Prophet Matching with General Arrivals

2021 ◽  
Author(s):  
Tomer Ezra ◽  
Michal Feldman ◽  
Nick Gravin ◽  
Zhihao Gavin Tang
Keyword(s):  
Probability Distribution ◽  
State Of The Art ◽  
A Priori ◽  
The State ◽  
Contention Resolution ◽  
Unified Framework ◽  
Weighted Matching ◽  
Resolution Scheme

We provide prophet inequality algorithms for online weighted matching in general (nonbipartite) graphs, under two well-studied arrival models: edge arrival and vertex arrival. The weights of the edges are drawn from a priori known probability distribution. Under edge arrival, the weight of each edge is revealed on arrival, and the algorithm decides whether to include it in the matching or not. Under vertex arrival, the weights of all edges from the newly arriving vertex to all previously arrived vertices are revealed, and the algorithm decides which of these edges, if any, to include in the matching. To study these settings, we introduce a novel unified framework of batched-prophet inequalities that captures online settings where elements arrive in batches. Our algorithms rely on the construction of suitable online contention resolution scheme (OCRS). We first extend the framework of OCRS to batched-OCRS, we then establish a reduction from batched-prophet inequality to batched-OCRS, and finally we construct batched-OCRSs with selectable ratios of 0.337 and 0.5 for edge and vertex arrival models, respectively. Both results improve the state of the art for the corresponding settings. For vertex arrival, our result is tight. Interestingly, a pricing-based prophet inequality with comparable competitive ratios is unknown.

scholarly journals A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

2021 ◽  
Vol 11 (23) ◽  
pp. 11344
Author(s):  
Wei Ke ◽  
Ka-Hou Chan
Keyword(s):  
Probability Distribution ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Final Analysis ◽  
The State ◽  
Experimental Results ◽  
Content Adaptive

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.

scholarly journals A Theoretically Derived Probability Distribution of Scour

2018 ◽  
Author(s):  
Salvatore Manfreda ◽  
Oscar Link ◽  
Alonso Pizarro
Keyword(s):  
Probability Distribution ◽  
Closed Form ◽  
State Of The Art ◽  
Mathematical Formulation ◽  
Gumbel Distribution ◽  
The State ◽  
Bridge Scour ◽  
Flood Peak ◽  
Hydraulic Conditions ◽  
Model Extension

Based on recent contributions regarding the treatment of unsteady hydraulic conditions into the state-of-the-art of scour literature, the theoretically derived probability distribution of bridge scour is introduced. The model is derived assuming a rectangular hydrograph shape with a given duration, and random flood peak following a Gumbel distribution. A model extension for a more complex flood event is also presented, assuming a synthetic exponential hydrograph shape. The mathematical formulation can be extended to any flood-peak probability distribution. The aim of the manuscript is to move forward the current approaches adopted for the bridge design coupling hydrological, hydraulic, and erosional models in a mathematical closed form.

Ontology Learning from Thesauri

2010 ◽  
pp. 247-260
Author(s):  
Javier Nogueras-Iso ◽  
Javier Lacasta ◽  
Jacques Teller ◽  
Gilles Falquet ◽  
Jacques Guyot
Keyword(s):  
State Of The Art ◽  
A Priori ◽  
Automatic Processing ◽  
The State ◽  
Ontology Learning ◽  
Ontology Construction ◽  
Knowledge Resources ◽  
Learning Techniques ◽  
Methods And Techniques ◽  
Domain Ontologies

Ontology learning is the term used to encompass methods and techniques employed for the (semi-)automatic processing of knowledge resources that facilitate the acquisition of knowledge during ontology construction. This chapter focuses on ontology learning techniques using thesauri as input sources. Thesauri are one of the most promising sources for the creation of domain ontologies thanks to the richness of term definitions, the existence of a priori relationships between terms, and the consensus provided by their extensive use in the library context. Apart from reviewing the state of the art, this chapter shows how ontology learning techniques can be applied in the urban domain for the development of domain ontologies.

A Review on Self-Healing and Self-Organizing Techniques for Wireless Sensor Networks

2019 ◽  
Vol 28 (05) ◽  
pp. 1930005 ◽  
Author(s):  
Sergio Diaz ◽  
Diego Mendez ◽  
Rolf Kraemer
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
State Of The Art ◽  
The State ◽  
Fault Classification ◽  
Wireless Sensor ◽  
Self Organization ◽  
Self Healing ◽  
Unified Framework ◽  
Self Organizing

We present the state-of-the-art related to self-organizing and self-healing techniques. On the one hand, self-organization is the nodes’ ability to construct a network topology without any human intervention and any previous topology knowledge. On the other hand, self-healing is the network’s ability to recover from failures by using hardware and software redundancies. By using both techniques, Wireless Sensor Networks (WSNs) can be deployed in unattended and harsh environments where on-site technical service is unfeasible. In the last few years, a large amount of work has been done in these two research areas, but these different techniques occur at different layers and with no general classification or effort to consolidate them. One of the contributions of this paper is the consolidation of the most significant and relevant mechanisms in these two areas, and additionally, we made an effort to organize and classify them. In this review, we explain in detail the two stages of self-organization, namely topology construction and management. Moreover, we present a comprehensive study of the four steps in a self-healing technique, namely, information collection, fault detection, fault classification and fault recovery. By introducing relevant work, comparative tables, and future trends, we provide the reader with a complete picture of the state-of-the-art. Another contribution is the proposal of a unified framework that employs self-organizing and self-healing mechanisms to achieve a fault-tolerant network.

