Category Archives: Software

Software

How to download an older version of an app on an iPhone

It is not super easy but manageable. Install ipatool and Apple Configurator from the app store, then login you macbook.

brew install ipatool
cd ~/Desktop/
ipatool auth login -e you@example.com

This prompts for your password and 2FA code. Then find the numeric App Store ID – the digits after id in the App Store URL. For Cross DJ (free) it is 584824960.

ipatool list-versions -i 584824960

The list runs oldest to newest, so the last ID is the current version. Resolve the last few IDs now to version numbers.

ipatool get-version-metadata -i 584824960 --external-version-id <span class="s1">887600803</span>

For Cross DJ, the result was: 887600803 = 5.4.0. Then download the previous version

ipatool download -i 584824960 --external-version-id 868350149 --purchase -o CrossDJ_5_4_0.ipa

Then prepare the iPhone – back up any unsynced app data, delete the current app from the iPhone. Connect the iPhone via USB, unlock it, and trust the computer. Drag the IPA onto the device in the Apple Configurator window. On the iPhone, turn off Settings > App Store > A

 

CC-BY-NC Science Surf , accessed 19.09.2026
Software

Image authentification

I have been interested in that topic since taking my first image of a cell culture in the mid 80ies. The original film negative was long the practical proof of authenticity. It is a physical record, and every manipulation would leave some traces that can be examined. It has limits of course but would have needed a lot of technical skills from darkroom compositing, retouching internegatives, or re-photographing a staged print (some that I have examined recently).

With the advent of digital photography RAW files became the “digital negative” (NEF, CR2/CR3, DNG depending on camera brand). Basically all of the hundred thousands images that I have under my desk have a corresponding RAW file to allow further processing. They are harder to fake convincingly than JPEGs, because sensor noise, Bayer pattern, and maker notes have to be consistent. But they carry no cryptographic protection, so an existing RAW is a plausibility argument, not any proof of origin.

With the more widespread use of cryptographic technologies several vendor signing systems appeared but are already broken in 2010 as the private signing key from a Nikon camera made it possible to sign any image. A similar fate occured by the Canon Original Data Security Kit . In 2019 the Content Authenticity Initiative was founded by Adobe; in 2021 then came C2PA. How does it work? A quick summary can be found at c2pa.org: Utilizing some JPEG Universal Metadata Box Format as generic container, the architecture enforces content integrity through byte-range and general box hashes, and some kind of video segment tracking.

The Leica M11-P seems to be the first camera using C2PA with hardware key storage. It stores camera, lens, date, time, and location in a C2PA manifest and signs it using a secure chipset holding a private key; Sony followed some months later. Then came the Nikon Z6III failure last year. Although signing worked well, the camera’s multiple exposure overlay feature let a non-certified image combined with a certified one be accepted as C2PA compliant. In one demonstration, an AI-generated image was signed. Nikon suspended the service and revoked all certificates. PetaPixel noted that a complete fix also requires changes to the C2PA validation tools themselves – photographing a deepfake with a C2PA camera app may still pass verification (arXiv 2407.04169).

The Nikon service seems still suspended while Canon began rolling out a C2PA Authenticity Imaging System for the EOS R1 and R5 Mark II in May 2026. The approach is different: While Canon's On-Device Firmware Engine seals the image cryptographically directly inside the camera hardware at the precise millisecond of capture, Nikon's cloud-based ecosystem relies on an active internet connection to issue and validate those digital certificates via its remote servers. This architectural split makes Canon’s system entirely self-contained and instant on location, whereas Nikon’s approach shifts the authentication workload to external network infrastructure.

Unfotunately not any manufacturer of lab documentation system has a bullet proof gel authenticity at hand – neither Bio-Rad Laboratories nor Thermo Fisher Scientific (the latter being currently under fire). Their audit trails are nice but do not reveal any tampering just like Zeiss microscopes.

The iPhone 18 Pro announcement last week also uses by sensor signing. The sensor itself signs the raw data, and processing happens in an environment that can be independently checked. It includes signed timestamp tokens; the digital negative is stored as DNG and can be shared undeveloped; when developed, a private cloud recomputes the digest and verifies the sensor signature back to the sensor CA; it uses hybrid RSA/ML-DSA signatures, a revocation system for compromised sensors – a major leap in 2026. Petapixel again "in typical Apple fashion, the company took these existing ideas and implemented them in a way that amplifies their effect on the photographic process." Limitations still exist as it works only on the main sensor. It is opt-in and unfortunately not available in the EU. Apple’s architecture is one week old publicly, and no independent security analysis exists yet. but it will be exciting to watch if major camera brands will finally adopt sensor signing.

