8,005 research outputs found
Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation
Reinforcement learning demonstrates significant potential in automatically
building control policies in numerous domains, but shows low efficiency when
applied to robot manipulation tasks due to the curse of dimensionality. To
facilitate the learning of such tasks, prior knowledge or heuristics that
incorporate inherent simplification can effectively improve the learning
performance. This paper aims to define and incorporate the natural symmetry
present in physical robotic environments. Then, sample-efficient policies are
trained by exploiting the expert demonstrations in symmetrical environments
through an amalgamation of reinforcement and behavior cloning, which gives the
off-policy learning process a diverse yet compact initiation. Furthermore, it
presents a rigorous framework for a recent concept and explores its scope for
robot manipulation tasks. The proposed method is validated via two
point-to-point reaching tasks of an industrial arm, with and without an
obstacle, in a simulation experiment study. A PID controller, which tracks the
linear joint-space trajectories with hard-coded temporal logic to produce
interim midpoints, is used to generate demonstrations in the study. The results
of the study present the effect of the number of demonstrations and quantify
the magnitude of behavior cloning to exemplify the possible improvement of
model-free reinforcement learning in common manipulation tasks. A comparison
study between the proposed method and a traditional off-policy reinforcement
learning algorithm indicates its advantage in learning performance and
potential value for applications
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Norsk rÄ kumelk, en kilde til zoonotiske patogener?
The worldwide emerging trend of eating ânaturalâ foods, that has not been
processed, also applies for beverages. According to Norwegian legislation, all
milk must be pasteurized before commercial sale but drinking milk that has
not been heat-treated, is gaining increasing popularity. Scientist are warning
against this trend and highlights the risk of contracting disease from milkborne
microorganisms. To examine potential risks associated with drinking
unpasteurized milk in Norway, milk- and environmental samples were
collected from dairy farms located in south-east of Norway. The samples
were analyzed for the presence of specific zoonotic pathogens; Listeria
monocytogenes, Campylobacter spp., and Shiga toxin-producing Escherichia
coli (STEC). Cattle are known to be healthy carriers of these pathogens, and
Campylobacter spp. and STEC have a low infectious dose, meaning that
infection can be established by ingesting a low number of bacterial cells. L.
monocytogenes causes one of the most severe foodborne zoonotic diseases,
listeriosis, that has a high fatality rate. All three pathogens have caused milk
borne disease outbreaks all over the world, also in Norway.
During this work, we observed that the prevalence of the three examined
bacteria were high in the environment at the examined farms. In addition, 7%
of the milk filters were contaminated by STEC, 13% by L. monocytogenes and
4% by Campylobacter spp. Four of the STEC isolates detected were eaepositive,
which is associated with the capability to cause severe human
disease. One of the eae-positive STEC isolates were collected from a milk
filter, which strongly indicate that Norwegian raw milk may contain potential
pathogenic STEC.
To further assess the possibilities of getting ill by STEC after consuming raw
milk, we examined the growth of the four eae-positive STEC isolates in raw milk at different temperatures. All four isolates seemed to have ability to multiply in raw milk at 8°C, and one isolate had significant growth after 72 hours. Incubation at 6°C seemed to reduce the number of bacteria during the
first 24 hours before cell death stopped. These findings highlight the
importance of stable refrigerator temperatures, preferable < 4°C, for storage
of raw milk.
The L. monocytogenes isolates collected during this study show genetic
similarities to isolates collected from urban and rural environmental
locations, but different clones were predominant in agricultural
environments compared to clinical and food environments. However, the
results indicate that the same clone can persist in a farm over time, and that
milk can be contaminated by L. monocytogenes clones present in farm
environment.
Despite testing small volumes (25 mL) of milk, we were able to isolate both
STEC and Campylobacter spp. directly from raw milk. A proportion of 3% of
the bulk tank milk and teat milk samples were contaminated by
Campylobacter spp. and one STEC was isolated from bulk tank milk. L
monocytogenes was not detected in bulk tank milk, nor in teat milk samples.
The agricultural evolvement during the past decades have led to larger
production units and new food safety challenges. Dairy cattle production in
Norway is in a current transition from tie-stall housing with conventional
pipeline milking systems, to modern loose housing systems with robotic
milking. The occurrence of the three pathogens in this project were higher in
samples collected from farms with loose housing compared to those with tiestall
housing.
Pasteurization of cowâs milk is a risk reducing procedure to protect
consumers from microbial pathogens and in most EU countries, commercial
distribution of unpasteurized milk is legally restricted. Together, the results
presented in this thesis show that the animal housing may influence the level
of pathogenic bacteria in the raw milk and that ingestion of Norwegian raw
cowâs milk may expose consumers to pathogenic bacteria which can cause
severe disease, especially in children, elderly and in persons with underlying
diseases. The results also highlight the importance of storing raw milk at low
temperatures between milking and consumption.Ă
spise mat som er mindre prosessert og mer «naturlig» er en pÄgÄende
trend i Norge og i andre deler av verden. Interessen for Ă„ drikke melk som
ikke er varmebehandlet, sÄkalt rÄ melk, er ogsÄ Þkende. I Norge er det pÄbudt
Ă„ pasteurisere melk fĂžr kommersielt salg for Ă„ beskytte forbrukeren mot
sykdomsfremkallende mikroorganismer. Fagfolk advarer mot Ä drikke rÄ
melk, og pÄpeker risikoen for Ä bli syk av patogene bakterier som kan finnes i
melken.
