Federated learning via inexact ADMM

One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full devices participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descent-based algorithms, this paper develops an inexact alternating direction method of multipliers (ADMM), which is both computation and communication-efficient, capable of combating the stragglers' effect, and convergent under mild conditions

Efficient and Convergent Federated Learning

Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational costs, and whether they converge. To address these issues, this paper proposes a new federated learning algorithm (FedGiA) that combines the gradient descent and the inexact alternating direction method of multipliers. It is shown that FedGiA is computation and communication-efficient and convergent linearly under mild conditions

Task-Oriented Multi-User Semantic Communications for VQA

Semantic communications focus on the transmission of semantic features. In this letter, we consider a task-oriented multi-user semantic communication system for multimodal data transmission. Particularly, partial users transmit images while the others transmit texts to inquiry the information about the images. To exploit the correlation among the multimodal data from multiple users, we propose a deep neural network enabled semantic communication system, named MU-DeepSC, to execute the visual question answering (VQA) task as an example. Specifically, the transceiver for MU-DeepSC is designed and optimized jointly to capture the features from the correlated multimodal data for task-oriented transmission. Simulation results demonstrate that the proposed MU-DeepSC is more robust to channel variations than the traditional communication systems, especially in the low signal-to-noise (SNR) regime

Learning large margin multiple granularity features with an improved siamese network for person re-identification

Person re-identification (Re-ID) is a non-overlapping multi-camera retrieval task to match different images of the same person, and it has become a hot research topic in many fields, such as surveillance security, criminal investigation, and video analysis. As one kind of important architecture for person re-identification, Siamese networks usually adopt standard softmax loss function, and they can only obtain the global features of person images, ignoring the local features and the large margin for classification. In this paper, we design a novel symmetric Siamese network model named Siamese Multiple Granularity Network (SMGN), which can jointly learn the large margin multiple granularity features and similarity metrics for person re-identification. Firstly, two branches for global and local feature extraction are designed in the backbone of the proposed SMGN model, and the extracted features are concatenated together as multiple granularity features of person images. Then, to enhance their discriminating ability, the multiple channel weighted fusion (MCWF) loss function is constructed for the SMGN model, which includes the verification loss and identification loss of the training image pair. Extensive comparative experiments on four benchmark datasets (CUHK01, CUHK03, Market-1501 and DukeMTMC-reID) show the effectiveness of our proposed method and its performance outperforms many state-of-the-art methods

Probing nuclear expansion dynamics with π−/π+\pi^-/\pi^+-spectra

We study the dynamics of charged pions in the nuclear medium via the ratio of differential $\pi^-$- and $\pi^+$-spectra in a coupled-channel BUU (CBUU) approach. The relative energy shift of the charged pions is found to correlate with the pion freeze-out time in nucleus-nucleus collisions as well as with the impact parameter of the heavy-ion reaction. Furthermore, the long-range Coulomb force provides a 'clock' for the expansion of the hot nuclear system. Detailed comparisons with experimental data for $Au + Au$ at 1 GeV/A and $Ni + Ni$ at 2.0 GeV/A are presented.Comment: 21 pages, latex, figures include

Structure of mitochondrial DNA control region of Argyrosomus amoyensis and molecular phylogenetic relationship among six species of Sciaenidae

The structure of the mitochondrial DNA (mtDNA) control region of Argyrosomus amoyensis was examined in this study. TAS, cTAS, CSB-D to CSB-F and CSB-1 to CSB-3 segments were detected in the species. The results indicated that the structures of these parts were different from that of most fishes. All the mtDNA control region sequences examined had missing tandem repeat sequences downstream of CSB-3, which were the same as most fishes’. In addition, part of the COI gene was used to analyze the phylogenetic relationships of six Sciaenids species. The phylogenetic tree results supported the classification by traditional morphology, and COI barcodes were useful for identifying these six species of Sciaenids.Key words: Control region, structure, Argyrosomus amoyensis, COI, phylogenetic relationship, Sciaenidae

Deep Learning Enabled Semantic Communication Systems

Recently, deep learned enabled end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in the traditional communication systems, which make joint transceiver optimization possible. Powered by deep learning, natural language processing (NLP) has achieved great success in analyzing and understanding large amounts of language texts. Inspired by research results in both areas, we aim to providing a new view on communication systems from the semantic level. Particularly, we propose a deep learning based semantic communication system, named DeepSC, for text transmission. Based on the Transformer, the DeepSC aims at maximizing the system capacity and minimizing the semantic errors by recovering the meaning of sentences, rather than bit- or symbol-errors in traditional communications. Moreover, transfer learning is used to ensure the DeepSC applicable to different communication environments and to accelerate the model training process. To justify the performance of semantic communications accurately, we also initialize a new metric, named sentence similarity. Compared with the traditional communication system without considering semantic information exchange, the proposed DeepSC is more robust to channel variation and is able to achieve better performance, especially in the low signal-to-noise (SNR) regime, as demonstrated by the extensive simulation results.Comment: 13 pages, Journal, accepted by IEEE TS

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

Quantitative trait loci analysis for chlorophyll content of cucumber (Cucumis sativus L.) seedlings under low-light stress

An increase in chlorophyll content is an adaptive response to low-light stress and can be used to evaluate low-light tolerance. The effects of low-light stress (100 ìmol·m-2.s-1) on the chlorophyll content of cucumber (Cucumis sativus L.) were investigated in a set of 123 F2:3 lines in the seedling stage in the autumn of 2008 and spring of 2009. Quantitative trait loci (QTL) analysis was undertaken on the basis of a genetic linkage map of the corresponding F2 population that was constructed using composite interval mapping. F2:3-based QTL analysis of the chlorophyll-a (chl.a), chlorophyll-b (chl.b) and chlorophyll-a+b (chl.a+b) content in the 2 environments revealed 21 QTLs located on the linkage groups 1, 2, 3, 4, 6 and 7, which accounted for 4.8 - 17.3% of the phenotypic variation. In the spring of 2009, the total phenotypic variation among the F2:3 lines accounted for by the QTLs for chl.a, chl.b and chl.a+b were 44.5, 29.4 and39.0%, respectively. In the autumn of 2008, 11 QTLs were identified, which accounted for 4.8 - 14.9% of the observed phenotypic variation and an additive effect of -8.10 to 20.85. Four major-effect QTLs (chla2.1, chlb2.2, chlb3.1 and chla+b2.2) were detected under both conditions. The QTL information presented in this research, together with the data from our previous study on heredity of low-light tolerant traits, will facilitate the breeding of low-light-stress-resistant cucumbers

Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis

In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the transmission tasks of the communication system, respectively. First, the speech recognition-related semantic features are extracted for transmission by a joint semantic-channel encoder and the text is recovered at the receiver based on the received semantic features, which significantly reduces the required amount of data transmission without performance degradation. Then, we perform speech synthesis at the receiver, which dedicates to re-generate the speech signals by feeding the recognized text and the speaker information into a neural network module. To enable the DeepSC-ST adaptive to dynamic channel environments, we identify a robust model to cope with different channel conditions. According to the simulation results, the proposed DeepSC-ST significantly outperforms conventional communication systems and existing DL-enabled communication systems, especially in the low signal-to-noise ratio (SNR) regime. A software demonstration is further developed as a proof-of-concept of the DeepSC-ST