Dual CNN based channel estimation for MIMO-OFDM systems

Recently, convolutional neural network (CNN)-based channel estimation (CE) for massive multiple-input multiple-output communication systems has achieved remarkable success. However, complexity even needs to be reduced, and robustness can even be improved. Meanwhile, existing methods do not accurately explain which channel features help the denoising of CNNs. In this paper, we first compare the strengths and weaknesses of CNN-based CE in different domains. When complexity is limited, the channel sparsity in the angle-delay domain improves denoising and robustness whereas large noise power and pilot contamination are handled well in the spatial-frequency domain. Thus, we develop a novel network, called dual CNN, to exploit the advantages in the two domains. Furthermore, we introduce an extra neural network, called HyperNet, which learns to detect scenario changes from the same input as the dual CNN. HyperNet updates several parameters adaptively and combines the existing dual CNNs to improve robustness. Experimental results show improved estimation performance for the time-varying scenarios. To further exploit the correlation in the time domain, a recurrent neural network framework is developed, and training strategies are provided to ensure robustness to the changing of temporal correlation. This design improves channel estimation performance but its complexity is still low

A paradox theory lens on proactivity, individual ambidexterity, and creativity:An empirical look

Paradox theory suggests that contradictory demands, like applying current work methods while exploring new ones, should be viewed as dualities with competing and complementary aspects. It advocates for employee ambidexterity, where employees must manage exploitation and exploration. We know little about how personal dispositions affect ambidexterity independently or when interacting with situational factors. Based on a time-lagged survey of 364 employee–supervisor pairs from 74 R&D teams, we found that proactive disposition was positively related to ambidexterity, enhancing creativity. Guided by trait activation theory, we found further that paradoxical supervision and job autonomy enhanced the relationship between proactive disposition and employee ambidexterity and the indirect effect of proactive disposition on creativity via ambidexterity. We discuss these findings' theoretical and practical implications, extending the literature on proactivity, ambidexterity, and paradox theory

A photometric monitoring of bright high-amplitude delta Scuti stars. II. Period updates for seven stars

We present new photometric data for seven high-amplitude delta Scuti stars. The observations were acquired between 1996 and 2002, mostly in the Johnson photometric system. For one star (GW UMa), our observations are the first since the discovery of its pulsational nature from the Hipparcos data.The primary goal of this project was to update our knowledge on the period variations of the target stars. For this, we have collected all available photometric observations from the literature and constructed decades-long O-C diagrams of the stars. This traditional method is useful because of the single-periodic nature of the light variations. Text-book examples of slow period evolution (XX Cyg, DY Her, DY Peg) and cyclic period changes due to light-time effect (LITE) in a binary system (SZ Lyn) are updated with the new observations. For YZ Boo, we find a period decrease instead of increase. The previously suggested LITE-solution of BE Lyn (Kiss & Szatmary 1995) is not supported with the new O-C diagram. Instead of that, we suspect the presence of transient light curve shape variations mimicking small period changes.Comment: 11 pages, 15 figures, accepted for publication in A&

Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08±1.22%, a specificity of 93.58±1.49 and an accuracy of 93.83±0.96. The proposed method gives superior performance than eight state-of-theart approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.British Heart Foundation Accelerator Award, UKRoyal Society International Exchanges Cost Share Award, UK RP202G0230Hope Foundation for Cancer Research, UK RM60G0680Medical Research Council Confidence in Concept Award, UK MC_PC_17171MINECO/FEDER, Spain/Europe RTI2018-098913-B100 A-TIC-080-UGR1

Phellinus linteus suppresses growth, angiogenesis and invasive behaviour of breast cancer cells through the inhibition of AKT signalling

The antitumour activity of a medicinal mushroom Phellinus linteus (PL), through the stimulation of immune system or the induction of apoptosis, has been recently described. However, the molecular mechanisms responsible for the inhibition of invasive behaviour of cancer cells remain to be addressed. In the present study, we demonstrate that PL inhibits proliferation (anchorage-dependent growth) as well as colony formation (anchorage-independent growth) of highly invasive human breast cancer cells. The growth inhibition of MDA-MB-231 cells is mediated by the cell cycle arrest at S phase through the upregulation of p27Kip1 expression. Phellinus linteus also suppressed invasive behaviour of MDA-MB-231 cells by the inhibition of cell adhesion, cell migration and cell invasion through the suppression of secretion of urokinase-plasminogen activator from breast cancer cells. In addition, PL markedly inhibited the early event in angiogenesis, capillary morphogenesis of the human aortic endothelial cells, through the downregulation of secretion of vascular endothelial growth factor from MDA-MB-231 cells. These effects are mediated by the inhibition of serine-threonine kinase AKT signalling, because PL suppressed phosphorylation of AKT at Thr308 and Ser473 in breast cancer cells. Taken together, our study suggests potential therapeutic effect of PL against invasive breast cancer

An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user's forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user's hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user's gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke

Synthesis and Characterization of Core-shell ZrO2/PAAEM/PS Nanoparticles

This work demonstrates the synthesis of core-shell ZrO2/PAAEM/PS nanoparticles through a combination of sol–gel method and emulsifier-free emulsion polymerizaiton. By this method, the modified nanometer ZrO2cores were prepared by chemical modification at a molecular level of zirconium propoxide with monomer of acetoacetoxyethylmethacrylate (AAEM), and then copolymerized with vinyl monomer to form uniform-size hybrid nanoparticles with diameter of around 250 nm. The morphology, composition, and thermal stability of the core-shell particles were characterized by various techniques including transmission electron microscopy (TEM), X-ray diffractometer (XRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and thermal-gravimetry analyzer (TGA). The results indicate that the inorganic–organic nanocomposites exhibit good thermal stability with the maximum decomposition temperature of ~447 °C. This approach would be useful for the synthesis of other inorganic–organic nanocomposites with desired functionalities

The history of degenerate (bipartite) extremal graph problems

This paper is a survey on Extremal Graph Theory, primarily focusing on the case when one of the excluded graphs is bipartite. On one hand we give an introduction to this field and also describe many important results, methods, problems, and constructions.Comment: 97 pages, 11 figures, many problems. This is the preliminary version of our survey presented in Erdos 100. In this version 2 only a citation was complete