Wednesday announced that it will proceed with a reverse stock split of its outstanding ordinary shares at a ratio of 1-for-15. The reverse stock split is expected to become effective on or around November 13,

Prenetics Global announces reverse stock split

Prenetics Global Limited (NASDAQ: PRE), a leading-genomics-driven health sciences company today announced that it will proceed with a reverse stock split of its outstanding ordinary shares at a ratio of 1-for-15.

Prenetics Global Limited Announces Reverse Stock Split

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Prenetics Global

prenetics

What do we do when we speak? First, we use certain parts of our body to produce an airstream inwards and outwards. This airstream is created intentionally to produce certain sounds. These sounds are transmitted as sound waves, which are perceived by a hearer. This hearer receives the sound wave, decodifies it and, then, interprets it. “Phonetics and Phonology are concerned with speech – with the ways in which humans produce and hear speech.” (Clark & Yallop, 1990: 1) These two disciplines study the production and reception of speech sounds in all their complexity. That is, they study the whole process and all the mechanisms involved in the production and reception of speech. On the one hand, Phonetics is concerned with the anatomy and physiology of speech, with the production organs (Articulatory Phonetics), with the sound wave (Acoustic Phonetics), and with the perception of that sound wave (Auditory or Perceptual Phonetics). Phonetics and Phonology On the other hand, Phonology is concerned with the “systems and patterns of sounds that occur in particular languages.” (Ibid.: 2). So, research on the way articulatory organs work to produce a given sound or a sound feature is normally considered a phonetic investigation, whereas research on the total number of vowel and consonant sounds in English is considered phonological in nature. Then, “phoneticians are likely to draw on methods and techniques used in the natural sciences (while) phonologists may profess to be more concerned with the mental organization of language.” (ibid.: 3) However, both disciplines must be studied together. Phonetics and phonology only study two different aspects of the same reality. According to Peter Roach (1991), phonology is “the study of the abstract side of the sounds of language” whereas phonetics studies “the actual realizations”. Speech is a complex human phenomenon that involves mental and physical components and both phonetics and phonology aim at accounting for this complexity. The Production of Speech Sounds How can we produce speech? In this section, we will study the production of speech sounds from an articulatory point of view in order to understand better subsequent sections about vowel and consonant sounds. It must be said that speech does not start in the lungs. It starts in the brain and is, then, studied by Psycholinguistics. After the creation of the message and the lexico-grammatical structure in our mind, we need a representation of the sound sequence and a number of commands which will be executed by our speech organs to produce the utterance. So, we need a phonetic plan and a motor plan (Belinchón, Igoa y Rivière, 1994: 590) After these metal operations we come to the physical production of sounds. Speech, then, is produced by an air stream from the lungs, which goes through the trachea and the oral and nasal cavities. It involves four processes: Initiation, phonation, oro-nasal process, and articulation. The initiation process is the moment when the air is expelled from the lungs. In English, speech sounds are the result of “a pulmonic egressive airstream” (Giegerich, 1992) although that is not the case in all languages (ingressive sounds). The phonation process occurs at the larynx. The larynx has two horizontal folds of tissue in the passage of air; they are the vocal folds. The gap between these folds is called the glottis. The glottis can be closed, Then, no air can pass. Or it can have a narrow opening which can make the vocal folds vibrate producing the “voiced sounds”. Finally, it can be wide open, as in normal breathing, and, thus, the vibration of the vocal folds is reduced, producing the “voiceless sounds”. After it has gone through the larynx and the pharynx, the air can go into the nasal or the oral cavity. The velum is the part responsible for that selection. Through the oro-nasal process we can differentiate between the nasal consonants (/m/, /n/ ) and other sounds. Finally, the articulation process is the most obvious one: it takes place in the mouth and it is the process through which we can differentiate most speech sounds. In the mouth, we can distinguish between the oral cavity, which acts as a resonator, and the articulators, which can be active or passive: upper and lower lips, upper and lower teeth, tongue (tip, blade, front, back), and roof of the mouth (alveolar ridge, palate, and velum). So, speech sounds are distinguished from one another in terms of the place where and the manner how they are articulated. Stress in pronunciation As a sound phenomenon, can be studied from two points of view: production and perception. The production of stressed syllables is said to imply greater muscular energy than the production of unstressed syllables. From the perceptive point of view, stressed syllables are prominent. Prominence is the sum of different factors such as loudness, length, pitch, and quality. There are three possibilities of stress in a word: primary stress, characterized by prominence and, basically, by a rise-fall tone; secondary stress, weaker than the primary stress but stronger than that of the unstressed syllables (,photo’graphic); and unstressed syllables, defined by the absence of any prominence, becoming then the background against the prominent stressed syllables appear. Unstressed syllables normally have the short-closed vowels /i/ or /u/ and the schwa. From the teaching perspective, there are two ideas to be marked: first, “incorrect stress placement is the major cause of intelligibility problems for foreign learners, and is, therefore, a subject that needs to be treated very seriously.” (Roach, 1991:91). Second, the rules for word stress from the phonological point of view are too complex to use in the language classroom, so implicit ways of teaching are required. Dictionaries with phonemic transcriptions can be helpful. Another important aspect related to stress is that of the “weak forms”. There is a number of words in English (almost all of which belong to the category called function or grammatical words) that can be pronounced in two different ways, a strong and a weak form. There are about forty such words and it is important to be aware of their existence as they can provoke misunderstandings. English-speaking people find the strong forms unnatural and learners of English can misunderstand English speakers, who will surely use weak forms. There are some rules to learn. The strong form will be used when a) they occur at the end of a sentence, as in “Chips are what I’m fond of”.
b) A weak-form word is being contrasted with another word, as in “The letter is from him, not to him.&rdquo
c) A weak-form word is given stress for the purpose of emphasis, as in “You must give me more money.”
d) A weak-form is being “cited” or “quoted”, as in “You shouldn’t put “and” at the end of a sentence.” The most common weak-form words THE ði
A eɪ
AND ænd
BUT bʌt
THAT ðæt
THAN ðæn
AT æt
FOR fɔr
FROM frʌm
OF ʌv
TO tu
AS æz
SOME sʌm
CAN, COULD kæn, kʊd
HAVE, HAS, HAD hæv, hæz, hæd
SHALL, SHOULD ʃæl, ʃʊd
MUST mʌst
DO, DOES du, dʌz
