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To fully capitalize on the unique properties of 2D materials, cost-effective techniques for producing high-quality 2D flakes at scale are crucial. In this work, we show that dry ball-milling, a commonly used powder-processing technique, can be effectively and efficiently upgraded into an automated exfoliation technique. It is done by adding polymer as adhesives into a ball mill to mimic the well-known tape exfoliation process, which is known to produce 2D flakes with the highest quality but is limited by its extremely low efficiency on large-scale production. Seventeen types of commonly seen polymers, including both artificial and natural ones, have been examined as additives to dry ball-mill hexagonal boron nitride. A parallel comparison between different additives identifies low-cost natural polymers such as starch as promising dry ball-mill additives to produce ultrathin flakes with the largest aspect ratio. The mechanical, thermal, and surface properties of the polymers are proposed as key features that simultaneously determine the exfoliation efficiency, and their ranking of importance in the mechanical exfoliation process is revealed using a machine learning model. Finally, the potential of the polymer-assisted ball-mill exfoliation method as a universal way to produce ultra-thin 2D nanosheets is also demonstrated. 

In order to understand multimodal interactions between humans or humans and machine, it is minimally necessary to identify the content of the agents’ communicative acts in the dialogue. This can involve either overt linguistic expressions (speech or writing), content-bearing gesture, or the integration of both. But this content must be interpreted relative to a deeper understanding of an agent’s Theory of Mind (one’s mental state, desires, and intentions) in the context of the dialogue as it dynamically unfolds. This, in turn, can require identifying and tracking nonverbal behaviors, such as gaze, body posture, facial expressions, and actions, all of which contribute to understanding how expressions are contextualized in the dialogue, and interpreted relative to the epistemic attitudes of each agent. In this paper, we adopt Generative Lexicon’s approach to event structure to provide a lexical semantics for ontic and epistemic actions as used in Bolander’s interpretation of Dynamic Epistemic Logic, called Lexical Event Modeling (LEM). This allows for the compositional construction of epistemic models of a dialogue state. We demonstrate how veridical and false belief scenarios are treated compositionally within this model. 

Abstract The amount of data produced by genome sequencing experiments has been growing rapidly over the past several years, making compression important for efficient storage, transfer and analysis of the data. In recent years, nanopore sequencing technologies have seen increasing adoption since they are portable, real-time and provide long reads. However, there has been limited progress on compression of nanopore sequencing reads obtained in FASTQ files since most existing tools are either general-purpose or specialized for short read data. We present NanoSpring, a reference-free compressor for nanopore sequencing reads, relying on an approximate assembly approach. We evaluate NanoSpring on a variety of datasets including bacterial, metagenomic, plant, animal, and human whole genome data. For recently basecalled high quality nanopore datasets, NanoSpring, which focuses only on the base sequences in the FASTQ file, uses just 0.35–0.65 bits per base which is 3–6$$\times$$ $\times$lower than general purpose compressors like gzip. NanoSpring is competitive in compression ratio and compression resource usage with the state-of-the-art tool CoLoRd while being significantly faster at decompression when using multiple threads (> 4$$\times$$ $\times$faster decompression with 20 threads). NanoSpring is available on GitHub athttps://github.com/qm2/NanoSpring. 

Within Dialogue Modeling research in AI and NLP, considerable attention has been spent on “dialogue state tracking” (DST), which is the ability to update the representations of the speaker’s needs at each turn in the dialogue by taking into account the past dialogue moves and history. Less studied but just as important to dialogue modeling, however, is “common ground tracking” (CGT), which identifies the shared belief space held by all of the participants in a task-oriented dialogue: the task-relevant propositions all participants accept as true. In this paper we present a method for automatically identifying the current set of shared beliefs and “questions under discussion” (QUDs) of a group with a shared goal. We annotate a dataset of multimodal interactions in a shared physical space with speech transcriptions, prosodic features, gestures, actions, and facets of collaboration, and operationalize these features for use in a deep neural model to predict moves toward construction of common ground. Model outputs cascade into a set of formal closure rules derived from situated evidence and belief axioms and update operations. We empirically assess the contribution of each feature type toward successful construction of common ground relative to ground truth, establishing a benchmark in this novel, challenging task. 

As the feature size of microelectronic circuits is scaling down to nanometer order, the increasing interconnect crosstalk, resistance-capacitance (RC) delay and power consumption can limit the chip performance and reliability. To address these challenges, new low-kdielectric (k < 2) materials need to be developed to replace current silicon dioxide (k = 3.9) or SiCOH, etc. However, existing low-kdielectric materials, such as organosilicate glass or polymeric dielectrics, suffer from poor thermal and mechanical properties. Two-dimensional polymers (2DPs) are considered promising low-kdielectric materials because of their good thermal and mechanical properties, high porosity and designability. Here, we report a chemical-vapor-deposition (CVD) method for growing fluoride rich 2DP-F films on arbitrary substrates. We show that the grown 2DP-F thin films exhibit ultra-low dielectric constant (in plane k = 1.85 and out-of-plane k = 1.82) and remarkable mechanical properties (Young’s modulus > 15 GPa). We also demonstrated the improved performance of monolayer MoS₂field-effect-transistors when utilizing 2DP-F thin films as dielectric substrates.