Design and optimization of joint iterative detection and decoding receiver for uplink polar coded SCMA system

SCMA and polar coding are possible candidates for 5G systems. In this paper, we firstly propose the joint iterative detection and decoding (JIDD) receiver for the uplink polar coded sparse code multiple access (PC-SCMA) system. Then, the EXIT chart is used to investigate the performance of the JIDD receiver. Additionally, we optimize the system design and polar code construction based on the EXIT chart analysis. The proposed receiver integrates the factor graph of SCMA detector and polar soft-output decoder into a joint factor graph, which enables the exchange of messages between SCMA detector and polar decoder iteratively. Simulation results demonstrate that the JIDD receiver has better BER performance and lower complexity than the separate scheme. Specifically, when polar code length N=256 and code rate R=1/2 , JIDD outperforms the separate scheme 4.8 and 6 dB over AWGN channel and Rayleigh fading channel, respectively. It also shows that, under 150% system loading, the JIDD receiver only has 0.3 dB performance loss compared to the single user uplink PC-SCMA over AWGN channel and 0.6 dB performance loss over Rayleigh fading channel

A Novel Dataset and a Deep Learning Method for Mitosis Nuclei Segmentation and Classification

Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. The manual annotation needs experienced pathologists, which is very time-consuming and inefficient. With the development of deep learning methods, some models with good performance have emerged, but the generalization ability should be further strengthened. In this paper, we propose a two-stage mitosis segmentation and classification method, named SCMitosis. Firstly, the segmentation performance with a high recall rate is achieved by the proposed depthwise separable convolution residual block and channel-spatial attention gate. Then, a classification network is cascaded to further improve the detection performance of mitosis nuclei. The proposed model is verified on the ICPR 2012 dataset, and the highest F-score value of 0.8687 is obtained compared with the current state-of-the-art algorithms. In addition, the model also achieves good performance on GZMH dataset, which is prepared by our group and will be firstly released with the publication of this paper. The code will be available at: https://github.com/antifen/mitosis-nuclei-segmentation.Comment: 19 pages,11 figures, 4 table

Does metal pollution matter with C retention by rice soil?

Date of Acceptance: 17/07/2015 The research work was supported by the China Natural Science Foundation under a grant number of 40830528 and of 40671180. P.S. is a Royal Scoiety-Wolfson Research Merit Award holder and was supported by additional travel funds from a UK BBSRC China Partnership Award. P.S.’s contribution was supported by the UK-China Sustainable Agriculture Innovation Network (SAIN). D.C. was supported by an additional travel and collaboration funding from the China Ministry of Education under a “111” project.Peer reviewedPublisher PD

Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee

This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. Lyapunov’s method in control theory is employed to prove the convergence of orientation estimation errors. The estimator gains and a Lyapunov function are parametrised by deep neural networks and learned from samples based on the theoretical results. The DRL estimator is compared with three well-known orientation estimation methods on both numerical simulations and real dataset collected from commercially available sensors. The results show that the proposed algorithm is superior for arbitrary estimation initialisation and can adapt to a drastic angular velocity profile for which other algorithms can be hardly applicable. To the best of our knowledge, this is the first DRL-based orientation estimation method with an estimation error boundedness guarantee

Alleviating the Long-Tail Problem in Conversational Recommender Systems

Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. To develop an effective CRS, high-quality CRS datasets are very crucial. However, existing CRS datasets suffer from the long-tail issue, \ie a large proportion of items are rarely (or even never) mentioned in the conversations, which are called long-tail items. As a result, the CRSs trained on these datasets tend to recommend frequent items, and the diversity of the recommended items would be largely reduced, making users easier to get bored. To address this issue, this paper presents \textbf{LOT-CRS}, a novel framework that focuses on simulating and utilizing a balanced CRS dataset (\ie covering all the items evenly) for improving \textbf{LO}ng-\textbf{T}ail recommendation performance of CRSs. In our approach, we design two pre-training tasks to enhance the understanding of simulated conversation for long-tail items, and adopt retrieval-augmented fine-tuning with label smoothness strategy to further improve the recommendation of long-tail items. Extensive experiments on two public CRS datasets have demonstrated the effectiveness and extensibility of our approach, especially on long-tail recommendation.Comment: work in progres