AM, IS, ARE, WAS, WERE æm, ɪz, ɑr, wʌz, wɜr Intonation in English - why is important It is not easy to define Intonation. We know that the basic feature of intonation is pitch, being high or low. The overall behavior of the pitch is called tone. Tones can be static, level tones, or moving tones, either rising or falling. For the purpose of analyzing intonation, a unit is normally used called the tone-unit. Tone-units consist of at least one tonic syllable (a tonic syllable being a syllable with tone and prominence). Tone-units also have a “head”, which is that part of the tone-unit that extends from the first stressed syllable up to (but not including) the tonic syllable. Before the head, there may be a pre-head, which includes all the unstressed syllables in a tone unit preceding the first stressed syllables. Sometimes there is even a “tail”, that is, some syllables following the tonic syllable up to the end of the tone unit. So, the structure of a tone-unit is (pre-head) (head) tonic syllable (tail). Intonation is very important for communication, as it helps the addressee interpret the message. There have been different proposals to explain how intonation can help communication, some of which are: Intonation enables us to express emotions and attitudes as we speak: the attitudinal function of intonation. Intonation helps to produce the effect of prominence on stressed syllables: the accentual function of intonation. Intonation helps to recognize the grammar and syntactic structure of the utterance: the grammatical function of intonation. Intonation conveys the given-new information or provides information for turn-taking: the discourse function of intonation. So, there are three simple possibilities for intonation: level, fall, and rise. However, more complex tones are also used, such as fall-rise or rise-fall. Each of these tones is functionally distinct, that is, they convey different attitudes, intentions, and meanings to the hearer, as has been stated above. Thus, the fall tone is regarded as quite “neutral” and it conveys a certain sense of “finality” (so, it is normally used to yield the floor in turn-taking). The rising tone, on the other hand, conveys an impression that something more is to follow (so, it is frequently used to keep the floor in turn-taking). The fall-rise tone is quite frequent and it conveys, among many other possibilities, “limited agreement” or “response with reservations”. The rise-fall tone is normally used to convey strong feelings of approval, disapproval, or surprise. Syllables in English The Sonority Hierarchy In any utterance some sounds stand as more prominent or sonorous than others...A sonority scale or hierarchy can be set up which represents the relative sonority of various classes of sound: Open vowels Close vowels Laterals Nasals Approximants Trills Fricatives Affricates Plosives and flaps Using the sonority hierarchy, we can draw a contour representing the varying prominences of an utterance. The number of syllables in an utterance equates with the number of peaks of sonority [being aware that sound] classes from fricatives downwards cannot constitute peaks in English. There are three elements in a syllable. The onset, peak, and coda of a syllable form a hierarchy of constituents, in which the coda is more closely associated with the peak than with the onset [thus conforming to the rhyme]. Onset generally involves increasing sonority up to the peak (...) whereas codas generally involve decreasing sonority. How to divide syllables in medial position? Various principles can be applied to decide between alternatives: align syllable boundaries with morpheme boundaries where present (the morphemic principle): align syllable boundaries to parallel syllable codas and onsets at the ends and beginnings of words (the phonotactic principle); align syllable boundaries to best predict allophonic variation, e.g., the devoicing of /r/ following /t/ (the allophonic principle). Unfortunately, such principles often conflict with one another. A further principle is often invoked in such cases, the maximal onset principle, which assigns consonants to onsets wherever possible and is said to be universal in languages; but this itself often conflicts with one or more of the principles above. "Words" definition according to phonetics The word, composed of one or more phonemes, has a separate linguistic identity, in that it is a commutable entity, higher than the phoneme, which may either constitute a complete utterance or may be substituted in a longer utterance for other words of its same class. The syllable or syllables of a word that stands out from the remainder are said to be accepted, to receive the accent. The accentual pattern of English words is fixed but free. Accent and prominence Any of four factors, pitch, loudness, quality, and quantity may help to render a syllable more prominent than its neighbors. But it is principally pitch change that marks an accented syllable. The final pitch accent in a word or in a group of words is usually the most prominent (and hence referred to as the primary accent) while a pitch accent on an earlier syllable is usually somewhat less prominent (and referred to as secondary accent). Accented syllables are often assumed to be louder than unaccented syllables and, in many cases, this may be so. However, loudness is not by itself an efficient device for signaling the location of the accent of English. While the accent is primarily achieved by pitch change, sometimes assisted by extra loudness, among unaccented syllables some will be more prominent than others due to the quality and quantity of the vowels at their center Long vowels and diphthongs are generally more prominent than short vowels, while among the short vowels themselves - short a, e, i, o, and u: /æ, ɛ, ɪ, ɑ, ʌ/ (cat, bed, sit, top, sun) - (when unaccented) are the least prominent and are often referred to as reduced vowels as opposed to other full vowels. There are four degrees of prominence in English: Accentual Patterns The accentual pattern of a word establishes the relationship of its parts; it may also have a distinctive function in that it opposes words of comparable sound structure (and identical spelling). Such word oppositions (for the most part disyllables of French origin) may or may not involve phonemic changes of quality. a) primary accent, marked by the last major pitch change in a word (or longer utterance); b) secondary accent, marked by a non-final pitch change in a word (or longer utterance); c) a minor prominence produced by the occurrence of a full vowel but containing no pitch change d) a non-prominent syllable containing no pitch change and one of the vowels Distinctive Word A relatively small number of pairs of nouns and verbs may differ only in the location of the primary accent, this falling on the first syllable in the nouns and on the second in the verbs (accent, digest, torment, transfer, transport). In a somewhat larger number of pairs the occurrence in the first syllable of the verb is more regular (combine, compress, concert, conduct, contract, contrast, convict, desert, export, object, present, proceeds, produce, progress). Elision It has always been a feature of the structure of English words that the weakly accented syllables have undergone a process of reduction, including loss of phonemes or of vowels. The same process of reduction, with resultant contraction, may be observed in operation in PresE. Vowel Elision Established: initially in the state, scholar or sample; medially in Gloucester, marriage, evening, chimney,..; finally in time, name, loved, hands, eaten, written, cousin. Present colloquial: consonant + // + // + weak vowel = elision of // as in preferable, repertory, temporary, murderer, etc. o // + weak vowel + consonant = elision of weak vowel as in Dorothy (recent development)
consonant + weak vowel + // = elision of weak vowel (or reduction of dark // into clear //) as in fatalist, bachelor or insolent. Elision of post-primary // as in university, probably, difficult, national, fashionable or government.
Loss of syllabicity in present participles of verbs such as flavour, lighten or thicken where the // may be elided or the syllabic consonant [] replaced by a non-syllabic consonant. In pre-primary positions, // or // of the weak syllable preceding the primary accent is apt to be lost in very rapid speech, especially when the syllable with primary accent has initial // or // as in police, parade, correct, balloon or gorilla, but also in photography, suppose, perhaps, geometry or geography. Consonant elision Established: the reduction of many consonant clusters has long been established as initial /wr, kn, gn, hl, hr, hn/, medial /tn, tl/ or final /mb, mn/: write, know, gnaw, loaf, ring, nut, fasten, castle, lamb, hymn,... Present colloquial: loss of alveolars /t, d/ when medial in a cluster of three consonants as in exactly, facts, handsome, lastly, Westminster, dustman... // is normally elided from asthma and isthmus, sometimes from months, twelfths, fifths, clothes.
Elision of /k/ and /l/ in asked and only may occur. // is elided when preceded by // as in always. /p/ may be lost in clusters where its position is homorganic with that of an epenthetic plosive as in glimpse. Whole syllables may be elided in a rapid speech in the vicinity of /r/ or in a sequence of /r/ sounds: library. Epenthesis The elision of /t/ in words like vents sometimes leads to the opposite tendency to insert an epenthetic /t/ in words like a dance. While epenthetic /t/ occurs between an /n/ and /, , /, similarly, an epenthetic /p/ or /k/ may occur between an /, / and a following fricative as in triumphs, confuse.

