Semantics Clause Samples

Semantics. Expressions reduce according to a call-by-value strategy, for which we define evaluation contexts thus:

Semantics. ‌ SECA comes with four semantics, for different purposes. The standard ▇▇▇▇▇- tics defines how programs are executed. The energy-aware semantics addition- ally traces the energy consumption during program execution in a skyline. The symbolic execution semantics executes all possible paths through a program. The energy-aware symbolic execution semantics traces all possible skylines a program can produce. The focus of this paper is the last one; the others are formally defined in a technical report [19]. Below, we will informally discuss the energy-aware semantics, as it is a useful foundation to understand the energy- aware symbolic execution semantics.

Semantics. Figure 3 defines the semantic domains and the inference rules for a big-step evaluation judgment of the form ▇, R, W € H; u; e ‹→ Hj; uj; v ∈ R W R W This judgment declares that given a variable environment ρ and indexed collections and of read and write permissions, the expression e transforms the initial heap H to the final ▇▇▇▇ ▇▇ and returns value v. Furthermore, it threads a time stamp u, uj Stamp that is incremented at each property write operation and at each permit expression. The permission collections and are indexed by the time stamps of the heaps for which the permissions were granted. The time stamp of a permission uniquely identifies different executions of permit expressions and determines their relative order with respect to heap modifications. ∈ M M A value v Val is either a reference or a closure consisting of an environment and a lambda expression. The representation of a reference is a pair of a heap address A and a collection of access paths, indexed by time stamps. The collection records all permitted access paths that have been traversed during evaluation so far to obtain this reference value. The indexing is again used for marking modifications with time stamps. This representation is dictated by the design choice of path dependency (see Sec. 2.1). A heap maps a location to an object and an object maps a property name to a pair of a time stamp and a value. The time stamp indicates the time of the write operation that last assigned the property. It is required to implement the “sticky update” from Sec. 2.4. ▇, R, W € H; u; e0 ‹→ H ; u ; (ρ , λx.e) ρ, R, W € Hj; uj; e1 ‹→ Hjj; ujj; v1 ρj[x ›→ v1], R, W € Hjj; ujj; e ‹→ Hjjj; ujjj; v ▇, R, W € H; u; e0(e1) ‹→ Hjjj; ujjj; v NEW A ∈/ dom(H) ρ, R, W € H; u; new ‹→ H[A ›→ ∅]; u; (A, ∅) PUT ▇, R, W € H; u; e1 ‹→ Hj; uj; (A, M) ▇, R, W € Hj; uj; e2 ‹→ Hjj; ujj; v W €chk M.p Hjjj = Hjj[A ›→ Hjj(A)[p ›→ (ujj, v)]] ▇, R, W € H; u; e1.p := e2 ‹→ Hjjj; ujj + 1; v GET ▇, R, W € H; u; e ‹→ Hj; uj; (A, M) R €chk M.p < ▇, R, W € H; u; e.p ‹→ Hj; uj; M.p Hj(A)(p) PERMIT ρj, R[u ›→ Lr ], W[u ›→ Lw ] € H; u + 1; e ‹→ Hj; uj; v ρj = ρ[x ›→ ρ(x) a [u ›→ ε]] ▇, R, W € H; u; permit x : Lr, Lw in e ‹→ Hj; uj; v

Semantics. The definition below mimics the usual semantics of formulas in Kripke models, reformulated here in terms of simplicial models:

Semantics. This section discusses the semantics of the flow language and the way to integrate it with Event-B. In particular we show how to reason about flow and machine consistency in the terms of machine properties rather than flow or machine traces. But first we use the traces semantics to formally integrate flows with Event-B. The following defines the traces of a flow expression. traces(jskip) = {()} ^j j ntraces( stop) = s n s stop traces(jstart) = {(jstart)} { | ∈ N ∧ ≤ ( ) } =^^ ^ ^ js z s z traces(p) z = stop traces(ei.a) traces(p; q) traces(p|q) traces(∗(p)) = {(ei.a)} {s^t | s^z ∈ traces(p) ∧ t ∈ traces(q) ∧ z ƒ= (jstop)} =^ traces(p (p; (p))) = traces(p) ∪ traces(q) ^ S ^ | ∗ traces(p E q) = (s Et s traces(p) t traces(q) Here s t states that trace s is a prefix of trace t; α(x) is an alphabet of x (set of all events occurring in x). The parallel composition operator is defined as a collection of possible event interleavings:

