Policy Optimization of Finite-Horizon Kalman Filter with Unknown Noise Covariance

This paper is on learning the Kalman gain by policy optimization method. Firstly, we reformulate the finite-horizon Kalman filter as a policy optimization problem of the dual system. Secondly, we obtain the global linear convergence of exact gradient descent method in the setting of known parameters. Thirdly, the gradient estimation and stochastic gradient descent method are proposed to solve the policy optimization problem, and further the global linear convergence and sample complexity of stochastic gradient descent are provided for the setting of unknown noise covariance matrices and known model parameters

Dual Feature Augmentation Network for Generalized Zero-shot Learning

Zero-shot learning (ZSL) aims to infer novel classes without training samples by transferring knowledge from seen classes. Existing embedding-based approaches for ZSL typically employ attention mechanisms to locate attributes on an image. However, these methods often ignore the complex entanglement among different attributes' visual features in the embedding space. Additionally, these methods employ a direct attribute prediction scheme for classification, which does not account for the diversity of attributes in images of the same category. To address these issues, we propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules, one for visual features and the other for semantic features. The visual feature augmentation module explicitly learns attribute features and employs cosine distance to separate them, thus enhancing attribute representation. In the semantic feature augmentation module, we propose a bias learner to capture the offset that bridges the gap between actual and predicted attribute values from a dataset's perspective. Furthermore, we introduce two predictors to reconcile the conflicts between local and global features. Experimental results on three benchmarks demonstrate the marked advancement of our method compared to state-of-the-art approaches. Our code is available at https://github.com/Sion1/DFAN.Comment: Accepted to BMVC202

Unambiguous Observation of Single-Molecule Raman Spectroscopy Enabled by Synergic Electromagnetic and Chemical Enhancement

Raman spectroscopy is a powerful tool to detect, analyze and identify molecules. It has been a long-history pursuit to push the detection limit of Raman spectroscopy down to the fundamental single-molecule (SM) level. Due to the tiny cross section of intrinsic Raman scattering of molecule, some enhancement mechanisms of light-matter interaction must be implemented to levitate the Raman scattering intensity by a huge number of ~14-15 orders of magnitude, to the level comparable with the molecule fluorescence intensity. In this work we report unambiguous observation of single-molecule Raman spectroscopy via synergic action of electromagnetic and chemical enhancement for rhodamine B (RhB) molecule absorbed within the plasmonic nanogap formed by gold nanoparticle sitting on the two-dimensional (2D) monolayer WS2 and 2 nm SiO2 coated gold thin film. Raman spectroscopy down to an extremely dilute value of 10-18 mol/L can still be clearly visible, and the statistical enhancement factor could reach 16 orders of magnitude compared with the reference detection sample of silicon plate with a detection limit of 10-2 mol/L. The electromagnetic enhancement comes from local surface plasmon resonance induced at the nanogap, which could reach ~10-11 orders of magnitude, while the chemical enhancement comes from monolayer WS2 2D material, which could reach 4-5 orders of magnitudes. The synergic implementation and action of these two prestigious Raman scattering enhancement mechanisms in this specially designed 2D material-plasmon nanogap composite nanoscale system enables unambiguous experimental observation of single-molecule Raman spectroscopy of RhB molecule. This route of Raman enhancement devices could open up a new frontier of single molecule science, allowing detection, identification, and monitor of single molecules and their spatial-temporal evolution under various internal and external stimuli

Evaluation of n-Butane Gas Adsorption Performance of Composite Adsorbents Used for Carbon Canister

AbstractA novel adsorbent design technique was proposed to composite adsorbent used for carbon canister for improving the adsorption performance of n-butane gas. Two kinds of activated carbons were tested to produce composite adsorbents and evaluate the performance by measuring the adsorption isotherms of butane and pore structure characteristics. The volume-based amount of adsorption for the adsorbents prepared at sodium silicate solution concentration of 0.1wt% is 1.04 and 1.53 times that of the raw activated carbons (AC1 and AC2). The packing density of the composite adsorbent increased with the increase of sodium silicate solution concentration

NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants

Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things devices. However, it is still challenging to unleash tiny deep learning's full potential on both large-scale datasets and downstream tasks due to the under-fitting issues caused by the limited model capacity of tiny neural networks (TNNs). To this end, we propose a framework called NetBooster to empower tiny deep learning by augmenting the architectures of TNNs via an expansion-then-contraction strategy. Extensive experiments show that NetBooster consistently outperforms state-of-the-art tiny deep learning solutions

Topological triply-degenerate point with double Fermi arcs

Unconventional chiral particles have recently been predicted to appear in certain three dimensional (3D) crystal structures containing three- or more-fold linear band degeneracy points (BDPs). These BDPs carry topological charges, but are distinct from the standard twofold Weyl points or fourfold Dirac points, and cannot be described in terms of an emergent relativistic field theory. Here, we report on the experimental observation of a topological threefold BDP in a 3D phononic crystal. Using direct acoustic field mapping, we demonstrate the existence of the threefold BDP in the bulk bandstructure, as well as doubled Fermi arcs of surface states consistent with a topological charge of 2. Another novel BDP, similar to a Dirac point but carrying nonzero topological charge, is connected to the threefold BDP via the doubled Fermi arcs. These findings pave the way to using these unconventional particles for exploring new emergent physical phenomena

In vitro and in vivo antitumor properties of 7-epidocetaxel, a major impurity of docetaxel

Purpose: To investigate the antitumor properties and toxicity of 7-epi docetaxel (7-epi DTX) as an active pharmaceutical ingredient, and in formulations.Methods: Docetaxel-loaded albumin nanoparticles (DTX NPs) were prepared by freeze-drying, while 7- epi DTX was detected and isolated by high performance liquid chromatography (HPLC). Their antitumor properties were evaluated in vitro in CT26 cells and in vivo in BALB/c sk-ov-3 xenograft nude mice model. The tissues were histological examined.Results: The in vivo antitumor effects of DTX NPs at different doses of 7-epi DTX were similar. Moreover, the in vitro anti-cancer effect of 7-epi DTX was comparable to that of DTX. However, the in vivo antitumor effectiveness of 7-epi DTX was inferior to that of DTX. In toxicity studies, 7-epi DTX did not elicit any acute toxic effects both as active pharmaceutical ingredients, and as a component of formulations.Conclusion: The results indicate that 7-epi DTX does not elicit acute toxic effects both as an active pharmaceutical ingredient and in bulk formulations. The antitumor property of 7-epi DTX is less than that of DTX.Keywords: 7-Epidocetaxel, Impurity, Antitumor properties, Toxicit