Education•Lafayette, Louisiana, United States•
About: University of Louisiana at Lafayette is a education organization based out in Lafayette, Louisiana, United States. It is known for research contribution in the topics: Population & Wireless sensor network. The organization has 3796 authors who have published 7883 publications receiving 147977 citations. The organization is also known as: UL Lafayette & Louisiana-Lafayette.
Topics: Population, Wireless sensor network, Artificial neural network, Cloud computing, Wireless network
Papers published on a yearly basis
TL;DR: In this article, a review of deep learning-based object detection frameworks is provided, focusing on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further.
Abstract: Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems.
TL;DR: The results suggest that mid-to-early K stars should be considered along with G stars as optimal candidates in the search for extraterrestrial life.
Dalhousie University1, University of Georgia2, Bigelow Laboratory For Ocean Sciences3, Ontario Veterinary College4, New York State Department of Health5, Blaise Pascal University6, Bedford Institute of Oceanography7, University of Louisiana at Lafayette8, Duke University9, Pedagogical University10, Colorado State University11, University of Toronto12, University of Connecticut13, United States Forest Service14, University of Guelph15, Royal Botanic Garden Edinburgh16, Academy of Natural Sciences of Drexel University17, Michigan State University18, University of Copenhagen19, George Mason University20, University of Illinois at Urbana–Champaign21, Saint Petersburg State University22, University of Arkansas23, University of British Columbia24
TL;DR: This revision of the classification of unicellular eukaryotes updates that of Levine et al. (1980) for the protozoa and expands it to include other protists, and proposes a scheme that is based on nameless ranked systematics.
Abstract: This revision of the classification of unicellular eukaryotes updates that of Levine et al. (1980) for the protozoa and expands it to include other protists. Whereas the previous revision was primarily to incorporate the results of ultrastructural studies, this revision incorporates results from both ultrastructural research since 1980 and molecular phylogenetic studies. We propose a scheme that is based on nameless ranked systematics. The vocabulary of the taxonomy is updated, particularly to clarify the naming of groups that have been repositioned. We recognize six clusters of eukaryotes that may represent the basic groupings similar to traditional ''kingdoms.'' The multicellular lineages emerged from within monophyletic protist lineages: animals and fungi from Opisthokonta, plants from Archaeplastida, and brown algae from Stramenopiles.
TL;DR: In this paper, the basic theory for the initial value problem of fractional differential equations involving Riemann-Liouville differential operators is discussed employing the classical approach, and the theory of inequalities, local existence, extremal solutions, comparison result and global existence of solutions are considered.
Abstract: In this paper, the basic theory for the initial value problem of fractional differential equations involving Riemann–Liouville differential operators is discussed employing the classical approach. The theory of inequalities, local existence, extremal solutions, comparison result and global existence of solutions are considered.
TL;DR: It is suggested that recent symbolic-connectionist models of cognition shed new light on the mechanisms that underlie the gap between human and nonhuman minds.
Abstract: Over the last quarter century, the dominant tendency in comparative cognitive psychology has been to emphasize the similarities between human and nonhuman minds and to downplay the differences as "one of degree and not of kind" (Darwin 1871). In the present target article, we argue that Darwin was mistaken: the profound biological continuity between human and nonhuman animals masks an equally profound discontinuity between human and nonhuman minds. To wit, there is a significant discontinuity in the degree to which human and nonhuman animals are able to approximate the higher-order, systematic, relational capabilities of a physical symbol system (PSS) (Newell 1980). We show that this symbolic-relational discontinuity pervades nearly every domain of cognition and runs much deeper than even the spectacular scaffolding provided by language or culture alone can explain. We propose a representational-level specification as to where human and nonhuman animals' abilities to approximate a PSS are similar and where they differ. We conclude by suggesting that recent symbolic- connectionist models of cognition shed new light on the mechanisms that underlie the gap between human and nonhuman minds.
Showing all 3838 results
|James B. Grace
|Lawrence T. Drzal
|Lauren J. Chapman
|Raja Devesh Kumar Misra
|Robert R. Twilley
|James C. Green
|Chita R. Das
|Todd M. Preuss
Related Institutions (5)
Arizona State University
109.6K papers, 4.4M citations
Texas A&M University
164.3K papers, 5.7M citations
95.2K papers, 2.9M citations
University of California, Santa Barbara
80.8K papers, 4.6M citations
Georgia Institute of Technology
119K papers, 4.6M citations