bpfbox: Simple Precise Process Confinement with eBPF and KRSI William Findlay October 28, 2020 bpfbox at a Glance ▶ bpfbox is a novel process confinement mechanism for Linux using eBPF ▶ Users write

you can’t use a more precise type for some reason. Any is compatible with every other type, and vice versa. You can freely assign a value of type Any to a variable with a more precise type: a: Any = None does not return an int arbitrary_call(1) # Error: 'int' is not callable In situations where more precise or complex types of callbacks are necessary one can use flexible callback protocols. Lambdas are derived from an Any value also usually have the type Any implicitly, as mypy can’t infer a more precise result type. For example, if you get the attribute of an Any value or call a Any value the result

you can’t use a more precise type for some reason. Any is compatible with every other type, and vice versa. You can freely assign a value of type Any to a variable with a more precise type: a: Any = None not return an int arbitrary_call(1) # Error: 'int' is not callable In situations where more precise or complex types of callbacks are necessary one can use flexible callback protocols. Lambdas are derived from an Any value also usually have the type Any implicitly, as mypy can’t infer a more precise result type. For example, if you get the attribute of an Any value or call a Any value the result

you can’t use a more precise type for some reason. Any is compatible with every other type, and vice versa. You can freely assign a value of type Any to a variable with a more precise type: a: Any = None not return an int arbitrary_call(1) # Error: 'int' is not callable In situations where more precise or complex types of callbacks are necessary one can use flexible callback protocols. Lambdas are derived from an Any value also usually have the type Any implicitly, as mypy can’t infer a more precise result type. For example, if you get the attribute of an Any value or call a Any value the result

you can’t use a more precise type for some reason. Any is compatible with every other type, and vice versa. You can freely assign a value of type Any to a variable with a more precise type: a: Any = None does not return an int arbitrary_call(1) # Error: 'int' is not callable In situations where more precise or complex types of callbacks are necessary one can use flexible callback protocols. Lambdas are derived from an Any value also usually have the type Any implicitly, as mypy can’t infer a more precise result type. For example, if you get the attribute of an Any value or call a Any value the result

University 2020 Recovery types 8 Vasiliki Kalavri | Boston University 2020 Recovery types • Precise recovery (exactly-once) • It hides the effects of a failure perfectly • Post-failure output is is identical to no-failure 8 Vasiliki Kalavri | Boston University 2020 Recovery types • Precise recovery (exactly-once) • It hides the effects of a failure perfectly • Post-failure output is identical rebuild state of the failed node 8 Vasiliki Kalavri | Boston University 2020 Recovery types • Precise recovery (exactly-once) • It hides the effects of a failure perfectly • Post-failure output is

known as the Calculus of Inductive Constructions. The CIC is a formal language with a small and precise set of rules that governs the formation of expressions. In this formal system, moreover, every expression serves as a specification language, that is, a means to specify and define mathematical objects in precise terms. With these specifica- tions, Lean can interpret basic objects and infer their types: CHAPTER makes Lean special as a programming language is that the programs we write define functions in a precise axiomatic framework. Which brings us to third, and central, intended use of Lean: namely we can make

interfaces instead of relying on DOM, CSS, or Javascript. By using this approach, Doodle gives you precise control over the rendering of arbitrary UI elements, vector shapes, gradients, and custom visualizations New alignment of Gradle plugins' versions New JVM incremental compilation by default in Gradle Precise backup of compilation tasks' outputs Lazy Kotlin/JVM task creation for all Gradle versions Non-default feedback on this. You can file an issue in YouTrack. Precise backup of compilation tasks' outputs Starting with Kotlin 1.8.20, you can enable precise backup, whereby only those classes that Kotlin recompiles

hypotheses 22Take some good advice from science A good experiment is: Repeatable Replicable Accurate Precise Reliable Hermetic Calibrated ... 23Repeatable and Replicable Repeatable: you get the same answer tests If Thing fails, its tests fail and nothing else (more precision) London / Mockist tests are precise; no red herrings, no extraneous signals to sort through 30Using Test Doubles reduces accuracy: science: Falsifiable Hypotheses Tests are here to detect bugs Repeatable Replicable Accurate Precise 46Rule 0 Write unit tests 47Part 2: Good Code Three Aspects: Readable Maintainable Documentary

it’s good for image manipulation. You use this next to a profiled monitor. This way you can have precise colors while also being able to view them correctly on multiple screens. Blending modes in RGB and gradients. In particular, when color correcting, the bigger space can help with giving more precise information. If you have a display profile that uses a LUT, then you can use perceptual to give something akin to a small programming language by way of predictors to describe image data succinct and precise. Progressive encoding Whether or not to enable progressive encoding/decoding. As explained in VarDCT