Seeing wood because of the trees? A case of failure in reverse-engineering

1998 ◽  
Vol 21 (4) ◽  
pp. 468-468
Author(s):  
Philip J. Benson
Keyword(s):  
Reverse Engineering ◽  
Semantic Information ◽  
Complex Representation ◽  
Linguistic Terms ◽  
Accurate Identification

Failure to take note of distinctive attributes in the distal stimulus leads to an inadequate proximal encoding. Representation of similarities in Chorus suffers in this regard. Distinctive qualities may require additional complex representation (e.g., reference to linguistic terms) in order to facilitate discrimination. Additional semantic information, which configures proximal attributes, permits accurate identification of true veridical stimuli.

scholarly journals Block2vec: An Approach for Identifying Urban Functional Regions by Integrating Sentence Embedding Model and Points of Interest

2021 ◽  
Vol 10 (5) ◽  
pp. 339
Author(s):  
Zhihao Sun ◽  
Hongzan Jiao ◽  
Hao Wu ◽  
Zhenghong Peng ◽  
Lingbo Liu
Keyword(s):  
Spatial Correlation ◽  
Semantic Information ◽  
Low Cost ◽  
High Dimensional ◽  
Urban Spatial Structure ◽  
Dimensional Vector ◽  
Accurate Identification ◽  
Functional Region ◽  
Essential Information ◽  
Functional Regions

Urban functional regions are essential information in parsing urban spatial structure. The rapid and accurate identification of urban functional regions is important for improving urban planning and management. Thanks to its low cost and fast data update characteristics, the Point of Interest (POI) is one of the most common types of open access data. It mainly identifies urban functional regions by analyzing the potential correlation between POI data and the regions. Even though this is an important manifestation of the functional region, the spatial correlation between regions is rarely considered in previous studies. In order to extract the spatial semantic information among regions, a new model, called the Block2vec, is proposed by using the idea of the Skip-gram framework. The Block2vec model maps the spatial correlation between the POIs, as well as the regions, to a high-dimensional vector, in which classification of urban functional regions can be better performed. The results from cluster analysis showed that the high-dimensional vector extracted can well distinguish the regions with different functions. The random forests classification result (Overall accuracy = 0.7186, Kappa = 0.6429) illustrated the effectiveness of the proposed method. This study also verified the potential of the sentence embedding model in the semantic information extraction of POIs.

scholarly journals Swin–UNet++: A Nested Swin Transformer Architecture for Location Identification and Morphology Segmentation of Dimples on 2.25Cr1Mo0.25V Fractured Surface

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7504
Author(s):  
Pan Liu ◽  
Yan Song ◽  
Mengyu Chai ◽  
Zelin Han ◽  
Yu Zhang
Keyword(s):  
Neural Network ◽  
Fracture Surface ◽  
Prediction Accuracy ◽  
Semantic Information ◽  
Reference Value ◽  
Sem Images ◽  
Accurate Identification ◽  
Complex Morphology ◽  
Micro Features ◽  
Segmentation Models

The precise identification of micro-features on 2.25Cr1Mo0.25V steel is of great significance for understanding the mechanism of hydrogen embrittlement (HE) and evaluating the alloy’s properties of HE resistance. Presently, the convolution neural network (CNN) of deep learning is widely applied in the micro-features identification of alloy. However, with the development of the transformer in image recognition, the transformer-based neural network performs better on the learning of global and long-range semantic information than CNN and achieves higher prediction accuracy. In this work, a new transformer-based neural network model Swin–UNet++ was proposed. Specifically, the architecture of the decoder was redesigned to more precisely detect and identify the micro-feature with complex morphology (i.e., dimples) of 2.25Cr1Mo0.25V steel fracture surface. Swin–UNet++ and other segmentation models performed state-of-the-art (SOTA) were compared on the dimple dataset constructed in this work, which consists of 830 dimple scanning electron microscopy (SEM) images on 2.25Cr1Mo0.25V steel fracture surface. The segmentation results show Swin–UNet++ not only realizes the accurate identification of dimples but displays a much higher prediction accuracy and stronger robustness than Swin–Unet and UNet. Moreover, efforts from this work will also provide an important reference value to the identification of other micro-features with complex morphologies.

