FoV Privacy aware VR Streaming

Proactive tile-based virtual reality (VR) video streaming can use the traceof FoV and eye movement to predict future requested tiles, then renders and delivers the predicted tiles before playback . Quality of experience (QoE) depends on the combined effect of tile prediction and consumed resources .…

Log concave poset inequalities

We study combinatorial inequalities for various classes of set systems . We use the language formulation of greedoids which allows a linear algebraic setup . The underlying non-commutative nature of matrices associated withgreedoids allows us to proceed beyond polymatroids and prove the equalityconditions .…

Privacy in Open Search A Review of Challenges and Solutions

Privacy is of worldwide concern regarding activities and processes thatinclude sensitive data . Artificial intelligenceareas, such as machine learning and natural language processing, have alreadysuccessfully employed privacy-preserving mechanisms in order to safeguard dataprivacy in a vast number of applications . Information retrieval (IR) is prone to privacy threats such as attacks and unintended disclosures of documents and search history, which may cripple the security of users and bepenalized by data protection laws .…

Different Applications and Technologies of Internet of Things IoT

Internet of things (IoT) has significantly altered the traditional lifestyle to a highly technologically advanced society . Some of the significanttransformations that have been achieved through IoT are smart homes, smarttransportation, smart city, and control of pollution . A considerable number of studies have been conducted and continue to be done to increase the use of technology through IoT .…

Investigating Reversibility of Steps in Petri Nets

In reversible computations one is interested in the development of mechanisms allowing to undo the effects of executed actions . In this paper, we consider theproblem of reversing the effect of the execution of groups of actions (steps) Using Petri nets as a system model, we introduce concepts related to this newscenario .…

A unifying framework for n dimensional quasi conformal mappings

With the advancement of computer technology, there is a surge of interest ineffective mapping methods for objects in higher-dimensional spaces . In this work, we develop a unifying framework for computing $n$-dimensional quasi-conformal mappings . We demonstrate the effectiveness of the proposed framework using various experiments in two- and three-dimensions, with applications to medical image registration, adaptiveremeshing and shape modeling .…

Deep Point Cloud Normal Estimation via Triplet Learning

Normal estimation on 3D point clouds is a fundamental problem in 3D vision and graphics . Current methods often show limited accuracy in predicting normals at sharp features (e.g., edges and corners) and less robustness to noise . We are motivated that local patches on isotropic and anisotropic surfaces have similar or distinct normals .…

An Open Natural Language Processing Development Framework for EHR based Clinical Research A case demonstration using the National COVID Cohort Collaborative N3C

The study demonstrates the feasibility of creating a federated NLP algorithmdevelopment and benchmarking platform to enhance multi-institution clinical NLP study . The generated corpora derived out of thetexts from multiple intuitions and gold standard annotated are tested on asingle institution’s rule set .…

Quantum networks theory

The formalism of quantum theory over discrete systems is extended in twosignificant ways . Tensors and traceouts are generalized, so that systemscan be partitioned according to almost arbitrary logical predicates . Second,quantum evolutions are generalized to act over network configurations, in sucha way that nodes be allowed to merge, split and reconnect coherently in asuperposition .…

Discontinuous Grammar as a Foreign Language

In order to achieve deep natural language understanding, syntacticconstituent parsing is a vital step . We extend the framework of sequence-to-sequence models for constituent parsing . We design novel linearizationsthat can fully produce discontinuities and, for the first time, we test asequence to-sequence model on the main discontinuous benchmarks, obtainingcompetitive results on par with task-specific discontinuous constituent parsers .…

MultiHead MultiModal Deep Interest Recommendation Network

The multi-headmulti-modal DIN improves the recommendation prediction effect, and outperforms state-of-the-art methods on various comprehensive indicators . Researchers have optimized more on therecommendation model network structure, they have less research on enrichingrecommendation models, and there is still room for in-depth model optimization .…

State based Episodic Memory for Multi Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) algorithms have made promisingprogress in recent years . But existing MARL algorithms still suffer from the sample inefficiency problem . State-based episodic memory (SEM) is the first work to introduce EM into MARL . It has lower space complexity and time complexity than state and action based EM (SAEM), which is originally proposed for single-agent learning .…

Monitoring Collective Communication Among GPUs

Communication among devices in multi-GPU systems plays an important role interms of performance and scalability . In order to optimize an application,programmers need to know the type and amount of the communication happening among GPUs . In this work, we extend ComScribe to identify communication among GPUs for collective and P2P communicationprimitives in NVIDIA’s NCCL library .…

Improved Multilingual Language Model Pretraining for Social Media Text via Translation Pair Prediction

