Sequential Composition Research Articles

Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events

Security analysis of composite protocols is a critical issue in the field of network security. In complex network environments, the traditional approach of analyzing a single protocol becomes inadequate when dealing with scenarios involving multiple interactions and combinations of protocols. To address this challenge, this paper extends the Logic of Events Theory (LoET) and proposes a method for proving the security of composite protocols. Building upon the Logic of Events Theory, we introduce sequential composition rules, ordering rules, and relevant axioms. We incorporate the concept of invariants and formally abstract the DTLS-SRTP protocol, thereby verifying the mutual authentication and confidentiality of the two sub-protocols. In conclusion, our study demonstrates that the extended Logic of Events Theory offers an effective means of verifying the security of composite protocols.

Sequential composition of propositional logic programs

AbstractThis paper introduces and studies the sequential composition and decomposition of propositional logic programs. We show that acyclic programs can be decomposed into single-rule programs and provide a general decomposition result for arbitrary programs. We show that the immediate consequence operator of a program can be represented via composition which allows us to compute its least model without any explicit reference to operators. This bridges the conceptual gap between the syntax and semantics of a propositional logic program in a mathematically satisfactory way.

Multiple-input neural networks for time series forecasting incorporating historical and prospective context

Individual and societal systems are open systems continuously affected by their situational context. In recent years, context sources have been increasingly considered in different domains to aid short and long-term forecasts of systems’ behavior. Nevertheless, available research generally disregards the role of prospective context, such as calendrical planning or weather forecasts. This work proposes a multiple-input neural architecture consisting of a sequential composition of long short-term memory units or temporal convolutional networks able to incorporate both historical and prospective sources of situational context to aid time series forecasting tasks. Considering urban case studies, we further assess the impact that different sources of external context have on medical emergency and mobility forecasts. Results show that the incorporation of external context variables, including calendrical and weather variables, can significantly reduce forecasting errors against state-of-the-art forecasters. In particular, the incorporation of prospective context, generally neglected in related work, mitigates error increases along the forecasting horizon.

Graph Algebras and Derived Graph Operations

We revise our former definition of graph operations and correspondingly adapt the construction of graph term algebras. As a first contribution to a prospective research field, Universal Graph Algebra, we generalize some basic concepts and results from algebras to graph algebras. To tackle this generalization task, we revise and reformulate traditional set-theoretic definitions, constructions and proofs in Universal Algebra by means of more category-theoretic concepts and constructions. In particular, we generalize the concept of generated subalgebra and prove that all monomorphic homomorphisms between graph algebras are regular. Derived graph operations are the other main topic. After an in-depth analysis of terms as representations of derived operations in traditional algebras, we identify three basic mechanisms to construct new graph operations out of given ones: parallel composition, instantiation, and sequential composition. As a counterpart of terms, we introduce graph operation expressions with a structure as close as possible to the structure of terms. We show that the three mechanisms allow us to construct, for any graph operation expression, a corresponding derived graph operation in any graph algebra.

Inputs, Outputs, and Composition in the Logic of Information Flows

The logic of information flows (LIF) is a general framework in which tasks of a procedural nature can be modeled in a declarative, logic-based fashion. The first contribution of this article is to propose semantic and syntactic definitions of inputs and outputs of LIF expressions. We study how the two relate and show that our syntactic definition is optimal in a sense that is made precise. The second contribution is a systematic study of the expressive power of sequential composition in LIF. Our results on composition tie in the results on inputs and outputs and relate LIF to first-order logic (FO) and bounded-variable LIF to bounded- variable FO. This article is the extended version of a paper presented at KR 2020 [ 2 ].

A contract-based semantics and refinement for hybrid Simulink block diagrams

Simulink is widely used for modelling, simulating, and analysing cyber–physical systems (CPS). However, for safety-critical CPS, Simulink is insufficient to ensure safety since it lacks an official formal semantics to support compositional reasoning and verification. In this paper, we present a contract-based semantic model for hybrid Simulink block diagrams, including both discrete-time and continuous-time blocks. In our semantic formalisation, the semantics of a block is defined as a contract, and we define five operations on contracts, which are sequential composition, parallel composition, feedback composition, variable renaming, and variable hiding. We then develop a refinement calculus for hybrid Simulink block diagrams. Finally, we use a water tank system and a vehicle speed control system as case studies to demonstrate our approach.