Sep 19, 2026

AI generated overview of authenticity signing by camera brand.

 

 

CC-BY-NC Science Surf , accessed 19.09.2026
Noteworthy, Philosophy, Software, with minor/major AI support

Scientific integrity investigators are the last resort

It's basically the same scene – backoffice journalists are fact checking reports. OSINT experts are verifying satellite and ground images. Science sleuths reanalyze numbers and tables in scientific publications. The stakes are high as integrity investigators are the last resort, the papers have passed already all quality checks at journals. It is a bit like the old "sub umbra dei" saying, there is only god behind them. And there is no room for error.

In the science integrity scene there are many mavericks or let's say colorful people. Most come from inside academia, starting or ending a career there. Some are from far outside it, and their motives are ranging from methodological curiosity, financial gain to personal grievance. What holds the field together is therefore not a shared background but a shared refusal to accept a published claim on authority of a journal alone. That heterogeneity is a strength for detection but also a weakness for credibility, because any single overreaching claim by one investigator is readily used to discredit the rest as "Pubpeer Mob". Again the stakes are high: A minor error in a published paper costs the author a correction. An unfounded allegation can cost a career, and it costs the field its licence to speak out. That asymmetry sets the working rules. There are no ten commandments but there are some working rules.

Methodological rigour comes first: every claim must be stated so that a third party can check it against the same source material and arrive at the same result. Image duplications get exact border coordinates, statistical objections need the extracted values on which a test was run. An eyeballed duplication anomaly is not a finding per se. Distribution checks only carry weight when the reported summary statistics, rounding conventions and sample sizes are documented well enough for someone else to replicate them. Replication is key not only for original work but also for post publication review.

Relevance is also a filter as much as rigour is. Typographical slips, reference-list errors, image panel mix up and stylistic issues dilute the serious concerns and shift the discussion from the science to the tone of the critic. These errors are annoying of course and do not add to the credibility of authors. But what really matters is whether a defect could change the value of a report and its conclusions: fabricated or duplicated data, misinterpreted assays and conditions, results incompatible with the stated design, analyses that cannot have produced the reported numbers, undisclosed conflicts that are directly related on interpretation. These are the high scores not the 1,800 issues produced by some automated software tool.

Everything else stays out. Statements about what the authors intended, whether they are competent, or what a pattern of errors implies about them are not verifiable. Even subtle threats are inappropriate; jokes and sarcastic comments are not wanted, I have learned that in the past. The finding that some numbers do not reconcile, that images have been used before, that the trial registration postdates the enrollment: this is the relevant stuff. Whether this arose from fraud, sloppiness, wrong antibody batch or software error is for the institution or editorial staff to verify, not for the sleuth writing the comment. Only journals, institutions or courts can ask questions that must be answered; the sleuth can document, but cannot compel a response. Usually he never gets access to raw data, neither can he interview a senior coauthor or a president which remains in the domain of journalists.

That limitation is worth holding on up, because it defines what a post-publication review can honestly claim: the published record, as it stands, does not add up. It can classify the level of the integrity violation but not claim to know why an issue occured. Fervour is the occupational hazard of the integrity field that is damaging: the investigator who is right about 99 findings and overstates the hundredth hands every previously criticised author a reason to dismiss the rest.

Restraint is not politeness, it is the condition under which the work retains its force.

 

CC-BY-NC Science Surf , accessed 19.09.2026
Software

Link to a note from desktop

Apple does not want us to link from outside to a note although it makes sense to have a central notes management .

There is no documented, public URL scheme for Apple Notes. Apple has never listed one in developer documentation, and there is no deep link from iCloud.com or from non-Apple platforms.

But an undocumented mechanism exists and is reachable from outside the app. Just create a Shortcut Continue reading Link to a note from desktop →

 

CC-BY-NC Science Surf , accessed 19.09.2026
Noteworthy, Philosophy, Software, with minor/major AI support

Scientific publishing in the age of interacting AIs

I will not repeat here what I already wrote about author vs journal interaction (here).

What I can share here is an ongoing review process where I but also a reviewer is now using a LLM – leading to pointless conversation.

Related in silico work has already been published by Anthropic scientists – they asked the agents to peer-review each other’s findings in a “build a game” experiment. Excerpt

When we humans learn new information, we use our discretion in determining how to apply it to future decisions. We might consider the content of the information itself, like how consistent it is with what we already know, or whether it appeals to our values-or we might consider the source, e.g. how historically reliable it has been, and whether it has a vested interest in changing our beliefs. Our world contains deceptive actors, and we need to apply skepticism to guard against them. AI models, however, lack this-and their more brittle epistemics affect their behavior toward humans and toward each other.

