The theme-recipient alternation in Chinese: tracking syntactic variation across seven centuries

Previous research has tracked the history of the theme-recipient alternation (or: “dative” alternation) in Chinese, but few studies have embedded their analysis in a probabilistic variationist framework. Against this backdrop, we explore the language-internal and language-external factors that probabilistically influence the alternation between theme-first and recipient-first ordering in a large diachronic corpus of Chinese writing (1300s–1900s). Our analysis reveals that the recipient-first variant is consistently more frequent than its competitor and even more common in more recent texts than in older texts. Regression analysis also suggests that there are stable linguistic constraints (i.e., animacy and definiteness of theme) and fluid constraints (i.e., end-weight, recipient animacy). Notably, the diachronic instability of end-weight and animacy points to cross-linguistic parallels for ditransitive constructions, including the English dative alternation. We thus contribute to theory building in variationist linguistics by advancing the field’s knowledge about the comparative fluidity versus stability of probabilistic constraints.

Evaluation of an Antibody Detecting Point of Care Test for Diagnosis of Taenia solium Cysticercosis in a Zambian Rural Community: A Prospective Diagnostic Accuracy Study

The lack of cheap, easy-to-use, rapid diagnostic tests has led to the development of several rapid diagnostic tests for cysticercosis. The new prototype two-strip, Taenia solium point of care test (TS POC) detects antibodies against taeniosis (TS POC T) and cysticercosis (TS POC CC). This study evaluated the diagnostic performance of the TS POC CC in the Sinda district in eastern Zambia. A sample of 1254 participants was recruited and tested with the TS POC. Out of the 1249 participants with a valid TS POC result, 177 (14%) tested positive while 1072 (86%) tested negative. All individuals with a positive TS POC and a subset of negative TS POC participants were selected for serum sampling, and were subjected to the recombinant glycoprotein T24H enzyme-linked immunoelectrotransfer blot (rT24H EITB) and the serum B60/158 (serum Ag) enzyme-linked immunosorbent assay (Ag ELISA). Performance characteristics were estimated using a Bayesian approach with probabilistic constraints. Based on 255 complete cases, the estimated sensitivity and specificity of the TS POC CC test were 35% (95% CI: 14–63%) and 87% (95% CI: 83–90%), respectively. The diagnostic performance needs to be improved, possibly by titrating antigen and other reagents’ concentration in the strip to produce a performance similar to existing cysticercosis tests such as the rT24H EITB.

A Reliability-Based Formulation for Simulation-Based Control Co-design Using Generalized Polynomial Chaos Expansion

Combined plant and control design (control co-design, or CCD) methods are often used during product development to address the synergistic coupling between the plant and control parts of a dynamic system. Recently, a few studies have started applying CCD to stochastic dynamic systems. In their most rigorous approach, reliability-based design optimization (RBDO) principles have been used to ensure solution feasibility under uncertainty. However, since existing reliability-based CCD (RBCCD) algorithms use all-at-once (AAO) formulations, only most-probable-point (MPP) methods can be used as reliability analysis techniques. Though effective for linear/quadratic RBCCD problems, the use of such methods for highly nonlinear RBCCD problems introduces solution error that could lead to system failure. A multidisciplinary feasible (MDF) formulation for RBCCD problems would eliminate this issue by removing the dynamic equality constraints and instead enforcing them through forward simulation. Since the RBCCD problem structure would be similar to traditional RBDO problems, any of the well-established reliability analysis methods could be used. Therefore, in this work, a novel reliability-based MDF formulation of multidisciplinary dynamic system design optimization (RB-MDF-MDSDO) has been proposed for RBCCD. To quantify the uncertainty propagated by the random decision variables, Monte Carlo simulation has been applied to the generalized polynomial chaos (gPC) expansion of the probabilistic constraints. The proposed formulation is applied to two engineering test problems, with the results indicating the effectiveness of both the overall formulation as well as the reliability analysis technique for RBCCD.

Humans, machines, and language: A deep alignment in underlying computational styles?

