Standard random walks and trapping on the Koch network with scale-free behavior and small-world effect

A vast variety of real-life networks display the ubiquitous presence of scale-free phenomenon and small-world effect, both of which play a significant role in the dynamical processes running on networks. Although various dynamical processes have been investigated in scale-free small-world networks, analytical research about random walks on such networks is much less. In this paper, we will study analytically the scaling of the mean first-passage time (MFPT) for random walks on scale-free small-world networks. To this end, we first map the classical Koch fractal to a network, called Koch network. According to this proposed mapping, we present an iterative algorithm for generating the Koch network, based on which we derive closed-form expressions for the relevant topological features, such as degree distribution, clustering coefficient, average path length, and degree correlations. The obtained solutions show that the Koch network exhibits scale-free behavior and small-world effect. Then, we investigate the standard random walks and trapping issue on the Koch network. Through the recurrence relations derived from the structure of the Koch network, we obtain the exact scaling for the MFPT. We show that in the infinite network order limit, the MFPT grows linearly with the number of all nodes in the network. The obtained analytical results are corroborated by direct extensive numerical calculations. In addition, we also determine the scaling efficiency exponents characterizing random walks on the Koch network.Comment: 12 pages, 8 figures. Definitive version published in Physical Review

ReFlow-TTS: A Rectified Flow Model for High-fidelity Text-to-Speech

The diffusion models including Denoising Diffusion Probabilistic Models (DDPM) and score-based generative models have demonstrated excellent performance in speech synthesis tasks. However, its effectiveness comes at the cost of numerous sampling steps, resulting in prolonged sampling time required to synthesize high-quality speech. This drawback hinders its practical applicability in real-world scenarios. In this paper, we introduce ReFlow-TTS, a novel rectified flow based method for speech synthesis with high-fidelity. Specifically, our ReFlow-TTS is simply an Ordinary Differential Equation (ODE) model that transports Gaussian distribution to the ground-truth Mel-spectrogram distribution by straight line paths as much as possible. Furthermore, our proposed approach enables high-quality speech synthesis with a single sampling step and eliminates the need for training a teacher model. Our experiments on LJSpeech Dataset show that our ReFlow-TTS method achieves the best performance compared with other diffusion based models. And the ReFlow-TTS with one step sampling achieves competitive performance compared with existing one-step TTS models.Comment: Accepted at ICASSP202

Review spam detection via temporal pattern discovery

Online reviews play a crucial role in today’s electronic com-merce. It is desirable for a customer to read reviews of products or stores before making the decision of what or from where to buy. Due to the pervasive spam reviews, customers can be misled to buy low-quality products, while decent stores can be defamed by malicious reviews. We ob-serve that, in reality, a great portion (> 90 % in the data we study) of the reviewers write only one review (singleton re-view). These reviews are so enormous in number that they can almost determine a store’s rating and impression. How-ever, existing methods did not examine this larger part of the reviews. Are most of these singleton reviews truthful ones? If not, how to detect spam reviews in singleton reviews? We call this problem singleton review spam detection. To address this problem, we observe that the normal re-viewers ’ arrival pattern is stable and uncorrelated to their rating pattern temporally. In contrast, spam attacks are usually bursty and either positively or negatively correlated to the rating. Thus, we propose to detect such attacks via unusually correlated temporal patterns. We identify and construct multidimensional time series based on aggregate statistics, in order to depict and mine such correlations. In this way, the singleton review spam detection problem is mapped to a abnormally correlated pattern detection prob-lem. We propose a hierarchical algorithm to robustly detect the time windows where such attacks are likely to have hap-pened. The algorithm also pinpoints such windows in dif-ferent time resolutions to facilitate faster human inspection. Experimental results show that the proposed method is ef-fective in detecting singleton review attacks. We discover that singleton review is a significant source of spam reviews and largely affects the ratings of online stores

C-terminal truncated hepatitis B virus X protein promotes hepatocellular carcinogenesis through induction of cancer and stem cell-like properties

published_or_final_versio

Heterogenous expression of beta-catenin, p16, e-cadherin, and c-myc in multi-stage colorectal carcinogenesis detected by tissue microarray

published_or_final_versio

Influencing mechanism analysis of holiday activity-travel patterns on transportation energy consumption and emissions in China