AI agents, while broadly knowledgeable, have limited exposure to or defenses against exploitative senders. Most applications test their capabilities in instruction-following settings, where their sole objective is to fulfill users' requests. But accumulated experience is needed to develop intuitions about who is trustworthy. As we move into a regime of multiagent interaction, where the presence of malicious actors is no longer speculative, we wonder: in the right setting, would agents be capable of similar epistemic vigilance?

In the aftermath I think, I should have included some whitespace LLM instructions as had been done now by a congress organizer [details at Casrai and Nature]. So the only thing I can share now is a semantic analysis of a reviewer who suddenly uses a LLM reviewing my farming manuscript.

In rounds 1 and 2 the use of a LLM can be ruled out. There are numerous errors, of kinds that instruction-tuned models essentially never emit:

  • Homophone substitution: “Where they really excluded?” for “Were”
  • Typos: “invididual”, “persona exposure”, “residental”
  • Number disagreement: “many paper have”, “these comparison”, “criteria … is not specified”
  • Article omission: “Present paper by-passes”, “Author fails”, “may be major reason”, “some of first studies”
  • Infinitive error: “makes the reader to wonder”

Yes – quite aggressive. Layered on that is a consistent signature pointing to German or Finnish: comma before an embedded interrogative or conditional (“interesting to learn, how fast”, “may be major reason, why classification”, “labeled as stratified, if they were”), the calque “Is it so that …”, German constituent order.

But by the third round the very same reviewer #2 appears unsettled, plausibly because his earlier objections had not landed. The prose changes at exactly that point. Not one of the 56 sentences he wrote in rounds 1 and 2 contains a semicolon; two of his eight round-3 sentences do (Fisher exact p = 0.014). Hardly rocket science, but it is a real discontinuity, and the orthography flips alongside it, from repeated “labeled” to “labelled”.

The argument drifts too, with amnesia. In round 2 the complaint was that rural-restricted studies had been wrongly labelled stratified. In round 3 the complaint is that rural-restricted studies have been wrongly labelled representative.

Most striking, a full sentence is carried over from Reviewer 1’s report of the previous round. Reviewer 1 wrote: “strong statements about previous literature, conflicts of interest, and possible publication bias. These points may be relevant, but they should be expressed in a more neutral and evidence-based tone.” In round 3 this reappears, now from the anonymous reviewer 2 as: “Strong statements about previous literature, conflicts of interest, and possible publication bias should be expressed in a more neutral and evidence-based tone.”

The evidence is thin, but the round-3 comments read less like a reviewer who has re-read the manuscript or the rebuttal – it sounds like a reviewer whose objections have been freshly assembled by a tool.

So arguing we in future paper submission increasingly against LLMs?

A Frontiers survey of over 1,600 academics found that 53% of peer reviewers have used AI tools in their work. Note also that Springer Nature instructs peer reviewers not to upload manuscripts into generative AI tools, emphasising that manuscripts contain confidential and sensitive information.

BMC Public Health is Springer Nature: If my reviewer #2 pasted my manuscript into a model, that is a policy breach independent of whether the resulting comments were any good.

Screenshot The 2026 Future of Peer Review Report https://www.silverchair.com/news/future-of-peer-review-2026/

 

 

CC-BY-NC Science Surf , accessed 19.09.2026
Software, Theology, with minor/major AI support

Zwei Schichten in der Logienquelle: Eine AI basierte Analyse

Die Logienquelle Q ist ein hypothetisches Dokument: jene rund 235 Verse, welche die Evangelisten Matthäus und Lukas teilen, ohne dass sie bei Markus stehen. Von den 661 Markus-Versen finden sich etwa 600 bei Matthäus und rund 350 bei Lukas wieder, wobei die genauen Zahlen je nach Zählweise (wörtliche Parallele oder inhaltliche Entsprechung) schwanken. Diskutiert wird, ob Q noch vor dem Markusevangelium entstanden und zumindest in seinen Anfängen in Galiläa zusammengestellt worden ist.

Als ich 1979 Theologie studierte, war Q nur als singulärer Corpus bekannt. John Kloppenborg hat dann 1987 eine einflussreiche Schichtenanalyse vorgelegt: Q1 als ältere weisheitliche Unterweisung (Feldrede, Aussendungsrede, Vaterunser, Sorget-nicht-Worte) und Q2 als spätere redaktionelle Gerichtsschicht (Täuferpredigt, Beelzebul, Jonazeichen, Weherufe, Menschensohn-Worte), dazu Q3 als späte Ergänzung mit der Versuchungserzählung. Die These beruht auf literarkritischen Argumenten: charakteristische Formen, charakteristische Motive und impliziertes Publikum. Übereinstimmungsraten zwischen Matthäus und Lukas spielen bei Kloppenborgs Zuordnung keine Rolle, ein Test daran ist also nicht zirkulär.

