procrustes-Wasserstein) and algorithms for computing barycenters.
result Superior performance in precise alignment and shape preservation compared to existing OT approaches.
GA method reduces gradient staleness in cloud computing.
problem Gradient staleness in asynchronous SGD methods.
method Gap-Aware (GA) method that penalizes stale gradients linearly to the Gap.
result GA outperforms existing methods in final test accuracy.
Estimates intrinsic dimension of data sets robustly to noise.
problem Estimating intrinsic dimension of noisy data sets.
method Quantum Cognition Machine Learning for data representation and spectral gap detection.
result Robust estimation of intrinsic dimension in the presence of Gaussian noise.
Develops neural networks for reductive Lie groups, enhancing symmetry respect.
problem Symmetry respect in neural networks for reductive Lie groups.
method General equivariant neural network architecture for any reductive Lie Group G.
result Demonstrates generality and performance in top quark decay tagging and shape recognition.
Collage inference uses redundancy to reduce cloud image classification latency variance.
problem Reducing latency variance in cloud image classification.
method Integrates collage-cnn for low-cost redundancy in multi-image classification.
result Significant reduction in 99th percentile tail latency and inference latency variation.
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.
Robustly computes intrinsic coordinates on point clouds using resampling and averaging.
problem Computing intrinsic coordinates on noisy or outlier-prone point clouds.
method Subsample data, vary hyperparameters, cluster candidate embeddings, identify representative embeddings, and average them using Procrustes analysis.
result Robust to noise and outliers, validated on synthetic and real data.
Quantum computing speeds up multi-period asset allocation.
problem High computational complexity in classic computing for multi-period asset allocation.
method Applied quantum computing to simulate multi-asset portfolio using historic data.
result Quantum computing offers significant advantages over classical computing in finance.
Survey on quantum computing and neural networks.
problem Understanding and comparing quantum computing and neural networks.
method Introduction to quantum computing concepts, explanation of quantum computing paradigms, and analysis of quantum neural networks.
result Current state-of-the-art in quantum neural networks.
Enhanced 3D shape analysis using information geometry.
problem Challenges in comparing 3D point clouds due to their unstructured nature and complex geometry.
method Information geometric framework for 3D point cloud shape analysis using Gaussian Mixture Models (GMMs) on a statistical manifold. Proposed MSKL divergence with upper and lower bounds.
result MSKL provides stable and monotonically varying values that directly reflect geometric variation, outperforming traditional distances and existing KL approximations.
Quantum machine learning tackles large datasets with randomized measurements.
problem Efficiently process large, high-dimensional datasets on quantum computers.
method Randomized measurements to scale linearly with dataset size and quadratic for post-processing.
result Substantial speed-up for noisy quantum computers, enabling image classification.
Paper introduces IAD for detecting anomalous VMMs in cloud without VMM access.
problem Anomalous VMMs in cloud-based environments without direct access.
method IAD: Indirect Anomalous VMMs Detection algorithm using VM resource utilization data.
result IAD algorithm outperforms other methods with an average F1-score of 83.7%.
Machine vision-guided 3D medical image compression improves segmentation accuracy.
problem High data traffic and computation costs in cloud-based medical image analysis.
method Developed a machine vision-oriented 3D image compression framework for medical segmentation.
result Significantly higher segmentation accuracy at the same compression rate or better compression rate under the same accuracy.
Quantum computing techniques improve graph analysis and community detection.
problem Analyzing large graphs efficiently and accurately.
method Used quantum annealing and quantum gate computers for community detection and regularity checking.
result Demonstrated the effectiveness of quantum computing in solving complex graph problems.
The concept of a Point Cloud has played an increasingly important role in many areas of Engineering, Science, and Mathematics. Examples are: LIDAR, 3D-Printing, Data Analysis, Computer Graphics, Machine Learning, Mathematical Visualization, Numerical Analysis, and Monte Carlo Methods. Entering point cloud into Google r…
Detects backdoors in outsourced models by replicating training steps across multiple servers.
problem Detecting backdoors in models trained on cloud providers without prior knowledge.
method Replicate training steps across multiple servers to identify deviations and malicious updates.
result 99.6% accuracy in identifying backdoored models out of 50% malicious providers.
A new method learns stable, temporally coherent features in point clouds.
problem Flickering and halo structures in inferred solutions for time-varying point clouds.
method Proposes a novel temporal loss function and super-resolution method.
result Demonstrates flexibility and effectiveness on large, deforming point sets.