Search

Named entity recognition is a key prerequisite for many tasks. However, the high cost of entity annotation limits feature learning and generalization capabilities of models. To address this problem, this paper integrates the weakly supervised method with first-order logic for Chinese named entity recognition. Firstly, a knowledge base is established by using first-order logic, tailored to the characteristics of the Chinese named entity recognition dataset. Secondly, self-training approach is introduced to address the issue of suboptimal feature learning in the model, stemming from a limited number of entity types. Lastly, the first-order logic knowledge base is incorporated into self-training approach to rectify mislabeling in the training process, which improves the generalization ability. The F1-score on the public datasets ACE05 and MSRA are improved by 2.56% and 0.35% respectively.

Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems. In this paper, we shift the attention to multi-label classification, where the availability of domain knowledge on the relationships among the considered classes may offer a natural way to spot incoherent predictions, i.e., predictions associated to adversarial examples lying outside of the training data distribution. We explore this intuition in a framework in which first-order logic knowledge is converted into constraints and injected into a semi-supervised learning problem. Within this setting, the constrained classifier learns to fulfill the domain knowledge over the marginal distribution, and can naturally reject samples with incoherent predictions. Even though our method does not exploit any knowledge of attacks during training, our experimental analysis surprisingly unveils that domain-knowledge constraints can help detect adversarial examples effectively, especially if such constraints are not known to the attacker. We show how to implement an adaptive attack exploiting knowledge of the constraints and, in a specifically-designed setting, we provide experimental comparisons with popular state-of-the-art attacks. We believe that our approach may provide a significant step towards designing more robust multi-label classifiers. [ABSTRACT FROM AUTHOR]

In this sequel to our previous article (Cordero et al., 2020) on general inference systems for reasoning with if–then dependencies, we study transformations of if–then rules to semantically equivalent collections of if–then rules suitable to solve several problems related to reasoning with data dependencies. We work in a framework of general lattice-based if–then rules whose semantics is parameterized by systems of isotone Galois connections. This framework allows us to obtain theoretical insight as well as algorithms on a general level and observe their special cases by choosing various types of parameterizations. This way, we study methods for automated reasoning with different types of if–then rules in a single framework that covers existing as well as novel types of rules. Our approach supports a large family of if–then rules, including fuzzy if–then rules with various types of semantics. The main results in this article include new observations on the syntactic inference of if–then rules, complete collections of rules, reduced normal forms of collections of rules, and automated reasoning methods. We demonstrate the generality of the framework and the results by examples of their particular cases focusing on fuzzy if–then rules. [ABSTRACT FROM AUTHOR]

To aggregate useful information among diversified sources, a hotspot research topic called truth discovery has emerged in recent years. Existing truth discovery methods attempt to infer the true attribute values for the entities by identifying and trusting reliable data sources. That is, the values provided by reliable sources are more likely to be the true values. However, all these methods neglect the relations among different entities, which play important roles in truth discovery task. When reliable data sources cannot provide sufficient information of entities, the true attribute values of these entities can still be inferred by propagating trustworthy information from related entities. Motivated by this, in this paper, we introduce the constrained truth discovery problem. We incorporate denial constraints, a universally quantified first-order logic formalism which can express a large number of effective and widely existing relations among entities, into the process of truth discovery. We formulate it as a constrained optimization problem and analyze its hardness. To address the problem, we propose algorithms to partition the entities into disjoint groups, and generate arithmetic constraints for each disjoint group separately. Then, the true attribute values of the entities in each disjoint group are derived by minimizing the objective function under the corresponding arithmetic constraints. Experimental results on both real-world and synthetic datasets demonstrate that the proposed approach achieves good performance even with very few constraints and reliable sources. [ABSTRACT FROM AUTHOR]

