European Law Identifier (ELI) and the European Case Law Identifier (ECLI), which provide technical specifications for Web identifiers and suggestions for vocabularies to be used to describe metadata pertaining to legal documents in a machine readable format. Notably, these ECLI and ELI metadata standards adhere to the RDF data format which forms the basis of Linked Data, and therefore have the potential to form a basis for a pan-European legal Knowledge Graph.

Minimum sample size for external validation of a clinicalprediction model with a binary outcome

Adaptive sample size determination for thedevelopment of clinical prediction models

The promise of using machine learning on your own notes to connect with external sources is not new. Andromeda Yelton’s HAMLET is six years old.

MACHINE LEARNING FOR STOCK PRICES FORECASTING

we have turned to machine learning, an ingenious way of disclaiming responsibility for anything. Machine learning is like money laundering for bias. It's a clean, mathematical apparatus that gives the status quo the aura of logical inevitability. The numbers don't lie.

RSFCR can directlymodel non-linear effects and interactions to performaccurate prediction without making any prior assump-tions about the underlying data.

The aims of this manuscript can be summarised as:(i) examination of extensions of PLANNCR method(PLANNCR extended) for the development and vali-dation of prognostic clinical prediction models withcompeting events, (ii) systematic evaluation of model-predictive performance for ML techniques (PLANNCRoriginal, PLANNCR extended, RSFCR) and SM (cause-specific Cox, Fine-Gray) regarding discrimination andcalibration, (iii) investigation of the potential role ofML in contrast to conventional regression methods forCRs in non-complex eSTS data (small/medium samplesize, low dimensional setting), (iv) practical utility of themethods for prediction

Nowadays, there is a growing interest in applyingmachine learning (ML) for prediction (diagnosis or prog-nosis) of clinical outcomes [12, 13] which has sparked adebate regarding the added value of ML techniques ver-sus SM in the medical field. Criticism is attributed toML prediction models. Despite no assumptions aboutthe data structure are made, and being able to naturallyincorporate interactions between predictive features,they are prone to overfitting of the training data andthey lack extensive assessment of predictive accuracy(i.e., absence of calibration curves) [14, 15]. On the otherhand, traditional regression methods are consideredstraightforward to use and harder to overfit. That beingsaid, they do make certain (usually strong) assumptionssuch as the proportional hazards over time for the Coxmodel, and require manual pre-specification of interac-tion terms.

In health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statisticalmodels (SM) were developed to estimate the cumulative incidence of an event in the presence of CRs. As recentlythere is a growing interest in applying machine learning (ML) for clinical prediction, these techniques have also beenextended to model CRs but literature is limited. Here, our aim is to investigate the potential role of ML versus SM forCRs within non-complex data (small/medium sample size, low dimensional setting).

There’s a holy trinity in machine learning: models, data, and compute. Models are algorithms that take inputs and produce outputs. Data refers to the examples the algorithms are trained on. To learn something, there must be enough data with enough richness that the algorithms can produce useful output. Models must be flexible enough to capture the complexity in the data. And finally, there has to be enough computing power to run the algorithms.

Modern factory discipline was born on ships and on plantations. Itwas only later that budding industrialists adopted those techniques ofturning humans into machines into cities like Manchester andBirmingham.

Re"...what is it like? How does it manifest?"For me, the idea that my zettelkasten becomes an entity outside myself is most often (and most obviously) felt in two situations (tho there are probably others):When I'm importing new ideas and a connection arises that I hadn't thought of previouslyWhen following trains of thought and connections arise that I didn't overtly intend to makeIn the first instance, I come across ideas I had forgotten about, and although it's not the direction I assumed the new idea would go, it becomes an exciting and possibly more lucrative way to take it.In the second instance, where I might be tracing a thought line to develop an article, I might, for example, zoom in on the graph view in Obsidian and see an idea that, while not formally connected to the ones I'm following, happens to be in close proximity spatially, and so it triggers a new direction I might want to take the article. (You can see this happen IRL in this video: https://www.youtube.com/watch?v=9OUn2-h6oVc&)In both cases, my zk feels like it's offering me more than what I would have gotten had I not been communicating with it. There is a sense that I and it are working together. I import new ideas with a rough sense of how they should connect. It shows alternatives to my thinking on the matter.Obviously, in both cases, all the ideas are my own. So, the zk is not necessarily developing ideas for me. But, because of the way in which the ideas are handled—non-hierarchically, rhizomatic, cross-categorical, cross-theme, etc.—non-habituated connections come to light, connections that are less conditioned by my own conventional ways of thinking.

