49 research outputs found
An Empirical Study on the Influencing Factors of Customers\u27 Acceptance Intention towards Online Behavioral Advertising
Big data mining and analysis technology greatly influence the development of the advertising industry. In order to capture large information on consumers\u27 online behaviour, cookie files and Hadoop are widely adopted by advertisers to reach targeted consumers, which leads to online behavioural advertising. Based on an empirical study, this research mainly analyzes the factors influencing customers\u27 acceptance intention towards OBA from developing a conceptual framework. By collecting data through questionnaires and using SPSS and AMOS for data analysis, the result indicates that the factors of performance expectancy, effort expectancy, social influence and facilitating conditions have a positive relationship with customer acceptance intention. Moreover, performance expectancy, effort expectancy, and facilitating conditions have a positive relationship with attitudes towards OBA. However, attitudes do not positively impact customer acceptance intention and social influence has no significant relationship with attitudes, which could attribute to privacy concern and the rising of personality consciousness respectively. The result of this study is of great significance to the way of improving advertising effectiveness
Cross-Task Representation Learning for Anatomical Landmark Detection
Recently, there is an increasing demand for automatically detecting
anatomical landmarks which provide rich structural information to facilitate
subsequent medical image analysis. Current methods related to this task often
leverage the power of deep neural networks, while a major challenge in fine
tuning such models in medical applications arises from insufficient number of
labeled samples. To address this, we propose to regularize the knowledge
transfer across source and target tasks through cross-task representation
learning. The proposed method is demonstrated for extracting facial anatomical
landmarks which facilitate the diagnosis of fetal alcohol syndrome. The source
and target tasks in this work are face recognition and landmark detection,
respectively. The main idea of the proposed method is to retain the feature
representations of the source model on the target task data, and to leverage
them as an additional source of supervisory signals for regularizing the target
model learning, thereby improving its performance under limited training
samples. Concretely, we present two approaches for the proposed representation
learning by constraining either final or intermediate model features on the
target model. Experimental results on a clinical face image dataset demonstrate
that the proposed approach works well with few labeled data, and outperforms
other compared approaches.Comment: MICCAI-MLMI 202
Research on the Vibration Damping Performance of a Novel Single-Side Coupling Hydro-Pneumatic Suspension
A mine dump truck is exposed to heavy load and harsh working environment. When the truck passes over the road bumps, it will cause the body to tilt and the tires to "jump off the ground" (JOTG), which will affect the stability and safety of the truck, and will cause impact damage to the body and suspension system. To avoid this situation, a kind of Novel Single-side Coupling Hydro-pneumatic Suspension (NSCHs) is presented. NSCHs consists of two cylinders in parallel, which are connected to the accumulator by rubber pipes and mounted on the same side of the dump truck. Theoretical analysis and experimental research were respectively carried out under the road and loading experimental condition. The experimental results show that compared to the conventional single cylinder hydro-pneumatic suspension, under the loading experiment condition, the maximum overshoot pressure of the NSCHs was reduced by 0.4 MPa and the impact oscillation time was shortened by 4.13 s, which plays the effective role in reducing vibration and absorbing energy. Further, it is found that the two cylinders are coupled during the working process, and the NSCHs system can achieve uniform loading and displacement compensation, thus the novel dump truck can avoid the occurrence of the JOTG phenomenon
Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation
Monocular 3D object detection task aims to predict the 3D bounding boxes of
objects based on monocular RGB images. Since the location recovery in 3D space
is quite difficult on account of absence of depth information, this paper
proposes a novel unified framework which decomposes the detection problem into
a structured polygon prediction task and a depth recovery task. Different from
the widely studied 2D bounding boxes, the proposed novel structured polygon in
the 2D image consists of several projected surfaces of the target object.
Compared to the widely-used 3D bounding box proposals, it is shown to be a
better representation for 3D detection. In order to inversely project the
predicted 2D structured polygon to a cuboid in the 3D physical world, the
following depth recovery task uses the object height prior to complete the
inverse projection transformation with the given camera projection matrix.
