44 research outputs found
Minimizing Polarization in Noisy Leader-Follower Opinion Dynamics
The operation of creating edges has been widely applied to optimize relevant
quantities of opinion dynamics. In this paper, we consider a problem of
polarization optimization for the leader-follower opinion dynamics in a noisy
social network with nodes and edges, where a group of nodes are
leaders, and the remaining nodes are followers. We adopt the popular
leader-follower DeGroot model, where the opinion of every leader is identical
and remains unchanged, while the opinion of every follower is subject to white
noise. The polarization is defined as the steady-state variance of the
deviation of each node's opinion from leaders' opinion, which equals one half
of the effective resistance between the node group and all
other nodes. Concretely, we propose and study the problem of minimizing
by adding new edges with each incident to a node in . We
show that the objective function is monotone and supermodular. We then propose
a simple greedy algorithm with an approximation factor that
approximately solves the problem in time. To speed up the
computation, we also provide a fast algorithm to compute
(1-1/e-\eps)-approximate effective resistance , the running
time of which is \Otil (mk\eps^{-2}) for any \eps>0, where the \Otil
(\cdot) notation suppresses the factors. Extensive
experiment results show that our second algorithm is both effective and
efficient.Comment: This paper has been accepted in CIKM'23 conferenc
Optimal Scale-Free Small-World Graphs with Minimum Scaling of Cover Time
The cover time of random walks on a graph has found wide practical
applications in different fields of computer science, such as crawling and
searching on the World Wide Web and query processing in sensor networks, with
the application effects dependent on the behavior of cover time: the smaller
the cover time, the better the application performance. It was proved that over
all graphs with nodes, complete graphs have the minimum cover time . However, complete graphs cannot mimic real-world networks with small
average degree and scale-free small-world properties, for which the cover time
has not been examined carefully, and its behavior is still not well understood.
In this paper, we first experimentally evaluate the cover time for various
real-world networks with scale-free small-world properties, which scales as
. To better understand the behavior of the cover time for real-world
networks, we then study the cover time of three scale-free small-world model
networks by using the connection between cover time and resistance diameter.
For all the three networks, their cover time also behaves as . This
work indicates that sparse networks with scale-free and small-world topology
are favorable architectures with optimal scaling of cover time. Our results
deepen understanding the behavior of cover time in real-world networks with
scale-free small-world structure, and have potential implications in the design
of efficient algorithms related to cover time
ConvFormer: Revisiting Transformer for Sequential User Modeling
Sequential user modeling, a critical task in personalized recommender
systems, focuses on predicting the next item a user would prefer, requiring a
deep understanding of user behavior sequences. Despite the remarkable success
of Transformer-based models across various domains, their full potential in
comprehending user behavior remains untapped. In this paper, we re-examine
Transformer-like architectures aiming to advance state-of-the-art performance.
We start by revisiting the core building blocks of Transformer-based methods,
analyzing the effectiveness of the item-to-item mechanism within the context of
sequential user modeling. After conducting a thorough experimental analysis, we
identify three essential criteria for devising efficient sequential user
models, which we hope will serve as practical guidelines to inspire and shape
future designs. Following this, we introduce ConvFormer, a simple but powerful
modification to the Transformer architecture that meets these criteria,
yielding state-of-the-art results. Additionally, we present an acceleration
technique to minimize the complexity associated with processing extremely long
sequences. Experiments on four public datasets showcase ConvFormer's
superiority and confirm the validity of our proposed criteria
Financial transfers from adult children and depressive symptoms among mid-aged and elderly residents in China - evidence from the China health and retirement longitudinal study.
