Logic Specifications Research Articles

Logical specifications of effectively separable data models

It is established that any effectively separable many-sorted universal algebra has an enrichment that is the only (up to isomorphism) model constructed from constants for a suitable computably enumerable set of sentences

Semantics, Specification Logic, and Hoare Logic of Exact Real Computation

We propose a simple imperative programming language, ERC, that features arbitrary real numbers as primitive data type, exactly. Equipped with a denotational semantics, ERC provides a formal programming language-theoretic foundation to the algorithmic processing of real numbers. In order to capture multi-valuedness, which is well-known to be essential to real number computation, we use a Plotkin powerdomain and make our programming language semantics computable and complete: all and only real functions computable in computable analysis can be realized in ERC. The base programming language supports real arithmetic as well as implicit limits; expansions support additional primitive operations (such as a user-defined exponential function). By restricting integers to Presburger arithmetic and real coercion to the `precision' embedding $\mathbb{Z}\ni p\mapsto 2^p\in\mathbb{R}$, we arrive at a first-order theory which we prove to be decidable and model-complete. Based on said logic as specification language for preconditions and postconditions, we extend Hoare logic to a sound (w.r.t. the denotational semantics) and expressive system for deriving correct total correctness specifications. Various examples demonstrate the practicality and convenience of our language and the extended Hoare logic.

Quiver: Guided Abductive Inference of Separation Logic Specifications in Coq

Quiver: Guided Abductive Inference of Separation Logic Specifications in Coq

The Molecular Logic of Synaptic Specificity in the Retinocollicular Pathway.

The Molecular Logic of Synaptic Specificity in the Retinocollicular Pathway.

The logic of the Marcos Jr. Government’s South China Sea Policy

This study provides a comprehensive discussion of changes in the Philippines’ South China Sea strategy under the Marcos Jr. government. Since his election in June 2022, the Philippines has implemented a diversified strategy regarding its South China Sea policy and related disputes with China. In striving for its interests and activities in the South China Sea, the Philippines aims to launch hard and soft checks and balances on China at multiple levels, for example, through multilateral military cooperation with the United States, international arbitration, and applying propaganda tactics. This study attempts to deconstruct the specific actions and overall logic of the Philippines' South China Sea policy by comprehensively analyzing international dynamics and domestic changes. It sheds light on the intricate way global geopolitical contests, particularly the intensifying rivalry between China and the United States, are influencing political sentiment and actions within the Philippines. Concurrently, it examines the Philippines' strategic deployment of its South China Sea policy to catalyze international reactions, such as support from the United States, and how this involves recalibrating its internal political and economic landscape. Overall, the paper shows how the Philippines’ South China Sea policy seeks an optimal balance among various forces while facing many uncertainties and challenges.

Directed Acyclic Graph-Based Datapath Synthesis Using Graph Isomorphism and Gate Reconfiguration

Datapath synthesis is a crucial step in synthesis flow and aims at globally minimizing an area by identifying shareable logic structures. This paper introduces a novel Directed Acyclic Graph (DAG)-based datapath synthesis method based on graph isomorphism and gate reconfiguration. Unlike algorithms that identify common specification logic, our approach simplifies the problem by focusing on searching for common topology. Leveraging the concept of gate reconfiguration, our algorithm extends the applicability of DAG-based datapath synthesis by transforming a topology-equivalent network into a specification-equivalent network. Experimental results demonstrate up to 23.6% improvement when optimizing the adder–subtractor circuit, a scenario not addressed by existing DAG-based datapath synthesis algorithms.