I denne avhandlingen undersĂžker vi den potensielle risikoen det medfĂžrer Ă„
drikke upasteurisert melk fra Norge. I tillegg til Ă„ samle inn tankmelk- og
speneprÞver fra melkegÄrder i sÞrÞst Norge, samlet vi ogsÄ miljÞprÞver fra
de samme gÄrdene for Ä kartlegge forekomst og for Ä identifisere potensielle
mattrygghetsrisikoer i melkeproduksjonen. Alle prĂžvene ble analysert for de
zoonotiske sykdomsfremkallende bakteriene Listeria monocytogenes,
Campylobacter spp., og Shiga toksin-produserende Escherichia coli (STEC).
Kyr kan vĂŠre friske smittebĂŠrere av disse bakteriene, som dermed kan
etablere et reservoar pÄ gÄrdene. Bakteriene kan overfÞres fra gÄrdsmiljÞet
til melkekjeden og dermed utfordre mattryggheten. Disse bakteriene har
forÄrsaket melkebÄrne sykdomsutbrudd over hele verden, ogsÄ i Norge.
Campylobacter spp. og STEC har lav infeksiĂžs dose, som vil si at man kan bli
syk selv om man bare inntar et lavt antall bakterieceller. L. monocytogenes
kan gi sykdommen listeriose, en av de mest alvorlige matbÄrne zoonotiske
sykdommene vi har i den vestlige verden.
Resultater fra denne oppgaven viser en hĂžy forekomst av de tre patogenene i
gÄrdsmiljÞet. I tillegg var 7% av melkefiltrene vi testet positive for STEC, 13%
positive for L. monocytogenes og 4% positive for Campylobacter spp.. Fire av
STEC isolatene bar genet for Intimin, eae, som er ansett som en viktig
virulensfaktor som Ăžker sjansen for alvorlig sykdom. Ett av de eae-positive
isolatene ble funnet i et melkefilter, noe som indikerer at norsk rÄ melk kan
inneholde patogene STEC. For Ă„ videre vurdere risikoen for Ă„ bli syk av STEC
fra rÄ melk undersÞkte vi hvordan de fire eae-positive isolatene vokste i rÄ
melk lagret ved forskjellige temperaturer. For alle isolatene Ăžkte antall
bakterier etter lagring ved 8°C, og for et isolat var veksten signifikant. Etter
lagring ved 6°C ble antallet bakterier redusert de fÞrste 24 timene, deretter
stoppet reduksjonen i antall bakterier. Disse resultatene viser hvor viktig det
er Ä ha stabil lav lagringstemperatur for rÄ melk, helst < 4°C.
L. monocytogenes isolatene som ble samlet inn fra melkegÄrdene viste
genetiske likheter med isolater samlet inn fra urbane og rurale miljĂžer rundt
omkring i Norge. Derimot var kloner som dominerte i landbruksmiljĂžet
forskjellige fra kliniske isolater og isolater fra matproduksjonslokaler. Videre
sÄ man at en klone kan persistere pÄ en gÄrd over tid og at melk kan
kontamineres av L. monocytogenes kloner som er til stede i gÄrdsmiljÞet.
Til tross for smÄ testvolum av tankmelken (25 mL) fant vi bÄde STEC og
Campylobacter spp. i melkeprĂžvene. 3% av tankmelkprĂžvene og
speneprĂžvene var positive for Campylobacter spp. og ett STEC isolat ble
funnet i tankmelk. L. monocytogenes ble ikke funnet direkte i melkeprĂžvene.
Landbruket i Norge er i stadig utvikling der besetningene blir stĂžrre, men
fĂŠrre. Melkebesetningene er midt i en overgang der tradisjonell oppstalling
med melking pÄ bÄs byttes ut med lÞsdriftssystemer og melkeroboter.
Forekomsten av de tre patogenene funnet i denne studien var hĂžyere i
besetningene med lĂžsdrift sammenliknet med besetningene som hadde
melkekyrne oppstallet pÄ bÄs.
Pasteurisering er et viktig forebyggende tiltak for Ă„ beskytte konsumenter fra
mikrobielle patogener, og i de fleste EU-land er kommersielt salg av rÄ melk
juridisk begrenset. Denne studien viser at oppstallingstype kan pÄvirke
nivÄene av patogene bakterier i gÄrdsmiljÞet og i rÄ melk. Inntak av rÄ melk
kan eksponere forbruker for patogene bakterier som kan gi alvorlig sykdom,
spesielt hos barn, eldre og personer med underliggende sykdommer.
