The document proposes and evaluates strategies for selecting which component of a disjunctive concept definition to generalize in incremental learning. It introduces 5 strategies: older elements first (O), newer elements first (N), longer elements first (L), shorter elements first (S), and more similar elements first (~). The strategies are evaluated on a dataset of scientific paper layouts using measures like accuracy, number of components, runtime, and exceptions. The evaluation finds that strategies based on similarity or length of components like N and ~ generally perform best in classification, while L and ~ perform best in understanding the significant components of papers.
Research Paradigm/framework
Research Paradigm/ Framework
Described as the abstract, logical structure or meaning that guide the development of the study.
All frameworks are based on the identification of key concepts and the relationships among those concepts.
Concepts
Abstractly describes and names an object or phenomenon, thus providing it with a separate identity and meaning.
An intellectual representation of some aspects of reality that is derived from observations made from phenomena.
Conceptual Framework
Conceptual Framework
This consists of concepts that are placed within a logical and sequential design.
Represents less formal structure and used for studies in which existing theory is inapplicable or insufficient.
Based on specific concepts and propositions, derived from empirical observation and intuition.
May deduce theories from a conceptual framework.
Purposes of Conceptual Framework
To clarify concepts and propose relationships among the concepts in a study.
To provide a context for interpreting the study findings.
To explain observations
To encourage theory development that is useful to practice.
Theoretical Framework
Theoretical Framework
The theory provides a point of focus for attacking the unknown in a specific area.
If a relationship is found between two or more variables a theory should be formulated to explain why the relationship exists.
Theories are purposely created and formulated, never discovered; they can be tested but never proven.
Purposes of Theoretical Framework
To test theories
To make research findings meaningful and generalizable
To establish orderly connections between observations and facts.
To predict and control situations
To stimulate research
Research Paradigm/framework
Research Paradigm/ Framework
Described as the abstract, logical structure or meaning that guide the development of the study.
All frameworks are based on the identification of key concepts and the relationships among those concepts.
Concepts
Abstractly describes and names an object or phenomenon, thus providing it with a separate identity and meaning.
An intellectual representation of some aspects of reality that is derived from observations made from phenomena.
Conceptual Framework
Conceptual Framework
This consists of concepts that are placed within a logical and sequential design.
Represents less formal structure and used for studies in which existing theory is inapplicable or insufficient.
Based on specific concepts and propositions, derived from empirical observation and intuition.
May deduce theories from a conceptual framework.
Purposes of Conceptual Framework
To clarify concepts and propose relationships among the concepts in a study.
To provide a context for interpreting the study findings.
To explain observations
To encourage theory development that is useful to practice.
Theoretical Framework
Theoretical Framework
The theory provides a point of focus for attacking the unknown in a specific area.
If a relationship is found between two or more variables a theory should be formulated to explain why the relationship exists.
Theories are purposely created and formulated, never discovered; they can be tested but never proven.
Purposes of Theoretical Framework
To test theories
To make research findings meaningful and generalizable
To establish orderly connections between observations and facts.
To predict and control situations
To stimulate research
Polishing the examination – key points, Liz Norman, ANZCVS Exam Writing Works...Liz Norman
Polishing the examination – key points
A presentation given at the Australian and New Zealand College of Veterinary Scientists Examination Writing Workshop, November 2013
Liz Norman, Massey University, New Zealand
Development of conceptual framework in Nursing ResearchDhara Vyas
Conceptual Framework in Research
Conceptual framework: it is constructed by researcher’s own experience, previous research findings or conceps of theories and model
Conceptual Framework in Nursing Research
Developing Conceptual Framework
Types
Purposes
Health Belief Model
Health Promotional Model
Advantage of Conceptual Framework
Limitation of Conceptual Framework
Problems with the Framework
Grading criteria and marking schemes, Liz Norman, SAVS-CSU Learning and Teach...Liz Norman
Grading criteria and marking schemes
Presentation for School of Animal & Veterinary Sciences - Charles Sturt University, Learning and Teaching Extravaganza 2014, National Wine and Grape Industry Centre, Wagga Wagga, Australia, 6th February 2014
Liz Norman, Massey University, New Zealand
Lecture 2: Latent/Manifest/Observed Variables using in SEM Analysis (www.stat...Stats Statswork
Statswork (Statswork.com), Lecture:2 – Basic concepts of Latent, Manifest and Observed Variables using in SEM analysis.
