additional analog converters, imposing issues with scalability and power consumption. [2][3][4][5] Development of next-generation materials and devices for neuromorphic electronics entails detailed understanding of the fundamental device characteristics and their possible emulation capabilities at an elemental level. Ionically gated transistors harness diffusive mechanics to achieve continuous modulation of channel conductance at low-power, but require coupling of two disparate electronically and ionically active material sets. [6,7] Solutions based on drift-memristors are inherently disadvantaged due to digital-like abrupt switching transitions, which limit their plasticity. [8] Very recently, second-order drift memristors, [9,10] electrochemical metallization cells, [11] and diffusive memristors [8] have been engineered to approximate the biological Ca 2+ dynamics based on metal atom diffusion, thermal dissipation, [9] mobility decay, [12] and spontaneous nanoparticle formation, but often require additional nonvolatile elements in series for long-term memory storage. An ionic semiconductor which intimately combines rapid electronic transitions with slow drift-diffusive ionic kinetics will enable dynamic tuning of metastable memristive conductance states, allowing efficient emulation of synaptic characteristics and catering for novel low-power architectures that exploit electronic properties of the semiconductor.Emulation of brain-like signal processing is the foundation for development of efficient learning circuitry, but few devices offer the tunable conductance range necessary for mimicking spatiotemporal plasticity in biological synapses. An ionic semiconductor which couples electronic transitions with drift-diffusive ionic kinetics would enable energy-efficient analog-like switching of metastable conductance states. Here, ionic-electronic coupling in halide perovskite semiconductors is utilized to create memristive synapses with a dynamic continuous transition of conductance states. Coexistence of carrier injection barriers and ion migration in the perovskite films defines the degree of synaptic plasticity, more notable for the larger organic ammonium and formamidinium cations than the inorganic cesium counterpart. Optimized pulsing schemes facilitates a balanced interplay of short-and longterm plasticity rules like paired-pulse facilitation and spike-time-dependent plasticity, cardinal for learning and computing. Trained as a memory array, halide perovskite synapses demonstrate reconfigurability, learning, forgetting, and fault tolerance analogous to the human brain. Network-level simulations of unsupervised learning of handwritten digit images utilizing experimentally derived device parameters, validates the utility of these memristors for energy-efficient neuromorphic computation, paving way for novel ionotronic neuromorphic architectures with halide perovskites as the active material. Artificial SynapsesThe ORCID identification number(s) for the author(s) of this article can be found under https://doi.C...

Over 97 million people speak Vietnamese as their native language in the world. However, there are few research studies on machine reading comprehension (MRC) for Vietnamese, the task of understanding a text and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning like word matching and demands single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC methods for English and Chinese as the first experimental models on UIT-ViQuAD. We also estimate human performance on the dataset and compare it to the experimental results of powerful machine learning models. As a result, the substantial differences between human performance and the best model performance on the dataset indicate that improvements can be made on UIT-ViQuAD in future research. Our dataset is freely available on our website 1 to encourage the research community to overcome challenges in Vietnamese MRC.

No abstract

No abstract

Ultralow power dual-gated subthreshold oxide neuristors : an enabler for higher order neuronal temporal correlations