557 research outputs found
certain properties of generalized tracially approximated C*-algebras
We show that the following properties of unital -algebra in a
class of are preserved by unital simple -algebra in the
class of :
uniform property , a certain type of tracial nuclear
dimension at most , weakly -divisible.Comment: 18 pages. arXiv admin note: text overlap with arXiv:2203.0570
Examination of capital structure decisions: evidence from UK listed firms
In this paper, we attempt to analyses the determinants of capital structure in the UK. Besides, we also examine the empirical results compared with the implication of past capital structure theories and research. The panel data model with a sample of 100 non-financial listed firms in the United Kingdom between 1999 and 2008 is conducted to test the hypotheses. We use proxy variables that suggested by past literatures to explain capital structure decisions of the UK firms.
Profitability and growth are estimated to be natively related to debt ratios but the explanatory power of growth is limited. Asset tangibility and firm size both have a strongly positive relationship with the leverage. Again, both determinants will have opposite results when the leverage is measured by short-term debt. Similarly, earning volatility is also positively related to debt ratios with a very significant level. However, it is estimated that the effect of non-debt tax shields and inflation on capital structure is not significant, whereas inflation is only significant when using market value. In addition, it seems that the trade-off theory is more applicable to explain capital structure in the UK
Spiking Semantic Communication for Feature Transmission with HARQ
In Collaborative Intelligence (CI), the Artificial Intelligence (AI) model is
divided between the edge and the cloud, with intermediate features being sent
from the edge to the cloud for inference. Several deep learning-based Semantic
Communication (SC) models have been proposed to reduce feature transmission
overhead and mitigate channel noise interference. Previous research has
demonstrated that Spiking Neural Network (SNN)-based SC models exhibit greater
robustness on digital channels compared to Deep Neural Network (DNN)-based SC
models. However, the existing SNN-based SC models require fixed time steps,
resulting in fixed transmission bandwidths that cannot be adaptively adjusted
based on channel conditions. To address this issue, this paper introduces a
novel SC model called SNN-SC-HARQ, which combines the SNN-based SC model with
the Hybrid Automatic Repeat Request (HARQ) mechanism. SNN-SC-HARQ comprises an
SNN-based SC model that supports the transmission of features at varying
bandwidths, along with a policy model that determines the appropriate
bandwidth. Experimental results show that SNN-SC-HARQ can dynamically adjust
the bandwidth according to the channel conditions without performance loss
Scale-wise Convolution for Image Restoration
While scale-invariant modeling has substantially boosted the performance of
visual recognition tasks, it remains largely under-explored in deep networks
based image restoration. Naively applying those scale-invariant techniques
(e.g. multi-scale testing, random-scale data augmentation) to image restoration
tasks usually leads to inferior performance. In this paper, we show that
properly modeling scale-invariance into neural networks can bring significant
benefits to image restoration performance. Inspired from spatial-wise
convolution for shift-invariance, "scale-wise convolution" is proposed to
convolve across multiple scales for scale-invariance. In our scale-wise
convolutional network (SCN), we first map the input image to the feature space
and then build a feature pyramid representation via bi-linear down-scaling
progressively. The feature pyramid is then passed to a residual network with
scale-wise convolutions. The proposed scale-wise convolution learns to
dynamically activate and aggregate features from different input scales in each
residual building block, in order to exploit contextual information on multiple
scales. In experiments, we compare the restoration accuracy and parameter
efficiency among our model and many different variants of multi-scale neural
networks. The proposed network with scale-wise convolution achieves superior
performance in multiple image restoration tasks including image
super-resolution, image denoising and image compression artifacts removal. Code
and models are available at: https://github.com/ychfan/scn_srComment: AAAI 202
Membrane Contact Demulsification: A Superhydrophobic ZIF-8@rGO Membrane for Water-in-Oil Emulsion Separation