SEM consists of two parts:
1. Measurement model(s) for each latent variable
2.2. Path analysis between the latent and observed variables.
Latent Variables:
These phenomena are termed as latent variables, or common factors.
Examples of latent variables:
·Psychology: Self concept and Motivation.
·Education:Verbal ability& Teacher Expectancy.
Manifest and Observed Variables:
The unobserved variable is linked to one that is observable, thereby measurement is possible.
Examples:
·Self-report to an attitudinal scale.
·Scores on achievement test.
Watch more.
Contact Us:
UK NO:+44-1143520021
India No: +91-8754446690
US NO:+1-972-502-9262
Email: info@statswork.com
Website: http://www.statswork.com/
Landline: +91-44-42124284
Polishing the examination – key points, Liz Norman, ANZCVS Exam Writing Works...Liz Norman
Polishing the examination – key points
A presentation given at the Australian and New Zealand College of Veterinary Scientists Examination Writing Workshop, November 2013
Liz Norman, Massey University, New Zealand
Development of conceptual framework in Nursing ResearchDhara Vyas
Conceptual Framework in Research
Conceptual framework: it is constructed by researcher’s own experience, previous research findings or conceps of theories and model
Conceptual Framework in Nursing Research
Developing Conceptual Framework
Types
Purposes
Health Belief Model
Health Promotional Model
Advantage of Conceptual Framework
Limitation of Conceptual Framework
Problems with the Framework
Grading criteria and marking schemes, Liz Norman, SAVS-CSU Learning and Teach...Liz Norman
Grading criteria and marking schemes
Presentation for School of Animal & Veterinary Sciences - Charles Sturt University, Learning and Teaching Extravaganza 2014, National Wine and Grape Industry Centre, Wagga Wagga, Australia, 6th February 2014
Liz Norman, Massey University, New Zealand
Lecture 2: Latent/Manifest/Observed Variables using in SEM Analysis (www.stat...Stats Statswork
Statswork (Statswork.com), Lecture:2 – Basic concepts of Latent, Manifest and Observed Variables using in SEM analysis.
SEM consists of two parts:
1. Measurement model(s) for each latent variable
2.2. Path analysis between the latent and observed variables.
Latent Variables:
These phenomena are termed as latent variables, or common factors.
Examples of latent variables:
·Psychology: Self concept and Motivation.
·Education:Verbal ability& Teacher Expectancy.
Manifest and Observed Variables:
The unobserved variable is linked to one that is observable, thereby measurement is possible.
Examples:
·Self-report to an attitudinal scale.
·Scores on achievement test.
Watch more.
Contact Us:
UK NO:+44-1143520021
India No: +91-8754446690
US NO:+1-972-502-9262
Email: info@statswork.com
Website: http://www.statswork.com/
Landline: +91-44-42124284
Machine learning workshop, session 3.
- Data sets
- Machine Learning Algorithms
- Algorithms by Learning Style
- Algorithms by Similarity
- People to follow
Tutorial for beginning graduate students. Hypothesis or research question serves as the compass that gives direction to the project. Posing it poorly guarantees poor results, so here is some guidance.
A constraint is defined as a logical relation among several unknown quantities or variables, each taking a value in a given
domain. Constraint Programming (CP) is an emergent field in operations research. Constraint programming is based on feasibility
which means finding a feasible solution rather than optimization which means finding an optimal solution and focuses on the
constraints and variables domain rather than the objective functions. While defining a set of constraints, this may seem a simple way to
model a real-world problem but finding a good model that works well with a chosen solver is not that easy. A model could be very
hard to solve if it is poorly chosen
SEARN Algorithm is a search-based algorithm for structured prediction.
Most of the content is taken from http://users.umiacs.umd.edu/~hal/docs/daume06thesis.pdf. I just read the thesis and presented what's in there. Thus the credits of the content should go to the author of the thesis.
Lecture slides in DASI spring 2018, National Cheng Kung University, Taiwan. The content is about deep reinforcement learning: policy gradient including variance reduction and importance sampling
"Inductive & Deductive method" is one of the child centered approach. this PPT is useful for B.Ed, M.Ed and Dl.Ed students & also useful for teacher educators as a reference.
Aggregates in Recursion: Issues and SolutionsRuleML
Aggregates are commonplace in database query languages. It is natural to include them also into logic programming. However, doing so raises a number of issues, in particular when aggregates are used in conjunction with recursive definitions. This talk will shed some light on the underlying issues and some of the solutions proposed in the literature so far.
