HOS-Miner: a system for detecting outlying subspaces of high-dimensional data

[Abstract]: We identify a new and interesting high-dimensional outlier detection problem in this paper that is, detecting the subspaces in which given data points are outliers. We call the subspaces in which a data point is an outlier as its Outlying Subspaces. In this paper, we will propose the prototype of a dynamic subspace search system, called HOS-Miner (HOS stands for High-dimensional Outlying Subspaces) that utilizes a sample-based learning process to effectively identify the outlying subspaces of a given point

Discrete Diffusion Language Modeling by Estimating the Ratios of the Data Distribution

Despite their groundbreaking performance for many generative modeling tasks, diffusion models have fallen short on discrete data domains such as natural language. Crucially, standard diffusion models rely on the well-established theory of score matching, but efforts to generalize this to discrete structures have not yielded the same empirical gains. In this work, we bridge this gap by proposing score entropy, a novel discrete score matching loss that is more stable than existing methods, forms an ELBO for maximum likelihood training, and can be efficiently optimized with a denoising variant. We scale our Score Entropy Discrete Diffusion models (SEDD) to the experimental setting of GPT-2, achieving highly competitive likelihoods while also introducing distinct algorithmic advantages. In particular, when comparing similarly sized SEDD and GPT-2 models, SEDD attains comparable perplexities (normally within $+10\%$ of and sometimes outperforming the baseline). Furthermore, SEDD models learn a more faithful sequence distribution (around $4\times$ better compared to GPT-2 models with ancestral sampling as measured by large models), can trade off compute for generation quality (needing only $16\times$ fewer network evaluations to match GPT-2), and enables arbitrary infilling beyond the standard left to right prompting.Comment: 30 page

Dark matter dominated dwarf disc galaxy Segue 1

Several observations reveal that dwarf galaxy Segue 1 has a dark matter (DM) halo at least ~ 200 times more massive than its visible baryon mass of only ~ 103 solar masses. The baryon mass is dominated by stars with perhaps an interstellar gas mass of < 13 solar masses. Regarding Segue 1 as a dwarf disc galaxy by its morphological appearance of long stretch, we invoke the dynamic model of Xiang-Gruess, Lou & Duschl (XLD) to estimate its physical parameters for possible equilibria with and without an isopedically magnetized gas disc. We estimate the range of DM mass and compare it with available observational inferences. Due to the relatively high stellar velocity dispersion compared to the stellar surface mass density, we find that a massive DM halo would be necessary to sustain disc equilibria. The required DM halo mass agrees grossly with observational inferences so far. For an isopedic magnetic field in a gas disc, the ratio f between the DM and baryon potentials depends strongly on the magnetic field strength. Therefore, a massive DM halo is needed to counteract either the strong stellar velocity dispersion and rotation of the stellar disc or the magnetic Lorentz force in the gas disc. By the radial force balances, the DM halo mass increases for faster disc rotation.Comment: 5 pages, 2 figures, accepted for publication in MNRA

Modal Perturbation Method for the Dynamic Characteristics of Timoshenko Beams

Timoshenko beams have been widely used in structural and mechanical systems. under dynamic loading, the analytical solution of a Timoshenko beam is often difficult to obtain due to the complexity involved in the equation of motion. In this paper, a modal perturbation method is introduced to approximately determine the dynamic characteristics of a Timoshenko beam. In this approach, the differential equation of motion describing the dynamic behavior of the Timoshenko beam can be transformed into a set of nonlinear algebraic equations. Therefore, the solution process can be simplified significantly for the Timoshenko beam with arbitrary boundaries. Several examples are given to illustrate the application of the proposed method. Numerical results have shown that the modal perturbation method is effective in determining the modal characteristics of Timoshenko beams with high accuracy. The effects of shear distortion and moment of inertia on the natural frequencies of Timoshenko beams are discussed in detail

