Optimal transport for vector Gaussian mixture models

Vector Gaussian mixture models form an important special subset of vector-valued distributions. Any physical entity that can mutate or transit among alternative manifestations distributed in a given space falls into this category. A key example is color imagery. In this note, we vectorize the Gaussian mixture model and study different optimal mass transport related problems for such models. The benefits of using vector Gaussian mixture for optimal mass transport include computational efficiency and the ability to preserve structure

Classification of derivation-simple color algebras related to locally finite derivations

We classify the pairs $(A,D)$ consisting of an $(\epsilon,\Gamma)$-olor-commutative associative algebra $A$ with an identity element over an algebraically closed field $F$ of characteristic zero and a finite dimensional subspace $D$ of $(\epsilon,\Gamma)$-color-commutative locally finite color-derivations of $A$ such that $A$ is $\Gamma$-graded $D$-simple and the eigenspaces for elements of $D$ are $\Gamma$-graded. Such pairs are the important ingredients in constructing some simple Lie color algebras which are in general not finitely-graded. As some applications, using such pairs, we construct new explicit simple Lie color algebras of generalized Witt type, Weyl type.Comment: 15 page

LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection

Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection. However, existing CNN based pulmonary nodule detection methods lack the ability to capture long-range dependencies, which is vital for global information extraction. In computer vision tasks, non-local operations have been widely utilized, but the computational cost could be very high for 3D computed tomography (CT) images. To address this issue, we propose a long short slice-aware network (LSSANet) for the detection of pulmonary nodules. In particular, we develop a new non-local mechanism termed long short slice grouping (LSSG), which splits the compact non-local embeddings into a short-distance slice grouped one and a long-distance slice grouped counterpart. This not only reduces the computational burden, but also keeps long-range dependencies among any elements across slices and in the whole feature map. The proposed LSSG is easy-to-use and can be plugged into many pulmonary nodule detection networks. To verify the performance of LSSANet, we compare with several recently proposed and competitive detection approaches based on 2D/3D CNN. Promising evaluation results on the large-scale PN9 dataset demonstrate the effectiveness of our method. Code is at https://github.com/Ruixxxx/LSSANet.Comment: MICCAI 202

PRELIMINARY STUDY OF TRAINING COMPONENTS ON SENSORIMOTOR SYSTEM IN TAI CHI

The purpose of this study was to identify if Tai Chi (TC) movements are full with the training components on sensorimotor system by movement kinematics and electromyography (EMG) analysis. Two TC masters performed a typical TC movement "brush knees and twist steps" twice. Motion analysis showed that joint angles (ankles, knees and hips) of eight different postures, height and velocity of center of gravity (C.G.) of the whole movement had no significant difference in two trials. The results indicated that the TC masters had good awareness of joint position and movement and spatial position sense. Moreover, EMG analysis showed that muscles activated from full relaxation to vigorous contraction and the similar EMG patterns of each muscle in two trials suggested the good training effect of TC on muscle coordinative contraction

Reverse chemical ecology approach for the identification of an oviposition attractant for Culex quinquefasciatus.

Pheromones and other semiochemicals play a crucial role in today's integrated pest and vector management strategies. These semiochemicals are typically discovered by bioassay-guided approaches. Here, we applied a reverse chemical ecology approach; that is, we used olfactory proteins to lead us to putative semiochemicals. Specifically, we used 7 of the top 10 odorant receptors (ORs) most expressed in the antennae of the southern house mosquito, Culex quinquefasciatus, and which are yet to be deorphanized. We expressed these receptors in the Xenopus oocyte recording system and challenged them with a panel of 230 odorants, including physiologically and behaviorally active compounds. Six of the ORs were silent either because they are not functional or a key odorant was missing. CquiOR36, which showed the highest transcript levels of all OR genes in female antennae, was also silent to all odorants in the tested panel, but yielded robust responses when it was accidentally challenged with an old sample of nonanal in ethanol. After confirming that fresh samples were inactive and through a careful investigation of all possible "contaminants" in the old nonanal samples, we identified the active ligand as acetaldehyde. That acetaldehyde is activating CquiOR36 was further confirmed by electroantennogram recordings from antennae of fruit flies engineered to carry CquiOR36. Antennae of female mosquitoes also responded to acetaldehyde. Cage oviposition and dual-choice assays demonstrated that acetaldehyde is an oviposition attractant in a wide range of concentrations and thus of potential practical applications

Denoising Time Cycle Modeling for Recommendation

Recently, modeling temporal patterns of user-item interactions have attracted much attention in recommender systems. We argue that existing methods ignore the variety of temporal patterns of user behaviors. We define the subset of user behaviors that are irrelevant to the target item as noises, which limits the performance of target-related time cycle modeling and affect the recommendation performance. In this paper, we propose Denoising Time Cycle Modeling (DiCycle), a novel approach to denoise user behaviors and select the subset of user behaviors that are highly related to the target item. DiCycle is able to explicitly model diverse time cycle patterns for recommendation. Extensive experiments are conducted on both public benchmarks and a real-world dataset, demonstrating the superior performance of DiCycle over the state-of-the-art recommendation methods

Odorant Inhibition in Mosquito Olfaction.

How chemical signals are integrated at the peripheral sensory system of insects is still an enigma. Here we show that when coexpressed with Orco in Xenopus oocytes, an odorant receptor from the southern house mosquito, CquiOR32, generated inward (regular) currents when challenged with cyclohexanone and methyl salicylate, whereas eucalyptol and fenchone elicited inhibitory (upward) currents. Responses of CquiOR32-CquiOrco-expressing oocytes to odorants were reduced in a dose-dependent fashion by coapplication of inhibitors. This intrareceptor inhibition was also manifested in vivo in fruit flies expressing the mosquito receptor CquiOR32, as well in neurons on the antennae of the southern house mosquito. Likewise, an orthologue from the yellow fever mosquito, AaegOR71, showed intrareceptor inhibition in the Xenopus oocyte recording system and corresponding inhibition in antennal neurons. Inhibition was also manifested in mosquito behavior. Blood-seeking females were repelled by methyl salicylate, but repellence was significantly reduced when methyl salicylate was coapplied with eucalyptol