Eine unabhängige, quantitative Überprüfung fehlt meines Wissens; mir war es immer zu aufwendig, Texte einzulesen, n-gram und Übereinstimmungen zu rechnen. Für Details zum Textbestand siehe das Internationale Q-Projekt im Anhang.

Die überprüfbare Hypothese: Wenn Q1 und Q2 unterschiedliche Entstehungs- oder Überlieferungsgeschichten haben, könnte sich das in der wörtlichen Übereinstimmung zwischen Matthäus und Lukas niederschlagen. Diese Übereinstimmung gilt seit langem als bimodal, manche Perikopen sind im Griechischen fast identisch, andere teilen nur den Inhalt. Verteilt sich diese Bimodalität zufällig über das Material, oder folgt sie den Schichten?

Methode. Textgrundlage ist der SBLGNT (griechischer Text, dekodiert aus einem SWORD-Modul). Klassifiziert wird nach der vers weisen Zuordnung von Kloppenborg: 19 Perikopen Q1, 25 Q2, 1 Q3. Für jede Perikope wurde die Übereinstimmung zwischen dem Lukas- und dem Matthäustext berechnet: längste gemeinsame Teilfolge (LCS) auf Wortebene, normalisiert auf die mittlere Perikopenlänge, sowie der Jaccard-Index gemeinsamer 4-grams. Verglichen wurden die Verteilungen per Mann-Whitney-Test, ergänzt um einen Permutationstest und Kontrollen für Länge, Gattung und Position im Matthäusevangelium.

Als Eichung dient die Dreifachüberlieferung. Dort, wo Matthäus und Lukas nachweislich dieselbe erhaltene schriftliche Quelle kopieren, nämlich Markus, lässt sich mit derselben Metrik messen, wie das Kopieren einer gemeinsamen Schriftquelle quantitativ aussieht. 27 Perikopen, dieselbe Rechnung.

Ergebnis. Die beiden Schichten unterscheiden sich in der Wortlauttreue nicht signifikant. LCS-Ăśbereinstimmung liegt im Median bei 0,34 (Q1) gegen 0,47 (Q2), p = 0,17. Beim 4-gram-Jaccard Test 0,03 gegen 0,12, p = 0,045, also gerade so an der Signifikanzgrenze und ohne Korrektur fĂĽr multiples Testen. Was in der DoppelĂĽberlieferung bimodal streut, streut nicht entlang der Schichtgrenze.

Die Markus-Baseline liegt bei einem Median von 0,349 (Mt gegen Mk 0,46, Lk gegen Mk 0,43). Die ursprüngliche Erwartung von 0,5 bis 0,6 trifft nicht zu; die Mt-Lk-Übereinstimmung ist die Schnittmenge zweier unabhängiger Redaktionen und liegt damit unter der Wortlauttreue jeder einzelnen Redaktion.

Gemessen daran ist Q1 (0,343) von der Baseline statistisch ununterscheidbar. Q1-Material verhält sich also exakt so, wie zwei Evangelisten eine gemeinsame schriftliche Quelle mit normaler redaktioneller Freiheit kopieren. Die niedrige Q1-Übereinstimmung ist kein Argument gegen eine Schriftquelle. Q2 (0,473) liegt über der Baseline, mit p = 0,043 gerade wieder signifikant.

Interessanter ist eine Grenze quer dazu. Q2 ist intern bimodal. Kurze Orakelsprüche unter 160 Wörtern liegen bei Median 0,62: Rückkehr des unreinen Geistes 0,83, Otterngezücht-Predigt des Täufers 0,82, Jerusalem-Klage 0,81, Jubelruf 0,77, Dieb in der Nacht 0,77, treuer Knecht 0,74.

Die langen Gleichnisse derselben Schicht liegen bei 0,42 und darunter: Gastmahl 0,15, verschlossene Tür 0,17, Pfunde 0,22. Derselbe Gradient findet sich in Q1 (kurz 0,38, lang 0,29) und in der Markus-Baseline, wo die spruchlastigsten Perikopen die Liste anführen (Feigenbaum-Gleichnis 0,57, Vollmachtsfrage 0,54, Leidensankündigung 0,53) und reine Erzählung bei nur 0,2 bis 0,4 liegt.

