This document discusses treating the truth predicate (Tr) as a logical connective in truth theories like Friedman-Sheared's theory (FS). It analyzes FS from the perspective of proof theoretic semantics, where Tr's introduction and elimination rules are like those of a connective. However, FS violates the "harmony" requirement for connectives, as it is not conservatively extending and proves the consistency of PA. The document then discusses interpreting paradoxical sentences like McGee's using coinduction and how guarded corecursion relates to the failure of Tr's formal commutability in FS.
A talk I gave at the Yonsei University, Seoul in July 21st, 2015.
The aim was to show my background contribution to the CORCON (Correctness by Construction) research project.
I have to thank Prof. Byunghan Kim and Dr Gyesik Lee for their kind hospitality.
A talk I gave at the Yonsei University, Seoul in July 21st, 2015.
The aim was to show my background contribution to the CORCON (Correctness by Construction) research project.
I have to thank Prof. Byunghan Kim and Dr Gyesik Lee for their kind hospitality.
Sentient Arithmetic and Godel's Incompleteness TheoremsKannan Nambiar
For me, there is only one logic that we rational human beings are able to accept and appreciate, and that is the mathematical logic of ZF theory. But in the last century we found that ZF theory is not in a position to provide all that we want, and went in search of a new mode of thinking and got one which we called meta mathematics. My question is: if we can put the unambiguous logic of ZF theory on paper, why can't we do the same with meta mathematics. This paper is my feeble attempt in that direction.
Content:
1- Mathematical proof (what and why)
2- Logic, basic operators
3- Using simple operators to construct any operator
4- Logical equivalence, DeMorgan’s law
5- Conditional statement (if, if and only if)
6- Arguments
Semantic Web technologies are a set of languages standardized by the World Wide Web Consortium (W3C) and designed to create a web of data that can be processed by machines. One of the core languages of the Semantic Web is Web Ontology Language (OWL), a family of knowledge representation languages for authoring ontologies or knowledge bases. The newest OWL is based on Description Logics (DL), a family of logics that are decidable fragments of first-order logic. leanCoR is a new description logic reasoner designed for experimenting with the new connection method algorithms and optimization techniques for DL. leanCoR is an extension of leanCoP, a compact automated theorem prover for classical first-order logic.
Sentient Arithmetic and Godel's Incompleteness TheoremsKannan Nambiar
For me, there is only one logic that we rational human beings are able to accept and appreciate, and that is the mathematical logic of ZF theory. But in the last century we found that ZF theory is not in a position to provide all that we want, and went in search of a new mode of thinking and got one which we called meta mathematics. My question is: if we can put the unambiguous logic of ZF theory on paper, why can't we do the same with meta mathematics. This paper is my feeble attempt in that direction.
Content:
1- Mathematical proof (what and why)
2- Logic, basic operators
3- Using simple operators to construct any operator
4- Logical equivalence, DeMorgan’s law
5- Conditional statement (if, if and only if)
6- Arguments
Semantic Web technologies are a set of languages standardized by the World Wide Web Consortium (W3C) and designed to create a web of data that can be processed by machines. One of the core languages of the Semantic Web is Web Ontology Language (OWL), a family of knowledge representation languages for authoring ontologies or knowledge bases. The newest OWL is based on Description Logics (DL), a family of logics that are decidable fragments of first-order logic. leanCoR is a new description logic reasoner designed for experimenting with the new connection method algorithms and optimization techniques for DL. leanCoR is an extension of leanCoP, a compact automated theorem prover for classical first-order logic.
This is a slide of My talk at Kyoto Nonclassical Logic Workshop (19, November, 2015). This is based on my paper "A constructive naive set theory and infinity" which was accepted to Notre Dame Journal of Formal Logic.
New Method for Finding an Optimal Solution of Generalized Fuzzy Transportatio...BRNSS Publication Hub
In this paper, a proposed method, namely, zero average method is used for solving fuzzy transportation problems by assuming that a decision-maker is uncertain about the precise values of the transportation costs, demand, and supply of the product. In the proposed method, transportation costs, demand, and supply are represented by generalized trapezoidal fuzzy numbers. To illustrate the proposed method, a numerical example is solved. The proposed method is easy to understand and apply to real-life transportation problems for the decision-makers.
Machine Translation (MT) refers to the use of computers for the task of translating
automatically from one language to another. The differences between languages and
especially the inherent ambiguity of language make MT a very difficult problem. Traditional
approaches to MT have relied on humans supplying linguistic knowledge in the form of rules
to transform text in one language to another. Given the vastness of language, this is a highly
knowledge intensive task. Statistical MT is a radically different approach that automatically
acquires knowledge from large amounts of training data. This knowledge, which is typically
in the form of probabilities of various language features, is used to guide the translation
process. This report provides an overview of MT techniques, and looks in detail at the basic
statistical model.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
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.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Richard's entangled aventures in wonderlandRichard 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.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
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.
What is greenhouse gasses and how many gasses are there to affect the Earth.
