Friction uncertainty and contact position uncertainty may have a disastrous effect on the closure properties of grasps. This paper reflects our approach to handling these uncertainties in force-closure analysis. The former uncertainty is measured by the possible reduction rate κ of friction coefficients, while the radius ρ of contact regions is used to quantify the latter uncertainty. The actual contact point may deviate from the desired position but not farther than ρ • ρ S , the supremum of ρ without loss of force-closure, indicates the grasp tolerance to contact position uncertainty. For investigating the above uncertainties systematically, we propose three new problems in force-closure: whether a grasp with given κ and ρ achieves forceclosure, what value ρ S equals if κ is given, and how ρ S varies versus κ. To facilitate their solutions, we extend the scope of the infinitesimal motion approach from form-closure analysis to force-closure. A necessary and sufficient condition for force-closure is deduced by means of the duality between some convex cones, which play the key role in solving the problems. Finally, efficient algorithms are developed and applied to two illustrative examples.

In the age of social media, faced with a huge amount of knowledge and information, accurate and effective keyphrase extraction methods are needed to be applied in information retrieval and natural language processing. It is difficult for traditional keyphrase extraction models to contain a large amount of external knowledge information, but with the rise of pre-trained language models, there is a new way to solve this problem. Based on the above background, we propose a new baseline for unsupervised keyphrase extraction based on pre-trained language model called SIFRank. SIFRank combines sentence embedding model SIF and autoregressive pre-trained language model ELMo, and it has the best performance in keyphrase extraction for short documents. We speed up SIFRank while maintaining its accuracy by document segmentation and contextual word embeddings alignment. For long documents, we upgrade SIFRank to SIFRank+ by position-biased weight, greatly improve its performance on long documents. Compared to other baseline models, our model achieves state-of-the-art level on three widely used datasets. INDEX TERMS Keyphrase extraction, pre-trained language model, sentence embeddings, position-biased weight, SIFRank. I. INTRODUCTION Keyphrase extraction is the task of selecting a set of words or phrases from a document that could summarize the main topics discussed in the document [1]. Keyphrase extraction can greatly accelerate the speed of information retrieval, help people get the first-hand information from a long text quickly and accurately. A. MOTIVATION Keyphrase Extraction can be divided into two main kinds of approaches: supervised and unsupervised. Supervised methods perform better on specific domain tasks, but it takes a lot of labor to annotate the corpus, and the model after training may overfit and do not work well on other datasets. The main traditional unsupervised methods are mainly divided into the models based on statistics and the models based on The associate editor coordinating the review of this manuscript and approving it for publication was Shuai Han .

Researchers investigate the use of ionic direct current to reverse the standard neural stimulation recruitment order.

In the long pursuit of smart robotics, it has been envisioned to empower robots with human-like senses, especially vision and touch. While tremendous progress has been made in image sensors and computer vision over the past decades, tactile sense abilities are lagging behind due to the lack of large-scale flexible tactile sensor array with high sensitivity, high spatial resolution, and fast response. In this work, we have demonstrated a 64 × 64 flexible tactile sensor array with a record-high spatial resolution of 0.9 mm (equivalently 28.2 pixels per inch) by integrating a high-performance piezoresistive film (PRF) with a large-area active matrix of carbon nanotube thin-film transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity of ∼385 kPa–1 for multi-walled carbon nanotubes concentration of 6%, while the 14% one exhibited fast response time of ∼3 ms, good linearity, broad detection range beyond 1400 kPa, and excellent cyclability over 3000 cycles. Using this fully integrated tactile sensor array, the footprint maps of an artificial honeybee were clearly identified. Furthermore, we hardware-implemented a smart tactile system by integrating the PRF-based sensor array with a memristor-based computing-in-memory chip to record and recognize handwritten digits and Chinese calligraphy, achieving high classification accuracies of 98.8% and 97.3% in hardware, respectively. The integration of sensor networks with deep learning hardware may enable edge or near-sensor computing with significantly reduced power consumption and latency. Our work could empower the building of large-scale intelligent sensor networks for next-generation smart robotics.

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