Some stylometric remarks on Ovid’sHeroidesand theEpistula Sapphus

AbstractThis article contributes to two well-worn areas of debate in classical Latin philology, relating to Ovid’s Heroides. The first is the question of the authenticity (and, to a lesser extent the correct position) of the letter placed 15th in almost every collection—the so-called Epistula Sapphus (henceforth ES). The secondary question, although perhaps now less fervently debated, is the authenticity of the ‘Double Heroides’, placed by those who accept them as letters 16–21. I employ a variety of methods drawn from the domain of computational stylometry to consider the poetics and the lexico-grammatical features of these elegiac poems in the broader context of a corpus of ‘shorter’ (from 20 to 546 lines) elegiac works from five authors (266 poems in all) representing most of classical elegy. Based on a variety of techniques, every measure gives clear indication that the poetic style of the Heroides is genuinely Ovidian, but distinctive; they can be accurately isolated from Ovid more broadly. The Single and Double Heroides split into two clear groups, with the ES grouped consistently with the single letters. Furthermore, by comparing the style of the letters with the ‘early’ (although there are complications in this label) works of the Amores and the late works of the Ex Ponto, the evidence supports sequential composition—meaning that the ES is correctly placed—and, further, supports the growing consensus that the double letters were composed significantly later, in exile.

Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference

We present a static analysis for discovering differentiable or more generally smooth parts of a given probabilistic program, and show how the analysis can be used to improve the pathwise gradient estimator, one of the most popular methods for posterior inference and model learning. Our improvement increases the scope of the estimator from differentiable models to non-differentiable ones without requiring manual intervention of the user; the improved estimator automatically identifies differentiable parts of a given probabilistic program using our static analysis, and applies the pathwise gradient estimator to the identified parts while using a more general but less efficient estimator, called score estimator, for the rest of the program. Our analysis has a surprisingly subtle soundness argument, partly due to the misbehaviours of some target smoothness properties when viewed from the perspective of program analysis designers. For instance, some smoothness properties, such as partial differentiability and partial continuity, are not preserved by function composition, and this makes it difficult to analyse sequential composition soundly without heavily sacrificing precision. We formulate five assumptions on a target smoothness property, prove the soundness of our analysis under those assumptions, and show that our leading examples satisfy these assumptions. We also show that by using information from our analysis instantiated for differentiability, our improved gradient estimator satisfies an important differentiability requirement and thus computes the correct estimate on average (i.e., returns an unbiased estimate) under a regularity condition. Our experiments with representative probabilistic programs in the Pyro language show that our static analysis is capable of identifying smooth parts of those programs accurately, and making our improved pathwise gradient estimator exploit all the opportunities for high performance in those programs.

Randomized Reference Models for Temporal Networks

Many dynamical systems can be successfully analyzed by representing them as networks. Empirically measured networks and dynamic processes that take place in these situations show heterogeneous, non-Markovian, and intrinsically correlated topologies and dynamics. This makes their analysis particularly challenging. Randomized reference models (RRMs) have emerged as a general and versatile toolbox for studying such systems. Defined as random networks with given features constrained to match those of an input (empirical) network, they may, for example, be used to identify important features of empirical networks and their effects on dynamical processes unfolding in the network. RRMs are typically implemented as procedures that reshuffle an empirical network, making them very generally applicable. However, the effects of most shuffling procedures on network features remain poorly understood, rendering their use nontrivial and susceptible to misinterpretation. Here we propose a unified framework for classifying and understanding microcanonical RRMs (MRRMs) that sample networks with uniform probability. Focusing on temporal networks, we survey applications of MRRMs found in the literature, and we use this framework to build a taxonomy of MRRMs that proposes a canonical naming convention, classifies them, and deduces their effects on a range of important network features. We furthermore show that certain classes of MRRMs may be applied in sequential composition to generate new MRRMs from the existing ones surveyed in this article. We finally provide a tutorial showing how to apply a series of MRRMs to analyze how different network features affect a dynamic process in an empirical temporal network.

Relating Compatibility and Divisibility of Quantum Channels

We connect two key concepts in quantum information: compatibility and divisibility of quantum channels. Two channels are compatible if they can be both obtained via marginalization from a third channel. A channel divides another channel if it reproduces its action by sequential composition with a third channel. (In)compatibility is of central importance for studying the difference between classical and quantum dynamics. The relevance of divisibility stands in its close relationship with the onset of Markovianity. We emphasize the simulability character of compatibility and divisibility, and, despite their structural difference, we find a set of channels – self-degradable channels – for which the two notions coincide. We also show that, for degradable channels, compatibility implies divisibility, and that, for anti-degradable channels, divisibility implies compatibility. These results provide physical insights and motivate further research on these classes of channels and shed new light on the meaning of these two largely studied notions.

