Reinforcement Learning Based Minimum State-flipped Control for the Reachability of Boolean Control Networks

To realize reachability as well as reduce control costs of Boolean Control Networks (BCNs) with state-flipped control, a reinforcement learning based method is proposed to obtain flip kernels and the optimal policy with minimal flipping actions to realize reachability. The method proposed is model-free and of low computational complexity. In particular, Q-learning (QL), fast QL, and small memory QL are proposed to find flip kernels. Fast QL and small memory QL are two novel algorithms. Specifically, fast QL, namely, QL combined with transfer-learning and special initial states, is of higher efficiency, and small memory QL is applicable to large-scale systems. Meanwhile, we present a novel reward setting, under which the optimal policy with minimal flipping actions to realize reachability is the one of the highest returns. Then, to obtain the optimal policy, we propose QL, and fast small memory QL for large-scale systems. Specifically, on the basis of the small memory QL mentioned before, the fast small memory QL uses a changeable reward setting to speed up the learning efficiency while ensuring the optimality of the policy. For parameter settings, we give some system properties for reference. Finally, two examples, which are a small-scale system and a large-scale one, are considered to verify the proposed method

Algebraic structure of F_q-linear conjucyclic codes over finite field F_{q^2}

Recently, Abualrub et al. illustrated the algebraic structure of additive conjucyclic codes over F_4 (Finite Fields Appl. 65 (2020) 101678). In this paper, our main objective is to generalize their theory. Via an isomorphic map, we give a canonical bijective correspondence between F_q-linear additive conjucyclic codes of length n over F_{q^2} and q-ary linear cyclic codes of length 2n. By defining the alternating inner product, our proposed isomorphic map preserving the orthogonality can also be proved. From the factorization of the polynomial x^{2n}-1 over F_q, the enumeration of F_{q}-linear additive conjucyclic codes of length n over F_{q^2} will be obtained. Moreover, we provide the generator and parity-check matrices of these q^2-ary additive conjucyclic codes of length n

Comparative Analysis of Evidentiality in Spoken and Written Academic Discourse

This paper aims to explore the differences existing in the use of evidentiality in spoken and written academic discourse. Great differences are revealed in the occurrence frequency and linguistic forms of sub-types of evidentiality (shared evidentials, reporting evidentials and personal evidentials). Comparatively, writers demonstrate a proclivity towards employing shared and reporting evidentials and opting for linguistic forms conveying objectivity and discreetness due to the temporal and spatial distance inherent in the author-reader dynamic. Speakers tend to use personal evidentials to project a confident and positive self-image and display less stringent adherence to the verifiability of evidence sources because of the instantaneous nature of of (delete) speech and the interactive nature of oral communication with an audience

Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks

In this paper, we investigate the problem of controlling probabilistic Boolean control networks (PBCNs) to achieve reachability with maximum probability in the finite time horizon. We address three questions: 1) finding control policies that achieve reachability with maximum probability under fixed, and particularly, varied finite time horizon, 2) leveraging prior knowledge to solve question 1) with faster convergence speed in scenarios where time is a variable framework, and 3) proposing an enhanced Q-learning (QL) method to efficiently address the aforementioned questions for large-scale PBCNs. For question 1), we demonstrate the applicability of QL method on the finite-time reachability problem. For question 2), considering the possibility of varied time frames, we incorporate transfer learning (TL) technique to leverage prior knowledge and enhance convergence speed. For question 3), an enhanced model-free QL approach that improves upon the traditional QL algorithm by introducing memory-efficient modifications to address these issues in large-scale PBCNs effectively. Finally, we apply the proposed method to two examples: a small-scale PBCN and a large-scale PBCN, demonstrating the effectiveness of our approach

MUG: Interactive Multimodal Grounding on User Interfaces

We present MUG, a novel interactive task for multimodal grounding where a user and an agent work collaboratively on an interface screen. Prior works modeled multimodal UI grounding in one round: the user gives a command and the agent responds to the command. Yet, in a realistic scenario, a user command can be ambiguous when the target action is inherently difficult to articulate in natural language. MUG allows multiple rounds of interactions such that upon seeing the agent responses, the user can give further commands for the agent to refine or even correct its actions. Such interaction is critical for improving grounding performances in real-world use cases. To investigate the problem, we create a new dataset that consists of 77,820 sequences of human user-agent interaction on mobile interfaces in which 20% involves multiple rounds of interactions. To establish our benchmark, we experiment with a range of modeling variants and evaluation strategies, including both offline and online evaluation-the online strategy consists of both human evaluation and automatic with simulators. Our experiments show that allowing iterative interaction significantly improves the absolute task completion by 18% over the entire test dataset and 31% over the challenging subset. Our results lay the foundation for further investigation of the problem

Magnetic Crosstalk Suppression and Probe Miniaturization of Coupled Core Fluxgate Sensors

This paper demonstrates the probe structure optimization of coupled core fluxgate magnetic sensors through finite element analysis. The obtained modelling results have been used to optimize the probe structures from horizontal- to vertical- arrangements for magnetic crosstalk suppression and probe miniaturization. The finite element analysis show that with the same distance between each adjacent fluxgate elements, the magnetic crosstalk is suppressed by 6 times and the volume is reduced by 2 times after the optimization. Furthermore, the miniaturized probes with low magnetic crosstalk have been designed and implemented. The experimental results which showed more than 5 times suppression of magnetic crosstalk verified the simulation results. Therefore, the results provide detailed reference to cope with the contradiction between volume miniaturization and magnetic crosstalk suppression in magnetic sensor-array design

On the locality of local neural operator in learning fluid dynamics

This paper launches a thorough discussion on the locality of local neural operator (LNO), which is the core that enables LNO great flexibility on varied computational domains in solving transient partial differential equations (PDEs). We investigate the locality of LNO by looking into its receptive field and receptive range, carrying a main concern about how the locality acts in LNO training and applications. In a large group of LNO training experiments for learning fluid dynamics, it is found that an initial receptive range compatible with the learning task is crucial for LNO to perform well. On the one hand, an over-small receptive range is fatal and usually leads LNO to numerical oscillation; on the other hand, an over-large receptive range hinders LNO from achieving the best accuracy. We deem rules found in this paper general when applying LNO to learn and solve transient PDEs in diverse fields. Practical examples of applying the pre-trained LNOs in flow prediction are presented to confirm the findings further. Overall, with the architecture properly designed with a compatible receptive range, the pre-trained LNO shows commendable accuracy and efficiency in solving practical cases