AIchronIA: time with artificial intelligence and space (aitopia, utopia)

 As artificial intelligence invades all domains of so-called "reality", remember that utopia was a antecedent to science-fiction. After Thomas More Utopia book (1516), this fictional "perfect" democratic society, in contrast to the then monarchies. Describing this on a far-away non-reachable place was a means to avoiding difficulties (from clerics, kings). Since then, this word describes an imaginary society possessing desirable traits (equality in justice, origin, economics, harmony etc.). Or the converse: a extremely unwishable place. It was altered to eutopia (eu- for good, or optimstic : Plato's "Republic", Tommaso Campanella's "City of the sun", Samuel Butler's "Erewhon", aka nowhere, almost backward) or dystopia (dys- for bad or pessimistic, think about 1984, "We" by Yevgeny Zamiatin, "Brave new world" by Aldous Huxley). In speculative fiction, one distinguishes subgenres like utopian/dystopian fiction. 

It can be extended to uchronia (or dyschronia) when we include different paths in time, like alternate history.

With the advent of "artifical intelligence", let us use the neologism "AIchronIA", inspired by a tweet by Greg Egan of LLM (or Large Language Models): 

, science

Don’t forget that in 2025, the days of the week will be determined by your favourite LLM, trained on day/month pairs from 2004-2023. So Jan 2 will have odds of: 

• 1/10 of being Tuesday or Thursday
• 3/20 of being Wednesday, Friday, Saturday, Sunday
  
• 1/5 of being Monday

With generative IA, we are bound to new calendars.As time and space are intricate, we may not need the relative term AItopIA. Not yet.

Note: this idea could be related to "IA" meaning "Intelligence artificielle" in French (and possibly other latin-based languages), being a suffix -ia, like in Brian Aldiss sci-fi trilogy Helliconia.

CHOPtrey: contextual online polynomial extrapolation for enhanced multi-core co-simulation of complex systems

XKCD. My hobby: extrapolating

CHOPtrey. A long, extrapolated and progressive scientific story. Ace, deuce, trey... ready?

It all started with Cyril Faure, then a PhD student  with Nicolas Pernet in real-time computing. He used to stop by my office. We had coffee, discussed books (from Descartes to Sci-Fi) and music (mostly Loudblast, Slayer, Pink Floyd, Manowar, Bolt Thrower). We exchanged ideas and concerns. One day, he told me about a worry in his thesis. Caveat: I am very bad at computer science, advanced programming, and had little hints about partitioned/slacked real-time co-simulation systems.

So this was not about programming, but simulation and co-simulation. Big physical systems (engines, aircrafts, boats) are complicated to simulate. Protocols and methods include FMI standard (Functional Mockup Interface) and FMU (Functional Mockup Unit). Partitioning them into subsystems may help the simulation, but split discrete subsystems should communicate. Fast enough to be precise, slow enough for speed-ups.

Hoher, 2011, A contribution to the real-time simulation...

So when simulated subsystems communicate at regular communication times, and one want interpolated data at a fractional time interval, one generally uses the last known value. This is called zeroth-order hold (ZOH). 

So I wondered: "this sound a little bit like aliasing, in a causal setting, let's interpolate, say, with a line or a parabola". No so easy. In this cosimulation domain, interpolation is "known" to be unstable. Even with FOH (first-order hold) or SOH (second-order hold).

A few implementations (Matlab prototypes, C++ and a final embedding in the XMOD software) and some tuning later, we produced a hierarchical scheme (with Abir, Cyril, Daniel and Mongi), based on morphological contexts, like in lossless image compression (PNG for instance). It proved very cheap, quite robust, and apparently stable. We called it CHOPtrey, from the old French words "ace, deuce, trey" (1, 2, 3 referring to sides of dice). In reference to a chop tray (or cutting board). Because it allows to chop (or cut) a big simulated system into several smaller ones. Because it is composed of three parts or chops:

• CHOPred: a Computationally Hasty Online Prediction system (improving the trade-off between integration speed-ups, needing large communication steps, and simulation precision)
• CHOPoly: Causal Hopping Oblivious Polynomials, with smoothed adaptive forward prediction improves co-simulation accuracy. They are similar to Savitzky-Golay filters or LOESS or LOWESS regression methods)
• CHOPatt: a Contextual & Hierarchical Ontology of Patterns, where data behavior is segmented  into different classes to handle the discontinuities of the exchanged signals

During this work, I learned a lot about co-simulation, parallel computing, multi-core, etc. But also a lot about least-squares parabolic regression, which I thought I knew for decades. I did not. Not the deepest theory ever, but one of my nicest experience of cross-domain collaboration (along with bioinformatics and our BRANE work on gene regulatory networks), some genuine computer engineering, with a final packaged product (implemented in xMod). Here it is:

CHOPtrey: contextual online polynomial extrapolation for enhanced multi-core co-simulation of complex systems 