Deep Gaussian processes and infinite neural networks for the analysis of EEG signals in Alzheimer’s diseases

Deep neural network models (DGPs) can be represented hierarchically by a sequential composition of layers. When the prior distribution over the weights and biases are independently identically distributed, there is an equivalence with Gaussian processes (GP) in the limit of an infinite net[1]work width. DGPs are non-parametric statistical models used to character[1]ize patterns of complex non-linear systems due to their flexibility, greater generalization capacity, and a natural way of making inferences about the parameters and states of the system. This article proposes a hierarchi[1]cal Bayesian structure to model the weights and biases of a deep neural network. We deduce a general formula to calculate the integrals of Gaussian processes with non-linear transfer densities and obtain a kernel to estimate the covariance functions. In the methodology, we conduct an empirical study analyzing an electroencephalogram (EEG) database for diagnosing Alzheimer’s disease. Additionally, the DGPs models are esti[1]mated and compared with the NN models for 5, 10, 50, 100, 500, and 1000 neurons in the hidden layer, considering two transfer functions: Recti[1]fied Linear Unit (ReLU) and hyperbolic Tangent (Tanh). The results show good performance in the classification of the signals. Finally, we use the mean square error as a goodness of fit measure to validate the proposed models, obtaining low estimation errors.

Age-Dependent Differential Privacy

The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of age of information. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact of data freshness on privacy guarantees, and hence may lead to unnecessary accuracy loss when trying to achieve meaningful privacy guarantees in time-varying databases. In this work, we introduce age-dependent DP, taking into account the underlying stochastic nature of a time-varying database. In this new framework, we establish a connection between classical DP and age-dependent DP, based on which we characterize the impact of data staleness and temporal correlation on privacy guarantees. Our characterization demonstrates that aging, i.e., using stale data inputs and/or postponing the release of outputs, can be a new strategy to protect data privacy in addition to noise injection in the traditional DP framework. Furthermore, to generalize our results to a multi-query scenario, we present a sequential composition result for age-dependent DP. We then characterize and achieve the optimal tradeoffs between privacy risk and utility. Finally, case studies show that, when achieving a target of an arbitrarily small privacy risk in a single-query case, the approach of combining aging and noise injection can achieve a bounded accuracy loss, whereas using noise injection only (as in the DP benchmark) will lead to an unbounded accuracy loss.

DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.

Drug–target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.

The Sequential Composition of Collaborative Groups’ Emotion Regulation in Negative Socio-emotional Interactions

Research indicates that to adjust a group’s emotional atmosphere for successful collaborative learning, group members need to engage in group-level emotion regulation. However, less is known about the whys and ways regulation is activated at a group level. This research explores what triggers 12-year-old primary school students’ (N = 37) negative socio-emotional interactions during a collaborative science task and whether the nature of the trigger makes a difference to group-level emotion regulation strategies and their sequential composition in these interactions. Groups’ collaborative working was videotaped, and triggers and strategies were analysed. The results reveal that the triggers of negative interactions are linked to the groups’ activated regulation strategies. Motivation control strategies were more represented in situations where negative interactions were triggered by task-related issues, whereas socially related triggers were associated with behavioural regulation strategies. Furthermore, the results illustrate that strategies are concatenated to a series of strategic actions, which mostly begin with sharing an awareness of the trigger. The results indicate a need to focus on the series of strategic actions activated in group interactions. This will help reveal how socially shared regulatory processes build a group’s emotional atmosphere.

The leaky semicolon: compositional semantic dependencies for relaxed-memory concurrency

Program logics and semantics tell a pleasant story about sequential composition: when executing (S1;S2), we first execute S1 then S2. To improve performance, however, processors execute instructions out of order, and compilers reorder programs even more dramatically. By design, single-threaded systems cannot observe these reorderings; however, multiple-threaded systems can, making the story considerably less pleasant. A formal attempt to understand the resulting mess is known as a “relaxed memory model.” Prior models either fail to address sequential composition directly, or overly restrict processors and compilers, or permit nonsense thin-air behaviors which are unobservable in practice. To support sequential composition while targeting modern hardware, we enrich the standard event-based approach with preconditions and families of predicate transformers. When calculating the meaning of (S1; S2), the predicate transformer applied to the precondition of an event e from S2 is chosen based on the set of events in S1 upon which e depends. We apply this approach to two existing memory models.

