CLT for linear spectral statistics of normalized sample covariance matrices with the dimension much larger than the sample size

Let $\mathbf{A}=\frac{1}{\sqrt{np}}(\mathbf{X}^T\mathbf{X}-p\mathbf {I}_n)$ where $\mathbf{X}$ is a $p\times n$ matrix, consisting of independent and identically distributed (i.i.d.) real random variables $X_{ij}$ with mean zero and variance one. When $p/n\to\infty$, under fourth moment conditions a central limit theorem (CLT) for linear spectral statistics (LSS) of $\mathbf{A}$ defined by the eigenvalues is established. We also explore its applications in testing whether a population covariance matrix is an identity matrix.Comment: Published at http://dx.doi.org/10.3150/14-BEJ599 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Identifying protein complexes from interaction networks based on clique percolation and distance restriction

Background: Identification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. Each protein might be subordinate multiple protein complexes in the real protein-protein interaction networks.Identifying overlapping protein complexes from protein-protein interaction networks is a considerable research topic. Result: As an effective algorithm in identifying overlapping module structures, clique percolation method (CPM) has a wide range of application in social networks and biological networks. However, the recognition accuracy of algorithm CPM is lowly. Furthermore, algorithm CPM is unfit to identifying protein complexes with meso-scale when it applied in protein-protein interaction networks. In this paper, we propose a new topological model by extending the definition of k-clique community of algorithm CPM and introduced distance restriction, and develop a novel algorithm called CP-DR based on the new topological model for identifying protein complexes. In this new algorithm, the protein complex size is restricted by distance constraint to conquer the shortcomings of algorithm CPM. The algorithm CP-DR is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Conclusion: The proposed algorithm CP-DR based on clique percolation and distance restriction makes it possible to identify dense subgraphs in protein interaction networks, a large number of which correspond to known protein complexes. Compared to algorithm CPM, algorithm CP-DR has more outstanding performance

A comparison of the functional modules identified from time course and static PPI network data

<p>Abstract</p> <p>Background</p> <p>Cellular systems are highly dynamic and responsive to cues from the environment. Cellular function and response patterns to external stimuli are regulated by biological networks. A protein-protein interaction (PPI) network with static connectivity is dynamic in the sense that the nodes implement so-called functional activities that evolve in time. The shift from static to dynamic network analysis is essential for further understanding of molecular systems.</p> <p>Results</p> <p>In this paper, Time Course Protein Interaction Networks (TC-PINs) are reconstructed by incorporating time series gene expression into PPI networks. Then, a clustering algorithm is used to create functional modules from three kinds of networks: the TC-PINs, a static PPI network and a pseudorandom network. For the functional modules from the TC-PINs, repetitive modules and modules contained within bigger modules are removed. Finally, matching and GO enrichment analyses are performed to compare the functional modules detected from those networks.</p> <p>Conclusions</p> <p>The comparative analyses show that the functional modules from the TC-PINs have much more significant biological meaning than those from static PPI networks. Moreover, it implies that many studies on static PPI networks can be done on the TC-PINs and accordingly, the experimental results are much more satisfactory. The 36 PPI networks corresponding to 36 time points, identified as part of this study, and other materials are available at <url>http://bioinfo.csu.edu.cn/txw/TC-PINs.</url></p

LightGrad: Lightweight Diffusion Probabilistic Model for Text-to-Speech

Recent advances in neural text-to-speech (TTS) models bring thousands of TTS applications into daily life, where models are deployed in cloud to provide services for customs. Among these models are diffusion probabilistic models (DPMs), which can be stably trained and are more parameter-efficient compared with other generative models. As transmitting data between customs and the cloud introduces high latency and the risk of exposing private data, deploying TTS models on edge devices is preferred. When implementing DPMs onto edge devices, there are two practical problems. First, current DPMs are not lightweight enough for resource-constrained devices. Second, DPMs require many denoising steps in inference, which increases latency. In this work, we present LightGrad, a lightweight DPM for TTS. LightGrad is equipped with a lightweight U-Net diffusion decoder and a training-free fast sampling technique, reducing both model parameters and inference latency. Streaming inference is also implemented in LightGrad to reduce latency further. Compared with Grad-TTS, LightGrad achieves 62.2% reduction in paramters, 65.7% reduction in latency, while preserving comparable speech quality on both Chinese Mandarin and English in 4 denoising steps.Comment: Accepted by ICASSP 202

A Prospective Randomized Study of Adjuvant Chemotherapy in Resected Stage IIIA-N2 Non-small Cell Lung Cancer

Background and objective Lung cancer is one of the leading cause of cancer-related death around the world. Surgery is the primary treatment for patients with stage I, II, or IIIA non-small cell lung cancer (NSCLC). However, longterm survival of NSCLC patients after surgery alone is largely unsatisfactory. We undertook to determine whether adjuvant vinorelbine/paclitaxel plus carboplatin prolong overall survival among patients with completely resected stage IIIA-N2 nonsmall cell lung cancer. Methods We randomly assigned patients with completely resected stage IIIA-N2 non-small cell lung cancer to vinorelbine/paclitaxel plus carboplatin or to observation. Results A total of 150 patients (1999-2003) underwent randomization to vinorelbine/paclitaxel plus carboplatin (79 patients) or observation. In both groups, the median age was 57 years, 73 percent were male, and 28 percents had squamous carcinoma. Chemotherapy caused neutropenia in 82 percents of patients (including grade 3 and 4 neutropenia in 42 percent) and there was no treatment-related death observed in this trial. After median follow-up of 39 months (range 1-110), overall survival was significantly prolonged in the chemotherapy group as compared with the observation group (33 months versus 24 months, χ2=4.363, P=0.037), as was disease-free survival (32 months versus 20 months, χ2=5.413, P=0.020). Five-year overall survival rates were 31.1 percent and 19.1 percent, respectively. Conclusion Adjuvant vinorelbine/paclitaxel plus carboplatin have an acceptable level of toxicity and prolongs disease-free and overall survival among patients with completely resected stage IIIA-N2 non-small cell lung cancer

ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs

In this paper, we present ZeroPrompt (Figure 1-(a)) and the corresponding Prompt-and-Refine strategy (Figure 3), two simple but effective \textbf{training-free} methods to decrease the Token Display Time (TDT) of streaming ASR models \textbf{without any accuracy loss}. The core idea of ZeroPrompt is to append zeroed content to each chunk during inference, which acts like a prompt to encourage the model to predict future tokens even before they were spoken. We argue that streaming acoustic encoders naturally have the modeling ability of Masked Language Models and our experiments demonstrate that ZeroPrompt is engineering cheap and can be applied to streaming acoustic encoders on any dataset without any accuracy loss. Specifically, compared with our baseline models, we achieve 350 $\sim$ 700ms reduction on First Token Display Time (TDT-F) and 100 $\sim$ 400ms reduction on Last Token Display Time (TDT-L), with theoretically and experimentally equal WER on both Aishell-1 and Librispeech datasets.Comment: accepted by interspeech 202

Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing

© 2020 Binbin Huang et al. Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices. Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of the limited resource and cause waste of resource, resulting in low system performance (the weighted sum of the number of processed tasks, the number of punished tasks, and the number of dropped tasks). Therefore, it is a challenging problem to effectively allocate the computing and communication resources to multiple mobile users. To cope with this problem, we propose a performance-aware resource allocation (PARA) scheme, the goal of which is to maximize the long-term system performance. More specifically, we first build the multiuser resource allocation architecture for computing workloads and transmitting result data to mobile devices. Then, we formulate the multiuser resource allocation problem as a Markova Decision Process (MDP). To achieve this problem, a performance-aware resource allocation (PARA) scheme based on a deep deterministic policy gradient (DDPG) is adopted to derive optimal resource allocation policy. Finally, extensive simulation experiments demonstrate the effectiveness of the PARA scheme

Benthic carbon mineralization in hadal trenches: Insights from in-situ determination of benthic oxygen consumption

Hadal trenches have been proposed as depocenters of organic material and hot spots for organic matter mineralization. In this study, we for the first time quantified the total benthic O-2 uptake in hadal trenches using in situ chamber incubations. Three trenches in the tropical Pacific were targeted and exhibited relatively high diagenetic activity given the great water depths, that is, the Mariana Trench (2.0x10(2)molO(2)m(-2)d(-1), 10,853m), the Mussau Trench (2.70.1x10(2)molO(2)m(-2)d(-1), 7,011m), and the New Britain Trench (6.00.1x10(2)molO(2)m(-2)d(-1), 8,225m). Combined with the analyses of total organic carbon and C-13 of total organic carbon in the sediments and previously published in situ O-2 microprofiles from hadal settings, we suggest that hadal benthic carbon mineralization partly is governed by the surface production and also is linked to the distance from land. Therefore, we highlight that terrestrial organic matter can be of importance in sustaining benthic communities in some hadal settings. Plain Language Summary Hadal trenches that refer to seafloor areas covered by a water column with depths >6,000m have been proposed as depocenters of organic material and hot spots for organic matter mineralization. We applied in situ benthic chamber incubation techniques within three trenches in the tropical Pacific Ocean (the Mariana Trench, the Mussau Trench, and the New Britain Trench) and thereby reported the first benthic total O-2 uptake rates measured in hadal settings. The benthic carbon mineralization rates generally show a positive correlation with the net primary production in respective provinces and the sedimentary total organic carbon (TOC) level. Analyses of TOC contents and C-13 of TOC indicated a downslope transport of sediment containing a large amount of terrestrial organic matter, possibly via mass-wasting events to the axis of New Britain Trench off the New Britain Island. Therefore, we speculate that both surface production regimes and the distance from land are closely connected with the benthic carbon mineralization rate at the trench axes. The elevated organic carbon turnover rate may in part result from preferential concentration of relatively labile organic matter in the surface sediments of trench axes or efficient utilization of refractory terrestrial material under extreme pressure

Constrained Update Projection Approach to Safe Policy Optimization

Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a novel policy optimization method based on Constrained Update Projection framework that enjoys rigorous safety guarantee. Central to our CUP development is the newly proposed surrogate functions along with the performance bound. Compared to previous safe RL methods, CUP enjoys the benefits of 1) CUP generalizes the surrogate functions to generalized advantage estimator (GAE), leading to strong empirical performance. 2) CUP unifies performance bounds, providing a better understanding and interpretability for some existing algorithms; 3) CUP provides a non-convex implementation via only first-order optimizers, which does not require any strong approximation on the convexity of the objectives. To validate our CUP method, we compared CUP against a comprehensive list of safe RL baselines on a wide range of tasks. Experiments show the effectiveness of CUP both in terms of reward and safety constraint satisfaction. We have opened the source code of CUP at this link https://github.com/zmsn-2077/ CUP-safe-rl.Comment: Accepted by NeurIPS2022. arXiv admin note: substantial text overlap with arXiv:2202.0756