A Probabilistic Individual-based Model for Infectious Diseases Outbreaks

2013 ◽  
Vol 63 (3) ◽  
Author(s):  
Pierpaolo Vittorini ◽  
Antonella Villani ◽  
Ferdinando Di Orio
Keyword(s):  
Infectious Diseases ◽  
Disease Transmission ◽  
State Of The Art ◽  
Research Area ◽  
Compartmental Models ◽  
Simulation Algorithm ◽  
Recent Past ◽  
Computing Power ◽  
Fine Grained ◽  
Large Research

The mathematical modelling of infectious diseases is a large research area with a wide literature. In the recent past, most of the scientific contributions focused on compartmental models. However, the increasing computing power is pushing towards the development of individual models that consider the disease transmission and evolution at a very fine-grained level. In the paper, the authors give a short state of the art of compartmental models, summarise one of the most know individual models, and describe both a generalization and a simulation algorithm.

scholarly journals Two-Branch Attention Learning for Fine-Grained Class Incremental Learning

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2987
Author(s):  
Jiaqi Guo ◽  
Guanqiu Qi ◽  
Shuiqing Xie ◽  
Xiangyuan Li
Keyword(s):  
Incremental Learning ◽  
Network Architecture ◽  
State Of The Art ◽  
Research Area ◽  
Feature Representation ◽  
Learning Network ◽  
Fine Grained ◽  
Effective Training ◽  
Critical Regions ◽  
Number Of Classes

As a long-standing research area, class incremental learning (CIL) aims to effectively learn a unified classifier along with the growth of the number of classes. Due to the small inter-class variances and large intra-class variances, fine-grained visual categorization (FGVC) as a challenging visual task has not attracted enough attention in CIL. Therefore, the localization of critical regions specialized for fine-grained object recognition plays a crucial role in FGVC. Additionally, it is important to learn fine-grained features from critical regions in fine-grained CIL for the recognition of new object classes. This paper designs a network architecture named two-branch attention learning network (TBAL-Net) for fine-grained CIL. TBAL-Net can localize critical regions and learn fine-grained feature representation by a lightweight attention module. An effective training framework is proposed for fine-grained CIL by integrating TBAL-Net into an effective CIL process. This framework is tested on three popular fine-grained object datasets, including CUB-200-2011, FGVC-Aircraft, and Stanford-Car. The comparative experimental results demonstrate that the proposed framework can achieve the state-of-the-art performance on the three fine-grained object datasets.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

Perbedaan Metode Three Compartement Sink dengan Air Panas dan Larutan Klorin terhadap Angka Kuman Alat Makan di RSU Queen Latifa

2017 ◽  
Vol 19 (2) ◽  
pp. 126-130
Author(s):  
Rifatun Hasanah ◽  
Setyowati Setyowati ◽  
Noor Tifauzah
Keyword(s):  
Infectious Diseases ◽  
Confidence Level ◽  
Disease Transmission ◽  
T Test ◽  
Hot Water ◽  
Independent Test ◽  
The Difference ◽  
Hospital Use

Background:One of the efforts in preventing congenital food disease is by washing the cutlery perfectly. The cutlery used by patients with infectious diseases should be noted more, because it has a risk in disease transmission through cutlery. The process of washing the cutlery for infected patients in Queen Latifa Hospital use three compartement sink method with hot water, while the three compartement sink method with clorine solvent has never been tested. Purpose: Research was to determine the difference in the number of germs in the tool was washed using three compartement sink method with hot water and with clorine solvent. Method:Types of research is experiment with rancangan percobaan acak kelompok (RAK). The object of this research is 4 plates and 4 bowls. The number of experimental units in this research were 2 treatments x 2 cutlery x 2 checks x 2 reapetitions = 16 experimental units. The analysis used independent t-test with 95% confidence level. Result :The average number of germs in the cutlery washed using the three compartment sink method with hot water was 1 x 101 cfu / cm2, whereas with chlorine solvent is 0.2 cfu / cm2. Independent test t-test shows p = 0.049 which means the hypothesis is accepted. onclusion : There are differences in the number of germs in the washing cutlery using the three compartment sink method with hot water and with chlorine solvent.   Keywords: number of germs, cutlery, three compartment sink

Perbedaan Metode Three Compartement Sink dengan Air Panas dan Larutan Klorin terhadap Angka Kuman Alat Makan di RSU Queen Latifa

2017 ◽  
Vol 19 (2) ◽  
pp. 126
Author(s):  
Rifatun Hasanah ◽  
Setyowati Setyowati ◽  
Noor Tifauzah
Keyword(s):  
Infectious Diseases ◽  
Confidence Level ◽  
Disease Transmission ◽  
T Test ◽  
Hot Water ◽  
Independent Test ◽  
The Difference ◽  
Hospital Use