A software agent controlling 2 robot arms in co-operating concurrent tasksRuleML
TeleoR is a major extension of Nilsson’s Teleo-Reactive (TR)
rule based robotic agent programming language. Programs comprise sequences of guarded action rules grouped into parameterised procedures.
The guards are deductive queries to a set of rapidly changing percept and other dynamic facts in the agent’s Belief Store. The actions are either tuples of primitive actions for external robotic resources, to be executed in parallel, or a single call to a TeleoR procedure, which can be a recursive call. The guards form a sub-goal tree routed at the guard of the first rule. When partially instantiated by the arguments of some call, this guard is the goal of the call.
TeleoR extends TR in being typed and higher order, with extra forms of rules that allow finer control over sub-goal achieving task behaviour.
Its Belief Store inference language is a higher order logic+function rule language, QuLog. QuLog also has action rules and primitive actions for updating the Belief Store and sending messages. The action of a TeleoR rule may be a combination of the action of a TR rule and a sequence of
QuLog actions. TeleoR’s most important extension of TR is the concept of task atomic procedures, some arguments of which belong to a special but application specific resource type. This allows the high level programming of multitasking agents using multiple robotic resources. When two or more tasks
need to use overlapping resources their use is alternated between task atomic calls in each task, in such a way that there is no interference, deadlock or task starvation.
This multi-task programming is illustrated by giving the essentials of a program for an agent controlling two robotic arms in multiple block tower assembly tasks. It has been used to control both a Python interactive graphical simulation and a Baxter robot building real block towers, in each case with help or hindrance from a human. The arms move in parallel whenever it can be done without risk of clashing.
Port Clearance Rules in PSOA RuleML: From Controlled-English Regulation to Ob...RuleML
The Decision Management (DM) Community Challenge of
March 2016 consisted of creating decision models from ten English Port Clearance Rules inspired by the International Ship and Port Facility Security
Code. Based on an analysis of the moderately controlled English
rules and current online solutions, we formalized the rules in PositionalSlotted,
Object-Applicative (PSOA) RuleML. This resulted in: (1) a
reordering, subgrouping, and explanation of the original rules on the
specialized decision-model expressiveness level of (deontically contextualized)
near-Datalog, non-recursive, near-deterministic, ground-queried,
and non-subpredicating rules; (2) an object-relational PSOA RuleML
rulebase which was complemented by facts to form a knowledge base queried in PSOATransRun for decision-making. Thus, the DM and logical formalizations get connected, which leads to generalized decision models with Hornlog, recursive, non-deterministic, non-ground-queried, and subpredicating rules.
Big data, with its four main characteristics (Volume, Velocity,
Variety, and Veracity) pose challenges to the gathering, management, analytics, and visualization of events. These very same four characteristics, however, also hold a great promise in unlocking the story behind data. In this talk, we focus on the observation that event creation is guided by processes. For example, GPS information, emitted by buses in an urban setting follow the bus scheduled route. Also, RTLS information about the whereabouts of patients and nurses in a hospital is guided by the predefined schedule of work. With this observation at hand, we thoroughly seek a method for mining, not the data, but rather the rules that guide data creation and show how, by knowing such rules, big data tasks become more efficient and more effective. In particular, we demonstrate how, by knowing the rules that govern event creation, we can detect complex events sooner and make use of historical data to predict future behaviors.
RuleML 2015: Ontology Reasoning using Rules in an eHealth ContextRuleML
Traditionally, nurse call systems in hospitals are rather simple:
patients have a button next to their bed to call a nurse. Which specific
nurse is called cannot be controlled, as there is no extra information
available. This is different for solutions based on semantic knowledge:
if the state of care givers (busy or free), their current position, and for
example their skills are known, a system can always choose the best
suitable nurse for a call. In this paper we describe such a semantic nurse
call system implemented using the EYE reasoner and Notation3 rules.
The system is able to perform OWL-RL reasoning. Additionally, we use
rules to implement complex decision trees. We compare our solution to
an implementation using OWL-DL, the Pellet reasoner, and SPARQL
queries. We show that our purely rule-based approach gives promising
results. Further improvements will lead to a mature product which will
significantly change the organization of modern hospitals.
RuleML 2015: Semantics of Notation3 Logic: A Solution for Implicit Quantifica...RuleML
Since the development of Notation3 Logic, several years have
passed in which the theory has been refined and used in practice by different reasoning engines such as cwm, FuXi or EYE. Nevertheless, a clear model-theoretic definition of its semantics is still missing. This leaves room for individual interpretations and renders it difficult to make clear
statements about its relation to other logics such as DL or FOL or even about such basic concepts as correctness. In this paper we address one of the main open challenges: the formalization of implicit quantification.