Priced timed automata (PTA) are discrete-event system models with temporal constraints and a cost function and are used to pose optimal scheduling and routing problems. To date, solutions to these problems have been found offline and executed open loop.This open-loop control strategy makes it impossible to account for disturbances, i.e., changes in costs or scheduling constraints over time. To address this shortcoming, this work’s first contribution is a closed-loop model predictive control (MPC) framework for PTA, enabling decision-making based on real-time model updates. To ensure the feasibility of an MPC problem, it is often desirable to soften constraints. However, the contemporary PTA theory does not consider soft constraints. Thus, this work’s second contribution is to integrate constraint softening with PTA control by harnessing the capabilities of new solvers enabled by the recasting of the models and control problem into first-order logic by employing modified encoding schemes based on existing works. Finally, the proposed control framework and implementation are demonstrated in a simulation case study on the guidance of a product through a manufacturing system. [ABSTRACT FROM AUTHOR]

This article is a contribution to the theoretical study of weighted structures in fuzzy logic. We consider an important item from classical model theory: the construction of models that do not have any collection satisfying certain prescribed properties, that is, an omitting types theorem. We generalize the work done by Cintula and Diaconescu (Omitting Types Theorem for Fuzzy Logics, IEEE Transactions on Fuzzy Systems 27(2):273–277, 2019), who solved the problem for standard one-sided types. Instead, we introduce types for fuzzy structures as pairs of sets of formulas with free variables (expressing, respectively, properties to be satisfied and those to be avoided) and prove the corresponding omitting types theorem in the framework of uninorm-based logics. [ABSTRACT FROM AUTHOR]

In this article, we propose FlexHRC+, a hierarchical human–robot cooperation architecture designed to provide collaborative robots with an extended degree of autonomy when supporting human operators in high-variability shop-floor tasks. The architecture encompasses three levels, namely for perception, representation, and action. Building up on previous work, here we focus on an in-the-loop decision-making process for the operations of collaborative robots coping with the variability of actions carried out by human operators, and the representation level, integrating a hierarchical and/or graph whose online behavior is formally specified using first-order logic. The architecture is accompanied by experiments including collaborative furniture assembly and object positioning tasks. [ABSTRACT FROM AUTHOR]

This paper proposes an auto-decision making system, knowledge-based major choosing decision (KMCD), which helps remote students choose their majors. It is built within a first-order reasoning system with an uncertain reasoning function. The knowledge of both students who want to choose their majors and who have finished their studies are used in the decision making process of the system. KMCD provides personalized suggestions to those students who want to choose majors. Both the learning abilities of students and the career prospects of majors are carefully analyzed on the basis of the knowledge in database. As these analyzing results are well utilized in the decision making process of KMCD, it can be expected that the students who take the suggestions will have a better chance to get graduated smoothly and start their careers successfully after graduation. A detailed description of KMCD is given, which shows that the system is actually a reasonable decision making framework with a capability of expansion of other knowledge which is supposed to be useful for major choosing. An experimental study on data of real college students is devised to justify the effectiveness of KMCD. [ABSTRACT FROM PUBLISHER]

The severe bandwidth shortage in conventional microwave bands has spurred the exploration of the millimeter wave (MMW) spectrum for the next revolution in wireless communications. However, there is still a lack of proper channel modeling for the MMW wireless propagation, particularly in the case of outdoor environments. In this paper, we develop a geometry-based stochastic channel model to statistically characterize the effect of all the first-order reflection paths between the transmitter and the receiver. These first-order reflections are generated by the single bounce of signals reflected from the walls of randomly distributed buildings. Based on this geometric model, a closed-form expression for the power delay profile contributed by all the first-order reflection paths is obtained and then used to evaluate their impact on the MMW outdoor propagation characteristics. Numerical results are provided to validate the accuracy of the proposed model under various channel parameter settings. The findings in this paper provide a promising step toward more complex and practical MMW propagation channel modeling. [ABSTRACT FROM PUBLISHER]