Note 9/8j says - "There is a note in the Zettelkasten that contains the argument that refutes the claims on every other note. But this note disappears as soon as one opens the Zettelkasten. I.e. it appropriates a different number, changes position (or: disguises itself) and is then not to be found. A joker." Is he talking about some hypothetical note? What did he mean by disappearing? Can someone please shed some light on what he really meant?

ProPublica recently reported that breathing machines purchased by people with sleep apnea are secretly sending usage data to health insurers, where the information can be used to justify reduced insurance payments.

rule-based algorithms like decision trees are not affected by feature scaling.

When such consumers therefore mistake the meaning attributed tothe MT output as the actual communicative intent of the originaltext’s author, real-world harm can ensue.

We can have a machine learning model which gives more than 90% accuracy for classification tasks but fails to recognize some classes properly due to imbalanced data or the model is actually detecting features that do not make sense to be used to predict a particular class.

9/8,3 Geist im Kasten? Zuschauer kommen. Sie bekommen alles zusehen, und nichts als das – wie beimPornofilm. Und entsprechend ist dieEnttäuschung.

Dans le zero-shot learning, le modèle doit être capable de généraliser ce qu'il a appris sur des exemples précédents pour effectuer une tâche sur laquelle il n'a jamais été entraîné. Cela signifie que le modèle doit être capable de transférer ses connaissances acquises sur une tâche donnée à une nouvelle tâche, sans avoir besoin d'exemples d'entraînement spécifiques pour cette nouvelle tâche.

“I have a trick that I used in my studio, because I have these twenty-eight-hundred-odd pieces of unreleased music, and I have them all stored in iTunes,” Eno said during his talk at Red Bull. “When I’m cleaning up the studio, which I do quite often—and it’s quite a big studio—I just have it playing on random shuffle. And so, suddenly, I hear something and often I can’t even remember doing it. Or I have a very vague memory of it, because a lot of these pieces, they’re just something I started at half past eight one evening and then finished at quarter past ten, gave some kind of funny name to that doesn’t describe anything, and then completely forgot about, and then, years later, on the random shuffle, this thing comes up, and I think, Wow, I didn’t hear it when I was doing it. And I think that often happens—we don’t actually hear what we’re doing. . . . I often find pieces and I think, This is genius. Which me did that? Who was the me that did that?”

Lisp machine 这种用 Lisp 代码当机器的汇编指令的硬件实现为什么销声匿迹了？硬件和 Lisp 这样的高级语言两个极端如何融合在一起？

The collocation results can be used to correct the sensor data to more closely match thedata from the reference instrument. This correction process helps account for known biasand unknown interferences from weather and other pollutants and is typically done bydeveloping an algorithm. An algorithm can be a simple equation or more sophisticatedprocess (e.g., set of rules, machine learning) that is applied to the sensor data. This sectionfurther discusses the process of correcting sensor data

Emergent abilities are not present in small models but can be observed in large models.

No es magia.

As part of the Election Integrity Partnership, my team at the Stanford Internet Observatory studies online rumors, and how they spread across the internet in real time.

Eamonn Keogh is an assistant professor of Computer Science at the University ofCalifornia, Riverside. His research interests are in Data Mining, Machine Learning andInformation Retrieval. Several of his papers have won best paper awards, includingpapers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF CareerAward for “Efficient Discovery of Previously Unknown Patterns and Relationships inMassive Time Series Databases”.

“The metaphor is that the machine understands what I’m saying and so I’m going to interpret the machine’s responses in that context.”

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Technology like this, which lets you “talk” to people who’ve died, has been a mainstay of science fiction for decades. It’s an idea that’s been peddled by charlatans and spiritualists for centuries. But now it’s becoming a reality—and an increasingly accessible one, thanks to advances in AI and voice technology. 