We point out how the interpretation of implicit quantifiers differs in two of the above mentioned reasoning engines and how the specification, proposed in the W3C team submission, could be formalized. Our formalization is then put into context by integrating it into a model-theoretic definition of the whole language. We finish our contribution by arguing why universal quantification should be handled differently than currently
prescribed.
Challenge@RuleML2015 Developing Situation-Aware Applications for Disaster Man...RuleML
In order to enhance interoperability and productivity in the develop-ment of situation-aware applications for disaster management, proper mecha-nisms and guidelines are required. They must address the lack of semantics in modelling emergency situations. In addition, the ever-changing and unpredicta-ble nature of disaster scenarios present challenges for information processing and collaboration. This paper proposes a framework that combines the follow-ing elements: (i) a foundational ontology for temporal conceptualization; (ii) well-founded specifications of structural and behavioral models; (iii) a CEP en-gine based on a distributed rule-based platform for situation management; (iv) a model-driven approach. We illustrate the operation of the framework with a scenario for monitoring tuberculosis epidemy.
RuleML 2015 Constraint Handling Rules - What Else?RuleML
Constraint Handling Rules (CHR) is both a versatile theoretical formalism based on logic and an efficient practical high-level programming language based on rules and constraints.
Procedural knowledge is often expressed by if-then rules, events and actions are related by reaction rules, change is expressed by update rules. Algorithms are often specified using inference rules, rewrite rules, transition rules, sequents, proof rules, or logical axioms. All these kinds of rules can be directly written in CHR. The clean logical semantics of CHR facilitates non-trivial program analysis and transformation. About a dozen implementations of CHR exist in Prolog, Haskell, Java, Javascript and C. Some of them allow to apply millions of rules per second. CHR is also available as WebCHR for online experimentation with more than 40 example programs. More than 200 academic and industrial projects worldwide use CHR, and about 2000 research papers reference it.
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box RuleML
The traditional semantics for First Order Logic (sometimes called Tarskian semantics) is based on the notion of interpretations of constants. Herbrand semantics is an alternative semantics based directly on truth assignments for ground sentences rather than interpretations of constants. Herbrand semantics is simpler and more intuitive than Tarskian semantics; and, consequently, it is easier to teach and learn. Moreover, it is more expressive. For example, while it is not possible to finitely axiomatize integer arithmetic with Tarskian semantics, this can be done easily with Herbrand Semantics. The downside is a loss of some common logical properties, such as compactness and completeness. However, there is no loss of inferential power. Anything that can be proved according to Tarskian semantics can also be proved according to Herbrand semantics. In this presentation, we define Herbrand semantics; we look at the implications for research on logic and rules systems and automated reasoning; and and we assess the potential for popularizing logic.
Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the St...RuleML
Data distribution
•Public and private
•Data complexity
•Rich in attributes and location based
•Time dimension
•Example of data model from the Norwegian Mapping Authority
Datalog+-Track Introduction & Reasoning on UML Class Diagrams via Datalog+-RuleML
UML class diagrams (UCDs) are a widely adopted formalism
for modeling the intensional structure of a software system. Although
UCDs are typically guiding the implementation of a system, it is common
in practice that developers need to recover the class diagram from an
implemented system. This process is known as reverse engineering. A
fundamental property of reverse engineered (or simply re-engineered)
UCDs is consistency, showing that the system is realizable in practice.
In this work, we investigate the consistency of re-engineered UCDs, and
we show is pspace-complete. The upper bound is obtained by exploiting
algorithmic techniques developed for conjunctive query answering under
guarded Datalog+/-, that is, a key member of the Datalog+/- family
of KR languages, while the lower bound is obtained by simulating the
behavior of a polynomial space Turing machine.
RuleML2015: Binary Frontier-guarded ASP with Function SymbolsRuleML
It has been acknowledged that emerging Web applications
require features that are not available in standard rule languages like
Datalog or Answer Set Programming (ASP), e.g., they are not powerful
enough to deal with anonymous values (objects that are not explicitly
mentioned in the data but whose existence is implied by the background
knowledge). In this paper, we introduce a new rule language based on
ASP extended with function symbols, which can be used to reason about
anonymous values. In particular, we define binary frontier-guarded programs
(BFG programs) that allow for disjunction, function symbols, and
negation under the stable model semantics. In order to ensure decidability,
BFG programs are syntactically restricted by allowing at most
binary predicates and by requiring rules to be frontier-guarded. BFG programs
are expressive enough to simulate ontologies expressed in popular
Description Logics (DLs), capture their recent non-monotonic extensions,
and can simulate conjunctive query answering over many standard DLs.