Back to futures

Abstract Common approaches to concurrent programming begin with languages whose semantics are naturally sequential and add new constructs that provide limited access to concurrency, as exemplified by futures. This approach has been quite successful, but often does not provide a satisfactory theoretical backing for the concurrency constructs, and it can be difficult to give a good semantics that allows a programmer to use more than one of these constructs at a time. We take a different approach, starting with a concurrent language based on a Curry–Howard interpretation of adjoint logic, to which we add three atomic primitives that allow us to encode sequential composition and various forms of synchronization. The resulting language is highly expressive, allowing us to encode futures, fork/join parallelism, and monadic concurrency in the same framework. Notably, since our language is based on adjoint logic, we are able to give a formal account of linear futures, which have been used in complexity analysis by Blelloch and Reid-Miller. The uniformity of this approach means that we can similarly work with many of the other concurrency primitives in a linear fashion, and that we can mix several of these forms of concurrency in the same program to serve different purposes.

PepFormer: End-to-End Transformer-Based Siamese Network to Predict and Enhance Peptide Detectability Based on Sequence Only

The detectability of peptides is fundamentally important in shotgun proteomics experiments. At present, there are many computational methods to predict the detectability of peptides based on sequential composition or physicochemical properties, but they all have various shortcomings. Here, we present PepFormer, a novel end-to-end Siamese network coupled with a hybrid architecture of a Transformer and gated recurrent units that is able to predict the peptide detectability based on peptide sequences only. Specially, we, for the first time, use contrastive learning and construct a new loss function for model training, greatly improving the generalization ability of our predictive model. Comparative results demonstrate that our model performs significantly better than state-of-the-art methods on benchmark data sets in two species (Homo sapiens and Mus musculus). To make the model more interpretable, we further investigate the embedded representations of peptide sequences automatically learnt from our model, and the visualization results indicate that our model can efficiently capture high-latent discriminative information, improving the predictive performance. In addition, our model shows a strong ability of cross-species transfer learning and adaptability, demonstrating that it has great potential in robust prediction of peptides detectability on different species. The source code of our proposed method can be found via https://github.com/WLYLab/PepFormer.

Correct program parallelisations

A commonly used approach to develop deterministic parallel programs is to augment a sequential program with compiler directives that indicate which program blocks may potentially be executed in parallel. This paper develops a verification technique to reason about such compiler directives, in particular to show that they do not change the behaviour of the program. Moreover, the verification technique is tool-supported and can be combined with proving functional correctness of the program. To develop our verification technique, we propose a simple intermediate representation (syntax and semantics) that captures the main forms of deterministic parallel programs. This language distinguishes three kinds of basic blocks: parallel, vectorised and sequential blocks, which can be composed using three different composition operators: sequential, parallel and fusion composition. We show how a widely used subset of OpenMP can be encoded into this intermediate representation. Our verification technique builds on the notion of iteration contract to specify the behaviour of basic blocks; we show that if iteration contracts are manually specified for single blocks, then that is sufficient to automatically reason about data race freedom of the composed program. Moreover, we also show that it is sufficient to establish functional correctness on a linearised version of the original program to conclude functional correctness of the parallel program. Finally, we exemplify our approach on an example OpenMP program, and we discuss how tool support is provided.

Exploring the boundaries of Ada syntax with functional-style iterators

Functional-style iterators are present in many popular languages as a way of processing data in several connected steps, in a safe and readable manner. By contrast, Ada started providing general iterators only with its 2012 revision, in a way not directly intended for sequential composition. This paper presents a functional iterators library design inspired by the standard Rust iterators and the RxAda reactive extensions. This library is used as a case study for the limitations in implementing higher-kinded types in current Ada, and how currently proposed extensions for the 202X revision might change the situation.

Copolymer chain formation of 2-oxazolines by in situ 1H-NMR spectroscopy: dependence of sequential composition on substituent structure and monomer ratios.

In situ 1H NMR characterization of copolymerization reactions of various 2-oxazoline monomers at different molar ratios offers detailed insight into the build-up and composition of the polymer chains. Various 2-oxazolines were copolymerized in one single solvent, butyronitrile, with 2-dec-9′-enyl-2-oxazoline, where the double bond allows for post-polymerization modification and can function as a crosslinking unit to form polymer networks. The types of the monomers and their molar ratios in the feed have a strong effect on the microstructure of the forming copolymer chains. Copolymers comprising 2-dec-9′-enyl-2-oxazoline and either 2-ethyl-, 2-isopropyl-, 2-butyl-, 2-heptyl, 2-nonyl- or 2-phenyl-2-oxazoline, show significant differences in sequential structure of copolymers ranging from block to gradient and random ordering of the monomer units. 1H NMR was found to be a powerful tool to uncover detailed oxazoline copolymerization kinetics and evolution of chain composition.