TGR5: A Novel Target for Weight Maintenance and Glucose Metabolism

TGR5, an emerging G protein-coupled receptor, was identified as a membrane receptor for bile acids. The expression of TGR5 and its function are distinct from the previously identified nuclear bile acid receptor, farnesoid X receptor (FXR). These two bile acid receptors complement with each other for maintaining bile acid homeostasis and mediating bile acid signaling. Both receptors are also shown to play roles in regulating inflammation and glucose metabolism. An interesting finding for TGR5 is its role in energy metabolism. The discovery of TGR5 expression in brown adipocyte tissues (BATs) and the recent demonstration of BAT in adult human body suggest a potential approach to combat obesity by targeting TGR5 to increase thermogenesis. We summarize here the latest finding of TGR5 research, especially its role in energy metabolism and glucose homeostasis

TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition

Recent studies have integrated convolution into transformers to introduce inductive bias and improve generalization performance. However, the static nature of conventional convolution prevents it from dynamically adapting to input variations, resulting in a representation discrepancy between convolution and self-attention as self-attention calculates attention matrices dynamically. Furthermore, when stacking token mixers that consist of convolution and self-attention to form a deep network, the static nature of convolution hinders the fusion of features previously generated by self-attention into convolution kernels. These two limitations result in a sub-optimal representation capacity of the constructed networks. To find a solution, we propose a lightweight Dual Dynamic Token Mixer (D-Mixer) that aggregates global information and local details in an input-dependent way. D-Mixer works by applying an efficient global attention module and an input-dependent depthwise convolution separately on evenly split feature segments, endowing the network with strong inductive bias and an enlarged effective receptive field. We use D-Mixer as the basic building block to design TransXNet, a novel hybrid CNN-Transformer vision backbone network that delivers compelling performance. In the ImageNet-1K image classification task, TransXNet-T surpasses Swin-T by 0.3% in top-1 accuracy while requiring less than half of the computational cost. Furthermore, TransXNet-S and TransXNet-B exhibit excellent model scalability, achieving top-1 accuracy of 83.8% and 84.6% respectively, with reasonable computational costs. Additionally, our proposed network architecture demonstrates strong generalization capabilities in various dense prediction tasks, outperforming other state-of-the-art networks while having lower computational costs. Code is available at https://github.com/LMMMEng/TransXNet.Comment: 12 pages, 8 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Exogenous Application of Jasmonic Acid Induces Volatile Emissions in Rice and Enhances Parasitism of Nilaparvata lugens Eggs by theParasitoid Anagrus nilaparvatae

Jasmonate signaling pathway plays an important role in induced plant defense against herbivores and pathogens, including the emission of volatiles that serve as attractants for natural enemies of herbivores. We studied the volatiles emitted from rice plants that were wounded and treated with jasmonic acid (JA) and their effects on the host-searching behavior of the rice brown planthopper, Nilaparvata lugens (Stål), and its mymarid egg parasitoid Anagrus nilaparvatae Pang et Wang. Female adults of N. lugens significantly preferred to settle on JA-treated rice plants immediately after release. The parasitoid A. nilaparvatae showed a similar preference and was more attracted to the volatiles emitted from JA-treated rice plants than to volatiles from control plants. This was also evident from greenhouse and field experiments in which parasitism of N. lugens eggs by A. nilaparvatae on plants that were surrounded by JA-treated plants was more than twofold higher than on control plants. Analyses of volatiles collected from rice plants showed that JA treatment dramatically increased the release of volatiles, which included aliphatic aldehydes and alcohols, monoterpenes, sesquiterpenes, methyl salicylate, n-heptadecane, and several as yet unidentified compounds. These results confirm an involvement of the JA pathway in induced defense in rice plants and demonstrate that the egg parasitoid A. nilaparvatae exploits plant-provided cues to locate hosts. We explain the use of induced plant volatiles by the egg parasitoid by a reliable association between planthopper feeding damage and egg presenc