Mathematics is an abstract subject and most of the people hate mathematics. so Mathematics has a great role in developing interest of the students in Mathematics.
Using my BSnet deep learnin network, each neuron is designed not to overfit. It achieves this by concatenating the positive and negative inputs so that it becomes more separable in high dimension space. This allows it to be used for general purpose classification problems such as MNIST dataset to recognize handwriting number digits. BSnet is based on the principles of Boolean algebra and monotone circuit. Using the same principles, I also design BSautonet autoencoder, that can be used to denoise image, learn embeddings and unsupervised learning.
Mathematics is an abstract subject and most of the people hate mathematics. so Mathematics has a great role in developing interest of the students in Mathematics.
Using my BSnet deep learnin network, each neuron is designed not to overfit. It achieves this by concatenating the positive and negative inputs so that it becomes more separable in high dimension space. This allows it to be used for general purpose classification problems such as MNIST dataset to recognize handwriting number digits. BSnet is based on the principles of Boolean algebra and monotone circuit. Using the same principles, I also design BSautonet autoencoder, that can be used to denoise image, learn embeddings and unsupervised learning.
This powerpoint gives a technique to approximate (relaxation) discrete Markov Random Field (MRF) using convex programming. This approximated MRF can be used to approximate NP problem. This also proves that NP is not equal P because the MRF convex programming and the approximate MRF convex programming are not the same with removal of some product terms.
kung fu Computer Science, Geometric complexity theory
NP vs P Proof using Deterministic Finite AutomataSing Kuang Tan
Prove that Clique problem is NP and cannot be reduced to P because the Deterministic Finite Automata of the Clique problem has exponential number of states. We can use the same concept to prove that NP is not equal to P using Turing Machine. We figured out a way to unify Mathematics. This proof is for those Theoretical Computing guys who do not know Boolean algebra but know Turing Machine. Kung fu computer science, Geometric complexity theory
Use Inductive or Deductive Logic to solve NP vs P?Sing Kuang Tan
Use Inductive or Deductive Logic to solve NP vs P? I use circuit complexity and deductive logic to solve NP vs P. Kung fu computer science, Geometric complexity theory
Simplify a Clique Problem Boolean algebra by factorization. Show that Clique Problem is Non-Deterministic Polynomial Time (NP) and cannot be simplified to Polynomial Time (P). Kung Fu Computer Science, Geometric complexity theory
Beyond Shannon, Sipser and Razborov; Solve Clique Problem like an Electronic ...Sing Kuang Tan
Convert any Boolean algebra into monotone circuit and use that to prove that NP is not equal to P as monotone circuit cannot solve Clique problem in Polynomial time complexity. NP vs P is a Millennium Prize problem. Kung Fu Computer Science, Geometric complexity theory
A Weird Soviet Method to Partially Solve the Perebor ProblemSing Kuang Tan
Monotone Circuit can implement an algorithm to run Non-Deterministic Polynomial time complexity (NP) problem in Polynomial time complexity (P). I developed a method to implement all algorithms without "Not" operations. Using this information, I manage to prove that NP is not equal to P. Kung Fu Computer Science, Geometric complexity theory
Brief explanation of NP vs P. Prove Np not equal P using Markov Random Field ...Sing Kuang Tan
In this paper, we proved that Non-deterministic Polynomial time complexity (NP) is not equal to Polynomial time complexity (P). We developed the Boolean algebra that will infer the solution of two variables of a Non-deterministic Polynomial computation time Markov Random Field. We showed that no matter how we simplified the Boolean algebra, it can never run in Polynomial computation time (NP not equal to P). We also developed proof that all Polynomial computation time multi-layer Boolean algebra can be transformed to another Polynomial computation time multi-layer Boolean algebra where there are only 'Not' operations in the first layer. So in the process of simplifying the Boolean algebra, we only need to consider factorization operations that only assumes only 'Not' operations in the first layer. We also developed Polynomial computation time Boolean algebra for Markov Random Field Chain and 2sat problem represented in Markov Random Field form to give examples of Polynomial computation time Markov Random Field. Kung Fu Computer Science, Geometric complexity theory
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 .
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.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
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.
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.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
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.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.
Mathematical Proof types
1. 4 Types of Mathematical
Proofs
Sing Kuang Tan
singkuangtan@gmail.com
14 July 2021
Link to my paper
https://www.slideshare.net/SingKuangTan/brief-np-vspexplain-249524831
Prove Np not equal P using Markov Random Field and Boolean Algebra
Simplification
https://vixra.org/abs/2105.0181
2. Types of Mathematical Proofs
• In my opinion, there are 4 types of Mathematical Proofs
• Algebraic Proof
• Visual Proof
• Logic Proof
• Algorithmic Proof
5. Logic Proof
• Proof using Mathematical Logic
• E.g.
• p⇒q
• q⇒r
• ∴ p⇒r(3.2.3)
• Proof: No least positive rational number
• https://en.wikipedia.org/wiki/Proof_by_contradiction
• Consider the proposition, P: "there is no smallest rational number greater than 0". In a proof
by contradiction, we start by assuming the opposite, ¬P: that there is a smallest rational
number, say, r.
• Now, r/2 is a rational number greater than 0 and smaller than r. But that contradicts the
assumption that r was the smallest rational number (if "r is the smallest rational number"
were Q, then one can infer from "r/2 is a rational number smaller than r" that ¬Q.) This
contradictions shows that the original proposition, P, must be true. That is, that "there is no
smallest rational number greater than 0".
6. Algorithmic Proof
• Proof the Cycle Property of Minimum Spanning Tree
• https://en.wikipedia.org/wiki/Minimum_spanning_tree#Cycle_property
• Cycle property
• For any cycle C in the graph, if the weight of an edge e of C is larger than the
individual weights of all other edges of C, then this edge cannot belong to an
MST.
• Proof: Assume the contrary, i.e. that e belongs to an MST T1. Then
deleting e will break T1 into two subtrees with the two ends of e in different
subtrees. The remainder of C reconnects the subtrees, hence there is an
edge f of C with ends in different subtrees, i.e., it reconnects the subtrees into
a tree T2 with weight less than that of T1, because the weight of f is less than
the weight of e.
7. • Algorithmic Proof
• Problem Reduction
• are also used to proof a problem e.g. 3 coloring problem is NP problem
• by converting another NP problem e.g. 3sat to the original 3 coloring problem
• Proof correctness of an algorithm
• Proof time and space complexity of an algorithm
• Proof that 2 algorithms are equivalent
• …
8. My NP vs P proof
• My NP vs P proof is proved fully using only algebra
• Without any algorithm
• I think algebraic proof is the most advance form of proving in Mathematical Proof
• 我的NP vs P proof证明是完全用代数来证明
• 没有用到任何算法
• 我认为代数是最先进的数学证明方法
• Read my Paper:
• https://www.slideshare.net/SingKuangTan/brief-np-vspexplain-249524831
• Prove Np not equal P using Markov Random Field and Boolean Algebra Simplification
• https://vixra.org/abs/2105.0181
9. Share my links
• I am a Small Person with Big Dreams
• Please help me to repost my links to other platforms so that I can spread my ideas to the rest
of the world
• 我人小,但因梦想而伟大。
• 请帮我的文件链接传发到其他平台,让我的思想能传遍天下。
• Comments? Send to singkuangtan@gmail.com
• Link to my paper
• https://www.slideshare.net/SingKuangTan/brief-np-vspexplain-249524831
• Prove Np not equal P using Markov Random Field and Boolean Algebra Simplification
• https://vixra.org/abs/2105.0181