Moreover, a fine-grained 3D box refinement scheme is proposed to further
rectify the 3D detection results. Experiments are conducted on the challenging
KITTI benchmark, in which our method achieves state-of-the-art detection
accuracy.Comment: 11 pages, 8 figures, AAAI202
Sequential Manipulation Planning on Scene Graph
We devise a 3D scene graph representation, contact graph+ (cg+), for
efficient sequential task planning. Augmented with predicate-like attributes,
this contact graph-based representation abstracts scene layouts with succinct
geometric information and valid robot-scene interactions. Goal configurations,
naturally specified on contact graphs, can be produced by a genetic algorithm
with a stochastic optimization method. A task plan is then initialized by
computing the Graph Editing Distance (GED) between the initial contact graphs
and the goal configurations, which generates graph edit operations
corresponding to possible robot actions. We finalize the task plan by imposing
constraints to regulate the temporal feasibility of graph edit operations,
ensuring valid task and motion correspondences. In a series of simulations and
experiments, robots successfully complete complex sequential object
rearrangement tasks that are difficult to specify using conventional planning
language like Planning Domain Definition Language (PDDL), demonstrating the
high feasibility and potential of robot sequential task planning on contact
graph.Comment: 8 pages, 6 figures. Accepted by IROS 202
On the Emergence of Symmetrical Reality
Artificial intelligence (AI) has revolutionized human cognitive abilities and
facilitated the development of new AI entities capable of interacting with
humans in both physical and virtual environments. Despite the existence of
virtual reality, mixed reality, and augmented reality for several years,
integrating these technical fields remains a formidable challenge due to their
disparate application directions. The advent of AI agents, capable of
autonomous perception and action, further compounds this issue by exposing the
limitations of traditional human-centered research approaches. It is imperative
to establish a comprehensive framework that accommodates the dual perceptual
centers of humans and AI agents in both physical and virtual worlds. In this
paper, we introduce the symmetrical reality framework, which offers a unified
representation encompassing various forms of physical-virtual amalgamations.
This framework enables researchers to better comprehend how AI agents can
collaborate with humans and how distinct technical pathways of physical-virtual
integration can be consolidated from a broader perspective. We then delve into
the coexistence of humans and AI, demonstrating a prototype system that
exemplifies the operation of symmetrical reality systems for specific tasks,
such as pouring water. Subsequently, we propose an instance of an AI-driven
active assistance service that illustrates the potential applications of
symmetrical reality. This paper aims to offer beneficial perspectives and
guidance for researchers and practitioners in different fields, thus
contributing to the ongoing research about human-AI coexistence in both
physical and virtual environments.Comment: IEEE VR 202
Multi-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative clues for subtyping schizophrenia patients
Analysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene-expression, grey matter volume (GMV) and the Positive and Negative Syndrome Scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions. The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia. Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes. The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia
Increased brain volume from higher cereal and lower coffee intake : shared genetic determinants and impacts on cognition and metabolism
It is unclear how different diets may affect human brain development and if genetic and environmental factors play a part. We investigated diet effects in the UK Biobank data from 18,879 healthy adults and discovered anticorrelated brain-wide gray matter volume (GMV)-association patterns between coffee and cereal intake, coincidence with their anticorrelated genetic constructs. The Mendelian randomization approach further indicated a causal effect of higher coffee intake on reduced total GMV, which is likely through regulating the expression of genes responsible for synaptic development in the brain. The identified genetic factors may further affect people’s lifestyle habits and body/blood fat levels through the mediation of cereal/coffee intake, and the brain-wide expression pattern of gene CPLX3, a dedicated marker of subplate neurons that regulate cortical development and plasticity, may underlie the shared GMV-association patterns among the coffee/cereal intake and cognitive functions. All the main findings were successfully replicated. Our findings thus revealed that high-cereal and low-coffee diets shared similar brain and genetic constructs, leading to long-term beneficial associations regarding cognitive, body mass index (BMI), and other metabolic measures. This study has important implications for public health, especially during the pandemic, given the poorer outcomes of COVID-19 patients with greater BMIs
A model-based approach to assess reproducibility for large-scale high-throughput MRI-based studies
Magnetic Resonance Imaging (MRI) technology has been increasingly used in neuroscience studies. Reproducibility of statistically significant findings generated by MRI-based studies, especially association studies (phenotype vs. MRI metric) and task-induced brain activation, has been recently heavily debated. However, most currently available reproducibility measures depend on thresholds for the test statistics and cannot be use to evaluate overall study reproducibility. It is also crucial to elucidate the relationship between overall study reproducibility and sample size in an experimental design. In this study, we proposed a model-based reproducibility index to quantify reproducibility which could be used in large-scale high-throughput MRI-based studies including both association studies and task-induced brain activation. We performed the model-based reproducibility assessments for a few association studies and task-induced brain activation by using several recent large sMRI/fMRI databases. For large sample size association studies between brain structure/function features and some basic physiological phenotypes (i.e. Sex, BMI), we demonstrated that the model-based reproducibility of these studies is more than 0.99. For MID task activation, similar results could be observed. Furthermore, we proposed a model-based analytical tool to evaluate minimal sample size for the purpose of achieving a desirable model-based reproducibility. Additionally, we evaluated the model-based reproducibility of gray matter volume (GMV) changes for UK Biobank (UKB) vs. Parkinson Progression Marker Initiative (PPMI) and UK Biobank (UKB) vs. Human Connectome Project (HCP). We demonstrated that both sample size and study-specific experimental factors play important roles in the model-based reproducibility assessments for different experiments. In summary, a systematic assessment of reproducibility is fundamental and important in the current large-scale high-throughput MRI-based studies