An Introduction to English Phonetics

How Logical Clocks Keep Distributed Systems in Sync

Table of Links Abstract


Introduction
Background
Reinforcement Learning
Similar Work


Methodology
DRLAgent Design


Training Procedures


Testing Procedures


Results


SABR Experiments


Conclusions


Appendix A


References REFERENCES Assa, H., C. Kenyon, and H. Zhang. 2021. “Assessing Reinforcement Delta Hedging.” SSRN SSRN 3918375. http://dx.doi.org/10.2139/ssrn.3918375. Black, F., and M. Scholes. 1973. “The Pricing of Options and Corporate Liabilities” 81 (3): 637– 54. https://doi.org/10.1086/260062. Cao, Jay, Jacky Chen, Soroush Farghadani, John Hull, Zissis Poulos, Zeyu Wang, and Jun Yuan. 2023. “Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning.” Frontiers in Artificial Intelligence 6. https://www.frontiersin.org/articles/10.3389/frai.2023.1129370. Cao, Jay, Jacky Chen, John Hull, and Zissis Poulos. 2021. “Deep Hedging of Derivatives Using Reinforcement Learning.” The Journal of Financial Data Science 3 (1): 10–27. https://doi.org/10.3905/jfds.2020.1.052. Chebyshev, P. 1864. “Sur l’interpolation.” Zapiski Akademii Nauk 1 (4): 539–60. Du, Jiayi, Muyang Jin, Petter N. Kolm, Gordon Ritter, Yixuan Wang, and Bofei Zhang. 2020. “Deep Reinforcement Learning for Option Replication and Hedging.” The Journal of Financial Data Science 2 (4): 44–57. https://doi.org/10.3905/jfds.2020.1.045. Fathi, A., and B. Hientzsch. 2023. “A Comparison of Reinforcement Learning and Deep Trajectory Based Stochastic Control Agents for Stepwise Mean-Variance Hedging.” arXiv arXiv:2302.07996. Florescu, Ionuţ, and Cristian Gabriel Pãsãricã. 2009. “A Study about the Existence of the Leverage Effect in Stochastic Volatility Models.” Physica A: Statistical Mechanics and Its Applications 388 (4): 419–32. Giurca, B., and S. Borovkova. 2021. “Delta Hedging of Derivatives Using Deep Reinforcement Learning.” SSRN SSRN 3847272. https://ssrn.com/abstract=3847272. Glau, Kathrin, Micro Mahlstedt, and Christian Potz. 2018. “A New Approach for American Option Pricing: The Dynamic Chebyshev Method.” arXiv. https://doi.org/10.48550/arXiv.1806.05579. Hagan, Patrick, Deep Kumar, Andrew Lesniewski, and Diana Woodward. 2002. “Managing Smile Risk.” Wilmott Magazine 1 (January): 84–108. Halperin, Igor. 2017. “QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds.” The Journal of Derivatives 28 (1): 99–122. https://doi.org/10.3905/jod.2020.1.108. Heston, Steven L. 1993. “A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options.” The Review of Financial Studies 6 (2): 327–43. Hull, John, 1946-. 2012. Options, Futures, and Other Derivatives. Eighth edition. Boston : Prentice Hall, [2012] ©2012. https://search.library.wisc.edu/catalog/9910112878402121. Kolm, Petter N., and Gordon Ritter. 2019. “Dynamic Replication and Hedging: A Reinforcement Learning Approach.” The Journal of Financial Data Science 1 (1): 159–71. https://doi.org/10.3905/jfds.2019.1.1.159. Lillicrap, Timothy, Johnathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. “Continuous Control with Deep Reinforcement Learning.” arXiv arXiv:1509.02971. https://doi.org/10.48550/arXiv.1509.02971. Lin, Long-Ji. 1992. “Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching.” Machine Learning 8 (3): 293–321. https://doi.org/10.1007/BF00992699. Liu, Peng. 2023. “A Review on Derivative Hedging Using Reinforcement Learning.” The Journal of Financial Data Science. https://doi.org/10.3905/jfds.2023.1.124. Longstaff, Francis A., and Eduardo S. Schwartz. 2001. “Valuing American Options by Simulation: A Simple Least-Squares Approach.” UCLA: Finance. https://escholarship.org/uc/item/43n1k4jb. Mikkilä, Oskari, and Juho Kanniainen. 2023. “Empirical Deep Hedging.” Quantitative Finance 23 (1): 111–22. https://doi.org/10.1080/14697688.2022.2136037. Mnih, V., K. Kavukcuoglu, David Silver, A. Graves, I. Antonoglou, Daan Wierstra, and M. Riedmiller. 2013. “Playing Atari with Deep Reinforcement Learning.” arXiv arXiv:1312.5602. https://doi.org/10.48550/arXiv.1312.5602. Murray, Phillip, B. Wood, H. Buehler, Magnus Wiese, and Mikko Pakkanen. 2022. “Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions.” In ICAIF ’22: Proceedings of the Third ACM International Conference on AI in Finance, 361–68. Association for Computing Machinery. https://doi.org/10.1145/3533271.3561731. Pham, Uyen, Quoc Luu, and Hien Tran. 2021. “Multi-Agent Reinforcement Learning Approach for Hedging Portfolio Problem.” Soft Computing 25 (12): 7877–85. https://doi.org/10.1007/s00500-021-05801-6. Pickard, Reilly, and Yuri Lawryshyn. 2023. “Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review.” Mathematics 11 (24). https://doi.org/10.3390/math11244943. Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, et al. 2016. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529 (7587): 484–89. https://doi.org/10.1038/nature16961. Smith, Leslie N. 2018. “A Disciplined Approach to Neural Network Hyper-Parameters: Part 1 -- Learning Rate, Batch Size, Momentum, and Weight Decay.” https://doi.org/10.48550/arXiv.1803.09820. Sutton, Richard S., and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. A Bradford Book. Watkins, Christopher John Cornish Hellaby. 1989. “Learning from Delayed Rewards.” King’s College. https://www.cs.rhul.ac.uk/~chrisw/new_thesis.pdf. Xiao, B., W. Yao, and X. Zhou. 2021. “Optimal Option Hedging with Policy Gradient.” In 2021 International Conference on Data Mining Workshops (ICDMW), 1112–19. IEEE. https://doi.org/10.1109/ICDMW53433.2021.00145. Xu, Wei, and Bing Dai. 2022. “Delta-Gamma–Like Hedging with Transaction Cost under Reinforcement Learning Technique.” The Journal of Derivatives 29 (5): 60–82. https://doi.org/10.3905/jod.2022.1.156. Zheng, C., J. He, and C. Yang. 2023. “Option Dynamic Hedging Using Reinforcement Learning.” arXiv arXiv:2306.10743. Authors:
(1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (reilly.pickard@mail.utoronto.ca);
(2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. Table of Links Abstract Introduction
Background
Reinforcement Learning
Similar Work Methodology
DRLAgent Design Training Procedures Testing Procedures Results SABR Experiments Conclusions Appendix A References Abstract Abstract Abstract Introduction Background Reinforcement Learning Similar Work Introduction Background Background Reinforcement Learning Reinforcement Learning Similar Work Similar Work Methodology DRLAgent Design Methodology Methodology DRLAgent Design DRLAgent Design Training Procedures Training Procedures Training Procedures Testing Procedures Testing Procedures Testing Procedures Results Results Results SABR Experiments SABR Experiments SABR Experiments Conclusions Conclusions Conclusions Appendix A Appendix A Appendix A References References References REFERENCES Assa, H., C. Kenyon, and H. Zhang. 2021. “Assessing Reinforcement Delta Hedging.” SSRN SSRN 3918375. http://dx.doi.org/10.2139/ssrn.3918375. Black, F., and M. Scholes. 1973. “The Pricing of Options and Corporate Liabilities” 81 (3): 637– 54. https://doi.org/10.1086/260062. Cao, Jay, Jacky Chen, Soroush Farghadani, John Hull, Zissis Poulos, Zeyu Wang, and Jun Yuan. 2023. “Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning.” Frontiers in Artificial Intelligence 6. https://www.frontiersin.org/articles/10.3389/frai.2023.1129370. Cao, Jay, Jacky Chen, John Hull, and Zissis Poulos. 2021. “Deep Hedging of Derivatives Using Reinforcement Learning.” The Journal of Financial Data Science 3 (1): 10–27. https://doi.org/10.3905/jfds.2020.1.052. Chebyshev, P. 1864. “Sur l’interpolation.” Zapiski Akademii Nauk 1 (4): 539–60. Du, Jiayi, Muyang Jin, Petter N. Kolm, Gordon Ritter, Yixuan Wang, and Bofei Zhang. 2020. “Deep Reinforcement Learning for Option Replication and Hedging.” The Journal of Financial Data Science 2 (4): 44–57. https://doi.org/10.3905/jfds.2020.1.045. Fathi, A., and B. Hientzsch. 2023. “A Comparison of Reinforcement Learning and Deep Trajectory Based Stochastic Control Agents for Stepwise Mean-Variance Hedging.” arXiv arXiv:2302.07996. Florescu, Ionuţ, and Cristian Gabriel Pãsãricã. 2009. “A Study about the Existence of the Leverage Effect in Stochastic Volatility Models.” Physica A: Statistical Mechanics and Its Applications 388 (4): 419–32. Giurca, B., and S. Borovkova. 2021. “Delta Hedging of Derivatives Using Deep Reinforcement Learning.” SSRN SSRN 3847272. https://ssrn.com/abstract=3847272. Glau, Kathrin, Micro Mahlstedt, and Christian Potz. 2018. “A New Approach for American Option Pricing: The Dynamic Chebyshev Method.” arXiv. https://doi.org/10.48550/arXiv.1806.05579. Hagan, Patrick, Deep Kumar, Andrew Lesniewski, and Diana Woodward. 2002. “Managing Smile Risk.” Wilmott Magazine 1 (January): 84–108. Halperin, Igor. 2017. “QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds.” The Journal of Derivatives 28 (1): 99–122. https://doi.org/10.3905/jod.2020.1.108. Heston, Steven L. 1993. “A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options.” The Review of Financial Studies 6 (2): 327–43. Hull, John, 1946-. 2012. Options, Futures, and Other Derivatives. Eighth edition. Boston : Prentice Hall, [2012] ©2012. https://search.library.wisc.edu/catalog/9910112878402121. Kolm, Petter N., and Gordon Ritter. 2019. “Dynamic Replication and Hedging: A Reinforcement Learning Approach.” The Journal of Financial Data Science 1 (1): 159–71. https://doi.org/10.3905/jfds.2019.1.1.159. Lillicrap, Timothy, Johnathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. “Continuous Control with Deep Reinforcement Learning.” arXiv arXiv:1509.02971. https://doi.org/10.48550/arXiv.1509.02971. Lin, Long-Ji. 1992. “Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching.” Machine Learning 8 (3): 293–321. https://doi.org/10.1007/BF00992699. Liu, Peng. 2023. “A Review on Derivative Hedging Using Reinforcement Learning.” The Journal of Financial Data Science. https://doi.org/10.3905/jfds.2023.1.124. Longstaff, Francis A., and Eduardo S. Schwartz. 2001. “Valuing American Options by Simulation: A Simple Least-Squares Approach.” UCLA: Finance. https://escholarship.org/uc/item/43n1k4jb. Mikkilä, Oskari, and Juho Kanniainen. 2023. “Empirical Deep Hedging.” Quantitative Finance 23 (1): 111–22. https://doi.org/10.1080/14697688.2022.2136037. Mnih, V., K. Kavukcuoglu, David Silver, A. Graves, I. Antonoglou, Daan Wierstra, and M. Riedmiller. 2013. “Playing Atari with Deep Reinforcement Learning.” arXiv arXiv:1312.5602. https://doi.org/10.48550/arXiv.1312.5602. Murray, Phillip, B. Wood, H. 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Authors: (1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (reilly.pickard@mail.utoronto.ca); (2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. Authors: Authors: (1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada ( reilly.pickard@mail.utoronto.ca ); reilly.pickard@mail.utoronto.ca (2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. available on arxiv available on arxiv