We first prove that under certain mild assumptions, the entropy rate of a hidden Markov chain, observed when passing a finite-state stationary Markov chain through a discrete-time continuous-output channel, is analytic with respect to the input Markov chain parameters. We then further prove, under strengthened assumptions on the channel, that the entropy rate is jointly analytic as a function of both the input Markov chain parameters and the channel parameters. In particular, the main theorems establish the analyticity of the entropy rate for two representative channels: 1) Cauchy and 2) Gaussian. The analyticity results obtained are expected to be helpful in computation/estimation of entropy rate of hidden Markov chains and capacity of finite-state channels with continuous output alphabet. [ABSTRACT FROM AUTHOR]

This paper compares different numerical methods for the solution of myocyte models of cardiac electrophysiology. In particular, it presents how the technique called uniformization method substantially increases the stability of simple first-order methods such as Euler explicit method and Rush–Larsen (RL) method, for the solution of modern electrophysiology models that are based on continuous-time Markov chains (MCs) for the description of subcellular structures, such as ion channels. The MCs are often associated with stiff ordinary differential equations that severely limit the time step used by these traditional methods. By using the uniformization method, we could significantly increase the time steps for the solution of different cardiac electrophysiology models and improve the computational performance up to 150 times compared to the performance of Euler's and RL's methods. [ABSTRACT FROM PUBLISHER]

We introduce a game-theoretic framework to study the hypothesis testing problem in the presence of an adversary aiming to prevent a correct decision. Specifically, this paper considers a scenario in which an analyst has to accept or reject the null hypothesis \(H_0\) characterized by a probability mass function (pmf) \(P_X\) based on the evidence provided by a test sequence. In turn, the goal of the adversary is to take a sequence generated according to a different pmf and modify it in such a way to induce a decision error. \(P_X\) is known only through one or more training sequences. We derive the asymptotic equilibrium of the game under the assumption that the analyst relies only on first order statistics of the test and training sequences, and compute the asymptotic payoff of the game when the length of the sequences tends to infinity. We introduce the concept of indistinguishability region, defined as the set of pmfs that can not be distinguished reliably from \(P_X\) in the presence of attacks. Two different scenarios are considered: in the first one the analyst and the adversary share the same training sequence, in the second scenario, they rely on independent sequences. The obtained results are compared with a version of the game in which the pmf \(P_X\) is perfectly known to both the analyst and the adversary. [ABSTRACT FROM PUBLISHER]

Two trends are highlighted here, in the area of automated theorem proving: the integration of reasoning methods for propositional and first-order logic, with a best-of-both-worlds motivation; and built-in reasoning support modulo background theories. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to study the reversible magnetization processes in a ferromagnetic system. A deconvolution of total magnetization into reversible and irreversible components is obtained with a new technique based on measurements performed on a few families of minor hysteresis loops (mHL) near first-order reversal curve (FORC). This mHL/FORC technique has the potential to determine the reversible/irreversible ratio in each point of the hysteresis area. We have tested the method on experimental data measured on Barium ferrite powder samples, which exhibit a significant reversible magnetization component. The results are discussed within a modified version of scalar Preisach-type model. [ABSTRACT FROM PUBLISHER]

We generalize to any finite fields \BBFq a theorem about covering radius of codes of strength 2 proved by Helleseth and coworkers. Then,using this result and partial covering radius, we give bounds for the covering radius of first-order generalized Reed–Muller codes. Finally, using Magma, we get some improvements for \BBF3. [ABSTRACT FROM AUTHOR]

In this paper, an analytical study of high-impulse current measuring shunts with cage configuration is presented. Through the use of the partial mutual inductance method, an equivalent first-order model is developed, and a discussion of the minimization of the time constant taking into account physical constraints for two different materials is presented. The transfer function of the measuring circuit is provided, and the possibility of compensating the inductance via the use of rectangular loops formed by the sensing wires and placed inside the cage is shown. The derived model is tested on a homemade shunt prototype with application to an electromagnetic rail launcher. The experimental results confirm the validity of the model and its robustness with respect to the geometrical parameter uncertainty. [ABSTRACT FROM AUTHOR]

