Asynchronous parareal time discretization for partial differential equations

Asynchronous iterations are more and more investigated for both scaling and fault-resilience purpose on high performance computing platforms . This paper advocates a novel application direction targeting time-decomposed time-parallel approaches . It turned out that Parareal andasync-Parareal feature very close convergence conditions, asymptoticallyequivalent, including the finite-time termination property .…

GenNI Human AI Collaboration for Data Backed Text Generation

GenNI(Generation Negotiation Interface) is an interactive visual system for human-AI collaboration in producing descriptive text . The toolutilizes a deep learning model designed with explicit control states . The visual interface makes it possible for users to interact with AI systems following aRefine-Forecast paradigm to ensure that the generation system acts in a mannerhuman users find suitable .…

Speech Pattern based Black box Model Watermarking for Automatic Speech Recognition

How to design a black-box watermarking scheme for automatic speech recognition (ASR) models is still an unsolved problem . The proposed watermarkingscheme is robust against five kinds of attacks and has little impact on accuracy . We synthesize trigger audios by spreading the speechclips of model owners over the entire input audios and labeling the triggeraudios with the stego texts, which hides the authorship information withlinguistic steganography .…

Coalgebraic modal logic and games for coalgebras with side effects

We study coalgebraic modal logic and games to characterise behaviouralequivalence in the presence of side effects . Our aim is to develop a generalframework based on indexed categories/fibrations that is common, at least, to the aforementioned categories . In particular, we show how the coalgebras of behavioural equivalence arises from a relation lifting (a specialkind of indexed morphism) and we give a general recipe to construct suchliftings in the above three cases .…

Can Dynamic TDD Enabled Half Duplex Cell Free Massive MIMO Outperform Full Duplex Cellular Massive MIMO

Each half-duplex (HD)access point (AP) is scheduled to operate in the uplink (UL) or downlink (DL) mode based on the data demands of the user equipments (UEs) The goal is tomaximize the sum UL-DL spectral efficiency (SE) We theoretically establish thesub-modularity of the sum SE, which allows us to develop a new, low complexity,greedy algorithm for the combinatorial AP scheduling problem, with guaranteedoptimality properties .…

Digital transformation of droplet aerosol infection risk assessment realized on Fugaku for the fight against COVID 19

The fastest supercomputer in 2020, Fugaku, has achieved digitaltransformation of epidemiology in allowing end-to-end, detailed quantitative quantitative modeling of COVID-19 transmissions for the first time . Fugaku has transformed the behavior of the entire Japanese public through its detailed analysis of transmission risks in multitudes of societal situations entailing heavy risks .…

NeuralDiff Segmenting 3D objects that move in egocentric videos

Given a raw video sequence taken from a freely-moving camera, we study decomposing the observed 3D scene into a static background and adynamic foreground containing the objects that move in the video sequence . We achieve this factorization by reconstructing the video via a triple-stream neuralrendering network that explains the different motions based on corresponding biases .…

EEGminer Discovering Interpretable Features of Brain Activity with Learnable Filters

Patterns of brain activity are associated with different brain processes and can be used to identify different brain states and make behavioral predictions . However, the relevant features are not readily apparent and accessible . To mineinformative latent representations from multichannel EEG recordings, we propose a novel differentiable EEG decoding pipeline consisting of learnable filters and a pre-determined feature extraction module .…

Near Optimal Quantum Algorithms for String Problems

We study quantum algorithms for several fundamental string problems, including Longest Common Substring, Lexicographically Minimal String Rotation,and Longest Square Substring . These problems have been widely studied in thestringology literature since the 1970s, and are known to be solvable by classical algorithms .…

Comonadic semantics for hybrid logic and bounded fragments

In recent work, comonads and associated structures have been used to analyze range of important notions in finite model theory, descriptive complexity andcombinatorics . We extend this analysis to Hybrid logic, a widely-studied extension of basic modal logic . In addition to characterising various resource-indexedequivalences induced by Hybrid logic and the bounded fragment, we also give model-theoretic characterisations of bounded formulas in terms of invariance .…

Matrix Discrepancy from Quantum Communication

We develop a novel connection between discrepancy minimization and (quantum)communication complexity . We resolve a substantial specialcase of the Matrix Spencer conjecture . We give a polynomial-time algorithm based on partial coloring andsemidefinite programming to find such $x . Our techniques open a new avenue to use tools from communication complexityand information theory to study discrepancy .…

User Centric Federated Learning

Data heterogeneity across participating devices poses one of the main challenges in federated learning as it has been shown to greatly hamper its convergence time and generalization capabilities . Our approach potentially produces a personalized model for each user at the cost of some extra downlink communication overhead .…

FriendlyCore Practical Differentially Private Aggregation

Differentially private algorithms for common metric aggregation tasks often have limited practicality . We propose a simple and practical tool that takes a set of points from an unrestricted (pseudo) metric space as input . The tool can be used to preprocess the input before aggregating it, potentially simplifying the aggregation or boostingits accuracy .…