Immunolabelling of glomerular deposits in kidney biopsies routinely fixed in glutaraldehyde

1989 ◽  
Vol 47 ◽  
pp. 910-911
Author(s):  
Ś Lhoták ◽  
I. Alexopoulou ◽  
G. T. Simon
Keyword(s):  
Uv Light ◽  
Kidney Diseases ◽  
Light Chains ◽  
Microscopical Level ◽  
Accurate Identification ◽  
Light Microscopical Level ◽  
Dense Deposits ◽  
Cacodylate Buffer ◽  
Glomerular Deposits ◽  
Prognostic Importance

Various kidney diseases are characterized by the presence of dense deposits in the glomeruli. The type(s) of immunoglobulins (Igs) present in the dense deposits are characteristic of the disease. The accurate Identification of the deposits is therefore of utmost diagnostic and prognostic importance. Immunofluorescence (IF) used routinely at the light microscopical level is unable to detect and characterize small deposits found in early stages of glomerulonephritis. Although conventional TEM is able to localize such deposits, it is not capable of determining their nature. It was therefore attempted to immunolabel at EM level IgG, IgA IgM, C3, fibrinogen and kappa and lambda Ig light chains commonly found in glomerular deposits on routinely fixed ( 2% glutaraldehyde (GA) in 0.1M cacodylate buffer) kidney biopsies.The unosmicated tissue was embedded in LR White resin polymerized by UV light at -10°C. A postembedding immunogold technique was employed

Network reconstruction based on steady-state data

2008 ◽  
Vol 45 ◽  
pp. 161-176 ◽  
Author(s):  
Eduardo D. Sontag
Keyword(s):  
Steady State ◽  
Reverse Engineering ◽  
Theoretical Method ◽  
Network Reconstruction ◽  
Quasi Steady State ◽  
State Data

This paper discusses a theoretical method for the “reverse engineering” of networks based solely on steady-state (and quasi-steady-state) data.

Project BRAIN: Working Together to Improve Educational Outcomes for Students With Traumatic Brain Injury

2012 ◽  
Vol 22 (3) ◽  
pp. 106-118 ◽  
Author(s):  
Paula Denslow ◽  
Jean Doster ◽  
Kristin King ◽  
Jennifer Rayman
Keyword(s):  
Traumatic Brain Injury ◽  
At Risk ◽  
Brain Injury ◽  
Educational Outcomes ◽  
School Personnel ◽  
Department Of Education ◽  
Health Providers ◽  
Accurate Identification ◽  
Transition Intervention ◽  
Working Together

Children and youth who sustain traumatic brain injury (TBI) are at risk for being unidentified or misidentified and, even if appropriately identified, are at risk of encountering professionals who are ill-equipped to address their unique needs. A comparison of the number of people in Tennessee ages 3–21 years incurring brain injury compared to the number of students ages 3–21 years being categorized and served as TBI by the Department of Education (DOE) motivated us to create this program. Identified needs addressed by the program include the following: (a) accurate identification of students with TBI; (b) training of school personnel; (c) development of linkages and training of hospital personnel; and (d) hospital-school transition intervention. Funded by Health Services and Resources Administration (HRSA) grants with support from the Tennessee DOE, Project BRAIN focuses on improving educational outcomes for students with TBI through the provision of specialized group training and ongoing education for educators, families, and health professionals who support students with TBI. The program seeks to link families, hospitals, and community health providers with school professionals such as speech-language pathologists (SLPs) to identify and address the needs of students with brain injury.

Suppressed Semantic Information Accelerates Problem Solving

2012 ◽  
Author(s):  
Darya L. Zabelina ◽  
Emmanuel Guzman-Martinez ◽  
Laura Ortega ◽  
Marcia Grabowecky ◽  
Mark Beeman ◽  
...  
Keyword(s):  
Problem Solving ◽  
Semantic Information
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