Decreasing the uncertainty of atomic clocks via real-time noise distinguish

The environmental perturbation on atoms is the key factor restricting the performance of atomic frequency standards, especially in long term scale. In this letter, we demonstrate a real-time noise distinguish operation of atomic clocks. The operation improves the statistical uncertainty by about an order of magnitude of our fountain clock which is deteriorated previously by extra noises. The frequency offset bring by the extra noise is also corrected. The experiment proves the real-time noise distinguish operation can reduce the contribution of ambient noises and improve the uncertainty limit of atomic clocks.Comment: 5 pages, 4 figures, 1 tabl

Ternary Compression for Communication-Efficient Federated Learning

Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of privacy and security with the growing use of smart mobile devices and IoT devices. Federated learning provides a potential solution to privacy-preserving and secure machine learning, by means of jointly training a global model without uploading data distributed on multiple devices to a central server. However, most existing work on federated learning adopts machine learning models with full-precision weights, and almost all these models contain a large number of redundant parameters that do not need to be transmitted to the server, consuming an excessive amount of communication costs. To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor. A convergence proof of the quantization factor and the unbiasedness of FTTQ is given. In addition, we propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Empirical experiments are conducted to train widely used deep learning models on publicly available datasets, and our results demonstrate the effectiveness of FTTQ and T-FedAvg compared with the canonical federated learning algorithms in reducing communication costs and maintaining the learning performance

Reinforcement Learning Based Gasoline Blending Optimization: Achieving More Efficient Nonlinear Online Blending of Fuels

The online optimization of gasoline blending benefits refinery economies. However, the nonlinear blending mechanism, the oil property fluctuations, and the blending model mismatch bring difficulties to the optimization. To solve the above issues, this paper proposes a novel online optimization method based on deep reinforcement learning algorithm (DRL). The Markov decision process (MDP) expression are given considering a practical gasoline blending system. Then, the environment simulator of gasoline blending process is established based on the MDP expression and the one-year measurement data of a real-world refinery. The soft actor-critic (SAC) DRL algorithm is applied to improve the DRL agent policy by using the data obtained from the interaction between DRL agent and environment simulator. Compared with a traditional method, the proposed method has better economic performance. Meanwhile, it is more robust under property fluctuations and component oil switching. Furthermore, the proposed method maintains performance by automatically adapting to system drift.Comment: 30 pages,13 figure

Use of anchorchip-time-of-flight spectrometry technology to screen tumor biomarker proteins in serum for small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>The purpose of this study is to discover potential biomarkers in serum for the detection of small cell lung cancer (SCLC).</p> <p>Methods</p> <p>74 serum samples including 30 from SCLC patients and 44 from healthy controls were analyzed using ClinProt system combined with matrix-assisted laser desorption/ionization time-of-flight masss spectrometry (MALDI-TOF-MS). ClinProt software and genetic algorithm analysis selected a panel of serum markers that most efficiently predicted which patients had SCLC.</p> <p>Results</p> <p>The diagnostic pattern combined with 5 potential biomarkers could differentiate SCLC patients from healthy persons, with a sensitivity of 90%, specificity of 97.73%. Remarkably, 88.89% of stage I/II patients were accurately assigned to SCLC.</p> <p>Conclusions</p> <p>Anchorchip-time-of-flight spectrometry technology will provide a highly accurate approach for discovering new biomarkers for the detection of SCLC.</p

Neural network aided approximation and parameter inference of non-Markovian models of gene expression

10.1038/s41467-021-22919-1Nature Communications121261

Mutations in WNT10B Are Identified in Individuals with Oligodontia

Supplemental Data Supplemental Data include six figures and three tables and can be found with this article online at http://dx.doi.org/10.1016/j.ajhg.2016.05.012. Supplemental Data Document S1. Figures S1–S6 and Tables S1–S3 Download Document S2. Article plus Supplemental Data Download Web Resources Allen Brain Atlas, http://www.brain-map.org/ Eurexpress, http://www.eurexpress.org/ee/ ExAC Browser, http://exac.broadinstitute.org/ GEO Profiles, http://www.ncbi.nlm.nih.gov/geoprofiles HGMD, http://www.biobase-international.com/product/hgmd MutationTaster, http://www.mutationtaster.org/ OMIM, http://www.omim.org RefSeq, http://www.ncbi.nlm.nih.gov/refseq/ Tooth agenesis is one of the most common developmental anomalies in humans. Oligodontia, a severe form of tooth agenesis, is genetically and phenotypically a heterogeneous condition. Although significant efforts have been made, the genetic etiology of dental agenesis remains largely unknown. In the present study, we performed whole-exome sequencing to identify the causative mutations in Chinese families in whom oligodontia segregates with dominant inheritance. We detected a heterozygous missense mutation (c.632G>A [p.Arg211Gln]) in WNT10B in all affected family members. By Sanger sequencing a cohort of 145 unrelated individuals with non-syndromic oligodontia, we identified three additional mutations (c.569C>G [p.Pro190Arg], c.786G>A [p.Trp262∗], and c.851T>G [p.Phe284Cys]). Interestingly, analysis of genotype-phenotype correlations revealed that mutations in WNT10B affect the development of permanent dentition, particularly the lateral incisors. Furthermore, a functional assay demonstrated that each of these mutants could not normally enhance the canonical Wnt signaling in HEPG2 epithelial cells, in which activity of the TOPFlash luciferase reporter was measured. Notably, these mutant WNT10B ligands could not efficiently induce endothelial differentiation of dental pulp stem cells. Our findings provide the identification of autosomal-dominant WNT10B mutations in individuals with oligodontia, which increases the spectrum of congenital tooth agenesis and suggests attenuated Wnt signaling in endothelial differentiation of dental pulp stem cells

Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.Comment: This paper has been accepted by IEEE TNNL

Atrial Fibrillation Follow-up Investigation to Recover Memory and Learning Trial (AFFIRMING): Rationale and Design of a Multi-center, Double-blind, Randomized Controlled Trial

Background: People with atrial fibrillation (AF) have elevated risk of developing cognitive impairment. At present, there is a dearth of randomized controlled trials investigating cognitive impairment management in patients with AF. The Atrial Fibrillation Follow-up Investigation to Recover Memory and learning (AFFIRMING) study is aimed at evaluating the potential for computerized cognitive training to improve cognitive function in patients with AF. Methods: The study is a multi-center, double-blind, randomized controlled study using a 1:1 parallel design. A total of 200 patients with AF and mild cognitive decline without dementia are planned to be recruited. The intervention group will use the adaptive training software with changes in difficulty, whereas the positive control group will use basic training software with minimal or no variation in difficulty level. At the end of 12 weeks, the participants will be unblinded, and the positive control group will stop training. The intervention group will be rerandomized 1:1 to stop training or continue training. All participants will be followed up until 24 weeks. The primary endpoint is the proportion of the improvement of the global cognitive function at week 12 compared with baseline, using the Basic Cognitive Ability Test (BCAT)