Abstract : The growing complexity of Cyber-Physical Systems (CPS), together with increasingly available parallelism provided by multi-core chips, fosters the parallelization of simulation. Simulation speed-ups are expected from co-simulation and parallelization based on model splitting into weak-coupled sub-models, as for instance in the framework of Functional Mockup Interface (FMI). However, slackened synchronization between sub-models and their associated solvers running in parallel introduces integration errors, which must be kept inside acceptable bounds. CHOPtrey denotes a forecasting framework enhancing the performance of complex system co-simulation, with a trivalent articulation. First, we consider the framework of a Computationally Hasty Online Prediction system (CHOPred). It allows to improve the trade-off between integration speed-ups, needing large communication steps, and simulation precision, needing frequent updates for model inputs. Second, smoothed adaptive forward prediction improves co-simulation accuracy. It is obtained by past-weighted extrapolation based on Causal Hopping Oblivious Polynomials (CHOPoly). And third, signal behavior is segmented to handle the discontinuities of the exchanged signals: the segmentation is performed in a Contextual & Hierarchical Ontology of Patterns (CHOPatt). Implementation strategies and simulation results demonstrate the framework ability to adaptively relax data communication constraints beyond synchronization points which sensibly accelerate simulation. The CHOPtrey framework extends the range of applications of standard Lagrange-type methods, often deemed unstable. The embedding of predictions in lag-dependent smoothing and discontinuity handling demonstrates its practical efficiency.

Keywords: parallel simulation; Functional Mockup Interface (FMI); smoothed online prediction; causal polynomial extrapolation; context-based decision; internal combustion engine

Links:

• published: CHOPtrey (January 2017): SIMULATION: Transactions of The Society for Modeling and Simulation International
• arxiv: CHOPtrey: contextual online polynomial extrapolation for enhanced multi-core co-simulation of complex systems
• HAL: CHOPtrey: contextual online polynomial extrapolation for enhanced multi-core co-simulation of complex systems
• A novel CHOPtrey companion page
• Pêcheurs de perles, une parabole
• Ace, Deuce, Trey: The virtual linguist

The full set of numbers for the six sides of a die are ace, deuce, trey, cater, cinque, sice. They are from Old French (cf un, deux, trois, quatre, cinq, six of modern French). Ace is originally from the Latin for 'unit'.

Big data and Data science: LIX colloquium 2014

Sketch of the Hype Cycle for Emerging Technologies

Data science and Big data are two concepts at the tip of the tongue and the top of the Gartner Hype Cycle for Emerging Technologies. Close to the peak of inflated expectations. The Data science LIX colloquium 2014 at Ecole Polytechnique, organized by Michalis Vazirgiannis from DaSciM was held yesterday on the Plateau de Saclay, which may have prevented some to attend the event. Fortunately, it was webcast.

The talks covered a wide range of topics pertaining to Datacy (data + literacy). The community detection in graphs (with survey) keynote promoted local optimization (OSLOM, with order statistics). It was said than "We should define validation procedures even before starting developing algorithms", including negative tests; on random graphs, a clustering method should find non prominent cluster (except the whole graph), in other words no signal in noise. But there was no mention to phase transition in clustering. The variety of text data (SMS, tweets, chats, emails, news web pages, books, 100 languages, OCR and spelling mistakes) and its veracity was questioned with Facebook estimating that between 5% and 11% of accounts are fake, and 68.8 percent of the Internet is spam (how did they get the 3 figures precision?). News-hungry people would be interested in EMM News, a media watch tool aggregating 10000 RSS flux and 4000 news aggregators. With all these sources, some communities are concerned with virtual ghost town effects, and look for way to spark discussions (retweets and the likes) to keep social activity alive. Flat approaches or hierarchical grouping are still debated challenges in large-scale classification and web-scale taxonomies. Potentially novel graph structures (hypernode graphs, related to hypergraphs or maybe n-polygraphs) with convex stability and spectral theory are also proposed in the first part of the colloquium.

Big Data Cap Gap: the space between all and relevant data

While Paris-Saclay center for data science has opened its website, the unbalanced data was exposed around the HiggsML data-driven challenge. Less than 100 Higgs bosons (expected) to be detected in 10^10 yearly. Big-data analogs of the greek Pythia, as well as efficient indexing and mining methods would necessary to harness the data beast. More industrials talks concluded the colloquium, given by AXA, Amazon and Google representatives, which i could not attend, left with the so-called "crap gap" in mind, i. e. the gap between Relevant Data and Big Data. 

Innovation driven by large data sets still requires, at least, vague goals in mind. In Latin, "Ignoranti quem portum petat nullus suus uentus (ventus) est", wrote Sénèque in his 71th letter to Lucilius. A possible translation in English: "When a man does not know what harbour he is making for, no wind is the right wind". In German, "Dem weht kein Wind, der keinen Hafen hat, nach dem er segelt". And "Il n'y a point de vent favorable pour celui qui ne sait dans quel port il veut arriver" in French.

All of the information, and possibly information you need, may be found in the following program and videos. As the videos are not split into talks, the time codes are provided, thanks to the excellent suggestion (and typos corrections) by Igor Carron.

