213 research outputs found
The universal robot
Our artifacts are getting smarter, and a loose parallel with the evolution of animal intelligence suggests one future course for them. Computerless industrial machinery exhibits the behavioral flexibility of single-celled organisms. Today's best computer-controlled robots are like the simpler invertebrates. A thousand-fold increase in computer power in the next decade should make possible machines with reptile-like sensory and motor competence. Properly configured, such robots could do in the physical world what personal computers now do in the world of data - act on our behalf as literal-minded slaves. Growing computer power over the next half-century will allow this reptile stage to be surpassed, in stages producing robots that learn like mammals, model their world like primates, and eventually reason like humans. Depending on your point of view, humanity will then have produced a worthy successor, or transcended some of its inherited limitations and so transformed itself into something quite new
Computational Logic for Biomedicine and Neurosciences
We advocate here the use of computational logic for systems biology, as a
\emph{unified and safe} framework well suited for both modeling the dynamic
behaviour of biological systems, expressing properties of them, and verifying
these properties. The potential candidate logics should have a traditional
proof theoretic pedigree (including either induction, or a sequent calculus
presentation enjoying cut-elimination and focusing), and should come with
certified proof tools. Beyond providing a reliable framework, this allows the
correct encodings of our biological systems. % For systems biology in general
and biomedicine in particular, we have so far, for the modeling part, three
candidate logics: all based on linear logic. The studied properties and their
proofs are formalized in a very expressive (non linear) inductive logic: the
Calculus of Inductive Constructions (CIC). The examples we have considered so
far are relatively simple ones; however, all coming with formal semi-automatic
proofs in the Coq system, which implements CIC. In neuroscience, we are
directly using CIC and Coq, to model neurons and some simple neuronal circuits
and prove some of their dynamic properties. % In biomedicine, the study of
multi omic pathway interactions, together with clinical and electronic health
record data should help in drug discovery and disease diagnosis. Future work
includes using more automatic provers. This should enable us to specify and
study more realistic examples, and in the long term to provide a system for
disease diagnosis and therapy prognosis
Machine learning in analytical chemistry: applying innovative data analysis methods using chromatographic techniques
Dissertação de mestrado em Chemical Analysis and Characterisation Techniques Chemical SciencesScientific and technological advances allowed the extraction of a growing quantity of knowledge
from the analysed samples by means of analytical techniques. Over the last few years, the dimensionality
of data that the most recent analytical techniques produce is so high, that its analysis is now called
megavariate analysis. Recently, the usage of machine learning tools in chemical data analysis have
allowed the extraction of relevant information from samples at a level which, until then, would just not
be possible.
The objective of this work consists in classifying manufacturing conditions of printed circuit
boards based on data acquired by SLE-HPLC-ESI-MS. As such, this dissertation is divided in two parts:
the first synthesizes the work taken to assure the analytical method produces data with adequate quality
in such a way the second part shows the development of predictive model using the previous acquired
data. At the same time, a data augmentation technique which, to the best of our knowledge, constitutes
the first time a data augmentation technique for classification problems using chromatographic data, has
been developed.
Best models’ results show precisions above 94% for all manufacturing conditions prediction.
Moreover, the developed data augmentation technique reports superior performances when compared
to three other data augmentation techniques.
In summary, the results show that, besides distinguishing classes with different chemical
compositions, it is possible to obtain information about which are the chemical compounds that
differentiate the classes. This information might be of significant importance for areas such as quality
control, food chemistry, botany and pharmaceutical industry.O constante avanço cientÃfico-tecnológico permitiu que, ao longo do último século, as técnicas
de análise quÃmica extraÃssem cada vez mais conhecimento das amostras analisadas. Nos últimos anos,
a quantidade de dados que as mais recentes técnicas analÃticas produzem possui uma dimensão tão
elevada que a sua análise é denominada de análise megavariacional. Recentemente, a aplicação de
ferramentas de machine learning em análises de dados quÃmicos tem permitido extrair informação
relevante das amostras analisadas que até recentemente não era possÃvel.