There's no market for a machine-learning autopilot, or content moderation algorithm, or loan officer, if all it does is cough up a recommendation for a human to evaluate. Either that system will work so poorly that it gets thrown away, or it works so well that the inattentive human just button-mashes "OK" every time a dialog box appears.

You are all computer scientists. You know what FINITE AUTOMATA can do. You know what TURING MACHINES can do. For example, Finite Automata can add but not multiply. Turing Machines can compute any computable function. Turing machines are incredibly more powerful than Finite Automata. Yet the only difference between a FA and a TM is that the TM, unlike the FA, has paper and pencil. Think about it. It tells you something about the power of writing. Without writing, you are reduced to a finite automaton. With writing you have the extraordinary power of a Turing machine.

Rules formulated with openVALIDATION are thus at the same time a formal, machine-processable specification, but also a documentation that is easy for people to understand.

The rules recorded in natural language are readable not only by humans but also by the computer and therefore no longer need to be programmed by a software developer. This task is now taken over by openVALIDATION.

Self excited dc generators can further be divided into three types -     (a) Series wound - field winding in series with armature winding     (b) Shunt wound - field winding in parallel with armature winding     (c) Compound wound - combination of series and shunt winding

paper

Taking carbon steel as an example, as shown in Picture 1, using a 1000w fiber laser cutting machine, for carbon steel materials thickness below 10mm, when the thickness of carbon steel is less than 2mm, the cutting speed per minute can be up to 8 meters. When the thickness is 6mm, the cutting speed is about 1.6 meters per minute, and when the thickness of the carbon steel is 10 mm, the cutting speed is about 0.6 to 0.7 meters per minute.

And just a fewyears later, it was jubilantly discovered that machine translation and automaticabstracting were also just around the corner.

In 1896, Dewey formed a partnership with Herman Hollerith and the Tabulating Machine Company (TMC) to provide the punch cards used for the electro-mechanical counting system of the US government census operations. Dewey’s relationship with Hollerith is significant as TMC would be renamed International Business Machines (IBM) in 1924 and become an important force in the information age and creator of the first relational database.

1.10 Look at the machine from the higher level.

because it only needs to engage a portion of the model to complete a task, as opposed to other architectures that have to activate an entire AI model to run every request.

A huge amount of Bridgewater's efforts goes into gathering data on credit and equity, and understanding how that affects demand from individual market participants, such as a bank, or from a group of participants (such as subprime-mortgage borrowers). Bridgewater predicted the euro-zone debt crisis by totting up how much debt would need to be refinanced and when; and by examining all the potential buyers of that debt and their ability to buy it. Mr Volcker describes the degree of detail in Mr Dalio's work as “mind-blowing” and admits to feeling sometimes that “he has a bigger staff, and produces more relevant statistics and analyses, than the Federal Reserve.”

“The economy is like a machine.” This machine may look complex but is, he insists, relatively simple even if it is “not well understood”. Mr Dalio models the macroeconomy from the bottom up, by focusing on the individual transactions that are the machine's moving parts. Conventional economics does not pay enough attention to the individual components of supply and, above all, demand, he says. To understand demand properly, you must know whether it is funded by the buyers' own money or by credit from others.

In the early 1980s Mr Dalio started writing down rules that would guide his investing. He would later amend these rules depending on how well they predicted what actually happened. The process is now computerised, so that combinations of scores of decision-rules are applied to the 100 or so liquid-asset classes in which Bridgewater invests. These rules led him to hold both government bonds and gold last year, for example, because the deleveraging process was at a point where, unusually, those two assets would rise at the same time. He was right.

He has even simulated being an investor in markets in those periods by reading daily papers from these eras, receiving data and “trading” as if in real time.

Even if the original webpage disappears, you can often use this informationto locate an archived version using the Wayback Machine, a project of theInternet Archive that preserves a record of websites: https://archive.org/web/.

determine the caliphate; and another group led by Mu'awiya in the Levant, who demanded revenge for Uthman's blood. He defeated the first group in the Battle of the Camel; but in the end,

You can now tag citations in @CiteULike with #CITO! Add the tag "cito--(relationship)--permalink". Example:"cito--usesmethodin--423382".

