324 research outputs found
Influence of different angle of spoiler fin at specific position on vehicle aerodynamics
The significance of Aerodynamic Research on automobile is not only to improve the high-speed driving stability and crosswind stability, but also to reduce vehicle fuel consumption. As an additional device, the rear spoiler has shown a good effect in the external components of the automobile, so the reasonable design and assembly of the rear spoiler is particularly important. The rear spoiler with proper height can effectively reduce the air drag coefficient and lift coefficient. This paper mainly uses the software ICEM, Fluent and Post in ANSYS to analyze the influence of different angles of the specific spoiler on the aerodynamic performance of the vehicle. In the software experiment, I adjusted the spoiler angle and car speed and I found that in a certain range, the downforce generated by the spoiler is proportional to the angle between the spoiler and the ground
Neurobiological markers for remission and persistence of childhood attention-deficit/hyperactivity disorder
Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in children. Symptoms of childhood ADHD persist into adulthood in around 65% of patients, which elevates the risk for a number of adverse outcomes, resulting in substantial individual and societal burden. A neurodevelopmental double dissociation model is proposed based on existing studies in which the early onset of childhood ADHD is suggested to associate with dysfunctional subcortical structures that remain static throughout the lifetime; while diminution of symptoms over development could link to optimal development of prefrontal cortex. Current existing studies only assess basic measures including regional brain activation and connectivity, which have limited capacity to characterize the functional brain as a high performance parallel information processing system, the field lacks systems-level investigations of the structural and functional patterns that significantly contribute to the symptom remission and persistence in adults with childhood ADHD. Furthermore, traditional statistical methods estimate group differences only within a voxel or region of interest (ROI) at a time without having the capacity to explore how ROIs interact in linear and/or non-linear ways, as they quickly become overburdened when attempting to combine predictors and their interactions from high-dimensional imaging data set.
This dissertation is the first study to apply ensemble learning techniques (ELT) in multimodal neuroimaging features from a sample of adults with childhood ADHD and controls, who have been clinically followed up since childhood. A total of 36 adult probands who were diagnosed with ADHD combined-type during childhood and 36 matched normal controls (NCs) are involved in this dissertation research. Thirty-six adult probands are further split into 18 remitters (ADHD-R) and 18 persisters (ADHD-P) based on the symptoms in their adulthood from DSM-IV ADHD criteria. Cued attention task-based fMRI, structural MRI, and diffusion tensor imaging data from each individual are analyzed. The high-dimensional neuroimaging features, including pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process, regional cortical thickness and surface area, subcortical volume, volume and fractional anisotropy of major white matter fiber tract for each subject are calculated. In addition, all the currently available optimization strategies for ensemble learning techniques (i.e., voting, bagging, boosting and stacking techniques) are tested in a pool of semi-final classification results generated by seven basic classifiers, including K-Nearest Neighbors, support vector machine (SVM), logistic regression, Naïve Bayes, linear discriminant analysis, random forest, and multilayer perceptron.
As hypothesized, results indicate that the features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. The utilization of ELTs indicates that the bagging-based ELT with the base model of SVM achieves the best results, with the most significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD probands vs. NCs, and 0.9 for ADHD-P vs. ADHD-R). The outcomes of this dissertation research have considerable value for the development of novel interventions that target mechanisms associated with recovery
Identifying strawberry DOF family transcription factors and their expressions in response to crown rot
Crown rot is one of the most destructive diseases of cultivated strawberry. The DOF family transcription factors, which involved in biotic stress, has not been studied in responding to strawberry crown rot. In this study, the DOFs of Fragaria × ananassa, F. iinumae, F. nilgerrensis, F. viridis, and F. vesca were characterized. One hundred and eighteen FaDOFs, twenty-two FiDOFs, twenty-three FnDOFs, twenty-five FviDOFs and thirty-seven FvDOFs were identified. Gene cluster analysis showed nearly seventy segmental duplication and seventeen tandem duplications for DOF family expansion in octaploid strawberry. In addition, 59 FaDOFs showed syntenic relationships with 32 AtDOFs, which were located on all F.×ananassa chromosomes except Fvb4-1 and Fvb4-2. Except for five DOFs of diploid strawberries had syntenic relationships to one FaDOF, most of them corresponded to multiple FaDOFs. Gene expression analysis revealed that 107 FaDOFs were expressed in crown, and most of them were downregulated by crown rot, while some FaDOFs such as FaDOF107, 12, 82, 91, 90 and 101 were upregulated, whose regulation was not always consistent with the cis-elements in their promoters. Together, these results provided a basis for further functional studies of the FaDOFs
GADY: Unsupervised Anomaly Detection on Dynamic Graphs
Anomaly detection on dynamic graphs refers to detecting entities whose
behaviors obviously deviate from the norms observed within graphs and their
temporal information. This field has drawn increasing attention due to its
application in finance, network security, social networks, and more. However,
existing methods face two challenges: dynamic structure constructing challenge
- difficulties in capturing graph structure with complex time information and
negative sampling challenge - unable to construct excellent negative samples
for unsupervised learning. To address these challenges, we propose Unsupervised
Generative Anomaly Detection on Dynamic Graphs (GADY). To tackle the first
challenge, we propose a continuous dynamic graph model to capture the
fine-grained information, which breaks the limit of existing discrete methods.