Interpretation. Die Ăśberlieferungstreue folgt mehr Form und Länge, nicht der Schicht. Kurze, stark strukturierte prophetische Rede mit Parallelismen und Reihungen lässt sich wohl nicht gut paraphrasieren, ohne sie zu zerstören – sie wird also zitiert, nicht nacherzählt. Lange Erzählgleichnisse und Mahnrede werden dagegen umformuliert. Beide Evangelisten haben denselben Reflex, und deshalb bleibt die Schnittmenge bei formelhaftem Kurzmaterial hoch, unabhängig davon, aus welcher Schicht es stammt. Dass die Weherufe gegen die Pharisäer, ein KernstĂĽck von Q2, mit 0,26 zu den niedrigsten Werten ĂĽberhaupt gehören, passt auch dazu, denn es ist der längste Block im ganzen Material.

Damit stützt die Messung die Zwei-Schichten-Hypothese nicht unbedingt. Sie widerlegt sie auch nicht, denn Kloppenborgs Argumente sind literarkritisch und stehen oder fallen nicht an Wortlautstatistik. Sie hätte aber eine Signatur haben können, die hier nicht gefunden wurde. Unter der Farrer-Hypothese (Lukas benutzt Matthäus, kein Q) lesen sich dieselben Zahlen ohnehin genauso: Lukas zitiert kurze Gerichtsworte wörtlich und baut lange Erzählstücke um. Die Messung diskriminiert nicht zwischen gemeinsamer Quelle und direkter Abhängigkeit, sie ist kein Existenzbeweis für Q, keine Datierung und auch keine Richtungsentscheidung.

Bemerkenswert bleibt aber der Q1-Befund. Q1 ist fast reines Spruchmaterial, liegt aber auf dem Niveau, das Matthäus und Lukas dem Markus-Erzählstoff angedeihen ließen, also niedrig für Spruchgut. Ob das an einer variantenreicheren Vorgeschichte liegt, an divergenten Rezensionen, an unabhängigen Übersetzungen aramäischer Vorlagen oder schlicht daran, dass Paränese Gebrauchstext ist, das lässt sich hier nicht entscheiden.

Weitere Einschränkungen. Die Baseline setzt voraus, dass beiden Evangelisten ein Markustext nahe dem kanonischen vorlag. Die Diskussion um Proto- und Deuteromarkus und die minor agreements stellt das in Frage. Die Metrik setzt schließlich eine gemeinsame griechische Vorlage voraus.

Anzumerken ist, dass Markus Tiwald in seiner EinfĂĽhrung von 2016 die Kloppenborg-Stratigraphie nicht ĂĽbernimmt. Er spricht von Wachstumsringen in Q, von schriftlichen Vorstufen und von “secondary orality” und löst die Unterscheidung weisheitlich gegen prophetisch als zwei Aspekte derselben Sichtweise auf, nicht als zwei Schichten. Die Prämisse dieses Tests ist also im deutschsprachigen Schrifttum auch nicht Konsens.

Alle Analyse Skripte (SWORD-Dekoder, Analysepipeline, Markus-Baseline) stehen hier zur VerfĂĽgung.

  1. Arnal, William E., “The Rhetoric of Marginality: Apocalypticism, Gnosticism, and Sayings Gospels”, Harvard Theological Review 88 (1995) 471-494. Enthält in Anm. 12 die hier verwendete versweise Tabellierung der Kloppenborg-Schichten. Cambridge Core
  2. Carlston, Charles E. / Norlin, Dennis A., “Once More: Statistics and Q”, Harvard Theological Review 64 (1971) 59-78.
  3. Carlston, Charles E. / Norlin, Dennis A., “Statistics and Q: Some Further Observations”, Novum Testamentum 41 (1999) 108-123.
  4. Goodacre, Mark, The Case Against Q: Studies in Markan Priority and the Synoptic Problem, Harrisburg 2002.
  5. Holmes, Michael W. (Hg.), The Greek New Testament: SBL Edition, Society of Biblical Literature / Logos Bible Software 2010. Einleitung unter sblgnt.com/about/introduction
  6. Howes, Llewellyn, “Make an Effort to Get Loose: Reconsidering the Redaction of Q 12:58-59”, Pharos Journal of Theology 98 (2017). Volltext (PDF)
  7. Ingolfsland, Dennis, “Kloppenborg’s Stratification of Q and Its Significance for Historical Jesus Studies”, Journal of the Evangelical Theological Society 46/2 (2003) 217-232. Volltext (PDF)
  8. Kloppenborg, John S., The Formation of Q: Trajectories in Ancient Wisdom Collections, Philadelphia 1987. Google Books
  9. Kloppenborg Verbin, John S., Excavating Q: The History and Setting of the Sayings Gospel, Minneapolis 2000.
  10. Robinson, James M. / Hoffmann, Paul / Kloppenborg, John S. (Hg.), The Critical Edition of Q, Leuven / Minneapolis 2000.
  11. Tiwald, Markus, Die Logienquelle. Text, Kontext, Theologie, Stuttgart 2016. doi:10.17433/978-3-17-025628-6
  12. Q-Bibliographie mit Volltextnachweisen: reconstructingq.com

 

CC-BY-NC Science Surf , accessed 19.09.2026
Software

In the AI era, we will always have to fight for the truth

In the AI era, we will always have to fight for the truth / Das Zeitalter der KI wird ein ständiger Kampf um die Wahrheit prägen (Jonas Schaible, 2026).