Hedging American Put Options with Deep Reinforcement Learning: References

The Greedy Miner’s Dilemma: Flaws in DAG-Based Consensus Protocols

Unlocking the Secrets of Autocatalytic Sets: How Bit Strings and Reactions Shape Molecular Evolution

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) U Jin Choi, Department of mathematical science, Korea Advanced Institute of Science and Technology & ujchoi@kaist.ac.kr;
(2) Kyung Soo Rim, Department of mathematics, Sogang University & ksrim@sogang.ac.kr. Table of Links Introduction
Organization and notation
Problem Setting and Preliminaries
Generalized Wiener algebra and Fréchet derivative
Characterization of coefficients
Coefficients from an ergodic process
Conclusion & References Conclusion In this mathematical study, we delve into the realm of statistical inference and introduce a novel approach to variational non-Bayesian inference. Most significantly, we propose a new method for uniquely determining the hidden PDF solely from a random sample while leveraging theory. Beyond merely approximating the shape of the unrevealed PDF, we yield results that provide practical assistance in inferring important moments, such as the mean and variance, from the estimated PDF. These days, methods utilizing artificial intelligence are widely employed, but they generally exhibit a drawback of depending on initial conditions, i.e., a prior distribution, and iterative computations, i.e., a backpropagation. The limitations of such approaches lie in their reliance on these conditions to achieve global optimization. We emphasize the significant contribution of the proposed method to predictive and classification models, even without any information on populations, highlighting its potential applications in various domains such as finance, economics, weather forecasting, and machine learning, all of which present unique challenges. The most well-known method for approximating hidden PDFs is the variational approach in a Bayesian context. In contrast, our study extends this problem by seeking to precisely determine unknown PDFs through a system of equations. Our approach includes proving the Fréchet differentiability of entropy to establish the uniqueness of the energy function space in the Wiener algebra. We then derive the unique determination of the energy function through the minimization of KL-divergence. Leveraging the Ergodic theorem, we elucidate that solutions to equations comprising polynomial function series are the coefficients of the energy function and numerically substantiate the convergence of partial sums of the energy function obtained from a finite number of equations. In summary, our mathematical exploration has unveiled the potential of variational Non-Bayesian inference in Wiener space. We anticipate that these mathematical ideas can offer an innovative framework for probability density estimation and predictive modeling. Ultimately, our research emphasizes its potential contribution to reshaping mathematical horizons and expanding the boundaries of knowledge in the field of statistical methodology. REFERENCES [1] K. Bharath A. Saha and S. Kurtek, A geometric variational approach to bayesian inference, Journal of the American Statistical Association 115 (2020), no. 530, 822–835. [2] P. Alquier and J. Ridgway, Concentration of tempered posteriors and of their variational approximations, The Annals of Statistics 48 (2020), no. 3, 1475–1497. [3] A. R. Barron and C. Sheu, Approximation of density functions by sequences of exponential families, The Annals of Statistics 19 (1991), no. 3, 1347–1369. [4] C. M. Bishop and M. E. Tipping, Probabilistic principal component analysis, J. R. Statist. Soc. B 61 (1999), no. 3, 611–622. [5] F. W. Olver, D. Lozier, R. F. Boisvert and C. W. Clark, NIST Handbook of Mathematical Functions, Cambridge University Press, 2010. [6] C Brouder and F Patras, One-particle irreducibility with initial correlations, (2011). [7] I. Csiszar, I-divergence geometry of probability distributions and minimization problems, Ann. Probab. 3 (1975), no. 1, 146–158. [8] G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems 2 (1989), 303–314. [9] L. Grafakos, Classical and Modern Fourier Analysis, Prentice Hall, 2003. [10] D. E. Rumelhart, G. E. Hinton and R. Williams, Learning representations by back-propagating errors, Nature 323 (1986), 533–536. [11] G. E. Hinton, A Practical Guide to Training Restricted Boltzmann Machines, University of Toronto, 2010. [12] K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Networks 4 (1991), no. 2, 251–257. [13] Y. Katznelson, An introduction to harmonic analysis (third ed.), New York: Cambridge Mathematical Library, 2004. [14] D. M. Blei, A. Kucukelbir and J. D. McAuliffe, Variational inference: A review for statisticians, Jour. of the American Statistical Association 112 (2017), no. 518, 859–877. [15] S. Kullback and R. A. Leibler, On information and sufficiency, Ann. Math. Statistics 22 (1951), no. 1, 79–86. [16] H. M. Möller and R. Tenberg, Multivariate polynomial system solving using intersections of eigenspaces, Journal of Symbolic Computation 32 (2001), no. 5, 513–531. [17] B. Mourrain, V. Y. Pan and O. Ruatta, Accelerated solution of multivariate polynomial systems of equations, SIAM Journal on Computing 32 (2003), no. 2, 435–454. [18] W. Rudin, Functional Analysis, McGraw-Hill, 1991. [19] D. W. Stroock, Probability theory, an analytic view, Cambridge University Press, 1993. [20] B. Sturmfels, Solving systems of polynomial equations (cbms regional conference series in mathematics), Amer. Math. Soc., 2002. [21] N. Wiener, Tauberian theorems, Annals of Mathematics 33 (1932), no. 1, 1–100. [22] F. Zhang and C. Gao, Convergence rates of variational posterior distributions, The Annals of Statistics 48 (2020), no. 4, 2180–2207. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) U Jin Choi, Department of mathematical science, Korea Advanced Institute of Science and Technology & ujchoi@kaist.ac.kr; (2) Kyung Soo Rim, Department of mathematics, Sogang University & ksrim@sogang.ac.kr. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) U Jin Choi, Department of mathematical science, Korea Advanced Institute of Science and Technology & ujchoi@kaist.ac.kr; (2) Kyung Soo Rim, Department of mathematics, Sogang University & ksrim@sogang.ac.kr. Table of Links Introduction Organization and notation Problem Setting and Preliminaries Generalized Wiener algebra and Fréchet derivative Characterization of coefficients Coefficients from an ergodic process Conclusion & References Introduction Introduction Organization and notation Organization and notation Problem Setting and Preliminaries Problem Setting and Preliminaries Generalized Wiener algebra and Fréchet derivative Generalized Wiener algebra and Fréchet derivative Characterization of coefficients Characterization of coefficients Coefficients from an ergodic process Coefficients from an ergodic process Conclusion & References Conclusion & References Conclusion In this mathematical study, we delve into the realm of statistical inference and introduce a novel approach to variational non-Bayesian inference. Most significantly, we propose a new method for uniquely determining the hidden PDF solely from a random sample while leveraging theory. Beyond merely approximating the shape of the unrevealed PDF, we yield results that provide practical assistance in inferring important moments, such as the mean and variance, from the estimated PDF. These days, methods utilizing artificial intelligence are widely employed, but they generally exhibit a drawback of depending on initial conditions, i.e., a prior distribution, and iterative computations, i.e., a backpropagation. The limitations of such approaches lie in their reliance on these conditions to achieve global optimization. We emphasize the significant contribution of the proposed method to predictive and classification models, even without any information on populations, highlighting its potential applications in various domains such as finance, economics, weather forecasting, and machine learning, all of which present unique challenges. The most well-known method for approximating hidden PDFs is the variational approach in a Bayesian context. In contrast, our study extends this problem by seeking to precisely determine unknown PDFs through a system of equations. Our approach includes proving the Fréchet differentiability of entropy to establish the uniqueness of the energy function space in the Wiener algebra. We then derive the unique determination of the energy function through the minimization of KL-divergence. Leveraging the Ergodic theorem, we elucidate that solutions to equations comprising polynomial function series are the coefficients of the energy function and numerically substantiate the convergence of partial sums of the energy function obtained from a finite number of equations. In summary, our mathematical exploration has unveiled the potential of variational Non-Bayesian inference in Wiener space. We anticipate that these mathematical ideas can offer an innovative framework for probability density estimation and predictive modeling. Ultimately, our research emphasizes its potential contribution to reshaping mathematical horizons and expanding the boundaries of knowledge in the field of statistical methodology. REFERENCES [1] K. Bharath A. Saha and S. Kurtek, A geometric variational approach to bayesian inference, Journal of the American Statistical Association 115 (2020), no. 530, 822–835. [2] P. Alquier and J. Ridgway, Concentration of tempered posteriors and of their variational approximations, The Annals of Statistics 48 (2020), no. 3, 1475–1497. [3] A. R. Barron and C. Sheu, Approximation of density functions by sequences of exponential families, The Annals of Statistics 19 (1991), no. 3, 1347–1369. [4] C. M. Bishop and M. E. Tipping, Probabilistic principal component analysis, J. R. Statist. Soc. B 61 (1999), no. 3, 611–622. [5] F. W. Olver, D. Lozier, R. F. Boisvert and C. W. Clark, NIST Handbook of Mathematical Functions, Cambridge University Press, 2010. [6] C Brouder and F Patras, One-particle irreducibility with initial correlations, (2011). [7] I. Csiszar, I-divergence geometry of probability distributions and minimization problems, Ann. Probab. 3 (1975), no. 1, 146–158. [8] G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems 2 (1989), 303–314. [9] L. Grafakos, Classical and Modern Fourier Analysis, Prentice Hall, 2003. [10] D. E. Rumelhart, G. E. Hinton and R. Williams, Learning representations by back-propagating errors, Nature 323 (1986), 533–536. [11] G. E. Hinton, A Practical Guide to Training Restricted Boltzmann Machines, University of Toronto, 2010. [12] K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Networks 4 (1991), no. 2, 251–257. [13] Y. Katznelson, An introduction to harmonic analysis (third ed.), New York: Cambridge Mathematical Library, 2004. [14] D. M. Blei, A. Kucukelbir and J. D. McAuliffe, Variational inference: A review for statisticians, Jour. of the American Statistical Association 112 (2017), no. 518, 859–877. [15] S. Kullback and R. A. Leibler, On information and sufficiency, Ann. Math. Statistics 22 (1951), no. 1, 79–86. [16] H. M. Möller and R. Tenberg, Multivariate polynomial system solving using intersections of eigenspaces, Journal of Symbolic Computation 32 (2001), no. 5, 513–531. [17] B. Mourrain, V. Y. Pan and O. Ruatta, Accelerated solution of multivariate polynomial systems of equations, SIAM Journal on Computing 32 (2003), no. 2, 435–454. [18] W. Rudin, Functional Analysis, McGraw-Hill, 1991. [19] D. W. Stroock, Probability theory, an analytic view, Cambridge University Press, 1993. [20] B. Sturmfels, Solving systems of polynomial equations (cbms regional conference series in mathematics), Amer. Math. Soc., 2002. [21] N. Wiener, Tauberian theorems, Annals of Mathematics 33 (1932), no. 1, 1–100. [22] F. Zhang and C. Gao, Convergence rates of variational posterior distributions, The Annals of Statistics 48 (2020), no. 4, 2180–2207.

