Semantic-aware Transmission for Robust Point Cloud Classification

As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing. In this study, we introduce a semantic-aware communication system for robust point cloud classification that capitalizes on the advantages of pre-trained Point-BERT models. Our proposed method comprises four main components: the semantic encoder, channel encoder, channel decoder, and semantic decoder. By employing a two-stage training strategy, our system facilitates efficient and adaptable learning tailored to the specific classification tasks. The results show that the proposed system achieves classification accuracy of over 89\% when SNR is higher than 10 dB and still maintains accuracy above 66.6\% even at SNR of 4 dB. Compared to the existing method, our approach performs at 0.8\% to 48\% better across different SNR values, demonstrating robustness to channel noise. Our system also achieves a balance between accuracy and speed, being computationally efficient while maintaining high classification performance under noisy channel conditions. This adaptable and resilient approach holds considerable promise for a wide array of 3D scene understanding applications, effectively addressing the challenges posed by channel noise.Comment: submitted to globecom 202

The Model Inversion Eavesdropping Attack in Semantic Communication Systems

In recent years, semantic communication has been a popular research topic for its superiority in communication efficiency. As semantic communication relies on deep learning to extract meaning from raw messages, it is vulnerable to attacks targeting deep learning models. In this paper, we introduce the model inversion eavesdropping attack (MIEA) to reveal the risk of privacy leaks in the semantic communication system. In MIEA, the attacker first eavesdrops the signal being transmitted by the semantic communication system and then performs model inversion attack to reconstruct the raw message, where both the white-box and black-box settings are considered. Evaluation results show that MIEA can successfully reconstruct the raw message with good quality under different channel conditions. We then propose a defense method based on random permutation and substitution to defend against MIEA in order to achieve secure semantic communication. Our experimental results demonstrate the effectiveness of the proposed defense method in preventing MIEA.Comment: Accepted by 2023 IEEE Global Communications Conference (GLOBECOM

Minimizing End-to-End Latency for Joint Source-Channel Coding Systems

While existing studies have highlighted the advantages of deep learning (DL)-based joint source-channel coding (JSCC) schemes in enhancing transmission efficiency, they often overlook the crucial aspect of resource management during the deployment phase. In this paper, we propose an approach to minimize the transmission latency in an uplink JSCC-based system. We first analyze the correlation between end-to-end latency and task performance, based on which the end-to-end delay model for each device is established. Then, we formulate a non-convex optimization problem aiming at minimizing the maximum end-to-end latency across all devices, which is proved to be NP-hard. We then transform the original problem into a more tractable one, from which we derive the closed form solution on the optimal compression ratio, truncation threshold selection policy, and resource allocation strategy. We further introduce a heuristic algorithm with low complexity, leveraging insights from the structure of the optimal solution. Simulation results demonstrate that both the proposed optimal algorithm and the heuristic algorithm significantly reduce end-to-end latency. Notably, the proposed heuristic algorithm achieves nearly the same performance to the optimal solution but with considerably lower computational complexity.Comment: 7 Pages, 5 Figures, accepted by 2024 IEEE ICC Worksho

(Dimethyl­formamide-κO)[2-meth­oxy-6-(2-pyridylmethyl­imino­meth­yl)phenolato-κ3 N,N′,O 1](thio­cyanato-κN)copper(II)

In the title compound, [Cu(C14H13N2O2)(NCS)(C3H7NO)], the Cu2+ ion is coordinated by an N,N′,O-tridentate 2-meth­oxy-6-(2-pyridylmethyl­imino­meth­yl)phenolate ligand, an N-bonded thio­cyanate ion and an O-bonded dimethyl­formamide (DMF) mol­ecule, resulting in a distorted CuN3O2 square-based pyramidal geometry for the metal ion, with the DMF O atom in the apical site. The dihedral angle between the aromatic rings in the ligand is 8.70 (16)°. The S atom is disordered over two positions in a 0.901 (6):0.099 (6) ratio. In the crystal, mol­ecules inter­act by way of π–π stacking inter­actions [centroid–centroid separation = 3.720 (2) Å]

Characteristics of Pollen from Transgenic Lines of Apple Carrying the Exogenous CpTI Gene