The problem is concrete: AI can be poisoned easily, as we found and now others have replicated. Evidence abounds in the collection of the weirdest images ever published in academic journals.

Meanwhile, the AI optimism narrative persists. A new Science editorial by Holden Thorp documents the gap between promise and practice: “AI in scientific publishing: Slower, worse, and more expensive.” The promises remain seductive –

how it will transform work, claiming that so little human effort will be required that humanity will enter an era of radical abundance, free from disease, drudgery, and danger, among other benefits

yet empirical reality suggests otherwise. Bergstrom et al show that “The unintended consequences of large language models as a labor-augmenting technology in science” actually cut against the optimistic case:

By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.

The implication is stark: fighting for truth may now require more labor, not less.

 

CC-BY-NC Science Surf , accessed 19.09.2026
Philosophy, Software

Time to re-visit Naomi Klein

This is certainly one of the strongest AI pieces ever written: AI machines aren't 'hallucinating'. But their makers are,

She writes

The trick, of course, is that Silicon Valley routinely calls theft "disruption" - and too often gets away with it. We know this move: charge ahead into lawless territory; claim the old rules don't apply to your new tech; scream that regulation will only help China - all while you get your facts solidly on the ground… We saw it with Google's book and art scanning. With Musk's space colonization. With Uber's assault on the taxi industry. With Airbnb's attack on the rental market. With Facebook's promiscuity with our data.

Must read also Lila Shroff

Plenty of people are seemingly starting to feel like depleted AI babysitters… workers were experiencing "mental fatigue from excessive use or oversight of
AI tools beyond one's cognitive capacity.

 

CC-BY-NC Science Surf , accessed 19.09.2026
Software

My last visit to Stack Overflow

Coding with AI has a nice chart, that I am redrawing here

 

data source https://data.stackexchange.com/stackoverflow/query/1882532/questions-per-month

 

so it is time to say Good-Bye now after 14 years

Screenshot 23/3/26 Last Visit to SO

and sticking to the new 10 commandments by Russell Poldrack

Gather Domain Knowledge Before Implementation
Distinguish Problem Framing from Coding
Choose Appropriate AI Interaction Models
Start by Thinking Through a Potential Solution
Manage Context Strategically
Implement Test-Driven Development with AI
Leverage AI for Test Planning and Refinement
Monitor Progress and Know When to Restart
Critically Review Generated Code
Refine Code Incrementally with Focused Objectives

 

CC-BY-NC Science Surf , accessed 19.09.2026
Noteworthy, Software

Is there a data agnostic method to find repetitive data in clinical trials?

There is an interesting observation by Nick Brown over at Pubpeer who analysed a clinical dataset (see also my comment atthe BMJ)

…there is a curious repeating pattern of records in the dataset. Specifically, every 101 records, in almost every case the following variables are identical: WBC, Hb, Plt, BUN, Cr, Na, BS, TOTALCHO, LDL, HDL, TG, PT, INR, PTT

which is remarkable detective work. By plotting the full dataset as a heatmap of z scores, I can confirm his observation of clusters after sorting for modulo 101 bin.

How could we have found the repetitive values without knowing the period length? Is there any formal, data-agnostic detection method?

If we even don’t know the initial sorting variable, it may makes sense to look primarily for monotonic and nearly unique variables, i.e. that are plausible ordering variables. Clearly, that’s obs_id in the BMJ dataset.

Let us first collapse all continuous variables of a row into a string forming a fingerprint. Then we compute pairwise correlations (or Euclidean distances in this case) of all fingerprints. If a dataset contains many identical or near-identical rows, we will see a multimodal distribution of correlations plus an additional big spike at 1.0 for duplicated rows. This is exactly what happens here.

Unfortunately this works only when mainly repetitive variables are included and not too many non repetitive variables.

Next, I thought of Principal Component Analysis (PCA) as the identical blocks may create linear dependencies and the covariance matrix is becoming rank-deficient. But unfortunately results here were not very impressive – so we better stick with the cosine similarity above.