Although the awareness of mental health problems in late life is rising, the association between financial transfers to the older generations from children and mental health at older ages in China has received little attention. This study examines the association between financial transfers from children and depressive symptoms among the mid-aged and elderly residents (from 45 years of age and older) in China. We used the data from the China Health and Retirement Longitudinal Study (CHARLS, 2013) (n = 10,935) This included data on financial transfers from all non-co-resident children to their parents, and the individual scores on depressive symptoms as measured by the 10-item Center for Epidemiologic Studies-Depression Scale (CESD-10). A two-level - individual and community levels - mixed linear model was deployed to explore their association. Financial transfers from children to parents was the major component of inter-generational financial transfers in Chinese families. A higher financial support from non-co-resident children was signivicantly and positively related to fewer depressive symptoms (coef. = - 0.195,P-value< 0.001) among both the mid-aged and elderly parents. Financial transfers from non-co-resident children are associated with depressive symptoms among mid-aged and elderly residents in the China situation. Taxation and other policy measures should encourage and facilitate these type of financial transfers and prevent a decrease of support from children to parents
Impact of autologous platelet-rich plasma therapy vs. hyaluronic acid on synovial fluid biomarkers in knee osteoarthritis: a randomized controlled clinical trial
ObjectiveObserve the effects of platelet-rich plasma (PRP) therapy on inflammatory cytokines in the synovial fluid of the knee joint of patients with KOA, and explore the effects of PRP intra-articular injection on the inflammation of the knee joint environment and the possible mechanism of action.MethodsSeventy patients were randomized to undergo three blinded weekly intra-articular injections of PRP or hyaluronic acid (HA). The concentrations of inflammatory cytokines, including interleukin (IL)-6, IL-1β, tumor necrosis factor (TNF)-α, IL-8, IL-17A, IL-17F, IL-4, IL-5, and IL-10, in the synovial fluid were evaluated before the intervention and 1 month after the third injection. The Western Ontario and McMaster University (WOMAC) and visual analog scale (VAS) scores were used to assess pain and functional status of the knee joints in both groups before the intervention, immediately post-intervention, and 1, 3, 6, and 12 months after the intervention.ResultsBaseline characteristics were similar in both groups with no statistical difference. The IL-6, IL-1β, TNF-α, IL-17A, and IL-10 levels in the synovial fluid of the observation group decreased significantly after, vs. before, the intervention (p < 0.05), whereas the IL-8, IL-17F, and IL-4 levels decreased (p > 0.05) and IL-5 levels increased (p > 0.05). There was no statistically significant difference between inflammatory cytokine levels in the synovial fluid of the samples from the control group before and after the intervention (p > 0.05). There were no statistically significant differences between the two groups immediately after intervention (p > 0.05). At 1, 3, 6, and 12 months after intervention, the WOMAC and VAS scores were significantly better in the observation group than in the control group (p < 0.05).ConclusionPlatelet plasma therapy can reduce the concentrations of inflammatory cytokines IL-6, IL-1β, TNF-α, IL-17A, and IL-10 in the synovial fluid of KOA patients, reduce the expression levels of IL-8, IL-17F, and IL-4, clear the pro-inflammatory factors, improve the inflammatory environment of the affected knee joint, and alleviate pain caused by inflammation. Thus, alleviating pain and improving knee function in patients with KOA
CTpathway: A Crosstalk-Based Pathway Enrichment Analysis Method for Cancer Research
Background: Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated.
Methods: To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with \u3e440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes.
Results: Analysis of \u3e8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy.
Conclusions: Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/ . The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway
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Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation
Effects of emotion words activation and satiation on facial expression perception: evidence from behavioral and ERP investigations
ObjectiveThe present study investigated the impact of emotion concepts obtained from external environmental experiences on the perception of facial expressions by manipulating the activation and satiation of emotion words, which was based on the argument between basic emotion theory and constructed emotion theory.MethodsExperiment 1 explored the effects of emotion activation on happy, disgusted, emotion-label words and emotion-laden words in a facial expression judgment task through behavioral experimentation. Experiment 2 explored the effect of semantic satiation on emotion-label words and emotion-laden words using the event-related potential technique.ResultsExperiment 1 found that facial expression perception was influenced by both types of emotion words and showed a significant emotional consistency effect. Experiment 2 found that N170 exhibited a more negative amplitude in the consistent condition compared to the inconsistent condition in the right hemisphere. More importantly, in the later stage of facial expression processing, emotion-label words and emotion-laden words both obstructed the perception of disgusted facial expressions and elicited more negative N400 amplitude in the emotion consistency condition, showing a reversed N400 effect.ConclusionThese results suggested that emotion concepts in the form of language influenced the perception of facial expressions, but there were differences between happy and disgusted faces. Disgusted faces were more dependent on emotion concept information and showed different performances in semantic activation and satiation conditions