Data Time Tradeoffs for Optimal k Thresholding Algorithms in Compressed Sensing

Optimal $k$-thresholding algorithms overcome the shortcomings of traditional hard thresholding algorithms . Theory presents that the transition point of thenumber of measurements is on the order of $k \log({en}/{k)$ The algorithms can achieve linear convergence when the number of measurements required for successful recovery has a negative correlation with number of iterations and the algorithms achieve linearconvergence .…

Computing semigroups with error control

We develop an algorithm that computes strongly continuous semigroups on infinite-dimensional Hilbert spaces with explicit error control . The algorithm is based on a combination of a regularizedfunctional calculus, suitable contour quadrature rules, and adaptivecomputation of resolvents in infinite dimensions .…

Fast Hand Detection in Collaborative Learning Environments

Long-term object detection requires the integration of frame-based resultsover several seconds . For non-deformable objects, long-term detection is oftenaddressed using object detection followed by video tracking . Unfortunately,tracking is inapplicable to objects that undergo dramatic changes in appearance from frame to frame .…

HEDP A Method for Early Forecasting Software Defects based on Human Error Mechanisms

The main process behind a software defect is that an error-prone scenariotriggers human error modes, which psychologists have observed to recur acrossdiverse activities . The proposed ideaemphasizes predicting the exact location and form of a possible defect . The approach was able to predict, atthe requirement phase, the exact locations and forms of 7 out of the 22 (31.8%) specific types of defects that were found in the code .…

LENS Localization enhanced by NeRF synthesis

Neural Radiance Fields (NeRF) have recently demonstrated photo-realistic results for the task of novel view synthesis . In this paper, we propose to apply novel view synthesive view synthesis to the robot relocalization problem . We demonstrateimprovement of camera pose regression thanks to an additional synthetic dataset .…

Myerson value of directed hypergraphs

In this paper, we consider a directed hypergraph as cooperative network, anddefine the Myerson value for directedhypergraphs . We prove the axiomatization of the value, namely strong component efficiency and fairness . We modified the concept of safety defined by Li-Shan, and proved the condition about the safety of the hyperedge .…

SAR Net A Scenario Aware Ranking Network for PersonalizedFair Recommendation in Hundreds of Travel Scenarios

Alibaba serves an indispensable role forhundreds of different travel scenarios from Fliggy, Taobao, Alipay apps, etc. To provide personalized recommendation service for users visiting differentscenarios, there are two critical issues to be carefully addressed . In this paper, we propose a novelScenario-Aware Ranking Network (SAR-Net) to address these issues .…

Hyperspectral 3D Mapping of Underwater Environments

Hyperspectral imaging has been increasingly used for underwater survey applications . We propose to combine techniques from simultaneous localization and mapping, structure-from-motion and 3Dreconstruction and use them to create 3D models with hyperspectral texture . We show that the proposed method creates highly accurate 3D reconstructions of underwater environments .…

Dynamic Conflict Resolution of IoT Services in Smart Homes

We propose a novel conflict resolution framework for IoT services in multi-resident smart homes . The proposed framework employs a preferenceextraction model based on a temporal proximity strategy . We design a preferenceaggregation model using a matrix factorization-based approach . The concepts of current resident item matrix and idealresident item matrix are introduced as key criteria to cater to the conflictresolution framework .…

Orion Automatic Repair for Network Programs

The approach localizes the fault through symbolic reasoning, and synthesizes apatch such that the repaired program can pass all unit tests . It applies domain-specific abstraction to simplify network data structures and utilizesmodular analysis to facilitate function summary reuse for symbolic analysis .…

State of Security and Privacy Practices of Top Websites in the East African Community EAC

Growth in technology has resulted in the large-scale collection and processing of Personally Identifiable Information by organizations that rundigital services such as websites . Several African countries have recently started enacting new dataprotection regulations due to recent technological innovations . Only 16 percent of third-party tracking companies that track users on a particular website are disclosed in the site’s privacy policy statements .…

Variational and numerical analysis of a mathbf Q tensor model for smectic A liquid crystals

We analyse an energy minimisation problem recently proposed for modellingsmectic-A liquid crystals . The optimality conditions give a coupled nonlinearsystem of partial differential equations . Our two main results are aproof of the existence of solutions to the minimisation problems and a priori error estimates for its discretisation using the $\mathcal{C}^0$ interior penalty method .…

Object DGCNN 3D Object Detection using Dynamic Graphs

3D object detection often involves complicated training and testingpipelines, which require substantial domain knowledge about individualdatasets . Inspired by recent non-maximum suppression-free 2D objects detection models, we propose a new architecture on point clouds . This approach aligns the outputs of the teachermodel and the student model in a permutation-invariant fashion, significantly simplifying knowledge distillation for the 3D detection task .…

