scholarly journals Entropic Associative Memory for Manuscript Symbols

2021 ◽  
Author(s):  
Rafael Morales ◽  
Nóe Hernández ◽  
Ricardo Cruz ◽  
Victor D. Cruz ◽  
Luis A. Pineda
Keyword(s):  
Incomplete Information ◽  
Associative Memory ◽  
Memory Retrieval ◽  
Declarative Memory ◽  
Associative Memories ◽  
Trade Off ◽  
Practical Applications ◽  
Digital Memory ◽  
Operational Characteristics

Abstract Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation; symbols not contained in the memory are rejected directly without search; and memory operations can be performed through parallel computations. Manuscript symbols, both letters and numerals, are stored in Associative Memory Registers that have an associated entropy. The memory recognition operation obeys an entropy trade-off between precision and recall, and the entropy level impacts on the quality of the objects recovered through the memory retrieval operation. We discuss the operational characteristics of the system for retrieving objects with both complete and incomplete information, such as severe occlusions. The experiments reported in this paper add evidence that supports the scalability of the framework and its potential for developing practical applications. We also compare the present entropic associative memories to Hopfield’s paradigm and discuss its potential for the study of natural memory.

scholarly journals An entropic associative memory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis A. Pineda ◽  
Gibrán Fuentes ◽  
Rafael Morales
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Memory Retrieval ◽  
Visual Memory ◽  
Parallel Architecture ◽  
Memory Model ◽  
Symbolic Computing ◽  
Abstract Representations ◽  
Satisfactory Performance ◽  
Operational Characteristics

AbstractNatural memories are associative, declarative and distributed, and memory retrieval is a constructive operation. In addition, cues of objects that are not contained in the memory are rejected directly. Symbolic computing memories resemble natural memories in their declarative character, and information can be stored and recovered explicitly; however, they are reproductive rather than constructive, and lack the associative and distributed properties. Sub-symbolic memories developed within the connectionist or artificial neural networks paradigm are associative and distributed, but lack the declarative property, the capability of rejecting objects that are not included in the memory, and memory retrieval is also reproductive. In this paper we present a memory model that sustains the five properties of natural memories. We use Relational-Indeterminate Computing to model associative memory registers that hold distributed representations of individual objects. This mode of computing has an intrinsic computing entropy which measures the indeterminacy of representations. This parameter determines the operational characteristics of the memory. Associative registers are embedded in an architecture that maps concrete images expressed in modality specific buffers into abstract representations and vice versa. The framework has been used to model a visual memory holding the representations of hand-written digits. The system has been tested with a set of memory recognition and retrieval experiments with complete and severely occluded images. The results show that there is a range of entropy values, not too low and not too high, in which associative memory registers have a satisfactory performance. The experiments were implemented in a simulation using a standard computer with a GPU, but a parallel architecture may be built where the memory operations would take a very reduced number of computing steps.

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

Social Investment and Childcare Expansion

2017 ◽  
Author(s):  
Margarita León
Keyword(s):  
Early Childhood ◽  
Early Childhood Education ◽  
Perfect Match ◽  
Preschool Education ◽  
Conceptual Level ◽  
Social Investment ◽  
Trade Off ◽  
Childhood Education ◽  
Education And Care

The chapter first examines at a conceptual level the links between theories of social investment and childcare expansion. Although ‘the perfect match’ between the two is often taken for granted in the specialized literature as well as in policy papers, it is here argued that a more nuance approach that ‘unpacks’ this relationship is needed. The chapter will then look for elements of variation in early childhood education and care (ECEC) expansion. Despite an increase in spending over the last two decades in many European and Organisation for Economic Co-operation and Development (OECD) countries, wide variation still exists in the way in which ECEC develops. A trade-off is often observed between coverage and quality of provision. A crucial dividing line that determines, to a large extent, the quality of provision in ECEC is the increasing differentiation between preschool education for children aged 3 and above and childcare for younger children.

scholarly journals Stress before training alters memory retrieval of a non-declarative memory in Lymnaea

2020 ◽  
Vol 223 (15) ◽  
pp. jeb223727
Author(s):  
Erin Swinton ◽  
Tamila Shymansky ◽  
Cayley Swinton ◽  
Ken Lukowiak
Keyword(s):  
Memory Retrieval ◽  
Declarative Memory

scholarly journals Age differences in brain activity related to unsuccessful declarative memory retrieval

2015 ◽  
Vol 1612 ◽  
pp. 30-47 ◽  
Author(s):  
Cheryl L. Grady ◽  
Marie St-Laurent ◽  
Hana Burianová
Keyword(s):  
Age Differences ◽  
Memory Retrieval ◽  
Declarative Memory ◽  
Brain Activity

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

scholarly journals Diagnostic Capabilities of a Smartphone- Based Low-Cost Microscope

2020 ◽  
Vol 6 (3) ◽  
pp. 522-525
Author(s):  
Dorina Hasselbeck ◽  
Max B. Schäfer ◽  
Kent W. Stewart ◽  
Peter P. Pott
Keyword(s):  
Low Cost ◽  
Field Of View ◽  
Parasitic Diseases ◽  
Optical Parameters ◽  
Trade Off ◽  
Resource Limited ◽  
Wide Range ◽  
Diagnostic Applications ◽  
Diagnostic Capabilities

AbstractMicroscopy enables fast and effective diagnostics. However, in resource-limited regions microscopy is not accessible to everyone. Smartphone-based low-cost microscopes could be a powerful tool for diagnostic and educational purposes. In this paper, the imaging quality of a smartphone-based microscope with four different optical parameters is presented and a systematic overview of the resulting diagnostic applications is given. With the chosen configuration, aiming for a reasonable trade-off, an average resolution of 1.23 μm and a field of view of 1.12 mm2 was achieved. This enables a wide range of diagnostic applications such as the diagnosis of Malaria and other parasitic diseases.

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