Group Sparse Precoding for Cloud-RAN with Multiple User Antennas

Cloud radio access network (C-RAN) has become a promising network architecture to support the massive data traffic in the next generation cellular networks. In a C-RAN, a massive number of low-cost remote antenna ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed low-latency fronthaul links, which enables efficient resource allocation and interference management. As the RAPs are geographically distributed, the group sparse beamforming schemes attracts extensive studies, where a subset of RAPs is assigned to be active and a high spectral efficiency can be achieved. However, most studies assumes that each user is equipped with a single antenna. How to design the group sparse precoder for the multiple antenna users remains little understood, as it requires the joint optimization of the mutual coupling transmit and receive beamformers. This paper formulates an optimal joint RAP selection and precoding design problem in a C-RAN with multiple antennas at each user. Specifically, we assume a fixed transmit power constraint for each RAP, and investigate the optimal tradeoff between the sum rate and the number of active RAPs. Motivated by the compressive sensing theory, this paper formulates the group sparse precoding problem by inducing the $\ell_0$-norm as a penalty and then uses the reweighted $\ell_1$ heuristic to find a solution. By adopting the idea of block diagonalization precoding, the problem can be formulated as a convex optimization, and an efficient algorithm is proposed based on its Lagrangian dual. Simulation results verify that our proposed algorithm can achieve almost the same sum rate as that obtained from exhaustive search

Discovering Galaxy Features via Dataset Distillation

In many applications, Neural Nets (NNs) have classification performance on par or even exceeding human capacity. Moreover, it is likely that NNs leverage underlying features that might differ from those humans perceive to classify. Can we "reverse-engineer" pertinent features to enhance our scientific understanding? Here, we apply this idea to the notoriously difficult task of galaxy classification: NNs have reached high performance for this task, but what does a neural net (NN) "see" when it classifies galaxies? Are there morphological features that the human eye might overlook that could help with the task and provide new insights? Can we visualize tracers of early evolution, or additionally incorporated spectral data? We present a novel way to summarize and visualize galaxy morphology through the lens of neural networks, leveraging Dataset Distillation, a recent deep-learning methodology with the primary objective to distill knowledge from a large dataset and condense it into a compact synthetic dataset, such that a model trained on this synthetic dataset achieves performance comparable to a model trained on the full dataset. We curate a class-balanced, medium-size high-confidence version of the Galaxy Zoo 2 dataset, and proceed with dataset distillation from our accurate NN-classifier to create synthesized prototypical images of galaxy morphological features, demonstrating its effectiveness. Of independent interest, we introduce a self-adaptive version of the state-of-the-art Matching Trajectory algorithm to automate the distillation process, and show enhanced performance on computer vision benchmarks.Comment: Accepted to NeurIPS Workshop on Machine Learning and the Physical Sciences, 202

Factors contributing to sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis

BackgroundThis study aims to systematically assess the risk factors, the overall strength of association, and evidence quality related to sepsis-associated encephalopathy.MethodsA systematic search was conducted in the Cochrane Library, PubMed, Web of Science, and Embase for cohort or case-control studies published up to August 2023 on risk factors associated with sepsis-related encephalopathy. The selected studies were screened, data were extracted, and the quality was evaluated using the Newcastle–Ottawa Scale. Meta-analysis was performed using RevMan 5.3 software. The certainty of the evidence was assessed using the GRADE criteria.ResultsA total of 13 studies involving 1,906 participants were included in the analysis. Among these studies, 12 were of high quality, and one was of moderate quality. Our meta-analysis identified six risk factors significantly associated with Serious Adverse Events (SAE). These included APACHE II, SOFA, age, tau protein, and IL-6, which were found to be risk factors with significant effects (standard mean difference SMD: 1.24–2.30), and albumin, which was a risk factor with moderate effects (SMD: −0.55). However, the certainty of evidence for the risk factors identified in this meta-analysis ranged from low to medium.ConclusionThis systematic review and meta-analysis identified several risk factors with moderate to significant effects. APACHE II, SOFA, age, tau protein, IL-6, and albumin were associated with sepsis-related encephalopathy and were supported by medium- to high-quality evidence. These findings provide healthcare professionals with an evidence-based foundation for managing and treating hospitalized adult patients with sepsis-related encephalopathy

Acoustic Emission Characteristics of Compressive Deformation and Failure of Siltstone under Different Water Contents

The uniaxial compression and acoustic emission (AE) monitoring of siltstone specimens in the Gongchangling open-pit iron mine in Liaoning Province was conducted by evaluating the effects of three water saturation levels: dry, natural, and water-saturated. The siltstone AE characteristics were analyzed according to water content; the relationship between the AE characteristics and the growth and expansion of siltstone cracks was subsequently discussed. Research results indicated the following: siltstone specimens had distinctly different mechanical properties and AE characteristics according to water content; as the water content increased the compressive strength and elasticity modulus of specimens decreased. In the compacting phase of specimens under compression, the AE count rate of the water-saturated specimen was relatively small and the events were relatively stable. In the linear-elastic deformation phase, the AE count rate of the dry specimen increased sharply, reaching approximately 400 times/s. In the plastic yield deformation phase, the peak value of the AE count rate of the dry specimen ranged between 955 and 1,068 times/s, whereas that of the water-saturated specimen only attained a range of 635 to 782 times/s. In the failure phase, the time to reach the peak stress value of the dry specimen was increased as compared to that of the AE count rate

Automatic segmentation of kidney and kidney tumor based on 3D convolutional neural networks

Kidney cancer is a huge threat to humans, and the surgery is the most common treatment. For clinicians, knowing the morphology of the kidney and kidney tumor in advance may be helpful for surgery. Automatic segmentation of kidney and kidney tumor is a promising approach for these efforts. In this paper, we proposed a based 3D convolutional neural network to segment kidney and kidney tumor using the data from the 2019 Kidney Tumor Segmentation Challenge