Truth as a logical connective
1. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
Truth as a logical connective
Shunsuke Yatabe
Center for Applied Philosophy and Ethics,
Graduate School of Letters,
Kyoto University
November 15, 2015
Logic and Engineering of Natural Language Semantics 12
1 / 16
2. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
The pourpose of this talk
• I do not insist that “truth conception should be treated not
as a predicate but as a logical connective”,
• I do want to analyse Friedman-Sheared’s truth theory FS
from the behaviour of truth predicate,
• it seems to be possible to regard the truth predicate Tr as
a logical connective in truth theories with
ϕ
Tr(ϕ)
NEC
Tr(ϕ)
ϕ CONEC
• Since the condition of being a predicate is very flexible if
the theory is ω-inconsistent, such proof theoretic
semantics viewpoint is effective to analyse it.
2 / 16
3. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
Friedman-Sheared’s Axiomatic truth theory FS
FS = PA + the formal commutability of Tr
+ the introduction and the elimination rule of Tr
• Axioms and schemes of PA (mathematical induction for all
formulae including Tr)
• The formal commutability of the truth predicate:
• logical connectives
• for amy atomic formula ψ, Tr( ψ ) ≡ ψ,
• (∀x ∈ Form)[Tr( ˙¬x) ≡ ¬Tr(x)],
• (∀x, y ∈ Form)[Tr(x ˙∧y) ≡ Tr(x) ∧ Tr(y)],
• (∀x, y ∈ Form)[Tr(x ˙∨y) ≡ ¬Tr(x) ∨ Tr(y)],
• (∀x, y ∈ Form)[Tr(x ˙→y) ≡ ¬Tr(x) → Tr(y)],
• quantifiers
• (∀x ∈ Form)[Tr(˙∀z x(z)) ≡ ∀zTr(x(z))],
• (∀x ∈ Form)[Tr(˙∃z x(z)) ≡ ∃zTr(x(z))],
• The introduction rule and the elimination rule of Tr(x)
ϕ
Tr( ϕ )
NEC
Tr( ϕ )
ϕ CONEC
where Form is a set of Godel code of formulae.
3 / 16
4. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
A proof theoretic semantics (PTS) of Tr (naive version)
FS’s two rules look like the introduction and the elimination
rule; Tr looks like a logical connective [Hj12]
ϕ
Tr(ϕ)
NEC
Tr(ϕ)
ϕ CONEC
• A (naive) proof theoretic semantics: the meaning of a
logical connective is given by the introduction rule
and the elimination rule
4 / 16
5. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
What is “HARMONY”?
To say “Tr is a logical connective”, it must satisfy the
HARMONY between the introduction rule and the
elimination rule.
An example (TONK) The following connective without
HARMONY trivialize the system.
A
A tonk B
tonk +
A tonk B
B
tonk −
There are some criteria of “HARMONY”:
• Concervative extension (Belnap, Dummett)
For any ϕ which does not include Tr, if FS proves ϕ, then
PA proves it.
• Normalization of proofs (Dummett)
5 / 16
6. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
The violation of “HARMONY”
However, we have the negative answers whether Tr satisfy
the HARMONY:
• FS is arithmetically sound:
• Any arithmetically sentence ϕ provable in FS is true in N,
• FS proves the consistency of PA: adding NEC, CONEC is
not a conservative extension.
• Quite contrary: formal commutability + ω-consistency
implies a contradiction [M85],
• this shows FS is ω-inconsistent,
• NEC and CONEC make the domain of the models
drastically large!
What should we do if we want to say Tr is a logical connective?
6 / 16
7. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
ω-inconsistency?
Theorem[McGee] Any consistent truth theory T which include
PA and NEC and satisfies the following:
(1) (∀x, y)[x, y ∈→ (Tr(x ˙→y) → (Tr(x) → Tr(y)))],
(2) Tr(⊥) → ⊥,
(3) (∀x)[x ∈→ Tr(˙∀yx(y)) → (∀yTr(x(y)))]
Then T is ω-inconsistent
proof We define the following paradoxical sentence
γ ≡ ¬∀xTr(g(x, γ ))
where g is a recursive function s.t.
g(0, ϕ ) = Tr( ϕ )
g(x + 1, ϕ ) = Tr(g(x, ϕ )) )
7 / 16
8. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
ω-inconsistency? (conti.)
The intuitive meaning of γ is
γ ≡ ¬Tr( Tr( Tr( Tr( · · · ) ) ) )
Let us assume γ is true:
• Tr( Tr( · · ·
n
λ · · · ) ) is true for any n,
• By formal commutativity, ¬ Tr( Tr( · · ·
n
Tr( · · · )
¬λ
· · · ) )
is true,
• This is equivalent to ¬λ; contradiction!
8 / 16
9. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
How can we interpret paradoxical sentences?
The provability of the consistency of PA and ω-inconsistency
shares the same reason:
• Tr identify (real) formulae with Godel codes.
• So we can apparently define recursive, infinitely
operation on formulae.
• This makes us to define the following formulae:
McGee ¬Tr( Tr( Tr( Tr( · · · ) ) ) )
(Non-standard length, e.g. infinite length!)