This paper presents a set of divergence-conforming basis functions to model the behavior of surface currents near the edges and corners of an infinitely-thin, perfectly-conducting polygonal flat plate. The basis functions are derived by imposing the edge and corner conditions on the first-order basis functions. Current distributions obtained using the new set of basis functions are compared with the exact solutions for several scattering configurations. Even though the proposed basis functions are not highest order complete, they provide accurate results without incurring complexity in the analysis or additional computational requirements. [ABSTRACT FROM AUTHOR]

Fixed wireless channels in suburban macrocells are subject to fading due to scattering by moving objects such as windblown trees and foliage in the environment. When, as is often the case, the fading follows a Ricean distribution, the first-order statistics of fading are completely described by the corresponding average path gain and Ricean K-factor. Because such fading has important implications for the design of both narrow-band and wideband multipoint communication systems that are deployed in such environments, it must be well characterized. We conducted a set of 1.9-GHz experiments in suburban macrocell environments to generate a collective database from which we could construct a simple model for the probability distribution of K as experienced by fixed wireless users. Specifically, we find K to be lognormal, with the median being a simple function of season, antenna height, antenna beamwidth, and distance and with a standard deviation of 8 dB. We also present plausible physical arguments to explain these observations, elaborate on the variability of K with time, frequency, and location, and show the strong influence of wind conditions on K. [ABSTRACT FROM AUTHOR]

Intravenous glucose tolerance test (IVGTT) minimal model parameters are commonly estimated by weighted least squares (WLSs) on each subject data. Sometimes, with sparse data, individual parameters cannot be satisfactorily obtained. In such cases, a population approach could be preferable. These methods allow borrowing information across all subjects simultaneously, quantifying population features directly, and subsequently, deriving individual parameter estimates. In this paper, we assessed different estimation methods on simulated datasets. Besides the standard WLS approach, we applied iterative procedures (iterative two-stage (ITS) and global two-stage (GTS) methods) as well as nonlinear mixed-effects models (NLMIEMs), where the likelihood is based on model linearization: first-order (FO), FO conditional estimation (FOCE), and Laplace (LAP) approximations. The synthetic dataset, initially very rich, was progressively reduced (by 50% and 75%) in. order to assess the robustness of the results in sparsely sampled situations. Our results show that, even with intensive sampling, population approaches provide more reliable parameter estimates. Moreover, these estimates are remarkably more robust when the data become scarce. ITS and GTS encounter critical problems when single subjects have very poor sampling schedules, whereas the NLMEM (excluding FO) methods are more versatile and able to cope with such situations. FOCE appears as the most satisfactory approach. [ABSTRACT FROM AUTHOR]

A methodology of the first-order plus dead time process transfer function identification based on the measurement of the locus of a perturbed relay system from a single relay feedback test is proposed. Simple analytical formulas and rules that do not require iterative calculations and are suitable for the use within distributed control systems or stand-alone proportional-integral-differential controllers are obtained for this process model. [ABSTRACT FROM AUTHOR]

Presents a general methodology and a computer-aided design (CAD) tool for synthesis of linear analog functions with a network of operational transconductance amplifiers. Simulation results derived out of running CAD software BECAS 1.0 for synthesis of different classes of filters; Characteristics of the first-order and second-order transfer function; Discussion on the optimized realization of a transfer function.

Considers the capacity of constrained two-dimensional (2-D) codes which is equivalent to the maximum entropy of 2-D fields defined by the given set of constraints. Bounds on the capacity for first-order constraints; Upper bound for higher order constraints; Lowerbound on capacity; Numerical bounds on capacities; Conclusion.

Focuses on a study which considered environments where most energy incident on the array is from scatterers local to the mobile transmitters and explored the effects on direction of arrival (DOA) estimation with the MUSIC algorithm. Description of the local scattering; Result of the first-order analysis; Approximate distribution of the DOA estimates for Rayleigh and Ricean fading.