We evaluate a simple approach to improving zero-shot multilingual transfer ofmBERT on social media corpus by adding a pretraining task called translationpair prediction . Our approach assumes access to translations (exact or approximate) between source-target language pairs, where we fine-tune a modelon source language task data and evaluate the model in the target language .…

Multilingual Unsupervised Neural Machine Translation with Denoising Adapters

We consider the problem of multilingual unsupervised machine translation,translating to and from languages that only have monolingual data by using auxiliary parallel language pairs . We propose instead to use denoising adapters, adapter layers, on top of pre-trained mBART-50 . The resulting translations are on-par with back-translated as measured by BLEU, and further it allows adding unseen languages incrementally .…

LMSOC An Approach for Socially Sensitive Pretraining

Large-scale pretrained language models have been shown to learneffective linguistic representations for many NLP tasks . We propose a simple approach to incorporate speaker social context into the learnedrepresentations of large-scale language models . We evaluate our approach on geographically-sensitivelanguage-modeling tasks and show a substantial improvement (more than 100% relative lift on MRR) compared to baselines .…

Fully Three dimensional Radial Visualization

We develop methodology for three-dimensional (3D) radial visualization of multidimensional datasets . We show that this uniform distribution provides the best visualization with minimal artificial visualcorrelation for data with uncorrelated variables . We use RadViz3D to illustrate (i) the chemical composition of Longquan celadon ceramics and their Jingdezhen imitation overcenturies, and (ii) US regional SARS-Cov-2 variants’ prevalence in the Covid-19pandemic during the summer 2021 surge of the Delta variant .…

Beamforming Design for Intelligent Reflecting Surface Enhanced Symbiotic Radio Systems

This paper investigates multiuser multi- input single-input single-output downlinksymbiotic radio communication systems . The proposed scheme significantly improves the average sum-rate of the primary system, while guaranteeing the decodingperformance of the secondary system . We exploit the Schur complement thatfacilitates the design of a suboptimal beamforming algorithm based on convex approximation .…

Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning

We present a novel characterization of the mapping of multiple parallelismforms onto hierarchical accelerator system . We experimentally verify the substantial effect of these mappings onall-reduce performance (up to 448x) We offer a novel syntax-guided programsynthesis framework that is able to decompose reductions over one or moreparallelism axes to sequences of collectives in a hierarchy- and mapping-awareway .…

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 .…

Exploring the Relationship Between Positive Risk Balance and Absence of Unreasonable Risk

International discussions on the topic of how to define and define what a “safe enough” Automated Driving System is are currentlyhinged on the question of determining the relationship between “positive riskbalance” (PRB) and “absence of unreasonable risk” (AUR) The argumentation in this paper is aimed at showing that the two interpretations for PRB can actually complement each other, but can be considered independently, and can both besubsumed within non-prescriptive guidelines toward ADS safety assurance .…

An Open Natural Language Processing Development Framework for EHR based Clinical Research A case demonstration using the National COVID Cohort Collaborative N3C

The study demonstrates the feasibility of creating a federated NLP algorithmdevelopment and benchmarking platform to enhance multi-institution clinical NLP study . The generated corpora derived out of thetexts from multiple intuitions and gold standard annotated are tested on asingle institution’s rule set .…

Distributionally Robust Classifiers in Sentiment Analysis

In this paper, we propose sentiment classification models based on BERTintegrated with DRO (Distributionally Robust Classifiers) to improve model performance on datasets with distributional shifts . We added 2-Layer Bi-LSTM, projection layer (onto simplex or Lp ball), and linear layer on top of BERT toachieve distributionally robustness .…

SILG The Multi environment Symbolic Interactive Language Grounding Benchmark

Symbolic Interactive Language Grounding benchmark (SILG) unifies a collection of diverse grounded language learning environments . SILG consists of grid-world environments that require generalization to new dynamics, entities, and partially observed worlds (RTFM,Messenger, NetHack) and symbolic counterparts of visual worlds that require interpreting rich natural language with respect to complex scenes (ALFWorld, Touchdown) Together, these environments provide diverse groundingchallenges in richness of observation space, action space, languagespecification, and plan complexity .…

Towards Puffin The Creation of an Uncertainty Compiler

An uncertainty compiler is a tool that automatically translates original computer source code lacking explicit uncertainty analysis into code containing uncertainty representations and uncertainty propagation algorithms . It handles the specifications of input uncertainties and inserts callsto intrusive uncertainty quantification algorithms in the library .…

A unifying framework for n dimensional quasi conformal mappings

With the advancement of computer technology, there is a surge of interest ineffective mapping methods for objects in higher-dimensional spaces . In this work, we develop a unifying framework for computing $n$-dimensional quasi-conformal mappings . We demonstrate the effectiveness of the proposed framework using various experiments in two- and three-dimensions, with applications to medical image registration, adaptiveremeshing and shape modeling .…