Variational Non-Bayesian Inference of the Probability Density Function: Conclusion & References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix VII. CONCLUSION In this work, we have carried out a full-blown analysis and experimental implementation of a Slice Reconciliation scheme applied to a specific CV QKD protocol (with post-selection) under simulated channel conditions anticipated for satelliteto-Earth channels. We have provided the optimal solution for the classical reconciliation process for this CV-QKD protocol in the context of massive parallelisation under the finite key regime. More specifically, we have identified the optimal block length when a large-code block is to be subdivided so as to improve the final secure key rate in bits per second. Although our results were based on a specific CV-QKD protocol and a specific GPU architecture, the type of analysis we have introduced here will apply in general terms a large suite of CV-QKD protocols run over any form of architecture that offers massive parallelisation. Our results, therefore, pave the way to optimal reconciliation system design for a wide range of practical CV-QKD systems that operate in the finite key regime. As the demand on the finite key size grows (better security thresholds), and technology advances lead to larger quantum signalling rates, the importance of optimised multithreaded CV-QKD reconciliation will grow. REFERENCES [1] N. Hosseinidehaj, Z. Babar, R. Malaney, S. X. Ng, and L. 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Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: Conclusion & References

Introduction If you've been dipping your toes in the domain of automatic speech recognition ("ASR"), there's a good chance you've come across from Meta AI Research. There are some excellent technical resources, not the least of which is the itself, that describes how the machine learning ("ML") model works. Also, the Meta AI Research team has a nice on their website. wav2vec 2.0 ("wav2vec2") original wav2vec2 paper overview of wav2vec2 I would encourage you to take a look at it since it offers a nice summary of the academic paper and since the wav2vec2 model illustrations in this article are sourced from that page. With the preceding in mind, there don't seem to be many write-ups that explain wav2vec2 in "plain English." I try to do just that with this article. This article assumes that you understand some basic ML concepts and that you're interested in understanding how wav2vec2 works at a high level, without getting too deep "into the weeds." Accordingly, the subsequent sections try to avoid a lot of the technical details in favor of simple explanations and useful analogies when appropriate. That being said, it is helpful to know early on that wav2vec2 is comprised of 3 major components: the , the , and the . Feature Encoder Quantization Module Transformer Each will be discussed in the course of starting the discussion with some basic ideas while building up to more complex (but still digestible) points. Keep in mind that wav2vec2 can be used for other purposes beyond ASR. That being said, what follows here discusses the model in an ASR-specific context. A Gentle Overview At the time it was introduced in 2020, wav2vec2 offered a novel framework for building ASR systems. What was so special about it? Before wav2vec2, ASR systems were generally trained using . That is, prior models were trained on many examples of speech audio where example had an associated transcription. To explain the idea, consider this waveform: labeled data each It is not entirely clear what this waveform represents just looking at it. But, if you are told that the speaker who generated this audio said the words "hello world", you can probably make some intelligent guesses as to which parts of the waveform correspond with the text that represents it. You might surmise - correctly - that the first segment of the waveform is associated with the word "hello". Similarly, ASR models can learn how to make associations between spoken audio waveform segments and written text. However, as the original wav2vec2 investigators point out in their paper, "[many] speech recognition systems require thousands of hours of transcribed speech to reach acceptable performance which is not available for the vast majority of the nearly 7,000 languages spoken worldwide." So, the wav2vec2 investigators invented a new model where it is not necessary to have "thousands of hours of transcribed speech" in order to train the system. They reference a useful human analogy: babies learn to speak by hearing a word, and then immediately seeing a text representation of that word. don’t They learn of speech by listening to people in their environment (e.g., their parents, siblings, etc.). wav2vec2 learns in an analogous way: by first. representations listening Of course, how this is achieved is the point of the discussion in this article. Bear in mind that wav2vec2 is broadly designed to : accomplish 2 things Learn what the speech units should be given samples of unlabeled audio. Predict correct speech units. At this point, you don't need to completely understand what is meant by these points. They will be explained below. Just keep them in the back of your head for now. Learning Speech Units Imagine you have a huge dataset of audio samples - say for some number of English speakers. Even without a formal background in , you might understand intuitively that the English language is vocalized using a set of basic sounds that are "strung together" to form words, sentences, etc. phonetics Of course, if you're an English speaker, you don't think about speaking in this way and your vocalizations of whatever you want to say are more or less automatic! But, the point is that the spoken English language - and really any spoken language - can be decomposed into more basic, discrete sounds. If we could somehow coax an ASR model to "extract" these basic sounds, it would allow us to any audio sample of spoken language using them. This is what wav2vec2 does by on audio data. encode pretraining Pretraining, in this context, means that the first part of the model's training is insofar as it is "told" what the basic sounds should be for a given set of audio data. self-supervised not explicitly Diving down a bit more, the system is "fed" a large number of audio-only examples and, from those examples, is able to learn a set of . basic speech units Thus, every audio example is effectively composed of some combination of those speech units; in the same way that you can break a spoken audio sample into a sequence of . phonemes Importantly, the basic speech units that wav2vec2 learns are shorter than phonemes and are 25 milliseconds in length. The question that arises at this point is: How does wav2vec2 learn these speech units from audio alone? The process of learning speech units begins with the Feature Encoder. wav2vec2 "encodes speech audio via a multi-layer convolutional neural network." Convolutional neural networks, or CNNs, are models that allow us to learn features from a given input without those features being explicitly identified beforehand. Each of a CNN can be thought of as extracting features from an input, with those features becoming increasingly more complex as you move up to higher layers. layer In the case of audio data, you might imagine the first layer in a CNN examining of audio information and extracting low-level features, such as primitive sounds. windows A later layer in the same CNN, leveraging the lower-level features extracted in earlier layers, would encode higher-level features, such as sounds approximating phonemes. Following this idea, wav2vec2 can begin to by passing time slices of each audio example into the Feature Encoder and generating a of each slice. "learn what the speech units should be given samples of unlabeled audio" latent representation However, the collection of latent representations do not represent discrete speech units. These representations must be discretized in some way. This is accomplished by passing the output of the Feature Encoder to a . Quantization Module Effectively, the takes all the different audio representations generated by the Feature Encoder and reduces them to a finite set of speech units. It's worthwhile to ask at this point if wav2vec2 should be pretrained on a single language or a variety of languages. Quantization Module Logic tells us that capturing speech units that represent multiple languages versus a single language are likely to be more useful when designing ASR systems that can be used across many languages. To that end, pretraining wav2vec2 with a selection of multilingual audio samples enables the model to produce speech units that do in fact capture multiple languages. The wav2vec2 investigators noted the value behind this approach since Their original findings determined "for some languages, even [audio] data is limited." "that some units are used for only a particular language, whereas others are used in similar languages and sometimes even in languages that aren't very similar." Predicting Speech Units The inventory of speech units is a first step toward being able to encode spoken language audio samples. But, what we really want to achieve is to train wav2vec2 on how these units relate to one another. In other words, we want to understand what speech units are likely to occur in the same as one another. wav2vec2 tackles this task via the Transformer layer. context The Transformer essentially allows wav2vec2 to learn, in a statistical sense, how the speech units are distributed among the various audio examples. This understanding facilitates the encoding of audio samples that the model will "see" after pretraining. Finetuning Ultimately, an ASR system needs to be able to generate a text transcription for a given sequence of audio that it hasn't "seen" before. After pretraining via the steps described above, wav2vec2 is for this purpose. This time the model is shown examples of audio samples and their associated transcriptions. finetuned explicitly At this point, the model is able to utilize what it learned during pretraining to encode audio samples as sequences of speech units and to map those sequences of speech units to individual letters in the vocabulary representing the transcriptions (i.e. the letters "a" to "z" in the case of English). The learning during finetuning completes the training of the wav2vec2 model and allows it to predict the text for new audio examples that were not part of its training during finetuning. Conclusion Of course, the low-level mechanics of wav2vec2 are far more complex than what is presented above. However, to reiterate, the idea of this article is to provide you with a simple, conceptual understanding of how the model works and how it is trained. wav2vec2 is a very powerful ML framework for building ASR systems and its introduced in late 2021 was trained on 128 languages, thus providing an improved platform for designing ASR models across multiple languages. XLS-R variation As mentioned in the Introduction, there are a number of excellent technical resources available to help you learn more. In particular, you may find those provided by to be especially useful. Hugging Face