Achieving a water–oil interface imbalance has been identified as a critical factor in the demulsification of water-in-oil emulsions. However, conventional demulsifying membranes generally break the interface balance by depending on a relatively high transmembrane pressure. Here, we present a “contact demulsification” concept to naturally and quickly achieve disruption of the water–oil interface balance. For this purpose, a novel demulsifying membrane with a high flux of the organic component has been developed via the simple vacuum assembly of zeolitic imidazolate framework-8 (ZIF-8)@reduced graphene oxide (rGO) microspheres (ZGS) on a polytetrafluoroethylene (PTFE) support, followed by immobilization processing in a polydimethylsiloxane (PDMS) crosslinking solution. Due to the micro-nano hierarchies of the ZGS, the prepared ZIF-8@rGO@PDMS/PTFE (ZGPP) membranes feature a unique superhydrophobic surface, which results in a water–oil interface imbalance when a surfactant-stabilized water-in-oil emulsion comes into contact with the membrane surface. Under a low transmembrane pressure of 0.15 bar (15 kPa), such membranes show an excellent separation efficiency (∼99.57%) and a high flux of 2254 L·m−2·h−1, even for surfactant-stabilized nanoscale water-in-toluene emulsions (with an average droplet size of 57 nm). This “contact demulsification” concept paves the way for developing next-generation demulsifying membranes for water-in-oil emulsion separation
Research on joint strength of steel cord conveyor belt and preformed core rubber technology
Steel cord core conveyor belt is a traction and carrying component of belt conveyor, and joint failure is the main cause of steel cord core conveyor belt failure. In order to improve the joint strength and vulcanization efficiency of the conveyor belt, this paper proposes a preformed core rubber laying technology based on the existing joint vulcanization process. Taking the ST1 600 steel cord core conveyor belt joint as the research object, the application of this technology is theoretically analyzed and experimentally studied. Firstly, the mechanical model of the conveyor belt joint is established and stress analysis is conducted. The influence of uneven horizontal and vertical spacing of steel cords on the joint strength is studied using ANSYS Workbench software. Then, a preformed core rubber laying method is proposed, and the effect of filling core rubber and triangular, trapezoidal, and semicircular core rubber grooves on limiting the offset of steel cords is compared through fluid-solid coupling analysis of core rubber. Finally, the influence of this technology on the adhesive strength of the conveyor belt, joint vulcanization efficiency, and joint strength is studied through joint preformed core rubber vulcanization experiments. The research shows that the stress of steel cords at the conveyor belt joint is related to their longitudinal displacement. Due to the inability to achieve uniform distribution of steel cords in traditional joint vulcanization operations, uneven stress of steel cords is caused, which affects the joint strength. The change of horizontal spacing of steel cords compared to vertical spacing has a more significant effect on the equivalent stress of steel cords, which is more likely to cause the failure of joint steel cords. The preformed core rubber laying technology can effectively reduce the offset of steel cords, among which the semicircular core rubber groove has the best effect, reducing by 70% in the horizontal direction and 86.9% in the vertical direction. Under the same vulcanization conditions, compared with filling core rubber, the adhesive strength after vulcanization of triangular, trapezoidal, and semicircular core rubber grooves increased by 1.91%, −3.51%, and 2.48%, respectively, with the best combination with semicircular core rubber. The preformed core rubber technology with semicircular core rubber groove laying can reduce the working time by 70% while concurrently boosting the joint strength of the vulcanized conveyor belt by 5.74%. The research findings can offer both theoretical and practical insights for streamlining joint enhancements
From Unbalanced to Perfect: Implementation of Low Energy Stream Ciphers
Low energy is an important aspect of hardware implementation. For energy-limited battery-powered devices, low energy stream ciphers can play an important role. In \texttt{IACR ToSC 2021}, Caforio et al. proposed the Perfect Tree energy model for stream cipher that links the structure of combinational logic circuits with state update functions to energy consumption. In addition, a metric given by the model shows a negative correlation with energy consumption, i.e., the higher the balance of the perfect tree, the lower the energy consumption. However, Caforio et al. didn\u27t give a method that eliminate imbalances of the unrolled strand tree for the existing stream ciphers.
In this paper, based on the Perfect Tree energy model, we propose a new redundant design model that improve the balances of the unrolled strand tree for the purpose of reducing energy consumption. In order to obtain the redundant design, we propose a search algorithm for returning the corresponding implementation scheme. For the existing stream ciphers, the proposed model and search method can be used to provide a low-power redundancy design scheme. To verify the effectiveness, we apply our redundant model and search method in the stream ciphers (e.g., \texttt{Trivium} and \texttt{Kreyvium}) and conducted a synthetic test. The results of the energy measurement demonstrate that the proposed model and search method can obtain lower energy consumption
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