Intraosseous Hemangioma of the Inferior Turbinate

The nasal cavity harbors an enormous variety of neoplasms, including epithelial and mesenchymal tumors. Hemangioma is an infrequent mesenchymal tumor of the nasal cavity, mostly arising in the mucosa and rarely in the bones. We describe the case of a 73-year-old woman who was referred to our hospital with a tumor in her left nasal cavity. The tumor originated from the left inferior turbinate. Histological examination subsequent to complete excision revealed that the tumor was an intraosseous cavernous hemangioma. To our knowledge, this is the second case of intraosseous hemangioma of the inferior turbinate reported in the English literature

Impact of acoustic similarity on efficiency of verbal information transmission via subtle prosodic cues

In this study, we investigate the effect of tiny acoustic differences on the efficiency of prosodic information transmission. Study participants listened to textually ambiguous sentences, which could be understood with prosodic cues, such as syllable length and pause length. Sentences were uttered in voices similar to the participant’s own voice and in voices dissimilar to their own voice. The participants then identified which of four pictures the speaker was referring to. Both the eye movement and response time of the participants were recorded. Eye tracking and response time results both showed that participants understood the textually ambiguous sentences faster when listening to voices similar to their own. The results also suggest that tiny acoustic features, which do not contain verbal meaning can influence the processing of verbal information

Slippery flowers as a mechanism of defence against nectar-thieving ants

滑る花びらがアリの花への侵入を妨げることを発見 --新たな花の防衛機構の存在を実証--. 京都大学プレスリリース. 2021-01-12.Background and Aims: The great diversity of floral characteristics among animal-pollinated plants is commonly understood to be the result of coevolutionary interactions between plants and pollinators. Floral antagonists, such as nectar thieves, also have the potential to exert an influence upon the selection of floral characteristics, but adaptation against floral antagonists has attracted comparatively little attention. We found that the corollas of hornet-pollinated Codonopsis lanceolata (Campanulaceae) and the tepals of bee-pollinated Fritillaria koidzumiana (Liliaceae) are slippery to nectar-thieving ants living in the plant’s habitat; because the flowers of both species have exposed nectaries, slippery perianths may function as a defence against nectar-thieving ants. Methods: We conducted a behavioural experiment and observed perianth surface microstructure by scanning electron microscopy to investigate the mechanism of slipperiness. Field experiments were conducted to test whether slippery perianths prevent floral entry by ants, and whether ant presence inside flowers affects pollination. Key Results: Scanning electron microscopy observations indicated that the slippery surfaces were coated with epicuticular wax crystals. The perianths lost their slipperiness when wiped with hexane. Artificial bridging of the slippery surfaces using non-slippery materials allowed ants to enter flowers more frequently. Experimental introduction of live ants to the Codonopsis flowers evicted hornet pollinators and shortened the duration of pollinator visits. However, no statistical differences were found in the fruit or seed sets of flowers with and without ants. Conclusions: Slippery perianths, most probably based on epicuticular wax crystals, prevent floral entry by ants that negatively affect pollinator behaviour. Experimental evidence of floral defence based on slippery surfaces is rare, but such a mode of defence may be widespread amongst flowering plants

c-trie++: A Dynamic Trie Tailored for Fast Prefix Searches

Given a dynamic set $K$ of $k$ strings of total length $n$ whose characters are drawn from an alphabet of size $\sigma$, a keyword dictionary is a data structure built on $K$ that provides locate, prefix search, and update operations on $K$. Under the assumption that $\alpha = w / \lg \sigma$ characters fit into a single machine word $w$, we propose a keyword dictionary that represents $K$ in $n \lg \sigma + \Theta(k \lg n)$ bits of space, supporting all operations in $O(m / \alpha + \lg \alpha)$ expected time on an input string of length $m$ in the word RAM model. This data structure is underlined with an exhaustive practical evaluation, highlighting the practical usefulness of the proposed data structure, especially for prefix searches - one of the most elementary keyword dictionary operations

Recognition of paper currencies by hybrid neural network

For the recognition of paper currencies by image processing, the two steps data processing approach can yield high performance. The two steps include “recognition” and “verification” steps. In the current recognition machine, a simple statistical test is used as the verification step, where univariate Gaussian distribution is employed. Here we propose the use of the probability density formed by a multivariable Gaussian function, where the input data space is transferred to a lower dimensional subspace. Due to the structure of this model, we refer the total processing system as a hybrid neural network. Since the computation of the verification model only needs the inner product and square, the computational load is very small. In this paper, the method and numerical experimental results are shown by using the real data and the recognition machine</p

Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning

Analysis of invasive sports such as soccer is challenging because the game situation changes continuously in time and space, and multiple agents individually recognize the game situation and make decisions. Previous studies using deep reinforcement learning have often considered teams as a single agent and valued the teams and players who hold the ball in each discrete event. Then it was challenging to value the actions of multiple players, including players far from the ball, in a spatiotemporally continuous state space. In this paper, we propose a method of valuing possible actions for on- and off-ball soccer players in a single holistic framework based on multi-agent deep reinforcement learning. We consider a discrete action space in a continuous state space that mimics that of Google research football and leverages supervised learning for actions in reinforcement learning. In the experiment, we analyzed the relationships with conventional indicators, season goals, and game ratings by experts, and showed the effectiveness of the proposed method. Our approach can assess how multiple players move continuously throughout the game, which is difficult to be discretized or labeled but vital for teamwork, scouting, and fan engagement.Comment: 12 pages, 4 figure

Automatic Edge Error Judgment in Figure Skating Using 3D Pose Estimation from a Monocular Camera and IMUs

Automatic evaluating systems are fundamental issues in sports technologies. In many sports, such as figure skating, automated evaluating methods based on pose estimation have been proposed. However, previous studies have evaluated skaters' skills in 2D analysis. In this paper, we propose an automatic edge error judgment system with a monocular smartphone camera and inertial sensors, which enable us to analyze 3D motions. Edge error is one of the most significant scoring items and is challenging to automatically judge due to its 3D motion. The results show that the model using 3D joint position coordinates estimated from the monocular camera as the input feature had the highest accuracy at 83% for unknown skaters' data. We also analyzed the detailed motion analysis for edge error judgment. These results indicate that the monocular camera can be used to judge edge errors automatically. We will provide the figure skating single Lutz jump dataset, including pre-processed videos and labels, at https://github.com/ryota-takedalab/JudgeAI-LutzEdge