We provide an elegant automata-based algorithm to reason in BFG programs,
which yields a 3ExpTime upper bound for reasoning tasks like
deciding consistency or cautious entailment. Due to existing results, these
problems are known to be 2ExpTime-hard.
RuleML2015: API4KP Metamodel: A Meta-API for Heterogeneous Knowledge PlatformsRuleML
API4KP (API for Knowledge Platforms) is a standard
development effort that targets the basic administration services as
well as the retrieval, modification and processing of expressions in
machine-readable languages, including but not limited to knowledge
representation and reasoning (KRR) languages, within heterogeneous
(multi-language, multi-nature) knowledge platforms. KRR languages of
concern in this paper include but are not limited to RDF(S), OWL,
RuleML and Common Logic, and the knowledge platforms may support
one or several of these. Additional languages are integrated using mappings
into KRR languages. A general notion of structure for knowledge
sources is developed using monads. The presented API4KP metamodel,
in the form of an OWL ontology, provides the foundation of an abstract
syntax for communications about knowledge sources and environments,
including a classification of knowledge source by mutability, structure,
and an abstraction hierarchy as well as the use of performatives (inform,
query, ...), languages, logics, dialects, formats and lineage. Finally, the
metamodel provides a classification of operations on knowledge sources
and environments which may be used for requests (message-passing).
RuleML2015: Rule-Based Exploration of Structured Data in the BrowserRuleML
We present Dexter, a browser-based, domain-independent
structured-data explorer for users. Dexter enables users to explore data
from multiple local and Web-accessible heterogeneous data sources such
as files, Web pages, APIs and databases in the form of tables. Dexter’s
users can also compute tables from existing ones as well as validate
the tables (base or computed) through declarative rules. Dexter enables
users to perform ad hoc queries over their tables with higher expressivity
than that is supported by the underlying data sources. Dexter evaluates
a user’s query on the client side while evaluating sub-queries on remote
sources whenever possible. Dexter also allows users to visualize and share
tables, and export (e.g., in JSON, plain XML, and RuleML) tables along
with their computation rules. Dexter has been tested for a variety of data
sets from domains such as government and apparel manufacturing. Dexter
is available online at http://dexter.stanford.edu.
RuleML2015: Ontology-Based Multidimensional Contexts with Applications to Qua...RuleML
Data quality assessment and data cleaning are context
dependent activities. Starting from this observation, in previous work
a context model for the assessment of the quality of a database was
proposed. A context takes the form of a possibly virtual database or
a data integration system into which the database under assessment is
mapped, for additional analysis, processing, and quality data extraction.
In this work, we extend contexts with dimensions, and by doing so, multidimensional
data quality assessment becomes possible. At the core of
multidimensional contexts we find ontologies written as Datalog
±
programs
with provably good properties in terms of query answering. We
use this language to represent dimension hierarchies, dimensional constraints,
dimensional rules, and specifying quality data. Query answering
relies on and triggers dimensional navigation, and becomes an important
tool for the extraction of quality data.
RuleML2015: Compact representation of conditional probability for rule-based...RuleML
Context-aware systems gained huge popularity in recent
years due to rapid evolution of personal mobile devices. Equipped with
variety of sensors, such devices are sources of a lot of valuable information
that allows the system to act in an intelligent way. However, the
certainty and presence of this information may depend on many factors
like measurement accuracy or sensor availability. Such a dynamic
nature of information may cause the system not to work properly or
not to work at all. To allow for robustness of the context-aware system
an uncertainty handling mechanism should be provided with it. Several
approaches were developed to solve uncertainty in context knowledge
bases, including probabilistic reasoning, fuzzy logic, or certainty
factors. In this paper, we present a representation method that combines
strengths of rules based on the attributive logic and Bayesian networks.
Such a combination allows efficiently encode conditional probability distribution
of random variables into a reasoning structure called XTT2.
This provides a method for building hybrid context-aware systems that
allows for robust inference in uncertain knowledge bases.
