Integrating non-planar metamaterials with magnetic absorbing materials to yield ultra-broadband microwave hybrid absorbers

Broadening the bandwidth of electromagnetic wave absorbers has greatly challenged material scientists. Here, we propose a two-layer hybrid absorber consisting of a non-planar metamaterial (MM) and a magnetic microwave absorbing material (MAM). The non-planar MM using magnetic MAMs instead of dielectric substrates shows good low frequency absorption and low reflection across a broad spectrum. Benefiting from this and the high frequency strong absorption of the MAM layer, the lightweight hybrid absorber exhibits 90% absorptivity over the whole 2-18 GHz range. Our result reveals a promising and flexible method to greatly extend or control the absorption bandwidth of absorbers. (C) 2014 AIP Publishing LLC

Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target

Spoken Language Understanding (SLU) is a task that aims to extract semantic information from spoken utterances. Previous research has made progress in end-to-end SLU by using paired speech-text data, such as pre-trained Automatic Speech Recognition (ASR) models or paired text as intermediate targets. However, acquiring paired transcripts is expensive and impractical for unwritten languages. On the other hand, Textless SLU extracts semantic information from speech without utilizing paired transcripts. However, the absence of intermediate targets and training guidance for textless SLU often results in suboptimal performance. In this work, inspired by the content-disentangled discrete units from self-supervised speech models, we proposed to use discrete units as intermediate guidance to improve textless SLU performance. Our method surpasses the baseline method on five SLU benchmark corpora. Additionally, we find that unit guidance facilitates few-shot learning and enhances the model's ability to handle noise.Comment: Accepted by interspeech 202

Effects of anesthesia on conventional and speckle tracking echocardiographic parameters in a mouse model of pressure overload

Genetically‑modified mice are widely applied in cardiovascular studies as model organisms. Echocardiography is a key tool for evaluating cardiac and hemodynamic functions in mice. The present study aimed to examine the effects of isoflurane (ISF) on conventional and speckle tracking echocardiography (STE) parameters under healthy and pathological conditions using a murine model of pressure overload. In addition, the optimal dose of ISF in the process of echocardiographic measurement, with minimum cardiac contraction depression, was investigated. Conventional echocardiographic and STE examinations were performed on 38 adult C57BL/6 male mice. The mice were divided into the following three groups: The sham (n=15); mild thoracic aortic banding (TAB; n=15); and severe TAB (n=8) groups. ISF was administered under deep anesthesia (DA; 1‑2% ISF), light anesthesia (LA; 0.5‑1% ISF) and immediately prior to the mice waking up (awake; 0‑0.5% ISF). Conventional echocardiographic parameters were preserved within the sham and mild TAB groups (P>0.05 for each parameter) under LA and awake conditions. However, under DA conditions, the majority of these parameters were reduced compared with the LA and awake conditions (P<0.05). In the severe TAB group, conventional echocardiographic parameters remained constant under LA, DA and awake conditions. STE parameters in the groups remained similar between the LA and awake conditions, but were significantly reduced under DA conditions. Therefore, conventional echocardiography and STE may be performed using LA induced with low doses of ISF, under various pathological conditions without affecting cardiac function

Formal Kinematic Analysis of a General 6R Manipulator Using the Screw Theory

Kinematic analysis is a significant method when planning the trajectory of robotic manipulators. The main idea behind kinematic analysis is to study the motion of the robot based on the geometrical relationship of the robotic links and their joints, such as the Denavit-Hartenberg parameters. Given the continuous nature of kinematic analysis and the shortcoming of the traditional verification methods, we propose to use high-order-logic theorem proving for conducting formal kinematic analysis. Based on the screw theory in HOL4, which is newly developed by our research institute, we utilize the geometrical theory of HOL4 to develop formal reasoning support for the kinematic analysis of a robotic manipulator. To illustrate the usefulness of our fundamental formalization, we present the formal kinematic analysis of a general 6R manipulator

Reliable Conflictive Multi-View Learning

Multi-view learning aims to combine multiple features to achieve more comprehensive descriptions of data. Most previous works assume that multiple views are strictly aligned. However, real-world multi-view data may contain low-quality conflictive instances, which show conflictive information in different views. Previous methods for this problem mainly focus on eliminating the conflictive data instances by removing them or replacing conflictive views. Nevertheless, real-world applications usually require making decisions for conflictive instances rather than only eliminating them. To solve this, we point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which requires the model to provide decision results and attached reliabilities for conflictive multi-view data. We develop an Evidential Conflictive Multi-view Learning (ECML) method for this problem. ECML first learns view-specific evidence, which could be termed as the amount of support to each category collected from data. Then, we can construct view-specific opinions consisting of decision results and reliability. In the multi-view fusion stage, we propose a conflictive opinion aggregation strategy and theoretically prove this strategy can exactly model the relation of multi-view common and view-specific reliabilities. Experiments performed on 6 datasets verify the effectiveness of ECML.Comment: 9 pages and to be appeared in AAAI202

Endoglin Is Essential for the Maintenance of Self-Renewal and Chemoresistance in Renal Cancer Stem Cells.

Renal cell carcinoma (RCC) is a deadly malignancy due to its tendency to metastasize and resistance to chemotherapy. Stem-like tumor cells often confer these aggressive behaviors. We discovered an endoglin (CD105)-expressing subpopulation in human RCC xenografts and patient samples with a greater capability to form spheres in vitro and tumors in mice at low dilutions than parental cells. Knockdown of CD105 by short hairpin RNA and CRISPR/cas9 reduced stemness markers and sphere-formation ability while accelerating senescence in vitro. Importantly, downregulation of CD105 significantly decreased the tumorigenicity and gemcitabine resistance. This loss of stem-like properties can be rescued by CDA, MYC, or NANOG, and CDA might act as a demethylase maintaining MYC and NANOG. In this study, we showed that Endoglin (CD105) expression not only demarcates a cancer stem cell subpopulation but also confers self-renewal ability and contributes to chemoresistance in RCC