Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms

Many machine learning tasks can be formulated as a stochastic compositional optimization (SCO) problem such as reinforcement learning, AUC maximization, and meta-learning, where the objective function involves a nested composition associated with an expectation. While a significant amount of studies has been devoted to studying the convergence behavior of SCO algorithms, there is little work on understanding their generalization, i.e., how these learning algorithms built from training examples would behave on future test examples. In this paper, we provide the stability and generalization analysis of stochastic compositional gradient descent algorithms through the lens of algorithmic stability in the framework of statistical learning theory. Firstly, we introduce a stability concept called compositional uniform stability and establish its quantitative relation with generalization for SCO problems. Then, we establish the compositional uniform stability results for two popular stochastic compositional gradient descent algorithms, namely SCGD and SCSC. Finally, we derive dimension-independent excess risk bounds for SCGD and SCSC by trade-offing their stability results and optimization errors. To the best of our knowledge, these are the first-ever-known results on stability and generalization analysis of stochastic compositional gradient descent algorithms

Neural Common Neighbor with Completion for Link Prediction

Despite its outstanding performance in various graph tasks, vanilla Message Passing Neural Network (MPNN) usually fails in link prediction tasks, as it only uses representations of two individual target nodes and ignores the pairwise relation between them. To capture the pairwise relations, some models add manual features to the input graph and use the output of MPNN to produce pairwise representations. In contrast, others directly use manual features as pairwise representations. Though this simplification avoids applying a GNN to each link individually and thus improves scalability, these models still have much room for performance improvement due to the hand-crafted and unlearnable pairwise features. To upgrade performance while maintaining scalability, we propose Neural Common Neighbor (NCN), which uses learnable pairwise representations. To further boost NCN, we study the unobserved link problem. The incompleteness of the graph is ubiquitous and leads to distribution shifts between the training and test set, loss of common neighbor information, and performance degradation of models. Therefore, we propose two intervention methods: common neighbor completion and target link removal. Combining the two methods with NCN, we propose Neural Common Neighbor with Completion (NCNC). NCN and NCNC outperform recent strong baselines by large margins. NCNC achieves state-of-the-art performance in link prediction tasks. Our code is available at https://github.com/GraphPKU/NeuralCommonNeighbor

Combined Tracking Strategy Based on Unscented Kalman Filter for Global Positioning System L2C CM/CL Signal

In a global positioning system receiver, the tracking algorithm plays a dominant role since the code delay and Doppler frequency shift need to be accurately estimated as well as their variation over time need to be continuously updated. Combine unscented Kalman filter (UKF) with CM/CL signal to improve the signal tracking precision is proposed. It allow weighting assignment between CM code and CL code incoming signal, masked by a mass of noise, and to describe a UKF tracking loop aiming at decreasing numerical errors. UKF here involves state and measuring equations which calculate absolute offsets to adjust initial code and carrier phase then dramatically decrease the tracking error. In particular, the algorithm is implemented in both open space and jammed environment to highlight the advantages of tracking approach, by comparing single code and combined code, UKF and EKF tracking loop. It proves that signal tracking based on UKF, with low energy dissipation as well as high precision, is particularly appealing for a software receiver implementation

How do childhood abuse and neglect affect prosocial behavior? The mediating roles of different empathic components

BackgroundChildhood abuse and neglect are typically considered as two different forms of maltreatment. Previous international studies have found differential effects of abuse and neglect on prosocial behavior, but this and the mediating pathway underlying these associations have not been examined in a Chinese sample. Our study aims to examine the effects of childhood abuse and neglect on prosocial behavior in Chinese participants and test the unique mediating roles of different empathic components in these associations.MethodsA total of 1,569 young adults (average age = 18.17 years) were recruited from a college that enrolls students from all provinces of China. Participants completed a series of questionnaires, including the Childhood Trauma Questionnaire, Interpersonal Reactivity Index, and Prosocial Tendencies Measure. Path analysis was conducted to determine the mediational relationships.ResultsEmotional neglect had significant direct effect on prosocial behavior (β = −0.108, p < 0.001), and could also impact prosocial behavior through the mediating roles of perspective-taking and empathic concern (effect size = −0.091 and −0.097 respectively, p < 0.001). Emotional abuse affected prosocial behavior only through personal distress (effect size = −0.072, p < 0.001). Physical abuse, sexual abuse and physical neglect have little effect on prosocial behavior and empathy.ConclusionChildhood abuse and neglect have distinct influences on prosocial behavior. Emotional abuse and emotional neglect affect prosocial behavior through distinct pathways. This conclusion could help to establish precise interventions for improving prosocial behavior in maltreated individuals

In situ Chromatin Interaction Analysis Using Paired-End Tag Sequencing.

