Information-theoretic classification of SNOMED improves the organization of context-sensitive excerpts from Cochrane Reviews

The emphasis on evidence based medicine (EBM) has placed increased focus on finding timely answers to clinical questions in presence of patients. Using a combination of natural language processing for the generation of clinical excerpts and information theoretic distance based clustering, we evaluated multiple approaches for the efficient presentation of context-sensitive EBM excerpts

Multibody Multipole Methods

A three-body potential function can account for interactions among triples of particles which are uncaptured by pairwise interaction functions such as Coulombic or Lennard-Jones potentials. Likewise, a multibody potential of order $n$ can account for interactions among $n$-tuples of particles uncaptured by interaction functions of lower orders. To date, the computation of multibody potential functions for a large number of particles has not been possible due to its $O(N^n)$ scaling cost. In this paper we describe a fast tree-code for efficiently approximating multibody potentials that can be factorized as products of functions of pairwise distances. For the first time, we show how to derive a Barnes-Hut type algorithm for handling interactions among more than two particles. Our algorithm uses two approximation schemes: 1) a deterministic series expansion-based method; 2) a Monte Carlo-based approximation based on the central limit theorem. Our approach guarantees a user-specified bound on the absolute or relative error in the computed potential with an asymptotic probability guarantee. We provide speedup results on a three-body dispersion potential, the Axilrod-Teller potential.Comment: To appear in Journal of Computational Physic

Size does Matter: How do Micro-influencers Impact Follower Purchase Intention on Social Media?

Social media influencers have become a significant source of information for customers and a prevalent marketing tool for brands. It is crucial to explore factors that affect the follower’s purchase intention of the products endorsed by social media influencers. Recently, micro-influencers have gained recognition for their authenticity and relatability when compared with their established counterparts, such as macro- or mega-influencers. Increasing organizations also see the value micro-influencers can bring to their brands via more interaction with their target customers. Based on the parasocial interaction theory, we propose that the perceived credibility and transparency of micro-influencers enhance followers’ purchase intention through the mediation of parasocial interaction. Parasocial interaction is a kind of psychological relationship in which followers consider influencers as their friends, regardless of their limited interactions with those influencers. Our findings indicate that parasocial interaction between micro-influencers and their followers positively impacts purchase intentions of recommended products. It is also found that perceived micro-influencer credibility and transparency positively affect followers’ parasocial interaction with microinfluencers. Implications of our findings are discussed

Joint-contract function effects on BIM-enabled EPC project performance

Engineering, procurement, and construction (EPC) contracting does not promote collaboration and thus, may not be suitable for building information modeling (BIM) projects. Joint-contract functions that combine contractual control, coordination, and contingency adaptability may positively influence the performance of these BIM-enabled projects. This study hypothesized that perceived fairness, calculative trust, relational trust, and positive outcomes of distrust influence the relationship between joint contract functions and BIM-enabled project performance. It collected 252 observations from industry practitioners in EPC oil and gas projects and analyzed them using partial least squares structural equation modelling (PLS-SEM). The results show no direct effect of joint-contract functions on BIM-enabled EPC project performance but do show significant total and indirect relationship effects that are influenced by perceived fairness and relational trust. The findings contribute to construction contracting research by empirically showing how formal contracts focusing on joint-contract functions can influence BIM-enabled EPC project performance. The current findings also shed light on appropriate contract framing for BIM-enabled EPC project stakeholders, an area not explored in the previous literature

Dynamic network slicing for multitenant heterogeneous cloud radio access networks

Multitenant cellular network slicing has been gaining huge interest recently. However, it is not well-explored under the heterogeneous cloud radio access network (H-CRAN) architecture. This paper proposes a dynamic network slicing scheme for multitenant H-CRANs, which takes into account tenants' priority, baseband resources, fronthaul and backhaul capacities, quality of service (QoS) and interference. The framework of the network slicing scheme consists of an upper-level, which manages admission control, user association and baseband resource allocation; and a lower-level, which performs radio resource allocation among users. Simulation results show that the proposed scheme can achieve a higher network throughput, fairness and QoS performance compared to several baseline schemes

SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion Priors

We propose SceneTex, a novel method for effectively generating high-quality and style-consistent textures for indoor scenes using depth-to-image diffusion priors. Unlike previous methods that either iteratively warp 2D views onto a mesh surface or distillate diffusion latent features without accurate geometric and style cues, SceneTex formulates the texture synthesis task as an optimization problem in the RGB space where style and geometry consistency are properly reflected. At its core, SceneTex proposes a multiresolution texture field to implicitly encode the mesh appearance. We optimize the target texture via a score-distillation-based objective function in respective RGB renderings. To further secure the style consistency across views, we introduce a cross-attention decoder to predict the RGB values by cross-attending to the pre-sampled reference locations in each instance. SceneTex enables various and accurate texture synthesis for 3D-FRONT scenes, demonstrating significant improvements in visual quality and prompt fidelity over the prior texture generation methods.Comment: Project website: https://daveredrum.github.io/SceneTex

On Optimal Neighbor Discovery

Mobile devices apply neighbor discovery (ND) protocols to wirelessly initiate a first contact within the shortest possible amount of time and with minimal energy consumption. For this purpose, over the last decade, a vast number of ND protocols have been proposed, which have progressively reduced the relation between the time within which discovery is guaranteed and the energy consumption. In spite of the simplicity of the problem statement, even after more than 10 years of research on this specific topic, new solutions are still proposed even today. Despite the large number of known ND protocols, given an energy budget, what is the best achievable latency still remains unclear. This paper addresses this question and for the first time presents safe and tight, duty-cycle-dependent bounds on the worst-case discovery latency that no ND protocol can beat. Surprisingly, several existing protocols are indeed optimal, which has not been known until now. We conclude that there is no further potential to improve the relation between latency and duty-cycle, but future ND protocols can improve their robustness against beacon collisions.Comment: Conference of the ACM Special Interest Group on Data Communication (ACM SIGCOMM), 201