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

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

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

Symplectic self-orthogonal quasi-cyclic codes

In this paper, we obtain sufficient and necessary conditions for quasi-cyclic codes with index even to be symplectic self-orthogonal. Then, we propose a method for constructing symplectic self-orthogonal quasi-cyclic codes, which allows arbitrary polynomials that coprime $x^{n}-1$ to construct symplectic self-orthogonal codes. Moreover, by decomposing the space of quasi-cyclic codes, we provide lower and upper bounds on the minimum symplectic distances of a class of 1-generator quasi-cyclic codes and their symplectic dual codes. Finally, we construct many binary symplectic self-orthogonal codes with excellent parameters, corresponding to 117 record-breaking quantum codes, improving Grassl's table (Bounds on the Minimum Distance of Quantum Codes. http://www.codetables.de)

Speech reconstruction using a deep partially supervised neural network

Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays, however deep neural network-based systems have been hampered by the limited amount of training data available from individual voice-loss patients. We propose a novel deep neural network structure that allows a partially supervised training approach on spectral features from smaller datasets, yielding very good results compared to the current state-of-the-art

Numerical simulation of hydrodynamics and reaeration over a stepped spillway by the SPH method

Aerated flows are characterized by complex hydrodynamics and mass-transfer processes. As a Lagrangian method, smoothed particle hydrodynamics (SPH) has a significant advantage in tracking the air-water interface in turbulent flows. This paper presents the application of an SPH method to investigate hydrodynamics and reaeration over stepped spillways. In the SPH method, the entrainment of dissolved oxygen (DO) is studied using a multiphase mass transfer SPH method for reaeration. The numerical results are compared with the hydrodynamics data from Chanson and DO data from Cheng. The simulation results show that velocity distribution and the location of free-surface aeration inception agree with the experimental results. Compared with the experimental results, the distribution of DO concentration over the stepped spillway is consistent with the measurement results. The study shows that the two-phase DO mass transfer SPH model is reliable and reasonable for simulating the hydrodynamics characteristics and reaeration process