RuleML2015: Learning Characteristic Rules in Geographic Information SystemsRuleML
We provide a general framework for learning characterization
rules of a set of objects in Geographic Information Systems (GIS) relying
on the definition of distance quantified paths. Such expressions specify
how to navigate between the different layers of the GIS starting from
the target set of objects to characterize. We have defined a generality
relation between quantified paths and proved that it is monotonous with
respect to the notion of coverage, thus allowing to develop an interactive
and effective algorithm to explore the search space of possible rules. We
describe GISMiner, an interactive system that we have developed based
on our framework. Finally, we present our experimental results from a
real GIS about mineral exploration.
RuleML2015: Using Substitutive Itemset Mining Framework for Finding Synonymou...RuleML
Over the last two decades frequent itemset and association
rule mining has attracted huge attention from the scientific community
which resulted in numerous publications, models, algorithms, and optimizations
of basic frameworks. In this paper we introduce an extension
of the frequent itemset framework, called substitutive itemsets. Substitutive
itemsets allow to discover equivalences between items, i.e., they
represent pairs of items that can be used interchangeably in many contexts.
In the paper we present basic notions pertaining to substitutive
itemsets, describe the implementation of the proposed method available
as a RapidMiner plugin, and illustrate the use of the framework for mining
substitutive object properties in the Linked Data.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Rule Generalization Strategies in Incremental Learning of Disjunctive Concepts
1. Rule Generalization Strategies
in Incremental Learning
of Disjunctive Concepts
Stefano Ferilli, Andrea Pazienza, Floriana Esposito
stefano.ferilli@uniba.it
Dipartimento di Informatica
Centro Interdipartimentale per la Logica e sue Applicazioni
Università di Bari
9th International Web Rule Symposium (RuleML)
August 2-5, 2015 – Berlin, Germany
3. Introduction
● Symbolic knowledge representations
● Mandatory for applications that
– Reproduce the human inferential behavior
– May be required to explain their decisions in human-
understandable terms
● 2 kinds of concept definitions
– Conjunctive: a single definition accounts for all instances
of the concept
– Disjunctive: several alternate conjunctive definitions
(components)
● Each covers part of the full concept extension
● Psychological studies have established that capturing and
dealing with the latter is much harder for humans
● Pervasive and fundamental in most real-world domains
4. Introduction
● Knowledge acquisition bottleneck
● (Symbolic) Machine Learning (ML) systems
– Supervised setting: concept definitions inferred from
descriptions of valid (positive) or invalid (negative)
instances (examples)
● Batch: whole set of examples available (classical setting in ML)
– Components learned by progressive coverage strategies
– Definitions are immutable
● If additional examples are provided, learning must start
from scratch considering the extended set of examples
● Incremental: new examples may be provided after a tentative
definition is already available
– Components emerge as long as they are found
– Definitions may be changed/refined if wrong
5. Motivations
● Incremental approach
– If the available concept definition cannot properly account
for a new example, it must be refined (revised, changed,
modified) so that the new version properly accounts for
both the old and the new examples
– Progressive covering strategy not applicable
● Issue of disjunctive definitions becomes particularly relevant
– When many components can be refined, there is no unique
way for determining which one is most profitably refined
– Refining different components results in different updated
definitions, that become implicit constraints on how the
definition itself may evolve when additional examples will
become available in the future
● The learned model depends
– on the order in which the examples are provided
– on the choice of the component to be refined at each step
6. Motivations
● Abstract Diagnosis: Identification of the part of the
theory that misclassifies the example
– Tricky in the case of disjunctive concepts
● Several alternate conjunctive definitions are available
– If a positive example is not covered, no component of the
current definition accounts for it (omission error)
● All candidate to generalization
– Generalizing all components, each component would
account alone for all positive examples
● Contradiction: the concept is conjunctive
● Over-generalization: the theory would be more prone to
covering forthcoming negative examples
– Problem not present in batch learning
7. Motivations
● Problem
– A single component of a disjunctive concept is to be
generalized
● Guided solutions may improve effectiveness and efficiency of the
overall outcome compared to a random strategy
● Objective
– Propose and evaluate different strategies for determining
the order in which the components should be considered
for generalization
● If the generalization of a component fails, generalization of the
next component in the ordering is attempted
8. Motivations
● Questions:
– what sensible strategies can be defined?
– what are their expected pros and cons?
– what is their effect on the quality of the theory?
– what about their consequences on the effectiveness and
efficiency of the learning process?