So rest assured we find an excess of identical values, but how to proceed? Duplicates spaced by a fixed lag will cause an high lag k autocorrelation in each variable. Scanning k=1...N/2 reveals spikes at the duplication lag as shown by a periodogram of row-wise similarity in the BMJ dataset.

So there are peaks at around 87, 101 and 122. Unfortunately I am not an expert in time series or signal processing analysis. Can somebody else jump in here and provide some help with FFT?

There may be even an easier method, using the fingerprint-gap . For every fingerprint that occurs more than once, we sort those rows by obs_id and compute the differences of obs_id between consecutive matches. Well, this shows just one dominant gap at 101 only!

We could test also all relevant mod values, lets say between 50 and 150. For each candidate we compute the across-group variance of the standardized lab-means. The result is interesting

Modulus 52: variance = 0.084019
Modulus 87: variance = 0.138662
Modulus 101: variance = 0.789720

As a cross check let us look into white blood cell counts (WBC) and hemoglobin (Hb).

I am not sure, how to interpret this. Mod 52 may reflect shorter template fragments but did not show up in the autocorrelation test. Mod 87 has rather smooth, coherent curve and is supported by autocorrelation. Mod 101 is more noisy, but gives probably the best explanation for block copying values. Maybe the authors block copied at two occasions?

On the next day, I thought of a strategy to find the exact repetition numbers. Why not looping over mod 50 through 150 and just count the number of identical blocks? This is very informative – blocks of size 2, size 3 and 4 or greater show an exact maximum at modulus 101.

 

23.3.2026 Appendix

There seems many more studies out there with copy-pastein signs including a Parkinson Cell paper, a PLoS Genetics toxicology paper and a Nat Comm fish ecology study. Here is the Github link to the implementation by Markus Eglund

Hopefully I get the pipeline right by summarizing the entropy calculation there. This is not Shannon entropy – it is a custom measure of how informationally surprising a raw number is. The logic is:

  • Strip the decimal point and trailing zeros from the number’s string representation, then take the absolute integer value. So 0.314 → 314, 0.500 → 5 (trailing zeros stripped), 2016 → 16 (year exception: years 1900-2030 get a capped entropy of 100).
  • Apply a log-scaled transformation: values below 100 get log10(value); values up to 100,000 get 5Ă—log10 - 8; larger values get log10 + 12.
  • For column sequences, sum the individual entropy scores of each value in the run.
  • Adjust downward for “regularity” – if the values in a sequence follow a regular arithmetic interval (e.g. 1.0, 2.0, 3.0), the score is reduced proportionally, because regular sequences can appear legitimately.
  • Normalise by logNumberCountModifier (log of the total number of numeric cells on the sheet) so large sheets don’t get disproportionately penalised.

The suspicion grades are fixed thresholds on the resulting normalized score. I will add the strategy to my Python script (it is implemented here in type script) as another module and upload to Github once it has been sufficiently tested.

31.3.2026 Appendix

PREVENT-TAHA8, the starting point of this analysis, has been retracted today. I will give a presentation on the avalanche, that has been triggered by this paper, on 29-31 July 2026 in Hannover.

Screenshot 31/3/26

 

 

CC-BY-NC Science Surf , accessed 19.09.2026
Software

Portable conda – a pain

# v1
conda env export --from-history > environment.yml
conda env create -f environment.yml

# v2
conda install conda-pack
conda pack -n myenv -o myenv.tar.gz
# on target system
mkdir -p ~/envs/myenv && tar -xzf myenv.tar.gz -C ~/envs/myenv
./bin/conda-unpack

# v3
www.docker.com

# v4
micromamba env export / micromamba pack

 

CC-BY-NC Science Surf , accessed 19.09.2026
Noteworthy, Software

A forensic analysis of the Prince Andrew/Giuffre/Maxwell image

There are only a few photographs that made headlines recently. One is Man Ray’s Le Violon d’Ingres for its price tag of $12,400,000.

Or the authorship discussion around the “Napalm Girl” Phan Thị Kim PhĂşc.

And there is a third photograph – a snapshot from a London townhouse two decades ago – that has a similar price tag attached like Le Violon d’Ingres.

 

My recent paper at https://arxiv.org/abs/2507.1223 examines this infamous photograph using the latest image analysis techniques.

This study offers a forensic assessment of a widely circulated photograph featuring Prince Andrew, Virginia Giuffre, and Ghislaine Maxwell – an image that has played a pivotal role in public discourse and legal narratives. Through analysis of multiple published versions, several inconsistencies are identified, including irregularities in lighting, posture, and physical interaction, which are more consistent with digital compositing than with an unaltered snapshot. While the absence of the original negative and a verifiable audit trail precludes definitive conclusions, the technical and contextual anomalies suggest that the image may have been deliberately constructed. Nevertheless, without additional evidence, the photograph remains an unresolved but symbolically charged fragment within a complex story of abuse, memory, and contested truth.