Yablo S0 ≡ ¬Tr(S1) ¯∧(¬Tr(S2) ¯∧(¬Tr(S3) ¯∧ · · · ))
(we note that Si ≡ ¬Tr( Si+1 ) ¯∧Si+2)
• The original sentences are finite, but they generate (or
unfold) above infinite sentences (in that sense they are
“potentially infinite”).
• keyword: coinduction
9 / 16
10. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
Induction
To compare with the coinduction, first let us introduce a typical
example of the inductive definition.
• For any set A, the list ofA can be constructed as
A<ω, η : (1 + (A × A<ω)) → A<ω by:
• the first step: empty sequence
• the successor step: For all a0 ∈ A and sequence
a1, · · · , an ∈ A<ω
η(a0, a1, · · · , an ) = a0, a1, · · · , an ∈ A<ω
10 / 16
11. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
Thinking its meaning
Gentzen (1934, p. 80)
The introductions represent, as it were, the ‘definitions’ of the
symbols concerned, and the eliminations are no more, in the
final analysis, than the consequences of these definitions.
The meaning of induction is given by the introduction rule,
• the constructor η represents the introduction rule For
any a0 ∈ A and sequence a1, · · · , an ∈ A<ω,
η(a0, a1, · · · , an ) = a0, a1, · · · , an ∈ A<ω
A B
A ∧ B
• the elimination rule γ is determined in relation to the
introduction rule which satisfies the HARMONY,
A ∧ B
A
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12. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
Coinduction
• infinite streams of A is defined as
A∞, γ : A∞ → (A × A∞) :
• Their intuitive meaning is infinite streams of the form
a0, a1, · · · ∈ A∞
γ( a0, a1, · · · ) = (a0, a1, · · · ) ∈ (A × A∞
)
γ is a function which pick up the first element a0 of the
stream.
• Coinduction represents the intuition such that finitely
operations on the first element of the infinite sequence is
possible to compute (Productivity).
Actually CONEC only care about the productivity (or 1-step
computation)!
Tr(ϕ)
ϕ CONEC
12 / 16
13. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
Thinking its meaning
The meaning of coinduction is by the elimination rule.
• de-constructor γ represents the elimination rule [S12], for
any infinite stream a0, a1, · · · ∈ A∞,
γ( a0, a1, · · · ) = (a0, a1, · · · ) ∈ (A × A∞
)
γ is a function wich picks up the first element a0.
• the introduction rule is known to have to satisfy the
condition (the guarded corecursion) which corresponds
to the failure of the formal commutativity of Tr.
The final algebra which has the same structure to streams is
called weakly final coalgebra
13 / 16
14. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
What is a guarded corecursive function?
• Remember: a property of stream = infinite as a whole, but
1 step computation is possible
• Guarded corecursion guarantees the productivity of
functions over coinductive datatypes.
• for any recursive function f,
map : (A → B) → A∞
→ B∞
is defined as
map f x, x0, · · · = f(x)
finite part
( map f x0, x1, · · · )
infinite part
where means the concatenation of two sequence.
• Here recursive call of map only appears inside ,
14 / 16
15. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
The guarded corecursion and the failure of Formal
commutability
Remember:
ω-inconsistency = PA + the formal commutability of Tr
+NEC+CONEC
• The formal commutability violates the guardedness!
Tr( γ → ¬Tr( Tr( · · · ) ) )
Tr( γ ) → ¬Tr( Tr( Tr( · · · ) ) )
FS
Both γ and Tr( Tr( Tr( · · · ) ) ) are coinductive objects:
• To calculate the value of Tr( γ → ¬Tr( Tr( · · · ) ) ), we
should calculate the both the value of Tr( γ ) and
¬Tr( Tr( Tr( · · · ) ) ).
• But Tr( γ ) is also coinductive object, therefore the
calculation of the first value Tr( γ ) never terminates,
• therefore this violates the productivity.
• Therefore Tr with the formal commutability cannot be a
logical connective!!
15 / 16
16. Truth as a
logical
connective
Shunsuke
Yatabe
Introduction
FS
PTS of Tr
A problem
HARMONY
McGee’s Theorem
Analysis
Interpreting γ
PTS for inductive
formulae
Solution: The
HARMONY
extended
PTS for coinductive
formulae
The failure of FC
Reference
Jc Beal. Spandrels of Truth. Oxford University press (2008)
Hartry Field. “Saving Truth From Paradox” Oxford (2008)
Hannes Leitgeb. “Theories of truth which have no standard
models” Studia Logica, 68 (2001) 69-87.
Vann McGee. “How truthlike can a predicate be? A negative
result” Journal of Philosophical Logic, 17 (1985): 399-410.
Halbach, Volker, 2011, Axiomatic Theories of Truth, Cambridge
University Press.
Ole Hjortland, 2012, HARMONY and the Context of Deducibility,
in Insolubles and Consequences College Publications
Greg Restall “Arithmetic and Truth in Łukasiewicz’s Infinitely
Valued Logic” Logique et Analyse 36 (1993) 25-38.
Anton Setzer, 2012, Coalgebras as Types determined by their
Elimination Rules, in Epistemology versus Ontology.
Stephen Yablo. “Paradox Without Self-Reference” Analysis 53
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