Machine Tags

"Who controls the past controls the future. Who controls the present controls the past," as Rage Against the Machine sang in their 1999 song "Testify." OK, actually it's a quote from George Orwell's 1984, but hey.

he basic function of an anaesthesia machine is to prepare a gas mixture of precisely known, but variable composition. The gas mixture can then be delivered to a breathing system.

Safe use of anaesthesia machine depends upon an interaction between the basic design of the machine with its safety features and the knowledge and skills of the anaesthesiologist.

So my idea was to create a machine-tag format based on Wikipedia topics, allowing any content creator to tag content with any topic in Wikipedia. By using Wikipedia as an index, this format provides very specific identification of content across a vast knowledge domain. Call it the Dewey Decimal System for the web: “The Wiki Decimal System.” In general, the problem with machine tags is how to make them easy to add for regular folks. Although the format itself is simple, the tags are typically lengthy and require you to know the data ID for what you want to tag. Enter my hack: A web page that takes your text and builds the list of Wikipedia machine tags automatically.

Verfahren des Relational Machine Learning, welche unter Ausnutzung der Graphstruktur in vielen Fällen Modelle besserer Qualität liefern.

learner profiling

Special Issue on Machine Learning and Knowledge Graphs

Formats for Disk Images Another piece of the packaging puzzle is disk image formats. There are many. Each has its own benefits and detriments, but I’m not going to get into those here. Again, this is nowhere near a comprehensive list — just something to help with getting your bearings. I’d like to comment on a couple of the formats that I’ve recently encountered. VDI – VirtualBox’s internal default disk image format is VDI. Nevertheless, this is not what is used by Vagrant boxes. VMDK – One of the most common formats. VMWare’s products use various versions and variations of VMDK disk images. Several versions and variations exist, so it’s very important to understand which one you’re working with and where it can be used.

Open Virtual Appliance (OVA) An OVA is an OVF file packaged together with all of its supporting files (disk images, etc.). You can read about the requirements for a valid OVA package in the OVF specification. Oftentimes people will say “an OVF” and really mean “an OVA.”

File Formats for Virtual Machines Open Virtualization Format (OVF) The OVF Specification provides a means of describing the properties of a virtual system. It is XML based and has generous allowances for extensibility (with corresponding tradeoffs in actual portability). Most commonly, an OVF file is used to describe a single virtual machine or virtual appliance. It can contain information about the format of a virtual disk image file as well as a description of the virtual hardware that should be emulated to run the OS or application contained on such a disk image.

Virtual machines (VMs) revolutionized the data center. With the ability to easily spin up a machine and even roll back to a working state, VMs bring a level of ease IT would never have enjoyed. Rolling back your VM is handled by way of snapshots.

Open Virtualization Format (OVF). This is the industry-standard format. See Section 1.14.1, “About the OVF Format”.

Cloud service formats. Export to and import from cloud services such as Oracle Cloud Infrastructure is supported. See Section 1.15, “Integrating with Oracle Cloud Infrastructure”. 

Here, the card index func-tions as a ‘thinking machine’,67 and becomes the best communication partner for learned men.68

We are definitely living in interesting times!

Machine learning: A definition

as of February 2021, Europeana comprises 59%images and 38% text objects, but only 1% sound objects and 2% video objects.3 DPLA iscomposed of 25% images and 54% text, with only 0.3% sound objects, and 0.6% videoobjects.4Another reason, beyond cost, that audiovisual recordings are not widely accessible is the lack ofsufficiently granular metadata to support identification, discovery, and use, or to supportinformed rights determination and access control and permissions decisions on the part ofcollections staff and users.

This comparison is not to claim that the index catalog is already a Turing machine. Comparisons, transfers, and analogies are not that simple. If the elements of a universal discrete machine are present, they still lack the computational logic of an operating system, the development of which constitutes Turing ’ s foundational achievement. What is described here is merely the fact that the card catalog is liter-ally a paper machine, similar to a nontrivial Turing machine only in having similar components — no more, no less.

s Alan Turing proved only years later, these machines merely need (1) a (theoretically infi nite) partitioned paper tape, (2) a writing and reading head, and (3) an exact