Explanation, semantics, and ontology

The terms ‘semantics’ and ‘ontology’ are increasingly appearing together with ‘explanation’, not only in the scientific literature, but also in everyday social interactions, in particular, within organizations. Ontologies have been shown to play a key role in supporting the semantic interoperability of data and knowledge representation structures used by information systems. With the proliferation of applications of Artificial Intelligence (AI) in different settings and the increasing need to guarantee their explainability (but also their interoperability) in critical contexts, the term ‘explanation’ has also become part of the scientific and technical jargon of modern information systems engineering. However, all of these terms are also significantly overloaded. In this paper, we address several interpretations of these notions, with an emphasis on their strong connection. Specifically, we discuss a notion of explanation termed ontological unpacking, which aims at explaining symbolic domain descriptions (e.g., conceptual models, knowledge graphs, logical specifications) by revealing their ontological commitment in terms of their so-called truthmakers, i.e., the entities in one’s ontology that are responsible for the truth of a description. To illustrate this methodology, we employ an ontological theory of relations to explain a symbolic model encoded in the de facto standard modeling language UML. We also discuss the essential role played by ontology-driven conceptual models (resulting from this form of explanation processes) in supporting semantic interoperability tasks. Furthermore, we revisit a proposal for quality criteria for explanations from philosophy of science to assess our approach. Finally, we discuss the relation between ontological unpacking and other forms of explanation in philosophy and science, as well as in the subarea of Artificial Intelligence known as Explainable AI (XAI).

Hermes, a low-latency transactional storage for binary data streams from remote devices

In many contexts where data is streamed on a large scale, such as video surveillance systems, there is a dual requirement: secure data storage and continuous access to audio and video content by third parties, such as human operators or specific business logic, even while the media files are still being collected. However, using transactions to ensure data persistence often limits system throughput and latency. This paper presents a solution that enables both high ingestion rates with transactional data persistence and near real-time, low-latency access to the stream during collection. This immediate access enables the prompt application of specialized data engineering algorithms during data acquisition. The proposed solution is particularly suitable for binary data sources such as audio and video recordings in surveillance systems, and it can be extended to various big data scenarios via well-defined general interfaces. The scalability of the approach is based on the microservice architecture. Preliminary results obtained with Apache Kafka and MongoDB replica sets show that the proposed solution provides up to 3 times higher throughput and 2.2 times lower latency compared to standard multi-document transactions.

Mining of extended signal temporal logic specifications with ParetoLib 2.0

Cyber-physical systems are complex environments that combine physical devices (i.e., sensors and actuators) with a software controller. The ubiquity of these systems and dangers associated with their failure require the implementation of mechanisms to monitor, verify and guarantee their correct behaviour. This paper presents ParetoLib 2.0, a Python tool for offline monitoring and specification mining of cyber-physical systems. ParetoLib 2.0 uses signal temporal logic (STL) as the formalism for specifying properties on time series. ParetoLib 2.0 builds upon other tools for evaluating and mining STL expressions, and extends them with new functionalities. ParetoLib 2.0 implements a set of new quantitative operators for trace analysis in STL, a novel mining algorithm and an original graphical user interface. Additionally, the performance is optimised with respect to previous releases of the tool via data-type annotations and multi core support. ParetoLib 2.0 allows the offline verification of STL properties as well as the specification mining of parametric STL templates. Thanks to the implementation of the new quantitative operators for STL, the tool outperforms the expressiveness and capabilities of similar runtime monitors.

Research on the Impact of Enterprise ESG Ratings on Carbon Emissions from a Spatial Perspective

Based on 208 city-level data in China, this paper empirically analyzes the impact of ESG rating on carbon emissions through the SDM spatial metrology model, identifies the direct and indirect consequences and spatial spillover effects of ESG rating on carbon emissions, and compares the regional heterogeneity and city-size heterogeneity of such impacts. This paper draws three conclusions: (1) Empirical evidence shows that the ESG rating performance of enterprises has a significant inhibition effect on carbon dioxide emissions. Specifically, when the ESG rating performance increases by 1%, carbon emissions will decrease by 0.076; among other control variables, the effect of FDI on carbon emission reduction is that when ESG score performance increases by 1%, carbon emission decreases by 0.022. (2) In the decomposition of the total effects, indirect effects and direct effects have the same impact on carbon emissions, and the total effect is −0.393. (3) The inhibition effect is more significant in the Eastern Region and in megacities, where the effect of −0.096 in the Eastern Region is more obvious than that of −0.078 at the national level, and the effect of carbon reduction in megacities is significantly greater than 0.013 in big cities. This suggests regional heterogeneity in regards to the role of ESG ratings in reducing CO2 emissions. This paper reveals the specific effects and internal logic of the impact of ESG performance on CO2 emissions, which has certain implications for various regions to further promote the construction of an ESG system, according to local conditions, and to encourage enterprises to focus on emission reduction and high-quality development.