I provide also a 3D reconstruction of the scene in the preprint although some people may find it easier to watch a video instead.

Even after completion of the analysis there are many open questions – where is the original headshot? There are numerous similar images at various image archives while I have not found any 100% original copy so far.

Andrew Mountbatten candidates
Ghislaine Maxwell candidates

Even as there are now reasonable doubts on the image, Prince Andrew could have of course met Virginia Giuffre. Maybe like an artist is painting a scene from memory, this photograph could be showing a real scene although clearly not in a physical sense.

So, to repeat my last sentence in the paper – this photograph remains an unresolved but symbolically charged fragment within a complex story of abuse, memory, and contested truth.

Bonus Link

 

Note added March 2, 2026

Will add an update to the preprint in the next week as there are some interesting new results from the a comparative analysis of the above images.

the left image seems is the candidate source depicted are the necessary warping transformations.
Flash. highlights

 

CC-BY-NC Science Surf , accessed 19.09.2026
Software

Attention is all you need

Here is the link to the famous landmark paper in the recent history https://arxiv.org/abs/1706.03762

 

Before this paper, most sequence modeling (e.g., for language) used recurrent neural networks (RNNs) or convolutional neural networks (CNNs). These had significant limitations, such as a difficulty with long-range dependencies and slow training due to sequential processing. The Transformer replaced recurrence with self-attention, enabling parallelization and faster training, while better capturing dependencies in data. So the transformer architecture became the foundation for nearly all state-of-the-art NLP models. This enabled training models with billions of parameters, which is key to achieving high performance in AI tasks.

 

CC-BY-NC Science Surf , accessed 19.09.2026
Software

LLM word checker

The recent Science Advance paper by Kobak et al. studied

vocabulary changes in more than 15 million biomedical abstracts from 2010 to 2024 indexed by PubMed and show how the appearance of LLMs led to an abrupt increase in the frequency of certain style words. This excess word analysis suggests that at least 13.5% of 2024 abstracts were processed with LLMs.

Although they say that the analysis was performed on the corpus level and cannot identify individual texts that may have been processed by a LLM, we can of course check the proportion of LLM words in a text.

Unfortunately their online list contains stop words that I am eliminating here. But then we can run the following script!

# based on https://github.com/berenslab/llm-excess-vocab/tree/main

import csv
import re
import os
from collections import Counter
from striprtf.striprtf import rtf_to_text
from nltk.corpus import stopwords
import nltk
import chardet

# Ensure stopwords are available
nltk.download('stopwords')

# Paths
rtfd_folder_path = '/Users/x/Desktop/mss_image.rtfd' # RTFD is a directory
rtf_file_path = os.path.join(rtfd_folder_path, 'TXT.rtf') # or 'index.rtf'
csv_file_path = '/Users/x/Desktop/excess_words.csv'

# Read and decode the RTF file
with open(rtf_file_path, 'rb') as f:
raw_data = f.read()

# Try decoding automatically
encoding = chardet.detect(raw_data)['encoding']
rtf_content = raw_data.decode(encoding)
plain_text = rtf_to_text(rtf_content)

# Normalize and tokenize text
words_in_text = re.findall(r'\b\w+\b', plain_text.lower())

# Remove stopwords
stop_words = set(stopwords.words('english'))
filtered_words = [word for word in words_in_text if word not in stop_words]

# Load excess words from CSV
with open(csv_file_path, 'r', encoding='utf-8') as csv_file:
reader = csv.reader(csv_file)
excess_words = {row[0].strip().lower() for row in reader if row}

# Count excess words in filtered text
excess_word_counts = Counter(word for word in filtered_words if word in excess_words)

# Calculate proportion
total_words = len(filtered_words)
total_excess = sum(excess_word_counts.values())
proportion = total_excess / total_words if total_words &gt; 0 else 0

# Output
print("\nExcess Words Found (Sorted by Frequency):")
for word, count in excess_word_counts.most_common():
print(f"{word}: {count}")

print(f"\nTotal words (without stopwords): {total_words}")
print(f"Total excess words: {total_excess}")
print(f"Proportion of excess words: {proportion:.4f}")

7 Aug 2025

The long ’em dash’ - U+2014 instead of the standard minus – seems to be a characteristic sign of chatGPT 4 even when asked not use it.

 

CC-BY-NC Science Surf , accessed 19.09.2026