Energy shortage and atmospheric pollution problems are getting more serious in China, and transportation is the main source of energy consumption, pollutants, and carbon emissions. This study combined the activity-based analysis method with emission models, and investigated the influence mechanism of people’s activity travel scheduling on transportation energy consumption and emissions on holidays. Based on the holiday travel behavior survey data, the multinomial logistic regression model was first applied to explore the decision mechanisms of individual travel-mode choices in holidays. Next, the emission model was integrated with an activity-based travel demand model to calculate and compare transportation energy consumption and emissions under different policy scenarios. The results showed that socio-demographic characteristics had significant effects on holiday activity–travel patterns, and combined mode chains had a larger number of activity points than single mode chains. With an increase in the trip time of cars, and decrease of travel distance and the number of activity points, transportation energy consumption and emissions could be reduced greatly with an adjustment of holiday activity–travel patterns. The reduced portion is mainly attracted by slow traffic and public transport. However, the effects of a single policy strategy are very limited, thus portfolio policies need to be considered by policy makers

Oncogenic role of clusterin overexpression in multistage colorectal tumorigenesis and progression

Aim: To investigate the expression pattern of clusterin in colorectal adenoma-carcinoma-metastasis series, and to explore the potential role of clustelin in multistage colorectal tumorigenesis and progression. Methods: A colorectal carcinoma (CRC)-tissue microarray (TMA), which contained 85 advanced CRCs including 43 cases of Dukes B, 21 of Dukes C and 21 of Dukes D tumors, were used for assessing the expression of clusterin (clone 41D) and tumor cell apoptotic index (AI) by immunohistochemistry and TUNEL assay, respectively. Moreover the potential correlation of clusterin expression with the patient's clinical-pathological features were also examined. Results: The positive staining of clusterin in different colorectal tissues was primarily a cytoplasmic pattern. Cytoplasmic overexpression of clusterin was detected in none of the normal colorectal mucosa, 17% of the adenomas, 46% of the primary CRCs, and 57% of the CRC metastatic lesions. In addition, a significant positive correlation between overexpression of clusterin and advanced clinical (Dukes) stage was observed (P<0.01). Overexpression of cytoplasmic clusterin in CRCs was inversely correlated with tumor apoptotic index (P<0.01), indicating the anti-apoptotic function of cytoplasmic clusterin in CRCs. Conclusion: These data suggests that overexpression of cytoplasmic clusterin might be involved in the tumorigenesis and/or progression of CRCs. The anti-apoptotic function of cytoplasmic clusterin may be responsible, at least in part, for the development and biologically aggressive behavior of CRC. © 2005 The WJG Press and Elsevier Inc. All rights reserved.published_or_final_versio

Effect of APOE ɛ4 Status on Brain Amyloid-β and Cognitive Function in Amnestic and Nonamnestic Mild Cognitive Impairment: A 18F Florbetapir PET-CT Study

Mild cognitive impairment (MCI) is recognized as a predementia syndrome caused by multiple etiologies and nonmemory symptoms in MCI have recently gained increasing attention. However, the pattern of Aβ deposition and the effect of APOE (apolipoprotein E, APOE) ε4 on cognitive impairment in amnestic MCI (aMCI) and nonamnestic MCI (naMCI) patients has not been demonstrated. In this work, the amyloid-β (Aβ) load by [$^{18}$F]florbetapir PET imaging and cognitive performance is compared by comprehensive neuropsychological scales in participants with different MCI types or different APOE ε4 carriage status. According to the Aβ positivity and results of voxel-wise analysis, higher Aβ loads are observed in aMCI patients than naMCI patients, especially aMCI patients with APOE ε4. Additionally, it is observed that memory domain Z scores show a strong negative correlation with global florbetapir SUVR in the aMCI group (r = – 0.352, p < 0.001) but not in the naMCI group (r = –0.016, p = 0.924). Moreover, this correlation is independent of APOE e4 carriage status. This study aims to identify high-risk groups at an early stage of AD(Alzheimer's Disease, AD) through cognitive performance and APOE ε4 carrier status, which can be important for guiding clinical intervention trials

MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation

Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships. However, due to the annotation challenges brought by task complexity, a large-scale dataset covering the full process of event understanding has long been absent. In this paper, we introduce MAVEN-Arg, which augments MAVEN datasets with event argument annotations, making the first all-in-one dataset supporting event detection, event argument extraction (EAE), and event relation extraction. As an EAE benchmark, MAVEN-Arg offers three main advantages: (1) a comprehensive schema covering 162 event types and 612 argument roles, all with expert-written definitions and examples; (2) a large data scale, containing 98,591 events and 290,613 arguments obtained with laborious human annotation; (3) the exhaustive annotation supporting all task variants of EAE, which annotates both entity and non-entity event arguments in document level. Experiments indicate that MAVEN-Arg is quite challenging for both fine-tuned EAE models and proprietary large language models (LLMs). Furthermore, to demonstrate the benefits of an all-in-one dataset, we preliminarily explore a potential application, future event prediction, with LLMs. MAVEN-Arg and our code can be obtained from https://github.com/THU-KEG/MAVEN-Argument.Comment: Working in progres