GradAuto:Energy-oriented Attack on Dynamic Neural Networks

Dynamic neural networks could adapt their structures or parameters based on different inputs. By reducing the computation redundancy for certain samples, it can greatly improve the computational efficiency without compromising the accuracy. In this paper, we investigate the robustness of dynamic neural networks against energy-oriented attacks. We present a novel algorithm, named GradAuto, to attack both dynamic depth and dynamic width models, where dynamic depth networks reduce redundant computation by skipping some intermediate layers while dynamic width networks adaptively activate a subset of neurons in each layer. Our GradAuto carefully adjusts the direction and the magnitude of the gradients to efficiently find an almost imperceptible perturbation for each input, which will activate more computation units during inference. In this way, GradAuto effectively boosts the computational cost of models with dynamic architectures. Compared to previous energy-oriented attack techniques, GradAuto obtains the state-of-the-art result and recovers 100% dynamic network reduced FLOPs on average for both dynamic depth and dynamic width models. Furthermore, we demonstrate that GradAuto offers us great control over the attacking process and could serve as one of the keys to unlock the potential of the energy-oriented attack. Please visit https://github.com/JianhongPan/GradAuto for code

Quantitative Comparison of Korotkoff Sound Waveform Characteristics: Effects of Static Cuff Pressures and Stethoscope Positions

The underlying principles of Korotkoff sound (KorS) during blood pressure measurement and its waveform characteristic changes with cuff pressure and stethoscope position have not been fully understood. This study aimed to quantify the effects of cuff pressure and stethoscope position on the measured KorS waveform characteristics. Thirty healthy subjects were recruited in this study. Four stethoscopes were placed on the circumferential direction around the arm (m1, m2, m3 and m4; m1 was above the artery, and equal distance between each other), and then sequentially at three different longitudinal positions ('upper', 'middle' and 'low' part under the cuff). At each longitudinal position, three levels of static cuff pressure (high: SBP + 10 mmHg, low: DBP-10 mmHg, and medium: DBP + (SBP-DBP)/3) were applied during the recording of KorS waveform. The averaged KorS waveform was firstly computed by using an interpolation method, separately for measurements from different stethoscope locations and cuff pressures. Two quantitative indices were derived to characterize the recorded KorS waveform: intensity amplitude and high-level duration of KorS waveform. Post-hoc pairwise comparisons after analysis of variance were used to compare the waveform characteristic differences between different stethoscope locations and between cuff pressures. Variance analysis demonstrated that the effects of stethoscope circumferential and longitudinal positions and cuff pressure on the two KorS waveform indices were significant (all p < 0.001). In detail, KorS waveform recorded at cuff pressure P had larger intensity amplitude and shorter high-level duration than those recorded at cuff pressure P or P. In most conditions, the stethoscope above the artery (m1) produced the largest RMS intensity amplitude and shortest high-level duration, while the stethoscope at the opposite location of m1 generated the smallest RMS intensity amplitude and longest high-level duration. In terms of the effect of longitudinal position, the stethoscopes below the middle of the cuff always produced KorS recordings with larger intensity amplitude and shorter high-level duration. This study has quantified and provided scientific evidence that cuff pressure, stethoscope longitudinal and circumferential positions are important factors influencing KorS waveform characteristics

Industrial Development Layout and Competitiveness Evaluation Based on Correlation Analysis between Power and Industry

The change of industrial structure has a significant impact on energy consumption. The coordinated development between energy structure and industrial structure has a profound impact on the steady development of national economy and society. In this paper, focusing on the needs of industrial development layout and combining with the scenario of dual-carbon target constraint setting, a power-industry management analysis model is constructed, and a differentiated screening mechanism of industrial layout is realized under the three scenario Settings of dual-carbon target. According to the industrial development layout of Xinjiang, this paper studies the relationship between industrial development and energy and electricity consumption under different scenarios, selects the key industries according to the requirements of different scenarios, and carries out the scene comparison evaluation based on the industrial competitiveness evaluation. Finally, the paper puts forward specific suggestions from the perspectives of applying the coordinated layout of different scenes in stages, taking into account the coordination of regional layout, and striving for the practical linkage between industry and electric power