Finite-step approximately bi-similar symbolic model for switched systems

Obtaining an approximately bi-similar symbolic model for a continuous-state system is a crucial step in symbolic control. Symbolic control aims to design switching logic to achieve temporal logic specifications. Most existing approaches to approximate bi-simulation are developed over infinite time horizons. However, many control tasks are set within finite periods in practical scenarios. Hence, it makes sense to consider the behavior of trajectories within finite-time horizons. Therefore, this paper introduces two new notions: finite-time uniform incremental stability (FUI-stability) and finite-step approximate bi-simulation. Furthermore, a FUI-stability condition for switched system is derived using the multiple incremental Lyapunov-like functions technique. A symbolic model is constructed for switched system by the state space grid technique. Subsequently, a sufficient condition for finite-step approximate bi-simulation between the finite-time uniformly incrementally stable (FUI-stable) switched system and the constructed symbolic model is derived. Finally, the obtained results are illustrated by two numerical examples.

Statistical Verification using Surrogate Models and Conformal Inference and a Comparison with Risk-Aware Verification

Uncertainty in safety-critical cyber-physical systems can be modeled using a finite number of parameters or parameterized input signals. Given a system specification in Signal Temporal Logic (STL), we would like to verify that for all (infinite) values of the model parameters/input signals, the system satisfies its specification. Unfortunately, this problem is undecidable in general. Statistical model checking (SMC) offers a solution by providing guarantees on the correctness of CPS models by statistically reasoning on model simulations. We propose a new approach for statistical verification of CPS models for user-provided distribution on the model parameters. Our technique uses model simulations to learn surrogate models , and uses conformal inference to provide probabilistic guarantees on the satisfaction of a given STL property. Additionally, we can provide prediction intervals containing the quantitative satisfaction values of the given STL property for any user-specified confidence level. We compare this prediction interval with the interval we get using risk estimation procedures. We also propose a refinement procedure based on Gaussian Process (GP)-based surrogate models for obtaining fine-grained probabilistic guarantees over sub-regions in the parameter space. This in turn enables the CPS designer to choose assured validity domains in the parameter space for safety-critical applications. Finally, we demonstrate the efficacy of our technique on several CPS models.

Advanced Query Functionalities in Natural Logic Knowledge Bases

Natural logics are formal logics characterized by expressions that bear a resemblance to simplified natural language sentences. The inherent readability of natural logics renders them suitable also for large knowledge bases. Within the realm of natural logics, the rules of logical proof are applicable directly to the sentences constructed using this framework. This direct application ensures explainability of query inferences. In the paper we describe a specific natural logic named NaturaLog, suited for ontology-structured knowledge bases. We outline how NaturaLog can be effectively implemented into a database system to facilitate deductive querying. The primary focus of this paper lies in exploring various query functionalities and elucidating methods to achieve these capabilities, also when dealing with large-scale knowledge bases.

Trusting by Numbers: An Analysis of a Chinese Social Credit System Governance Infrastructure

To govern, states collect and evaluate information about citizens. This paper examines a social credit system (SCS) in China, a state initiative aimed at governing trust through the quantification of social behavior. Our analysis opens the ‘black box’ of an SCS metric, investigating how trust is translated into numbers and the implications of this translation. We reveal that the SCS is deeply relational and embedded in specific interests, biases, and logics of governance. The system has the potential to reinforce structural injustices and inequalities as it particularly disadvantages rural residents. Meanwhile, it subjects government employees to stricter surveillance, indicating its multifaceted objectives. Our finding uncovers the nuanced ways the system interacts with social stratification in Chinese society and the administrative structure inside the state. We problematize the individualistic, decontextualized, and behavioral assumptions undergirding the metric, and advocate for a critical reassessment of the sociopolitical dimensions of such quantitative governance infrastructures.

