certain properties of generalized tracially approximated C*-algebras

We show that the following properties of unital ${\rm C^*}$-algebra in a class of $\Omega$ are preserved by unital simple ${\rm C^*}$-algebra in the class of $\rm WTA\Omega$: $(1)$ uniform property $\Gamma$, $(2)$ a certain type of tracial nuclear dimension at most $n$, $(3)$ weakly $(m, n)$-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

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

Compatible Remediation on Vulnerabilities from Third-Party Libraries for Java Projects

With the increasing disclosure of vulnerabilities in open-source software, software composition analysis (SCA) has been widely applied to reveal third-party libraries and the associated vulnerabilities in software projects. Beyond the revelation, SCA tools adopt various remediation strategies to fix vulnerabilities, the quality of which varies substantially. However, ineffective remediation could induce side effects, such as compilation failures, which impede acceptance by users. According to our studies, existing SCA tools could not correctly handle the concerns of users regarding the compatibility of remediated projects. To this end, we propose Compatible Remediation of Third-party libraries (CORAL) for Maven projects to fix vulnerabilities without breaking the projects. The evaluation proved that CORAL not only fixed 87.56% of vulnerabilities which outperformed other tools (best 75.32%) and achieved a 98.67% successful compilation rate and a 92.96% successful unit test rate. Furthermore, we found that 78.45% of vulnerabilities in popular Maven projects could be fixed without breaking the compilation, and the rest of the vulnerabilities (21.55%) could either be fixed by upgrades that break the compilations or even be impossible to fix by upgrading.Comment: 11 pages, conferenc