AbstractIt is fundamental for gene transformation and ecosystem hazard evaluation to study the pollen characteristics of transgenic plants. In this research, the characteristics of pollen from 7- or 8-year-old transgenic apple plants carrying an exogenous CpTI gene were analyzed. The results showed that there was no significant difference in terms of size, morphology, or exine ornamentation between the pollen of the transgenic plants and the non-transgenic control. However, the transgenic plants had more abnormal pollen grains. Of the 13 transgenic lines tested, 12 had a significantly lower amount of pollen and six exhibited a significantly lower germination rate when cultured in vitro. The pollen viability of three transgenic lines was determined, with two showing significantly lower viability than the control. The transgenic Gala apple pollen grains germinated normally via controlled pollination on Fuji apple stigmas. However, the pollen tubes extended relatively slowly during the middle and late development stages, and another 8h were needed to reach the ovules compared with the control. The gibberellic acid concentration in transgenic Gala apple flowers was lower than in the non-transgenic control during all development stages tested. The abscisic acid concentration in the transgenic flowers was lower during the pink stage, and higher during the ball and fully open stages. Microscopic observation of the anther structure showed no difference. The tapetum of the pollen sac wall in transgenic plants decomposed late and affected pollen grain development, which could be one of the reasons for the lower number of pollen grains and poor viability in the transgenic plants

Immediate Antiretroviral Therapy Decreases Mortality Among Patients With High CD4 Counts in China: A Nationwide, Retrospective Cohort Study.

BackgroundClinical trials have demonstrated that immediate initiation of antiretroviral therapy (ART) reduces AIDS-related morbidity and mortality. We tested the hypothesis that initiating ART ≤30 days after human immunodeficiency virus (HIV) diagnosis would be associated with reduced mortality among people living with HIV (PLWH) with CD4 counts >500 cells/μL.MethodsPLWH enrolled in the Chinese National HIV Information System between January 2012 and June 2014 with CD4 counts >500 cells/μL were followed for 12 months. Cox proportional hazards model was used to determine hazard ratios (HRs) for PLWH who initiated ART after HIV diagnosis. ART initiation was treated as a time-dependent variable.ResultsWe enrolled 34581 PLWH with CD4 >500 cells/μL; 1838 (5.3%) initiated ART ≤30 days after diagnosis (immediate ART group), and 19 deaths were observed with a mortality rate of 1.04 per 100 person-years (PY). Fifty-eight deaths were documented among the 5640 PLWH in the delayed ART group with a mortality rate of 2.25 per 100 PY. There were 713 deaths among the 27103 PLWH in the no ART group with a mortality rate of 2.39 per 100 PY. After controlling for potential confounding factors, ART initiation at ≤30 days (adjusted HR, 0.37 [95% confidence interval, .23-.58]) was a statistically significant protective factor.ConclusionsWe found that immediate ART is associated with a 63% reduction in overall mortality among PLWH with CD4 counts >500 cells/μL in China, supporting the recommendation to initiate ART immediately following HIV diagnosis

Improved 11α-hydroxycanrenone production by modification of cytochrome P450 monooxygenase gene in Aspergillus ochraceus

Eplerenone is a drug that protects the cardiovascular system. 11α-Hydroxycanrenone is a key intermediate in eplerenone synthesis. We found that although the cytochrome P450 (CYP) enzyme system in Aspergillus ochraceus strain MF018 could catalyse the conversion of canrenone to 11α-hydroxycanrenone, its biocatalytic efficiency is low. To improve the efficiency of 11α-hydroxycanrenone production, the CYP monooxygenase-coding gene of MF018 was predicted and cloned based on whole-genome sequencing results. A recombinant A. ochraceus strain MF010 with the high expression of CYP monooxygenase was then obtained through homologous recombination. The biocatalytic rate of this recombinant strain reached 93 % at 60 h without the addition of organic solvents or surfactants and was 17–18 % higher than that of the MF018 strain. Moreover, the biocatalytic time of the MF010 strain was reduced by more than 30 h compared with that of the MF018 strain. These results show that the recombinant A. ochraceus strain MF010 can overcome the limitation of substrate biocatalytic efficiency and thus holds a high potential for application in the industrial production of eplerenone

AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training

It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applications across edge devices, researchers have proposed partitioning a large model into several sub-models, and deploying each of them to a different edge device to collaboratively train a DNN model. However, the communication overhead caused by the large amount of data transmitted from one device to another during training, as well as the sub-optimal partition point due to the inaccurate latency prediction of computation at each edge device can significantly slow down training. In this paper, we propose AccEPT, an acceleration scheme for accelerating the edge collaborative pipeline-parallel training. In particular, we propose a light-weight adaptive latency predictor to accurately estimate the computation latency of each layer at different devices, which also adapts to unseen devices through continuous learning. Therefore, the proposed latency predictor leads to better model partitioning which balances the computation loads across participating devices. Moreover, we propose a bit-level computation-efficient data compression scheme to compress the data to be transmitted between devices during training. Our numerical results demonstrate that our proposed acceleration approach is able to significantly speed up edge pipeline parallel training up to 3 times faster in the considered experimental settings