Enabling a Social Robot to Process Social Cues to Detect when to Help a User

Social robots need to be able to recognize human needs in real-time so that they can provide timely assistance . We propose anarchitecture that uses social cues to determine when a robot should provide assistance . Enabling a robot to recognize a user’s needs through social cues can help it to adapt to user behaviors andpreferences, which in turn will lead to improved user experiences, say the authors .…

Permutation Invariance of Deep Neural Networks with ReLUs

A paper proposes a sound, abstraction-based technique to establish permutation invariance in DNNs with ReLU as the activation function . The technique computes an over-approximation of the reachable states, and anunder-assessment of the safe states . The novelty of our approach lies in auseful tie-class analysis, that we introduce for forward propagation, and ascalable 2-polytope under-appreciation method that escapes the exponential blow-up in the number of regions during backward propagation .…

Riemannian classification of EEG signals with missing values

This paper proposes two strategies to handle missing data for the classification of electroencephalograms using covariance matrices . The first approach estimates the covariance from imputed data with the $k$-nearestneighbors algorithm . The second relies on the observed data by leveraging theobserved-data likelihood within an expectation-maximization algorithm .…

Continuation of Famous Art with AI A Conditional Adversarial Network Inpainting Approach

Inpainting GAN is tasked with learning to reconstruct the original image from the centre crop of images . Images are thenresized rather than cropped and presented as input to the generator . Results show that texture and texture (including canvas and paint) as well as scenery such as clouds, clouds, water, land (including hills and mountains), grass, and flowersare implemented by the generator when extending real artworks .…

Patch Based Deep Autoencoder for Point Cloud Geometry Compression

In this paper, we propose a patch-based compression process using deep learning, focusing on the lossy point cloudgeometry compression . Unlike existing point cloud compression networks, wedivide the point cloud into patches and compress each patch independently . Our method outperforms the state-of-the-art in terms of rate-distortion performance, especially at lowbitrates .…

An Unconstrained Convex Formulation of Compliant Contact

We present a convex formulation of compliant frictional contact and a robust,performant method to solve it in practice . Our solver has provenglobal convergence and warm-starts effectively, enabling simulation atinteractive rates . We show our method outperforms existing and open source alternatives without sacrificing accuracy .…

MTP Multi Hypothesis Tracking and Prediction for Reduced Error Propagation

This paper addresses theproblem of cascading errors by focusing on the coupling between the trackingand prediction modules . Rather than relying on a single set of tracking results forprediction, our framework simultaneously reasons about multiple sets oftracking results . We show that this framework improves overall prediction performance over the standard single-hypothesistracking-prediction pipeline by up to 34.2% on the nuScenes dataset, with evenmore significant improvements (up to ~70%) when restricting the evaluation to challenging scenarios involving identity switches and fragments — all with anacceptable computation overhead .…

Sky Is Not the Limit Tighter Rank Bounds for Elevator Automata in Büchi Automata Complementation Technical Report

We propose several heuristics for mitigating one of the main causes of an explosion in rank-based complementation of B\”{u}chi automata(BAs) We introduce two techniques for refiningbounds on the ranks of BA states using data-flow analysis of the automaton . We implement out techniques as an extension of the tool Ranker for BAcomplementation and show that they indeed greatly prune the generated statespace, obtaining significantly better results and outperforming other tools on a large set of benchmarks .…

Eternal Domination and Clique Covering

Using computational methods, we show that the smallest graph having its eternal domination number less than its cliqueecovering number has $10$ vertices . This answers a question of Klostermeyer andMynhardt [Protecting a graph with mobile guards] In addition, we study the problem ontriangle-free graphs, circulant graphs, planar graphs and cubic graphs .…

Matrix Discrepancy from Quantum Communication

We develop a novel connection between discrepancy minimization and (quantum)communication complexity . We resolve a substantial specialcase of the Matrix Spencer conjecture . We give a polynomial-time algorithm based on partial coloring andsemidefinite programming to find such $x . Our techniques open a new avenue to use tools from communication complexityand information theory to study discrepancy .…

BlockIoT Blockchain based Health Data Integration using IoT Devices

BlockIoT uses blockchain technology to transfer previously inaccessible and centralized data from medical devices to EHR systems . Data tends to be fragmented among healthinfrastructures and prevents interoperability of medical data at the point ofcare . BlockIOT is a suitable system to supplement physicians’ clinical practice and increases efficiency in most healthcare specialties, such as cardiology, pulmonology, endocrinology, and primary care, authors say .…

Repeated Games Optimal Channel Capture and Open Problems for Slotted Multiple Access

This paper revisits a classical problem of slotted multiple access with success, idle, and collision events on each slot . An efficient algorithm is developed andconjectured to have an optimal expected capture time for all positive integers $n$. Optimality is proven in the special cases $n \in \{1, 2, 3, 4, 6\}$ using a novel analytical technique that introduces virtual users with enhancedcapabilities .…