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 .…

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 .…

Scalable Learning Environments for Teaching Cybersecurity Hands on

This Innovative Practice full paper describes a technical innovation forscalable teaching of cybersecurity hands-on classes using interactive learningenvironments . The environments support virtual networks with full-fledged operating systems and devices that emulate real-world systems . KYPO Cyber Range Platform and Cyber SandboxCreator deliver the classes on-site or remotely for various target groupsof learners (K-12, university students, and professional learners) The instructors value time-efficiency when preparing and deploying the hands on activities .…

AequeVox Automated Fairness Testing of Speech Recognition Systems

AequeVox simulates different environments to assess the effectiveness of ASR systems for different populations . Non-native English, female and Nigerian English speakers generate 109%, 528.5% and 156.9% more errors than native English, male and UK Midlands speakers . 82% of the simulations had a comprehensibility rating aboveseven (out of ten), with the lowest rating being 6.78.78 .…

Result Diversification by Multi objective Evolutionary Algorithms with Theoretical Guarantees

Given a ground set of items, the result diversification problem aims to select a subset with high “quality” and “diversity” while satisfying someconstraints . Previous algorithms aremainly based on greedy or local search . We theoretically prove that the GSEMO can achieve the(asymptotically) optimal theoretical guarantees under both static and dynamic environments .…

Hermite multiwavelets for manifold valued data

In this paper we present a construction of interpolatory Hermitemultiwavelets for functions that take values in nonlinear geometries such as Riemannian manifolds or Lie groups . We rely on the strong connection between wavelets and subdivision schemes to define a prediction-correction approach .…

An Interoperable Open Data Portal for Climate Analysis

This work proposes an open interoperable data portal that offers access to aWeb-wide climate domain knowledge graph created for Ireland and England’s NOAAclimate daily data . There are three main components contributing to this dataportal: the upper layer schema of the knowledge graph — theclimate analysis (CA) ontology) – the second is an ad hoc SPARQL server bywhich to store the graph data and provide public Web access .…

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 .…

EILEEN A recommendation system for scientific publications and grants

This article describes EILEEN (ExploratoryInnovator of LitEraturE Networks), a recommendation system for scientific publications and grants with open source code and datasets . Using a unique dataset of log-inuser behavior, we validate our recommendation system against Latent SemanticAnalysis (LSA) and the standard ranking from Elasticsearch (Lucene scoring) We find that a learning-to-rank with Random Forest achieves an AUC of 0.9, outperforming both baselines .…

Using RDMA for Efficient Index Replication in LSM Key Value Stores

Key-Value (KV) stores have become afoundational layer in storage stacks of datacenter and cloud services . The main reason is that past designs for replicated KV stores favor reducing network traffic and increasing I/O size . We use a primary-backupreplication scheme that performs compactions only on the primary nodes and sends the pre-built index to the backup nodes of the region, avoiding allcompactions in backups .…

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 .…

EILEEN A recommendation system for scientific publications and grants

This article describes EILEEN (ExploratoryInnovator of LitEraturE Networks), a recommendation system for scientific publications and grants with open source code and datasets . Using a unique dataset of log-inuser behavior, we validate our recommendation system against Latent SemanticAnalysis (LSA) and the standard ranking from Elasticsearch (Lucene scoring) We find that a learning-to-rank with Random Forest achieves an AUC of 0.9, outperforming both baselines .…

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 .…

Generalised Wendland functions for the sphere

In this paper we compute the spherical Fourier expansions coefficients for the restriction of the generalised Wendland functions from $d-$dimensionalEuclidean space to the (d-1)-dimensional unit sphere . The development required to derive these coefficients relies heavily upon known asymptotic results forhypergeometric functions .…

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 .…

Optimal control using flux potentials A way to construct bound preserving finite element schemes for conservation laws

The main novelty of the proposedmethodology lies in the use of flux potentials as control variables and targetsof inequality-constrained optimization problems for numerical fluxes . We show that the feasible set of apotential-state potential-target (PP) optimization problems is nonempty . The discrete conservationproperty of flux-corrected finite element approximations is guaranteed without the need to impose additional equality constraints .…

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 .…

A New Extension of Chubanov s Method to Symmetric Cones

We propose a new variant of Chubanov’s method for solving the feasibilityproblem over the symmetric cone . The proposed method considersa feasibility problem associated with a norm induced by the maximum eigenvalue of an element . Its computational bound is(i) equivalent to Roos’s original method (2018) and superior to Louren\c{c}.o…

Neural Medication Extraction A Comparison of Recent Models in Supervised and Semi supervised Learning Settings

Drug prescriptions are essential information that must be encoded in electronic medical records . However, much of this information is hidden within free-text reports . This is why the medication extraction task has emerged . The study shows the very competitiveperformance of simple DNN models on the task as well as the high interest ofpre-trained models .…

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 .…

Using RDMA for Efficient Index Replication in LSM Key Value Stores

Key-Value (KV) stores have become afoundational layer in storage stacks of datacenter and cloud services . The main reason is that past designs for replicated KV stores favor reducing network traffic and increasing I/O size . We use a primary-backupreplication scheme that performs compactions only on the primary nodes and sends the pre-built index to the backup nodes of the region, avoiding allcompactions in backups .…

Trajectory Prediction with Linguistic Representations

Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions . Wepresent a novel trajectory prediction model that uses linguistic intermediaterepresentations to forecast trajectories . The model learns the meaning of each of the words without direct per-word supervision .…

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 .…

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 .…

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 .…

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 .…