LIX colloquium 2014 on Data Science LIVE part 1

• 00:00:00 > 00:22:22: Introduction and program
• 00:22:22 > 01:22:18: Keynote speech: Community detection in networks, Santo Fortunato, Aalto University
• 01:22:18 > 01:57:30: Text and Big Data, Gregory Grefenstette, Inria Saclay - Île de France
• 01:57:30 > 02:29:23: Accessing Information in Large Document Collections: classification in web-scale taxonomies, Eric Gaussier, Université Joseph Fourier (Grenoble I)
• 02:29:23 > 03:01:32: Shaping Social Activity by Incentivizing Users, Manuel Gomez Rodriguez, Max Planck Institute for Software Systems
• 03:01:32 > 03:38:00: Machine Learning on Graphs and Beyond, Marc Tommasi, Inria Lille

LIX colloquium 2014 on Data Science LIVE part 2

• 00:00:00 > 00:33:57: Learning to discover: data science in high-energy physics and the HiggsML challenge, Balázs Kégl, CNRS
• 00:34:11 > 01:06:15: Big Data on Big Systems: Busting a Few Myths, Peter Triantafillou, University of Glasgow
• 01:06:15 > 01:38:29: Big Sequence Management, Themis Palpanas, Paris Descartes University

LIX colloquium 2014 on Data Science LIVE part 3 

• 00:00:00 > 00:38:11: Understanding Videos at YouTube Scale, Richard Washington, Google
• 00:38:11 > 01:05:48: AWS's big data/HPC innovations, Stephan Hadinger, Amazon Web Services
• 01:05:48 > 02:02:41: Big Data in Insurance - Evolution or disruption? Stéphane Guinet, AXA 
• 02:02:41 > 02:06:35: Closing words on a word cloud (with time, series, graph and classification are the big four)

Time-frequency methods for condition based maintenance: call for papers

Call for Papers (CfP): Special issue on time-frequency methods for condition based maintenance and modal analysis

In recent years, research in the area of Condition-Based Maintenance (CBM) and Modal Analysis (MA) has been growing rapidly benefiting from the development of more advanced and accurate time-frequency methods. CBM is a decision making strategy which was introduced to recommend maintenance actions when the output of the sensors placed on the physical equipment fulfill a set of predefined criteria defining abnormal behaviors. CBM is based on using real-time data to prioritize and to optimize maintenance resources. It enables real-time diagnosis of impending failures and prognosis of remaining useful life of equipment without interrupting normal operations through analyzing and interpreting the measured data. Intensively used in CBM, the MA is concerned with the identification of the natural mode shapes and frequencies of structures or fluids under vibrational excitation.
Multi-purpose time-frequency methods like the short-time Fourier transform, the Wigner-Ville distribution or wavelets etc. are signal processing techniques which are applied in mechanical structures analysis for investigating functional parameters that are evolving in time like structural damages, cracks or deformations. Their usefulness in practice consists mainly in the fact that they are non-invasive techniques. This call for papers is aiming to stimulate not only new insides on the standard time-frequency techniques for signal processing but also recently developed methods like non-stationary Gabor frames, curvelets, adaptive wavelet families, sparse decompositions etc. with potential applications to dynamic data processing in manufacturing, mechanical structures and acoustics. Therefore, the main objective of this special issue is to bring the ideas of worldwide research community to present the latest developments and to advance the fields of CBM and MA through applications of the modern time-frequency processing techniques.

The following topics are of main interest:

• Time-frequency methods for diagnostics, prognostics and features extraction
• Time-frequency methods for modal analysis
• Wavelet-based vibration monitoring and control
• Natural frequencies identification
• Sparse signal analysis
• Online, segment-wise and parallel time-frequency implementations
• Assessment studies of different signal processing techniques
• Hardware and application constraints when building CBM systems
• Applications in radar, mechanics, acoustics, etc.

The specified topics should be viewed as guidelines but any original interpretation of the signal processing methods for CBM and MA will be considered.

Important dates:

• Manuscript due: November 30, 2012
• First Round of Reviews: January 31, 2013
• Second Round of Reviews: March 31, 2013
• Publication date: June 1, 2013

Submission Guidelines:
Authors are invited to submit their papers via the Elsevier submission web site at http://ees.elsevier.com/sigpro/login.asp and please select the Item: “Time-frequency for CBM&MA” after you log in as an author. Please also refer to the complete Author Guidelines.

Guest editors:

• Dr. Darian M. Onchis - University of Vienna & University of Resita, (darian.onchis@univie.ac.at)
• Dr. Ruqiang Yan - Southeast University, (ruqiang@seu.edu.cn)
• Dr. Pavel Rajmic - University of Brno, (rajmic@feec.vutbr.cz)

And of course remember this other recent special issue on the topic of multirate/multiscale methods in signal and image processing. Signal Processing: Special issue on Advances in Multirate Filter Bank Structures and Multiscale Representations, Vol. 91, Issue 12, Dec. 2011 (more here).