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 .…

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 .…

Shaking the foundations delusions in sequence models for interaction and control

The recent phenomenal success of language models has reinvigorated machinelearning research . One important problem class that has remained elusive however is purposeful adaptive behavior . Currently there is a common perception that sequence models “lack the understanding of the causeand effect of their actions” leading them to draw incorrect inferences due toauto-suggestive delusions .…

Development and analysis of entropy stable no slip wall boundary conditions for the Eulerian model for viscous and heat conducting compressible flows

Nonlinear entropy stability analysis is used to derive entropy stable no-slipwall boundary conditions for the Eulerian model proposed by Sv\”{a}rd (PhysicaA: Statistical Mechanics and its Applications, 2018) and its spatialdiscretization based on entropy stable collocated discontinuous Galerkinoperators with the summation-by-parts property for unstructured grids .…

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 .…

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 .…

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 .…

Computational Graph Completion

ComputationalGraph Completion (CGC) problem addressed by the proposed framework could be interpreted as a generalization of that of solving linearsystems of equations to that of approximating unknown variables and functions with noisy, incomplete, and nonlinear dependencies . The proposed framework can be used to recover unknown functions with much scarcer data by exploitinginterdependencies between multiple functions and variables .…

Colosseum Large Scale Wireless Experimentation Through Hardware in the Loop Network Emulation

Colosseum is an open-access and publicly-available large-scale wirelesstestbed for experimental research via virtualized and softwarized waveforms and protocol stacks on a fully programmable, “white-box” platform . Through 256 state-of-the-art Software-defined Radios and a Massive Channel Emulator core, the system can model virtually any scenario, enabling the design, developmentand testing of solutions at scale in a variety of deployments and channelconditions .…

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 .…

A C 0 interior penalty method for m th Laplace equation

In this paper, we propose a $C^{0$ interior penalty method for $m$th-Laplaceequation on bounded Lipschitz polyhedral domain in $\mathbb{R}^{d}$ The standard $H^{1$-conforming piecewise$r$-th order polynomial space is used to approximate the exact solution $u$where $r$ can be any integer greater than or equal to $m$.…

A Numerical Scheme for Wave Turbulence 3 Wave Kinetic Equations

We introduce a finite volume scheme to solve isotropic 3-wave kineticequations . Our numerical energy cascade rates are in good agreement with the theoretical one obtained by Soffer and Tran . Our finite volume algorithm relies on a new identity, that allows one to reduce the number of terms needed to be approximated in the collision operators .…

Bootstrapping confidence in future safety based on past safe operation

With autonomous vehicles (AVs) a major concern is the inability to give quantitative assurance of safety . We demonstrate an approach to achieving more moderate,but useful, confidence . This formalises mathematically the approach of operating a system on a limited basis in the hope that Mishap-free operation will confirm one’s confidence in its safety and allow more extensive operation: a process of “bootstrapping” of confidence .…

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 .…

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 .…

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 .…

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 .…

Hierarchical Skills for Efficient Exploration

In reinforcement learning, pre-trained low-level skills have the potential to facilitate exploration . However, prior knowledge of the downstream task is required to strike the right balance between generality (fine-grained control) and specificity (faster learning) in skill design . We alleviate the need for prior knowledge by proposing a .…

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 .…

The Bleeps the Sweeps and the Creeps Convergence Rates for Dynamic Observer Patterns via Data Assimilation for the 2D Navier Stokes Equations

We adapt a continuous data assimilation scheme to the case of moving observers for the 2Dincompressible Navier-Stokes equations . We propose and test computationallyseveral movement patterns (which we refer to as “the bleeps, the sweeps and thecreeps) We end with a discussion of possible applications to real-world datacollection strategies that may lead to substantial improvements in predictivecapabilities .…

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 .…

Reduced Basis Approximations of Parameterized Dynamical Partial Differential Equations via Neural Networks

Projection-based reduced order models are effective at approximatingparameter-dependent differential equations that are parametrically separable . When parametric separability is not satisfied, which occurs in both linear andnonlinear problems, projection-based methods fail to adequately reduce the complexity of these problems . Devising alternative models is crucial for obtaining efficient and accurate approximations to expensive high-fidelity models, such as those by a neural-network .…

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 .…

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 .…