Efficient 32-nm CNTFET-Based 1-Bit Adder: A Fast and Energy-Optimized Design

CNTFETs are a shows potential choice for traditional CMOS technology due to their potential for lesser power consumption and superior performance. In the present paper, a new 1-bit hybrid full adder has been deliberated and proposed using both pass transistor (PT) and transmission gate logics (TGL), which utilizes a total of 16 transistors. The combination of PT and TGL can lead to improved power efficiency, reduced delay, and enhanced circuit performance in various VLSI applications. For 0.9 V supply voltage at 32-nm CNTFET technology, the power consumption is 0.0748 μW, which is to be an exceptionally low value with a lesser delay of 7.586 Ps and the power-delay-product (PDP) of 0.5674 aJ. The results obtained from this analysis demonstrate the power efficiency, speed, and overall performance of the proposed full adder design. The combination of 32-nm CNTFET technology, deliberate circuit design choices, and the use of specific logic elements (CMOS inverters and strong transmission gates) contributes to the reported characteristics. Using a 0.9 V supply voltage and 32-nm Stanford CNTFET Model technology, the suggested 1-bit adder circuit's concert was investigated using the Mentor Graphics Tool. Finally, using the proposed 1-bit full adder circuit, an N-bit ripple carry adder (N=8, 16 & 32) is implemented and demonstrated. The Simulation results of the 8-bit RCA with a power consumption of 0.373 μW, the delay is 16.852 Ps and the PDP is 6.2857 aJ. These results show that proposed RCA’s have better performance compared to the already reported designs in terms of performance, speed, and power economy.

Adaptive Reactive Synthesis for LTL and LTLf Modulo Theories

Reactive synthesis is the process of generate correct con- trollers from temporal logic specifications. Typically, synthesis is restricted to Boolean specifications in LTL. Recently, a Boolean abstraction technique allows to translate LTLT specifications that contain literals in theories into equi-realizable LTL specifications, but no full synthesis procedure exists yet. In synthesis modulo theories, the system receives valuations of environment variables (from a first-order theory T ) and outputs valuations of system variables from T . In this paper, we address how to syntheize a full controller using a combination of the static Boolean controller obtained from the Booleanized LTL specification together with on-the-fly queries to a solver that produces models of satisfiable existential T formulae. This is the first synthesis method for LTL modulo theories. Additionally, our method can produce adaptive responses which increases explainability and can improve runtime properties like performance. Our approach is applicable to both LTL modulo theories and LTLf modulo theories.

AIRE relies on Z-DNA to flag gene targets for thymic T cell tolerization.

AIRE is an unconventional transcription factor that enhances the expression of thousands of genes in medullary thymic epithelial cells and promotes clonal deletion or phenotypic diversion of self-reactive T cells1-4. The biological logic of AIRE's target specificity remains largely unclear as, in contrast to many transcription factors, it does not bind to a particular DNA sequence motif. Here we implemented two orthogonal approaches to investigate AIRE's cis-regulatory mechanisms: construction of a convolutional neural network and leveraging natural genetic variation through analysis of F1 hybrid mice5. Both approaches nominated Z-DNA and NFE2-MAF as putative positive influences on AIRE's target choices. Genome-wide mapping studies revealed that Z-DNA-forming and NFE2L2-binding motifs were positively associated with the inherent ability of a gene's promoter to generate DNA double-stranded breaks, and promoters showing strong double-stranded break generation were more likely to enter a poised state with accessible chromatin and already-assembled transcriptional machinery. Consequently, AIRE preferentially targets genes with poised promoters. We propose a model in which Z-DNA anchors the AIRE-mediated transcriptional program by enhancing double-stranded break generation and promoter poising. Beyond resolving a long-standing mechanistic conundrum, these findings suggest routes for manipulating T cell tolerance.