Legal Transplants: Snapshots of the State of the Art and a Case Study from Central Europe – Post Transplantation-adjustment of Contractual Liability in the New Hungarian Civil Code

2020 ◽  
Vol 16 (2) ◽  
pp. 267-299
Author(s):  
Ádám Fuglinszky
Keyword(s):  
Central Europe ◽  
State Of The Art ◽  
A Priori ◽  
Civil Code ◽  
The State ◽  
Post Transplantation ◽  
Comparative Analyses ◽  
Before And After ◽  
Legal Transplantation

AbstractStrict contractual liability, foreseeability and non-cumul in the new Hungarian Civil Code are a living laboratory of legal transplantation. After an introduction (I) an overview is provided on the state of the art on legal transplants in seven theses (II). A case study follows next (III), sorted into three categories: ‘full legal transplants’ (comparative analyses took place both before and after the transplantation); ‘limping legal transplants’ (no a priori comparative considerations took place but the comparative toolbox is used in interpreting the new rules) and ‘surprising legal transplants,’ based on the spontaneous intuitions of the legislator having resulted in rejection and/or conversion into a ‘legal irritant’. The conclusions (IV) verify the significance of comparative analyses both in the pre- and post-transplantation phase.

Organizational discourse analysis - well done or too rare? A reply to our critics

2011 ◽  
Vol 64 (9) ◽  
pp. 1193-1202 ◽  
Author(s):  
Mats Alvesson ◽  
Dan Kärreman
Keyword(s):  
Discourse Analysis ◽  
State Of The Art ◽  
A Priori ◽  
The State ◽  
Organizational Discourse

In this article we respond to Bargiela-Chiappini, Iedema and Mumby.We notice that there is considerable agreement concerning the state of the art of organizational discourse analysis, while also discussing the disagreements. We expand on some of the ontological issues inherent in our argument, further discuss the character of reductionism in organizational discourse analysis, the trappings of a priori assumptions, and, finally, argue that our critics themselves, perhaps inadvertently, tend to repeat the problematic moves we identified in our original article.

scholarly journals A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2227
Author(s):  
Osama Mazhar ◽  
Sofiane Ramdani ◽  
Andrea Cherubini
Keyword(s):  
User Interfaces ◽  
Large Scale ◽  
Short Term Memory ◽  
State Of The Art ◽  
The State ◽  
Human Robot Interaction ◽  
Upper Body ◽  
Unified Framework ◽  
Depth Sensing ◽  
Dynamic Gestures

Intuitive user interfaces are indispensable to interact with the human centric smart environments. In this paper, we propose a unified framework that recognizes both static and dynamic gestures, using simple RGB vision (without depth sensing). This feature makes it suitable for inexpensive human-robot interaction in social or industrial settings. We employ a pose-driven spatial attention strategy, which guides our proposed Static and Dynamic gestures Network—StaDNet. From the image of the human upper body, we estimate his/her depth, along with the region-of-interest around his/her hands. The Convolutional Neural Network (CNN) in StaDNet is fine-tuned on a background-substituted hand gestures dataset. It is utilized to detect 10 static gestures for each hand as well as to obtain the hand image-embeddings. These are subsequently fused with the augmented pose vector and then passed to the stacked Long Short-Term Memory blocks. Thus, human-centred frame-wise information from the augmented pose vector and from the left/right hands image-embeddings are aggregated in time to predict the dynamic gestures of the performing person. In a number of experiments, we show that the proposed approach surpasses the state-of-the-art results on the large-scale Chalearn 2016 dataset. Moreover, we transfer the knowledge learned through the proposed methodology to the Praxis gestures dataset, and the obtained results also outscore the state-of-the-art on this dataset.

Practical Picture Processing

1974 ◽  
Vol 32 ◽  
pp. 338-339
Author(s):  
T. A. Welton
Keyword(s):  
Radiation Damage ◽  
Coherence Length ◽  
Spatial Information ◽  
State Of The Art ◽  
Coherent Radiation ◽  
The State ◽  
Energy Spread ◽  
Electron Micrograph ◽  
Picture Processing ◽  
Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

Hypothesis-Evidence Coordination: The State of the Art

2003 ◽  
Vol 48 (6) ◽  
pp. 826-829 ◽  
Author(s):  
Eric Amsel
Keyword(s):  
State Of The Art ◽  
The State

The State of the Art

1968 ◽  
Vol 13 (9) ◽  
pp. 479-480
Author(s):  
LEWIS PETRINOVICH
Keyword(s):  
State Of The Art ◽  
The State
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