Trip destination forecasting has received a great attention recently when the idea of intelligent transportation is discussed ubiquitously from related business to government. For instance, we gain ...convenience such as easily finding a ride at the right location for the right time in a car dispatch system given the sharing economy setting. In this work, we aim to propose a method for trip destination forecasting given its first partial trip trajectory as the input. In a nutshell, we formulate the problem as a multi-class prediction problem and mine the distinguishing pattern that we can see on one class but not on other classes. Moreover, we attempt to find non-redundant rules to separate the interested class from other classes by an efficient algorithm called Non-redundant Contrast Sequence Miner given multiple answers (destination) to choose from. This study tested the proposed method on public trip destination prediction dataset. The results show that the proposed method outperforms other mining techniques on the task of trip destination forecasting in terms of accuracy and resource allocation both time and memory usage efficiency and accuracy.
•Presenting mining contrast sequential rules.•Defining similar rules as any two rules that have the same consecutive distinct items.•Proposing an efficient algorithm to mine non-redundant distinguishing subsequence rules.•Proposing a framework to forecast the destinations from its partial trajectories.
Efficient detection of stealthy malware attacks in suspicious files is very challenging as dynamic malware analysis is time consuming. This paper proposes a virtual time control mechanics-based ...method to overcome the challenge. The proposed method utilizes a modified Xen hypervisor, in which a virtual clock source is generated according to a predefined speed ratio, such that sandbox systems running on the modified hypervisor can be accelerated. Thus, it does not modify operating system kernels nor intercept system function calls, and is therefore compatible with various operating systems. Further, it utilizes an entropy-based measure that adjusts its execution time according to various malware inputs as an early termination criterion. The results of experiments conducted to verify the efficacy of the proposed method indicate that it speeds up the system timer and significantly increases the logged record size by up to 42% or obtains the same log size within a shorter period compared with conventional sandboxes. Thus, the proposed virtual time control mechanics-based method efficiently detects nontrivial anomalous codes that may be neglected by conventional sandboxing techniques.
Artificial intelligence (AI) in healthcare, especially in medical imaging, faces challenges due to data scarcity and privacy concerns. Addressing these, we introduce Med-DDPM, a diffusion model ...designed for 3D semantic brain MRI synthesis. This model effectively tackles data scarcity and privacy issues by integrating semantic conditioning. This involves the channel-wise concatenation of a conditioning image to the model input, enabling control in image generation. Med-DDPM demonstrates superior stability and performance compared to existing 3D brain imaging synthesis methods. It generates diverse, anatomically coherent images with high visual fidelity. In terms of dice score in the tumor segmentation task, Med-DDPM achieves 0.6207, close to the 0.6531 dice score of real images, and outperforms baseline models. Combined with real images, it further increases segmentation accuracy to 0.6675, showing the potential of the proposed method for data augmentation. This model represents the first use of a diffusion model in 3D semantic brain MRI synthesis, producing high-quality images. Its semantic conditioning feature also shows potential for image anonymization in biomedical imaging, addressing data and privacy issues.
•Defining fuzzy local activity pattern based on proportional duration to deal with continuous time intervals.•Proposing an algorithm for mining fuzzy local periodic activity patterns given a smart ...home dataset.•Presenting a novel k-scale period parameter for reconstructing activity sequence set in representative form.
Perceiving resident movements from ubiquitous sensor devices can aid health and safety management in smart home environment. One can find resident movement patterns to get notification about what should be aware of to prepare for possible accidents. The goal of this work is to propose a method to search for periodic residents’ activity patterns given smart home applications. Specifically, the proposed method is aimed to capture fuzzy local periodic activity patterns (FLPAP) from the smart home data. A FLPAP is an activity pattern that periodically occurs for a particular duration in fuzzy time intervals. We construct an algorithm to help to find the FLPAP called Fuzzy Local Periodic Activity Pattern Miner (FLoPMiner). On the side, a parameter called k-scale period is well-chosen to reorganize the data to make it suitable for the need of periodic activity pattern mining. The proposed method was demonstrated for its effectiveness on a public smart home dataset. After all, we successfully retrieved periodic resident activity patterns, which can be applied to self-health management systems, especially when focusing on a precise scale period. Overall, the proposed method shows descent pattern representation against the state-of-the-art methods of the same category.