Chromatin Interaction Analysis Using Paired-End Tag Sequencing (ChIA-PET) is an established method to map protein-mediated chromatin interactions. A limitation, however, is that it requires a hundred million cells per experiment, which hampers its broad application in biomedical research, particularly in studies in which it is impractical to obtain a large number of cells from rare samples. To reduce the required input cell number while retaining high data quality, we developed an in situ ChIA-PET protocol, which requires as few as 1 million cells. Here, we describe detailed step-by-step procedures for performing in situ ChIA-PET from cultured cells, including both an experimental protocol for sample preparation and data generation and a computational protocol for data processing and visualization using the ChIA-PIPE pipeline. As the protocol significantly simplifies the experimental procedure, reduces ligation noise, and decreases the required input of cells compared to previous versions of ChIA-PET protocols, it can be applied to generate high-resolution chromatin contact maps mediated by various protein factors for a wide range of human and mouse primary cells. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Sample preparation and data generation Support Protocol: Bridge linker preparation Basic Protocol 2: Data processing and visualization

Biomarker study of symptomatic intracranial atherosclerotic stenosis in patients with acute ischemic stroke

ObjectiveAcute ischemic stroke (AIS) is characterized by high rates of morbidity, disability, mortality, and recurrence, often leaving patients with varying degrees of sequelae. Symptomatic intracranial atherosclerotic stenosis (sICAS) is a significant contributor to AIS pathogenesis and recurrence. The formation and progression of sICAS are influenced by pathways such as lipid metabolism and inflammatory response. Given its high risk of clinical recurrence, timely assessment of intracranial vascular stenosis in AIS is crucial for diagnosing sICAS, treating stroke, and preventing stroke recurrence.MethodsFourteen AIS patients were divided into stenosis and control groups based on the presence or absence of intracranial vessel stenosis. Initially, 4D Label-free proteome quantification technology was employed for mass spectrometry analysis to identify differential proteins between the groups. Subsequently, functional enrichment analysis, including GO classification, KEGG pathway, and Domain, revealed trends related to differential proteins. The STRING (v.11.5) protein interaction network database was used to identify differential protein interactions and target proteins. Finally, parallel reaction monitoring (PRM) validated the selected target proteins.ResultsMass spectrometry identified 1,096 proteins, with 991 being quantitatively comparable. Using a p-value <0.05 and differential expression change thresholds of >1.3 for significant up-regulation and < 1/1.3 for significant down-regulation, 46 differential proteins were identified: 24 significantly up-regulated and 22 significantly down-regulated. PRM experiments validated five proteins related to lipid metabolism and inflammatory response: namely alpha-2-macroglobulin (A2M), lipopolysaccharide-binding protein (LBP), cathepsin G (CTSG), cystatin (CST)3, and fatty acid-binding protein (FABP)1.ConclusionThe detection of changes in these five proteins in AIS patients can aid in the diagnosis of sICAS, inform stroke treatment, and assist in preventing stroke recurrence. Moreover, it can contribute to the development of drugs for preventing AIS recurrence by integrating traditional Chinese and Western medicine

Dynamic Succession of Microbial Communities in Soybean Paste Made with Broomcorn Millet as an Additive and Its Correlation with Flavor and Nutritional Properties during the Brewing Process

To obtain a full understanding the quality and microbial characteristics of soybean paste made from a mixture of soybean and broomcorn millet flour, its physicochemical properties (amino nitrogen and nitrite), and total phenols (TP), γ-aminobutyric acid (GABA), free amino acids (FAAs), volatile compounds, and microbial community composition were investigated. The results showed that the amino nitrogen content increased to 0.71%, and the nitrite content decreased to within the standard range (1.37 mg/kg). The contents of TP, key FAAs and volatile compounds increased significantly during the fermentation process. The core microbial communities included Enterobacter, Pseudomonas, Stenotrophomonas, Aspergillus, and Alternaria. The results of correlation analysis confirmed that bacteria (Bacillus, Knoellia, and Blastococcus) and fungi (Epicoccum and Saccharomyces) played a significant role in the bioactivity changes and flavor generation in soybean paste. This study will be of great significance for understanding the quality and flavor of novel soybean paste made with cereal flour as an additive