● Starting point
– InTheLEx (Incremental Theory Learner from Examples)
● fully incremental
● can learn disjunctive concept definitions
● refinement strategy can be tuned to suitably adapt its behavior
9. InTheLEx
● Learns hierarchical theories from positive and
negative examples
● Fully incremental
– May start from an empty theory and from the first
available example
● Necessary in most real-world application domains
●
DatalogOI
– Concept definitions and examples expressed as rules
● Example: example :- observation
● Rule: concept :- conjunctive definition
– Disjunctive concepts: several rules for the same concept
11. InTheLEx: Theory Revision
● Logic theory revision process
– Given a new example
● No effect on the theory if
– negative and not covered
● not predicted by the theory to belong to the concept
– positive and covered
● predicted by the theory to belong to the concept
● In all the other cases, the theory needs to be revised
– Positive example not covered generalization of the theory
– Negative example covered specialization of the theory
– Refinements (generalizations or specializations) must
preserve correctness with respect to the entire set of
currently available examples
● If no candidate refinement fulfills this requirement, the specific
problematic example is stored as an exception
12. InTheLEx: Generalization
– Procedure Generalize
(E: positive example, T: theory, M: negative examples);
● L := list of the rules in the definition of E's concept
while not generalized and L do
– Select from L a rule C for generalization
– L' := generalize(C,E) (* list of generalizations *)
– while not generalized and L' do
● Select next best generalization C' from L'
●
if (T {C} {C'} is consistent wrt M then
● Implement C' in T
– Remove C from L
● if not generalized then
– C' := E with constants turned into variables
– if (T {C} {C'} is consistent wrt M then
● Implement C' in T
– else
● Implement E in T as an exception
13. InTheLEx: Generalization
– Comments
● Due to theoretical and implementation details, the generalization
operator used might return several incomparable generalizations
● Implementation of the theoretical generalization operator would
be computationally infeasible, even for relatively small rules
– Similarity-based approximation
● Experiments have shown that it comes very close, and
often catches, least general generalizations
● First example of a new concept
– First rule rule added for that concept
● Initial tentative conjunctive definition of the concept
● Conjunctive definition turns out to be insufficient
– Second rule added for that concept
● The concept becomes disjunctive
● Subsequent addition of rules for that concept
– Extend the ‘disjunctiveness’ of the concept
14. Clause Selection Strategy
● 5 strategies for determining the order in which the
components of a disjunctive concept definition are
to be considered for generalization
– Each component in the ordering considered only after
generalization attempts have failed on all previous
components
● Initial components have more chances to be generalized
– No direct connection between age and length of a rule
● Older rules might have had more chances of refinement
● Whether this means that they are also shorter (i.e., more
general) mainly depends on
– Ranking strategy
– Specific examples that are encountered and on their order
● Not controllable in a real-world setting
15. Clause Selection Strategy
● O: Older elements first
– Same order as they were added to the theory
● The most straightforward. A sort of baseline
● Static: position of each component in the processing order fixed
when component is added
– Generalizations are monotonic (progressively remove
constraints)
● Strategy expected to yield very refined (short, more
human-readable and understandable) initial components
and very raw (long) final ones
– After several refinements, it is likely that initial components
have reached a nearly final and quite stable form
● All attempts to further refine them are likely to fail
● The computational time spent in these attempts, albeit
presumably not huge, will be wasted
● Runtime expected to grow as long as the life of the
theory proceeds
16. Clause Selection Strategy
● N: Newer elements first
– Reverse order as they were added to the theory
● Also quite straightforward
● Static, but not obvious to foresee what will be the shape and
evolution of the components
– Immediately after the addition of a new component, it will
undergo a generalization attempt at the first non-covered
positive example
● ‘average’ level of generality in the definition expected to
be less than in the previous option
● There should be no completely raw components in
the definition
● Many chances that such an attempt is successful for any
example, but the resulting generalization might leverage
features that might be not very related to the correct
concept definition
17. Clause Selection Strategy
● L: Longer elements first
– Decreasing number of conjuncts
● Specifically considers the level of refinement
– Low variance in degree of refinement among components
expected
● Can be considered as an evolution of N
– Not just the most recently added component is favored for
generalization
● The more conjuncts in a component, the more specialized the
component
– More room for generalization
● Avoid waste of time trying to generalize very refined rules
that would hardly yield consistent generalizations
● On the other hand, generalizing a longer rule is expected
to take more time than generalizing shorter ones
18. Clause Selection Strategy
● S: Shorter elements first
– Increasing number of conjuncts
● Specifically considers the level of refinement
– Opposite behavior than L
● May confirm the possible advantage of spending time in
trying harder but more promising generalizations versus
spending time in trying easier but less promising ones
first
● Can be considered as an evolution of O
– Tries to generalize first more refined components
● Largest variance in degree of refinement (number of
conjuncts in rule premises) among components expected
19. Clause Selection Strategy
● ~: More similar elements first
– Decreasing similarity with the uncovered example
● Only content-based strategy
– Same similarity as in InTheLEx’s generalization operator
●
Disjunctive components different actualizations of a concept
– Small intersection expected between the sets of examples
covered by different components
● Similarity assessment may allow to identify the appropriate
component to be generalized for a given example
– Odd generalizations for mismatched component-example
● Coverage and generalization problems
● Bad theory, inefficient refinements
– One might expect that over-generalization is avoided
– Generalization more easily computed, but overhead to
compute the similarity
● Does the improvement compensate the overhead?