ADMM DAD net a deep unfolding network for analysis compressed sensing

In this paper, we propose a new deep unfolding neural network based on theADMM algorithm for analysis Compressed Sensing . The proposed network jointlylearns a redundant analysis operator for sparsification and reconstructs thesignal of interest . We compare our proposed network with the state-of-the-art state of theart deep unfolding networks, that also learns an orthogonal sparsifier .…

Masking Effects in Combined Hardness and Stiffness Rendering Using an Encountered Type Haptic Display

Rendering stable hard surfaces is an important problem in haptics for many tasks, including training simulators for orthopedic surgery or dentistry . Current impedance devices cannot provide enough force and stiffness to render awall . We propose to address these limitations by combininghaptic augmented reality, untethered haptic interaction, and anencountered-type haptic display .…

How Does Momentum Benefit Deep Neural Networks Architecture Design A Few Case Studies

We present and review an algorithmic and theoretical framework for improving neural network architecture design via momentum . We consider how momentum can improve the architecture design for recurrent neural networks, neural ordinary differential equations (ODEs), and transformers . Weshow that integrating momentum into neural network architectures has several remarkable theoretical and empirical benefits, including overcoming the vanishing gradient issues in training RNNs and neural ODEs, resulting in effective learning long-termdependencies .…

Demonstrator Game Showcasing Indoor Positioning via BLE Signal Strength

New concepts from computer science and engineering are often hard to grasp . The Who-wants-to-be-a-millionaire?-style quiz game lets the playerexperience indoor positioning based on Bluetooth signal strength firsthand . We found that such an interactive game demonstrator can function as aconversation-opener and is useful in helping introduce concepts relevant for future jobs .…

The algebra of row monomial matrices

The class of row monomial matrices is closed under multiplication, but not under ordinary matrix addition . The most significant difference is the summation operation . The algebra plays an important role in the study of DFA,especially for synchronizing automata .…

Identification of Metallic Objects using Spectral Magnetic Polarizability Tensor Signatures Object Classification

Magnetic polarizability tensor (MPT) offers an economical characterisation of metallic objects . MPT spectral signature can be determined frommeasurements of the induced voltage over a range frequencies in a metalsignature for a hidden object . With classification in mind, it can also becomomomputed in advance for different threat and non-threat objects .…

Transformers for EEG Emotion Recognition

Electroencephalogram (EEG) can objectively reflect emotional state andchanges . But transmission mechanism of EEG in the brain and its relationship with emotion are still ambiguous to human beings . New method, named EEG emotion Transformer (EeT), adapts theconventional Transformer architecture to EEG signals by enabling spatiospectralfeature learning directly from sequences of EEG signals .…

Dynamic Conflict Resolution of IoT Services in Smart Homes

We propose a novel conflict resolution framework for IoT services in multi-resident smart homes . The proposed framework employs a preferenceextraction model based on a temporal proximity strategy . We design a preferenceaggregation model using a matrix factorization-based approach . The concepts of current resident item matrix and idealresident item matrix are introduced as key criteria to cater to the conflictresolution framework .…

HEDP A Method for Early Forecasting Software Defects based on Human Error Mechanisms

The main process behind a software defect is that an error-prone scenariotriggers human error modes, which psychologists have observed to recur acrossdiverse activities . The proposed ideaemphasizes predicting the exact location and form of a possible defect . The approach was able to predict, atthe requirement phase, the exact locations and forms of 7 out of the 22 (31.8%) specific types of defects that were found in the code .…

THOMAS Trajectory Heatmap Output with learned Multi Agent Sampling

In this paper, we propose THOMAS, a joint multi-agent trajectory predictionframework . We present a unified model architecture for fast andsimultaneous agent future heatmap estimation leveraging hierarchical and sparseimage generation . We demonstrate that heatmap output enables a higher level ofcontrol on the predicted trajectories compared to vanilla multi-modaltrajectory regression .…

Real Time Learning from An Expert in Deep Recommendation Systems with Marginal Distance Probability Distribution

Recommendation systems play an important role in today’s digital world . Less research effort has been devoted to physical exercise recommendation systems . Sedentary lifestyles have become the major driver of several diseases as well as healthcare costs . In this paper, we develop a recommendation system for daily exercise activitiesto users based on their history, profile and similar users .…

Offset Symmetric Gaussians for Differential Privacy

Gaussian distribution is widely used in mechanism design for differentialprivacy (DP) Thanks to its sub-Gaussian tail, it significantly reduces the chance of outliers when responding to queries . In practice, this may limit the use of the Gaussian mechanism for large datasets with strong privacy requirements .…

Ousiometrics and Telegnomics The essence of meaning conforms to a two dimensional powerful weak and dangerous safe framework with diverse corpora presenting a safety bias

The essence of meaning conveyed by words is best described by a compass-like power-danger framework . Analysis of a disparate collection of large-scale English language corpora –literature, news, Wikipedia, talk radio, and social media — shows that naturallanguage exhibits a systematic bias toward safe, low danger words .…