Resultatene underbygger viktigheten av Ă„ pasteurisere melk for Ă„ sikre
mattryggheten, og at det er avgjÞrende Ä lagre rÄ melk ved kontinuerlig lave
temperaturer for Ă„ forebygge vekst av zoonotiske patogener
Setting contextual conditions to resolve grand challenges through responsible innovation:A comparative patent analysis in the circular economy
Copyright © 2023 The Authors. This article draws on responsible innovation (RI) undertaken by hybrid organizations, institutional rigidity, and national innovation systems (NISs) to assess and contextualize the innovation performance of for-profit firms seeking to resolve grand challenges (GCs). The extant research on RI lacks the theoretical underpinnings to profile the unique characteristics of RI firms and the contextual conditions behind the resolution of GCs through RI. This study aims to fill this important gap by focusing on a specific type of RI firmâa firm seeking to reduce climate change through implementation of a circular economy model. By studying a multi-country sample of 1153 manufacturing firms, we implemented propensity score matching (PSM) and the Heckman selection model to compare the patent productivity of RI and non-RI firms. Our evidence demonstrates that RI firms display lower likelihood of patenting and lower patent productivity than non-RI firms when they do engage in patenting. Furthermore, we found that a stronger national R&D environment can be conducive to aligning public interests and private incentives by enabling RI firms to enhance their patent productivity. Additionally, RI firms in industries with lower levels of technological complexity capture more value from improvements in R&D environments than RI firms in industries with higher levels of technological complexity. Our argument as a whole contributes to the GC and RI literature streams by considering both the innovation barriers faced by RI-oriented firms and the macro/industry boundary conditions that enable such organizations to overcome them.Governments of Spain and Andalusia (Research Project A-SEJ-196-UGR20); Schoeller Foundation; Taishan Scholar Program of Shandong Province
Transfer learning for operator selection: A reinforcement learning approach
In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the field of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. However, existing research fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time
Strategies for Early Learners
Welcome to learning about how to effectively plan curriculum for young children. This textbook will address: âą Developing curriculum through the planning cycle âą Theories that inform what we know about how children learn and the best ways for teachers to support learning âą The three components of developmentally appropriate practice âą Importance and value of play and intentional teaching âą Different models of curriculum âą Process of lesson planning (documenting planned experiences for children) âą Physical, temporal, and social environments that set the stage for childrenâs learning âą Appropriate guidance techniques to support childrenâs behaviors as the self-regulation abilities mature. âą Planning for preschool-aged children in specific domains including o Physical development o Language and literacy o Math o Science o Creative (the visual and performing arts) o Diversity (social science and history) o Health and safety âą Making childrenâs learning visible through documentation and assessmenthttps://scholar.utc.edu/open-textbooks/1001/thumbnail.jp
Learning disentangled speech representations
A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody.
The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions.
In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks.
This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically
The determinants of value addition: a crtitical analysis of global software engineering industry in Sri Lanka
It was evident through the literature that the perceived value delivery of the global software
engineering industry is low due to various facts. Therefore, this research concerns global
software product companies in Sri Lanka to explore the software engineering methods and
practices in increasing the value addition. The overall aim of the study is to identify the key
determinants for value addition in the global software engineering industry and critically
evaluate the impact of them for the software product companies to help maximise the value
addition to ultimately assure the sustainability of the industry.
An exploratory research approach was used initially since findings would emerge while the
study unfolds. Mixed method was employed as the literature itself was inadequate to
investigate the problem effectively to formulate the research framework. Twenty-three face-to-face online interviews were conducted with the subject matter experts covering all the
disciplines from the targeted organisations which was combined with the literature findings as
well as the outcomes of the market research outcomes conducted by both government and nongovernment institutes. Data from the interviews were analysed using NVivo 12. The findings
of the existing literature were verified through the exploratory study and the outcomes were
used to formulate the questionnaire for the public survey. 371 responses were considered after
cleansing the total responses received for the data analysis through SPSS 21 with alpha level
0.05. Internal consistency test was done before the descriptive analysis. After assuring the
reliability of the dataset, the correlation test, multiple regression test and analysis of variance
(ANOVA) test were carried out to fulfil the requirements of meeting the research objectives.
Five determinants for value addition were identified along with the key themes for each area.
They are staffing, delivery process, use of tools, governance, and technology infrastructure.
The cross-functional and self-organised teams built around the value streams, employing a
properly interconnected software delivery process with the right governance in the delivery
pipelines, selection of tools and providing the right infrastructure increases the value delivery.
Moreover, the constraints for value addition are poor interconnection in the internal processes,
rigid functional hierarchies, inaccurate selections and uses of tools, inflexible team
arrangements and inadequate focus for the technology infrastructure. The findings add to the
existing body of knowledge on increasing the value addition by employing effective processes,
practices and tools and the impacts of inaccurate applications the same in the global software
engineering industry
Brain simulation as a cloud service: The Virtual Brain on EBRAINS
open access articleThe Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic con- version of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collabo- ration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation
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