wav2vec2 for Automatic Speech Recognition In Plain English

প্লেইন ইংরেজিতে স্বয়ংক্রিয় বক্তৃতা স্বীকৃতির জন্য wav2vec2

wav2vec2 für automatische Spracherkennung in einfachem Englisch

wav2vec2 para reconocimiento automático de voz en inglés sencillo

wav2vec2 pour la reconnaissance vocale automatique en anglais simple

सामान्य अंग्रेजी में स्वचालित वाक् पहचान के लिए wav2vec2

平易な英語の自動音声認識のための wav2vec2

일반 영어의 자동 음성 인식을 위한 wav2vec2

wav2vec2 para reconhecimento automático de fala em inglês simples

wav2vec2 для автоматического распознавания речи на простом английском языке

Düz İngilizcede Otomatik Konuşma Tanıma için wav2vec2

wav2vec2 để nhận dạng giọng nói tự động bằng tiếng Anh đơn giản

wav2vec2 用于简单英语的自动语音识别

Satoshi Nakamoto - everybody who is into crypto knows this name. But who is behind the name? Wikipedia explains it in an easy way: Satoshi Nakamoto is the name used by the presumed pseudonymous person or persons who developed Bitcoin, authored the Bitcoin white paper, and created and deployed Bitcoin's original reference implementation. However, for some reason, nobody has seen this mysterious Satoshi. All we know about him is that in 2007, he started to work on the code. In 2008, the domain Bitcoin.org was registered. And finally, on 31 October 2009, the Bitcoin white paper was published. On January 9, 2009, Satoshi launched the Bitcoin network by defining the genesis block of Bitcoin with the reward of 50 BTC. With it, the father of cryptocurrency was born, and the era of crypto started. Bitcoin The most funny thing is that nobody knows who Satoshi Nakamoto is and whether it is a single person or a group of people. But, as usual, in such cases, there are versions. The most popular personalities that are believed to be Satoshi Nakamoto are the following. Hal Finney Hal Finney (4 May 1956 – 28 August 2014) was a pioneer in cryptography and the person who was fixing bugs and making improvements in the Bitcoin code. He was also a neighbor of Dorian Nakamoto, another candidate for being the famous Bitcoin founder. The writing manner of Hal Finney was the closest to the manner used by the Bitcoin founder. Why is he not Satoshi? There are several facts that confirm this. Satoshi was actively communicating with one of the Mike Hearn, and transacting Bitcoin just at the same time when Hal was participating in a race in Santa Barbara, California. Bitcoin developers, Photographic proofs that it was him running are also available. Then, Hal was diagnosed with Amyotrophic Lateral Sclerosis (ALS) in August 2009, and since then, his health has been deteriorating. In 2010, he could hardly type. Satoshi was pretty active at that time by posting in the forum and writing emails. Another piece of evidence is the fact that when debugging Bitcoin, Hal writes to Satoshi about the issues encountered. Additionally, Hal denied being Satoshi Nakamoto until his death in 2014 due to ALS complications. At the moment, there are no reasons not to believe him. Dorian Nakamoto Dorian Prentice Satoshi Nakamoto, a Japanese living in the USA, California, is another candidate for being the Bitcoin founder. In 2014, Newsweek published that the identity of Satoshi Nakamoto was revealed. Journalist Leah McGrath Goodman went to Dorian’s home to take an interview and claimed later that during the interview, Dorian mentioned, "I was no longer involved in that, and I cannot discuss it. It's been turned over to other people. They are in charge of it now. I no longer have any connection." Later, Dorian denied being the famous and claimed he misunderstood the journalist’s question. In an interview with The Associated Press, he insisted that he had nothing to do with Bitcoin. Moreover, he used to make such mistakes as calling the main crypto Bitcoin instead of Bitcoin and referring to Bitcoin as a company, which is not correct. Also, he said he had never heard of Gavin Andresen, one of the main Bitcoin developers. Bitcoin creator Nick Szabo Nick Szabo is the most viable candidate for being Satoshi Nakamoto. Why? He is a decentralized currency enthusiast, and he created “bit gold,” which was the Bitcoin precursor and the main characteristics of which later became the ones of Bitcoin. A group of forensic scientists from Aston University analyzed the writing styles of Nick Szabo and the Bitcoin founder and came to the conclusion that it is highly likely that Nick is Satoshi Nakamoto. Additionally, the following similarities also make us believe that Nick may be Satoshi. The Bitcoin whitepaper was written using Latex, and Nick also uses Latex for his publications. Elon Musk also believes that, even though Nick may not be the actual Bitcoin founder, he is the one responsible for the development of the majority of Bitcoin ideas. Even though it is clear that Bit Gold was the Bitcoin precursor, Satoshi has never mentioned the project, which looks logical only if Nick was trying to obscure the fact that Bitcoin was a logical development of Bit Gold and he was the Bitcoin founder. Nick Szabo used to backdate his posts about Bit Gold to make it appear that he published them after the Bitcoin launch. All these facts indicate indirectly that Nick may be behind the Bitcoin whitepaper creation, and he was one of the Bitcoin founders. However, it doesn’t mean that he was the only one who did it. Craig Wright Craig Wright is the brightest and the most scandalous candidate for being Satoshi Nakamoto. Here are just some details about his claims. Wright insisted that some details mentioned in the Bitcoin whitepaper were previously mentioned in his blog. It seems, however, that entries in his blog are backdated to make them look as if they were published before the Bitcoin whitepaper went public. Other his claims are also being doubted. However, whether he is going to prove that he is the Bitcoin founder or not, he has already carved his place in the crypto community. Additionally, he is a founder of and a chief science officer at nChain. Bitcoin SV He is involved in several legal cases, including the one about the Bitcoin whitepaper copyright; other cases are about suing those who call him a scammer. In a trial slated to begin in January 2024, he is going to be finally determined as a scammer or as a rightful Bitcoin founder. However, there is one small detail to note. In his emails, Craig misspelled the main cryptocurrency name. Satoshi Nakamoto wrote the name of this cryptocurrency thousands of times on different occasions and never ever used the version by Craig. Just leaving here the link to to compare. Satoshi’s archive And just to have some fun, I'm leaving here a . I’ve enjoyed listening to it. link to a nice song

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