The emergence of AI systems that emulate the remarkable human capacity for language has raised fundamental questions about complex cognition in humans and machines. This lively debate has largely taken place, however, in the absence of specific empirical evidence about how the internal operations of artificial neural networks (ANNs) relate to processes in the human brain as listeners speak and understand language. To directly evaluate these parallels, we extracted multi-level measures of word-by-word sentence interpretation from ANNs, and used Representational Similarity Analysis (RSA) to test these against the representational geometries of real-time brain activity for the same sentences heard by human listeners. These uniquely spatiotemporally specific comparisons reveal deep commonalities in the use of multi-dimensional probabilistic constraints to drive incremental interpretation processes in both humans and machines. But at the same time they demonstrate profound differences in the underlying functional architectures that implement this shared algorithmic alignment.

A New Methodology for Solving Multiobjective Chance-Constrained Problems: An Application on IoT Systems

This study presents a newly developed methodology to transform the chance-constrained problem into a deterministic problem and then solving this multiobjective deterministic problem with the proposed method. Chance-constrained problem contains independent gamma random variables that are denoted as a i j . Two methods are proposed to obtain the deterministic equivalent of chance-constrained problem. The first of the methods is directly based on using the distribution, and the second consists of normalizing probabilistic constraints using Lyapunov’s central limit theorem. An algorithm which uses the Global Criterion Method is developed to solve the multiobjective deterministic equivalent of chance-constrained problem. The methodology is applied to a real-life engineering problem that consists of an IoT device and its data sending process. Using Lyapunov’s central limit theorem for large numbers of random variables is found to be more appropriate.

Probabilistic model predictive control for extended prediction horizons

Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.

Stochastic model predictive control framework for resilient cyber-physical systems: review and perspectives

In the era of Industrial 4.0, the next-generation control system regards the cyber-physical system (CPS) as the core ingredient thanks to the comprehensive integration of physical systems, online computation, networking and control. A reliable, stable and resilient CPS should pledge robustness and safety. A significant concern in CPS development arises from security issues since the CPS is vulnerable to physical constraints, ubiquitous uncertainties and malicious cyber attacks. The integration of the stochastic model predictive control (MPC) framework and the resilient mechanism is a possible approach to guarantee robustness in the presence of stochastic uncertainties and enable resilience against cyber attacks. This review paper aims to offer a detailed overview of existing stochastic MPC algorithms and their CPS applications. More specifically, we first review existing stochastic MPC algorithms for both linear and nonlinear systems subject to probabilistic constraints. We then discuss how to extend the stochastic MPC framework to incorporate resilience mechanisms for constrained CPS under various malicious attacks. Finally, we present an architectural stochastic MPC-based framework for resilient CPS and identify future research challenges. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

Robust Power Allocation for NOMA Heterogeneous Networks with EH under Imperfect CSI

A robust power allocation is proposed for downlink non-orthogonal multiple access (NOMA) heterogeneous networks with EH (Energy harvesting) under imperfect channel state information (CSI). In order to achieve green communication, an EH-aided scheme by leveraging energy from macro base station (MBS) signal and interference signal transmitted from other SBSs is proposed, which reduces the power burden and energy consumption of the SBS. In order to conform to the actual communication scenario, we construct an energy efficiency optimization function under imperfect CSI with considering the constraint of the outage probability interference power in macro cell user (MCU). However, the formulated optimization problem is non-convex due to the fractional form of the objective function and the probabilistic constraints of the outage probability limit. To cope with this problem, we propose a robust power allocation scheme. Firstly, the probabilistic problem is converted into a robust non-probabilistic problem by the minimax probability machine (MPM) and robust optimization theory. Then, the robust non-probabilistic problem can be transformed into the convex optimization problem via Dinkelbach method and sequential convex programming. Finally, the optimal transmission powers of the small cell users (SCUs) are obtained by Lagrange dual approach. The simulation results show that the robust power allocation scheme for NOMA heterogeneous networks with EH under imperfect CSI can significantly improve energy efficiency compared with traditional power allocation algorithms.