20. Evaluation
● Real-world dataset: Scientific papers
– 353 layout descriptions of first pages
– 4 classes: Elsevier journals, SVLN, JMLR, MLJ
● Classification: learn definitions for these classes
● Understanding: the significant components in the papers
– Title, Author, Abstract, Keywords
● First-Order Logic representation needed to express spatial
relationships among the page components
● Complex dataset
– Some layout styles are quite similar
– 67920 atoms in observations
● avg per observation >192 atoms, some >400 atoms
– Much indeterminacy
● Membership relation of layout components to pages
21. Evaluation: Parameters
● Qualitative
– # comp in the disjunctive concept definition
● Less components yield a more compact theory, which does not
necessarily provide for greater accuracy
– avg length of components
● More conjuncts, more specific -and less refined- concept
– # exc negative exceptions
● More exceptions, worse theory
● Quantitative
– acc (accuracy)
● Prediction capabilities of the theory on test examples
– time needed to carry out the learning task
● Efficiency (computational cost) for the different ranking strategies
22. Evaluation
● Incremental approach justified
– New instances of documents continuously available in
time
● Comparison of all strategies
– Random not tried
● O or N are somehow random
– Just append definitions as long as they are generated,
without any insight
● 10-fold cross-validation procedure
– Classification task used to check in detail the behavior of
the different strategies
– Understanding task used to assess the statistical
significance of the difference in performance between
different strategies
23. Evaluation: Classification
● Useful results only for MLJ and SVLN
– Elsevier and JMLR: always single-rule definitions
● Ranking approach not applicable
– Examples for Elsevier and JMLR still played a role as
negative examples for the other classes
– Some expectations confirmed
● Runtime: N always best (often the first generalization attempt
succeeds); ~ worst (need for computing the similarity of each
component and the example)
● Number of exceptions: ~ always best (improved selection of
components for generalization); S also good
● Aggregated indicator: N always wins
– Sum of ranking positions for the different parameters (the
smaller, the better)
25. Evaluation: Classification
– Rest of behavior somehow mixed
● SVLN: much less variance than in MLJ
– Definition quite clear and examples quite significant?
● MLJ: Impact of the ranking strategies much clearer
– Substantial agreement between quality-related indicators: for
each approach, either all tend to be good (as in ~) or all tend
to be bad (as in S and O)
● Interesting indications from single folds
– 3: peak in runtime and number of exceptions for S and O
● runtime a consequence of the unsuccessful search for
specializations, that in turn may have some connection
with the quality of the theory
● S is a kind of evolution of O
– 8: accuracy increases from 82% to 94% for ~ and L
● Content-based approach improves quality of the theory in
difficult situations
27. Evaluation: Understanding
● Quite different, albeit run on the same data
– When the difference is significant, in general analogous
behavior of L and ~ for accuracy (which is greater),
number of components and number of negative
exceptions. As expected, longer runtime for ~
– Also O and S have in general an analogous behavior, but
do not reach as good results as L and ~
– N not outstanding for performance, but better than O (the
baseline) for all parameters except runtime
– On the classification task, average length of components
using L significantly larger than all the others
● Returns more balanced components
but more accuracy and less negative exceptions
28. Conclusions & Future Work
● Disjunctive concept definitions tricky
– Each component covers a subset of positive examples,
ensuring consistency with all negative examples
● In incremental learning, when a new positive example is not
recognized by the current theory, one component must be
generalized
– Omission error (no specific component responsible)
● The system must decide the order in which the elements
are to be considered for trying a generalization
– 5 strategies proposed
● The outcomes confirm some of the expectations for the various
strategies, but we need
– More extensive experimentation to have confirmations and
additional details
– Identification of further strategies and refinement of the
proposed ones