scholarly journals Separation Logic with Linearly Compositional Inductive Predicates and Set Data Constraints

2019 ◽  
pp. 206-220
Author(s):  
Chong Gao ◽  
Taolue Chen ◽  
Zhilin Wu
Keyword(s):  
Separation Logic ◽  
Data Constraints

scholarly journals Linear capabilities for fully abstract compilation of separation-logic-verified code

2021 ◽  
Vol 31 ◽  
Author(s):  
THOMAS VAN STRYDONCK ◽  
FRANK PIESSENS ◽  
DOMINIQUE DEVRIESE
Keyword(s):  
Spatial Separation ◽  
Separation Logic ◽  
Target Language ◽  
Fine Grained ◽  
Dynamic Contract ◽  
Modular Verification ◽  
Source Program ◽  
Memory Accesses ◽  
Memory Resources ◽  
Efficient Memory

Abstract Separation logic is a powerful program logic for the static modular verification of imperative programs. However, dynamic checking of separation logic contracts on the boundaries between verified and untrusted modules is hard because it requires one to enforce (among other things) that outcalls from a verified to an untrusted module do not access memory resources currently owned by the verified module. This paper proposes an approach to dynamic contract checking by relying on support for capabilities, a well-studied form of unforgeable memory pointers that enables fine-grained, efficient memory access control. More specifically, we rely on a form of capabilities called linear capabilities for which the hardware enforces that they cannot be copied. We formalize our approach as a fully abstract compiler from a statically verified source language to an unverified target language with support for linear capabilities. The key insight behind our compiler is that memory resources described by spatial separation logic predicates can be represented at run time by linear capabilities. The compiler is separation-logic-proof-directed: it uses the separation logic proof of the source program to determine how memory accesses in the source program should be compiled to linear capability accesses in the target program. The full abstraction property of the compiler essentially guarantees that compiled verified modules can interact with untrusted target language modules as if they were compiled from verified code as well. This article is an extended version of one that was presented at ICFP 2019 (Van Strydonck et al., 2019).

scholarly journals An Ecological Service System Based Study on Suburban Rural Landscape Multifunction

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 232
Author(s):  
Jie Zheng ◽  
Lisha Na ◽  
Binglin Liu ◽  
Tiantian Zhang ◽  
Hao Wang
Keyword(s):  
Rural Communities ◽  
Urban Areas ◽  
Support Function ◽  
Service System ◽  
Rural Landscape ◽  
Dual Function ◽  
The Sustainable Development ◽  
Ecological Service ◽  
Single Function ◽  
Data Constraints

Suburban rural landscape multifunction has received increasing attention from scholars due to its high demand and impact on main urban areas. However, few studies have been focused on suburban rural landscape multifunction because of data constraints. The present study quantified the four landscape services based on ecological service system, i.e., regulating function (RF), provision function (PF), culture function (CF), and support function (SF), determined the interaction through the Spearman correlation coefficient, and ultimately identified the landscape multifunction hotspots and dominant functions through overlay analysis. The result indicated that suburban rural communities have exhibited the characteristics of regional multifunction, and the landscape multifunction hotspots accounted for 64.2%; it should be particularly noted that, among single-function, dual-function, and multifunction hotspots, both support function, and culture function was dominant, while only one case was found in which the regulating function was dominant. Furthermore, all landscape functions other than SF-CF exhibited certain correlations. The study suggests that planning and management should be performed in future in combination with landscape multifunction to ensure the sustainable development of suburban rural communities.

scholarly journals Cosmo: a concurrent separation logic for multicore OCaml

2020 ◽  
Vol 4 (ICFP) ◽  
pp. 1-29
Author(s):  
Glen Mével ◽  
Jacques-Henri Jourdan ◽  
François Pottier
Keyword(s):  
Separation Logic ◽  
Concurrent Separation Logic

scholarly journals Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial Regularization

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1792
Author(s):  
Juan Hagad ◽  
Tsukasa Kimura ◽  
Ken-ichi Fukui ◽  
Masayuki Numao
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Emotion Classification ◽  
Limited Data ◽  
Neural Network Architecture ◽  
Building Models ◽  
The Subject ◽  
Data Constraints ◽  
Input Level ◽  
Normally Distributed

Two of the biggest challenges in building models for detecting emotions from electroencephalography (EEG) devices are the relatively small amount of labeled samples and the strong variability of signal feature distributions between different subjects. In this study, we propose a context-generalized model that tackles the data constraints and subject variability simultaneously using a deep neural network architecture optimized for normally distributed subject-independent feature embeddings. Variational autoencoders (VAEs) at the input level allow the lower feature layers of the model to be trained on both labeled and unlabeled samples, maximizing the use of the limited data resources. Meanwhile, variational regularization encourages the model to learn Gaussian-distributed feature embeddings, resulting in robustness to small dataset imbalances. Subject-adversarial regularization applied to the bi-lateral features further enforces subject-independence on the final feature embedding used for emotion classification. The results from subject-independent performance experiments on the SEED and DEAP EEG-emotion datasets show that our model generalizes better across subjects than other state-of-the-art feature embeddings when paired with deep learning classifiers. Furthermore, qualitative analysis of the embedding space reveals that our proposed subject-invariant bi-lateral variational domain adversarial neural network (BiVDANN) architecture may improve the subject-independent performance by discovering normally distributed features.

scholarly journals Do Different Models Induce Changes in Mortality Indicators? That Is a Key Question for Extending the Lee-Carter Model

2021 ◽  
Vol 18 (4) ◽  
pp. 2204
Author(s):  
Ana Debón ◽  
Steven Haberman ◽  
Francisco Montes ◽  
Edoardo Otranto
Keyword(s):  
Historical Data ◽  
Mortality Rates ◽  
Parametric Model ◽  
Mortality Data ◽  
Block Bootstrap ◽  
Functional Anova ◽  
Model Block ◽  
The Usa ◽  
Data Constraints ◽  
Carter Model

The parametric model introduced by Lee and Carter in 1992 for modeling mortality rates in the USA was a seminal development in forecasting life expectancies and has been widely used since then. Different extensions of this model, using different hypotheses about the data, constraints on the parameters, and appropriate methods have led to improvements in the model’s fit to historical data and the model’s forecasting of the future. This paper’s main objective is to evaluate if differences between models are reflected in different mortality indicators’ forecasts. To this end, nine sets of indicator predictions were generated by crossing three models and three block-bootstrap samples with each of size fifty. Later the predicted mortality indicators were compared using functional ANOVA. Models and block bootstrap procedures are applied to Spanish mortality data. Results show model, block-bootstrap, and interaction effects for all mortality indicators. Although it was not our main objective, it is essential to point out that the sample effect should not be present since they must be realizations of the same population, and therefore the procedure should lead to samples that do not influence the results. Regarding significant model effect, it follows that, although the addition of terms improves the adjustment of probabilities and translates into an effect on mortality indicators, the model’s predictions must be checked in terms of their probabilities and the mortality indicators of interest.

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