Finding substantial features for image representation is one of the keys to cope with the challenges of person re-identification given video streams. The important features for re-identification can ...be found by image saliency computation and subject appearance modeling. State-of-the-art models explore this direction by balancing between the global low-level features and the features from local patches. We proposed a novel nested patch tree for a tree structured feature representation, and the feature representation is used to match between a probe image and a gallery image to solve the re-identification problem. The feature representation is learned based on an unsupervised approach, which is different from the majority of the community when they work on finding similar subjects. Usually, the video streams for the same figure may have highly repetitive information, and the pseudo repetitiveness should be useful for a center-learning based method. We further improve the prediction accuracy by learning components by components for the same subject and working in the multi-color space. We evaluate the proposed method for person re-identification on the VIPeR and GRID datasets. The result shows that the proposed method is indeed superior to other state-of-the-art methods.
•We model pairwise image similarity for person re-identification.•We learn the sparse tree structured representation in an unsupervised fashion from various color spaces.•The feature vectors are extracted from nested patch trees.•Deeper the tree and more color types will increase the performance.
The neurotoxicity of 3,4-methylenedioxy-methamphetamine (MDMA) to the serotonergic system is well-documented. Dextromethorphan (DM), an antitussive drug, decreased morphine- or methamphetamine ...(MA)-induced reward in rats and may prevent MDMA-induced serotonergic deficiency in primates, as indicated by increased serotonin transporter (SERT) availability. We aimed to investigate the effects of DM on reward, behavioral sensitization, and neurotoxicity associated with loss of SERT induced by chronic MDMA administration in rats.
Conditioned place preference (CPP) and locomotor activity tests were used to evaluate drug-induced reward and behavioral sensitization; 4- 18 F-ADAM/animal-PET and immunohistochemistry were used to explore the effects of DM on MDMA-induced loss of SERT.
MDMA significantly reduced SERT binding in the rat brain; however, co-administration of DM significantly restored SERT, enhancing the recovery rate at day 14 by an average of ~23% compared to the MDMA group. In confirmation of the PET findings, immunochemistry revealed MDMA reduced SERT immunoactivity in all brain regions, whereas DM markedly increased the serotonergic fiber density after MDMA induction.
Behavioral tests and in vivo longitudinal PET imaging demonstrated the CPP indexes and locomotor activities of the reward system correlate negatively with PET 4- 18 FADAM SERT activity in the reward system. Our findings suggest MDMA induces functional abnormalities in a network of brain regions important to decision-making processes and the motivation circuit. DM may exert neuroprotective effects to reverse MDMA-induced neurotoxicity.
Background and Objectives: The relationships of dietary choline and folate intake with hepatic function have yet to be established in the Taiwanese population. We investigated the associations of ...choline and folate intake with hepatic inflammatory injury in Taiwanese adults.
Methods and Study Design: Blood samples and data on dietary choline components and folate intake from 548 Taiwanese adults without pathological liver disease were collected. Dietary intake was derived using a semiquantitative food-frequency questionnaire. Serum liver injury markers of alanine transaminase, aspartate transaminase, and hepatitis viral infection were measured.
Results: Elevated serum hepatic injury markers (>40 U/L) were associated with low folate and free choline intake (p<0.05). Folate intake was the most significant dietary determinant of serum aspartate transaminase concentration (beta=-0.05, p=0.04), followed by free choline intake (beta=-0.249, p=0.055). Folate intake exceeding the median level (268 g/d) was correlated with a reduced rate of hepatitis viral infection (p=0.032) and with normalized serum aspartate transaminase (odds ratio OR=0.998, 95% confidence interval CI=0.996-1, p=0.042) and alanine transaminase (OR=0.998, 95% CI=0.007-1, p=0.019). Total choline intake exceeding the median level (233 mg/d) was associated with normalized serum aspartate transaminase (OR=0.518, 95% CI=0.360-0.745, p=0.018).