Semantics. The semantics of the language of agreements L is based on a possible worlds model We rst de ne a class of models M De nition M hW i is a tuple associating the possible multi agent world states W a function that assigns truth values to formulae and an acces sibility relation associated to programs Now focusing on the actions performed by agents within a multi agent system we de ne the set of paths along which the state of the multi agent system may pass This set of paths is used to de ne our notion of commitment i e what it means for an agent to be bound to uphold an agreement see constraints " in table

Semantics flags is inherited from the FullBox structure. The SampleEncryptionBox currently supports the following bit values:  0x2 – UseSubSampleEncryption  If the UseSubSampleEncryption flag is set, then the track fragment that contains this Sample Encryption Box SHALL use the sub-sample encryption as described in Section 3.2. When this flag is set, sub-sample mapping data follows each InitilizationVector. The sub-sample mapping data consists of the number of sub- samples for each sample, followed by an array of values describing the number of bytes of clear data and the number of bytes of encrypted data for each sub-sample. • sample_count is the number of encrypted samples in this track fragment. This value SHALL be either zero (0) or the total number of samples in the track fragment. • InitializationVector specifies the initialization vector (IV) needed for decryption of a sample. InitializationVector semantics SHALL conform to the semantics defined in [ISO] Annex I 7.2. IV_size SHALL be taken as the value in the corresponding Track Encryption Box (‘tenc’). The nth InitializationVector in the table SHALL be used for the nth sample in the track fragment. Selection of InitializationVector values SHOULD follow the recommendations of [ISO] Annex I 7.4.  See Section 3.2 for further details on how encryption is applied. • subsample_count specifies number of sub-sample encryption entries present for this sample. • BytesOfClearData specifies number of bytes of clear data at the beginning of this sub- sample encryption entry. (Note, that this value can be zero if no clear bytes exist for this entry.) • BytesOfEncryptedData specifies number of bytes of encrypted data following the clear data. (Note, that this value can be zero if no encrypted bytes exist for this entry.)  The sub-sample encryption entries SHALL NOT include an entry with a zero value in both the BytesOfClearData field and in the BytesOfEncryptedData field. The total length of all BytesOfClearData and BytesOfEncryptedData for a sample SHALL equal the length of the sample. Further, it is recommended that the sub- sample encryption entries be as compactly represented as possible. For example, instead of two entries with {15 clear, 0 encrypted}, {17 clear, 500 encrypted} use one entry of {32 clear, 500 encrypted}

Semantics. Continue development of the CDS annotation service. Investigate SKUA, an infrastructure to create and share annotations. Continue to improve the ontology of astronomical object types. Port the existing code to the Manchester OWL API. Design and implement an automated instantiation process for the ontology of astronomical object types. Design and implement a web service use-case exploiting the knowledge structured in the ontology. SimDAP: VisIVO implementation and convergence with the microsimulation requirements. SimDB: Extensions for microsimulation and Spectra extensions. Simulation semantics. No explicit plans for the moment. Contacts are maintained with the NSF-selected DataNet project at ▇▇▇▇▇ ▇▇▇▇▇▇▇ University, in which the US VObs project is playing an important role.

Semantics. Creating a framework for semantic markup and validation, to include ETL validation and the definition of common data types for cross-study query and analysis. Largely defined by the OncoTrack request and extended to requirements from other supported projects. Initial work to provide a prototype area for users and projects to save, export and share results. The original request covers a large development effort, which will be continues in succeeding phases. Extension to the viewing, selection and reporting of data, defined by OncoTrack and extended to other projects and new data types. The ongoing effort to provide continuous integration and unit testing combined. The release cycle will include a 4-week testing period involving developers and end users. Export of raw data files from processed data or from links to the original data, required for OncoTrack and other projects. Maintenance of provenance information covering database versions and dates. Selection of more than 2 cohorts, manually or by automated procedures. Reporting and analysis of all selected cohorts Performance issues identified for large data volumes may be addressed by the release of Postgres 9.3 which promises improved support. Initially test to what extent this resolves the issues, then improve performance further in critical areas. Comparable metadata and summary data for multiple data types. Requested by OncoTrack and other projects.