Sequential control barrier functions for mobile robots with dynamic temporal logic specifications

We address a motion planning and control problem for mobile robots to satisfy rich, time-varying tasks expressed as Signal Temporal Logic (STL) specifications. The specifications may include tasks with nested temporal operators or time-conflicting requirements (e.g., achieving periodic tasks or tasks defined within the same time interval). Moreover, the tasks can be defined in locations changing with time (i.e., dynamic targets), and their future motions are not known a priori. This unpredictability requires an online control approach which motivates us to investigate the use of control barrier functions (CBFs). The proposed CBFs take into account the actuation limits of the robots and a feasible sequence of STL tasks. They define time-varying feasible sets of states the system must always stay inside. We show the feasible sequence generation process that even includes the decomposition of periodic tasks and alternative scenarios due to disjunction operators. The sequence is used to define CBFs, ensuring STL satisfaction. We also show some theoretical results on the correctness of the proposed method. We illustrate the benefits of the proposed method and analyze its performance via simulations and experiments with aerial robots.

Chemical Logic Gates on Active Colloids.

Recent studies have shown that active colloidal motors using enzymatic reactions for propulsion hold special promise for applications in fields ranging from biology to material science. It will be desirable to have active colloids with capability of computation so that they can act autonomously to sense their surroundings and alter their own dynamics. It is shown how small chemical networks that make use of enzymatic chemical reactions on the colloid surface can be used to construct motor-based chemical logic gates. The basic features of coupled enzymatic reactions that are responsible for propulsion and underlie the construction and function of chemical gates are described using continuum theory and molecular simulation. Examples are given that show how colloids with specific chemical logic gates, can perform simple sensing tasks. Due to the diverse functions of different enzyme gates, operating alone or in circuits, the work presented here supports the suggestion that synthetic motors using such gates could be designed to operate in an autonomous way in order to complete complicatedtasks.

Together forever? How customer co‐creation affects the adoption of digital service innovations over time

AbstractThis study delves into the intricate relationship between customers' willingness to co‐create (WCC) and the required level of co‐creation (LCC) for effective service innovation adoption. In response to recent findings suggesting a “co‐creation sweet spot,” beyond which additional co‐creation activities have detrimental effects, this research aims to provide both theoretical and empirical insights into this phenomenon. Unlike previous investigations focusing on either the willingness to co‐create or the level of co‐creation at a particular point in time and overlooking potential interactions, our study considers their interactions over time, acknowledging that revenue for most digital services is generated through ongoing customer usage. Utilizing longitudinal data from 352 participants across four waves and employing structural equation modeling, our analysis reveals a nuanced pattern. Customers' willingness to co‐create predominantly shapes usage intention in the pre‐adoption stage, while the level of co‐creation becomes a critical driver for continuous usage in the post‐adoption stages. Further analysis using response surface analysis with polynomial regression confirms a specific matching logic: customers with low (high) willingness to co‐create expect a low (high) level of co‐creation from a service innovation. From a theoretical perspective, our findings contribute to bridging the research gap concerning an optimal co‐creation level, offering insights into psychological processes underlying the co‐creation sweet spot. Furthermore, we also shed light on temporal aspects of the interplay between willingness to co‐create and the level of co‐creation, offering a more holistic perspective on their joint influence on adoption behavior. From a managerial perspective, our study challenges the traditional assumption that more co‐creation naturally leads to better outcomes. It emphasizes the significance of tailoring the level of co‐creation in digital service innovations based on customers' intrinsic willingness. This strategic customization aligns with the evolving landscape of digital service innovation, offering practical insights for managers striving to optimize their co‐creation strategies.