Background:One of the efforts in preventing congenital food disease is by washing the cutlery perfectly. The cutlery used by patients with infectious diseases should be noted more, because it has a risk in disease transmission through cutlery. The process of washing the cutlery for infected patients in Queen Latifa Hospital use three compartement sink method with hot water, while the three compartement sink method with clorine solvent has never been tested. Purpose: Research was to determine the difference in the number of germs in the tool was washed using three compartement sink method with hot water and with clorine solvent. Method:Types of research is experiment with rancangan percobaan acak kelompok (RAK). The object of this research is 4 plates and 4 bowls. The number of experimental units in this research were 2 treatments x 2 cutlery x 2 checks x 2 reapetitions = 16 experimental units. The analysis used independent t-test with 95% confidence level. Result :The average number of germs in the cutlery washed using the three compartment sink method with hot water was 1 x 101 cfu / cm2, whereas with chlorine solvent is 0.2 cfu / cm2. Independent test t-test shows p = 0.049 which means the hypothesis is accepted. Conclusion : There are differences in the number of germs in the washing cutlery using the three compartment sink method with hot water and with chlorine solvent.

Spatial-temporal transmission dynamics and control of infectious diseases: Ebola virus disease (EVD) as a case study

2018 ◽  
Author(s):  
Cécile Viboud ◽  
Hélène Broutin ◽  
Gerardo Chowell
Keyword(s):  
Infectious Diseases ◽  
Disease Transmission ◽  
Ebola Virus ◽  
Temporal Dynamics ◽  
Virus Disease ◽  
Spatial Dynamics ◽  
Childhood Diseases ◽  
Middle Income ◽  
Theoretical Concepts ◽  
Dynamics And Control

Disentangling the spatial-temporal dynamics of infectious disease transmission is important to address issues of disease persistence, epidemic growth and optimal control. In this chapter, we review key concepts relating to the spatial-temporal dynamics of infectious diseases in meta-populations, whereby geographically separate subpopulations are connected by migration or mobility rates. We review the dynamics of colonization, persistence and extinction of emerging and recurrent pathogens in meta-populations; the role of demographic and environmental factors; and geographic heterogeneity in epidemic growth rate. We illustrate theoretical concepts by reviewing the spatial dynamics of childhood diseases and other acute infections in low- and middle-income countries, and provide a detailed description of the spatial-temporal dynamics of the 2014–16 Ebola epidemic in West Africa. We further discuss how increased availability of empirical data and recent methodological developments provide a deeper mechanistic understanding of transmission processes in space and time, and make recommendations for future work.

Communicable and Other Infectious Diseases

2020 ◽  
Author(s):  
Markus Frischhut
Keyword(s):  
Public Health ◽  
European Union ◽  
Infectious Diseases ◽  
Disease Transmission ◽  
Eu Law ◽  
The European Union ◽  
Ethical Perspective ◽  
Health Measures ◽  
Environment Agency ◽  
And Control

This chapter discusses the most important features of EU law on infectious diseases. Communicable diseases not only cross borders, they also often require measures that cross different areas of policy because of different vectors for disease transmission. The relevant EU law cannot be attributed to one sectoral policy only, and thus various EU agencies participate in protecting public health. The key agency is the European Centre for Disease Prevention and Control. Other important agencies include the European Environment Agency; European Food Safety Authority; and the Consumers, Health, Agriculture and Food Executive Agency. However, while integration at the EU level has facilitated protection of the public's health, it also has created potential conflicts among the different objectives of the European Union. The internal market promotes the free movement of products, but public health measures can require restrictions of trade. Other conflicts can arise if protective public health measures conflict with individual human rights. The chapter then considers risk assessment and the different tools of risk management used in dealing with the challenges of infectious diseases. It also turns to the external and ethical perspective and the role the European Union takes in global health.

scholarly journals Performance vs. hardware requirements in state-of-the-art automatic speech recognition

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Alexandru-Lucian Georgescu ◽  
Alessandro Pappalardo ◽  
Horia Cucu ◽  
Michaela Blott
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
State Of The Art ◽  
Decision Makers ◽  
Computing Power ◽  
Trade Off ◽  
Speech Features ◽  
Commercial Applications ◽  
Guided Tour ◽  
Embedded Applications

AbstractThe last decade brought significant advances in automatic speech recognition (ASR) thanks to the evolution of deep learning methods. ASR systems evolved from pipeline-based systems, that modeled hand-crafted speech features with probabilistic frameworks and generated phone posteriors, to end-to-end (E2E) systems, that translate the raw waveform directly into words using one deep neural network (DNN). The transcription accuracy greatly increased, leading to ASR technology being integrated into many commercial applications. However, few of the existing ASR technologies are suitable for integration in embedded applications, due to their hard constrains related to computing power and memory usage. This overview paper serves as a guided tour through the recent literature on speech recognition and compares the most popular ASR implementations. The comparison emphasizes the trade-off between ASR performance and hardware requirements, to further serve decision makers in choosing the system which fits best their embedded application. To the best of our knowledge, this is the first study to provide this kind of trade-off analysis for state-of-the-art ASR systems.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

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