Conclusions: The newly established relationships of dietary intake of total choline and folate with normalized hepatic inflammatory markers can guide the development of dietary choline and folate intake recommendations for Taiwanese adults.
Group behavior recognition is the task of inferring the collective action of people that have interaction among them in the contextual scenes. The challenge is harder when face with different ...individual actions. Hierarchical structure model based on deep learning tackles this problem with multi-stages spatio-temporal information modeling. Convolutional neural network (CNN) is designed for extracting the spatial features of scene and person-level. The other component, recurrent neural network (RNN) is aimed to capture temporal feature of the person trajectories in contextual scenes. However, in some prior works, to get the trajectory information in this framework still rely on a third-party tracker that makes the solution not in an end-to-end framework. The exist end-to-end solution incorporates matching strategy in Euclidean space that implicitly tracks the corresponding states as input of RNN unit.
In this work, we propose an improved RNN matching strategy by explicitly transform the feature in Euclidean space by distance learning function. Our distance function is based on simple Siamese network with two sub network share the same weights. The network consists of the learned feature based on unsupervised dictionary learning as an intermediate layer between raw input and fully connected layers with non-linear activation and regularization. Our proposed method can yield a little improvement in the applied group behavior recognition framework and yet empirically prove that it can be brought into another task without change the hyper-parameter.
SCOPE: Metabolic genotypes of 5,10‐methylenetetrahydrofolate reductase (MTHFR) and folate status on oxidative DNA lesions in hepatocellular carcinoma (HCC) has not been elucidated. The aims of the ...study were to investigate the folate‐polymorphic interactions on genetic oxidative damage in association with advanced HCC malignancy and prognosis. METHODS AND RESULTS: The study included 232 HCC patients with folate nutrition, MTHFR C677T polymorphic, p53 genetic and tumour pathological data collected and analyzed for their survivals after a 7.8‐years following up. By adjustment for oxidative risk factors of HCC, the compound CT and TT genotypes in relative to the CC wild‐type were associated with 83% reduced lymphocytic p53 oxidative lesions of HCC patients with RBC folate lower than 688 ng/mL (OR: 0.17, 95%CI: 0.07–0.43). Such genetic protective effects by the CT/TT genotypes were 2‐fold enhanced among those with high RBC folate (OR: 0.08, 95% CI: 0.03–0.21, P for interaction < 0.001). For those with non‐folate‐deficient status, the compound CT and TT vs. CC genotypes were associated with 80% reduced risks of advanced HCC stages (III&IV) (OR: 0.2, 95%CI: 0.08–0.56). Such protection was negated either by adjustment of lymphocytic p53 oxidative lesions or by 3‐fold increased risks among those with high RBC status (OR: 0.6, 95%CI; 0.31–1.41, P for interaction = 0.009). Multivariate Cox proportional hazards analysis showed that the CT/TT genotypes vs. CC wild‐type were the independent predictable factor for better survival outcome of HCC patients (HR: 0.48, CI = 0.30–0.79). For CC homozygote, the second vs. the bottom tertile levels of RBC status were associated with 2‐fold increased mortality rate of HCC patients (HR: 2.05, CI = 1.0–4.1). CONCLUSION: Our data demonstrated that reduced MTHFR activities associated with the MTHFR T allele may interact with RBC folate as the risk modifiers of lymphocytic p53 oxidative lesions of HCC patients. The CT/TT genotypes correlated with lower risks of late‐stage HCC and a favorable survival of HCC patients, depending on p53 oxidative lesions or RBC folate status.
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