Com isto em mente, o objetivo deste trabalho consiste em classificar condições de manufatura
de placas de circuito impresso tendo por base dados provenientes de análise por cromatografia lÃquida
acoplada a espetrometria de massa com extração sólido-lÃquido. Desta forma, esta dissertação está
dividida em duas partes: a primeira sintetiza o trabalho efetuado para garantir que o método de análise
produz dados com qualidade adequada para que na segunda parte esses dados sejam usados para
construir modelos preditivos. Paralelamente, foi desenvolvida uma técnica de aumento de dados que,
até onde o nosso conhecimento vai, constitui a primeira técnica de aumento de dados desenvolvida para
problemas de classificação com dados provenientes de análises cromatográficas.
Os resultados dos melhores modelos mostram precisões superiores a 94% para a previsão de
todas as condições de manufatura. Adicionalmente, a técnica de aumento de dados desenvolvida mostra
desempenhos superiores comparativamente a outras técnicas de aumento de dados.
Em sÃntese, os resultados obtidos indicam que, para além de distinguir classes com
composições quÃmicas diferentes, é possÃvel adquirir informação sobre quais são os compostos quÃmicos
que distinguem as classes em estudo. Esta informação pode vir a ter uma importância significativa em
áreas como controlo de qualidade, quÃmica alimentar e indústria fito-farmacêutica.Fundação para a Ciência e Tecnologia através do projeto POCI-01-0145-FEDER-029147 - PTDC/FIS-PAR/29147/2017 financiado por: OE/FCT, Lisboa 2020, Compete 2020 POCI, Portugal 2020 FEDE
Symbolic Search in Planning and General Game Playing
Search is an important topic in many areas of AI. Search problems often result in an immense number of states. This work addresses this by using a special datastructure, BDDs, which can represent large sets of states efficiently, often saving space compared to explicit representations. The first part is concerned with an analysis of the complexity of BDDs for some search problems, resulting in lower or upper bounds on BDD sizes for these. The second part is concerned with action planning, an area where the programmer does not know in advance what the search problem will look like. This part presents symbolic algorithms for finding optimal solutions for two different settings, classical and net-benefit planning, as well as several improvements to these algorithms. The resulting planner was able to win the International Planning Competition IPC 2008. The third part is concerned with general game playing, which is similar to planning in that the programmer does not know in advance what game will be played. This work proposes algorithms for instantiating the input and solving games symbolically. For playing, a hybrid player based on UCT and the solver is presented
Montana Kaimin, February 4, 1982
Student newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/8411/thumbnail.jp
Reliability and Validity of International Large-Scale Assessment
This open access book describes and reviews the development of the quality control mechanisms and methodologies associated with IEA’s extensive program of educational research. A group of renowned international researchers, directly involved in the design and execution of IEA’s international large-scale assessments (ILSAs), describe the operational and quality control procedures that are employed to address the challenges associated with providing high-quality, comparable data. Throughout the now considerable history of IEA’s international large-scale assessments, establishing the quality of the data has been paramount. Research in the complex multinational context in which IEA studies operate imposes significant burdens and challenges in terms of the methodologies and technologies that have been developed to achieve the stated study goals. The demands of the twin imperatives of validity and reliability must be satisfied in the context of multiple and diverse cultures, languages, orthographies, educational structures, educational histories, and traditions. Readers will learn about IEA’s approach to such challenges, and the methods used to ensure that the quality of the data provided to policymakers and researchers can be trusted. An often neglected area of investigation, namely the consequential validity of ILSAs, is also explored, examining issues related to reporting, dissemination, and impact, including discussion of the limits of interpretation. The final chapters address the question of the influence of ILSAs on policy and reform in education, including a case study from Singapore, a country known for its outstanding levels of achievement, but which nevertheless seeks the means of continual improvement, illustrating best practice use of ILSA data
The Independent, Vol. 10, No. 17, February 12, 1970
The Independent was a student run newspaper created in 1960 at Newark State College, now Kean University. The proceeding title was The Reflector. The editors of this issue were Kevin B. Alton and Susan M. Stein.https://digitalcommons.kean.edu/independent_1970-1974/1002/thumbnail.jp
- …