Specifically, we employ a message-passing framework combined with positional
features to get edge embeddings, which are decoded to identify anomalies. For
the second challenge, we pioneer the use of Generative Adversarial Networks to
generate negative interactions. Moreover, we design a loss function to alter
the training goal of the generator while ensuring the diversity and quality of
generated samples. Extensive experiments demonstrate that our proposed GADY
significantly outperforms the previous state-of-the-art method on three
real-world datasets. Supplementary experiments further validate the
effectiveness of our model design and the necessity of each module
Revisit Parameter-Efficient Transfer Learning: A Two-Stage Paradigm
Parameter-Efficient Transfer Learning (PETL) aims at efficiently adapting
large models pre-trained on massive data to downstream tasks with limited
task-specific data. In view of the practicality of PETL, previous works focus
on tuning a small set of parameters for each downstream task in an end-to-end
manner while rarely considering the task distribution shift issue between the
pre-training task and the downstream task. This paper proposes a novel
two-stage paradigm, where the pre-trained model is first aligned to the target
distribution. Then the task-relevant information is leveraged for effective
adaptation. Specifically, the first stage narrows the task distribution shift
by tuning the scale and shift in the LayerNorm layers. In the second stage, to
efficiently learn the task-relevant information, we propose a Taylor
expansion-based importance score to identify task-relevant channels for the
downstream task and then only tune such a small portion of channels, making the
adaptation to be parameter-efficient. Overall, we present a promising new
direction for PETL, and the proposed paradigm achieves state-of-the-art
performance on the average accuracy of 19 downstream tasks.Comment: 11 page
SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels
Pre-trained vision transformers have strong representation benefits to
various downstream tasks. Recently, many parameter-efficient fine-tuning (PEFT)
methods have been proposed, and their experiments demonstrate that tuning only
1% of extra parameters could surpass full fine-tuning in low-data resource
scenarios. However, these methods overlook the task-specific information when
fine-tuning diverse downstream tasks. In this paper, we propose a simple yet
effective method called "Salient Channel Tuning" (SCT) to leverage the
task-specific information by forwarding the model with the task images to
select partial channels in a feature map that enables us to tune only 1/8
channels leading to significantly lower parameter costs. Experiments outperform
full fine-tuning on 18 out of 19 tasks in the VTAB-1K benchmark by adding only
0.11M parameters of the ViT-B, which is 780 fewer than its full
fine-tuning counterpart. Furthermore, experiments on domain generalization and
few-shot learning surpass other PEFT methods with lower parameter costs,
demonstrating our proposed tuning technique's strong capability and
effectiveness in the low-data regime.Comment: This work has been accepted by IJCV202
Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks
Online movie review platforms are providing crowdsourced feedback for the
film industry and the general public, while spoiler reviews greatly compromise
user experience. Although preliminary research efforts were made to
automatically identify spoilers, they merely focus on the review content
itself, while robust spoiler detection requires putting the review into the
context of facts and knowledge regarding movies, user behavior on film review
platforms, and more. In light of these challenges, we first curate a
large-scale network-based spoiler detection dataset LCS and a comprehensive and
up-to-date movie knowledge base UKM. We then propose MVSD, a novel Multi-View
Spoiler Detection framework that takes into account the external knowledge
about movies and user activities on movie review platforms. Specifically, MVSD
constructs three interconnecting heterogeneous information networks to model
diverse data sources and their multi-view attributes, while we design and
employ a novel heterogeneous graph neural network architecture for spoiler
detection as node-level classification. Extensive experiments demonstrate that
MVSD advances the state-of-the-art on two spoiler detection datasets, while the
introduction of external knowledge and user interactions help ground robust
spoiler detection. Our data and code are available at
https://github.com/Arthur-Heng/Spoiler-DetectionComment: EMNLP 202
A Review of Heterogeneity in Attention Deficit/Hyperactivity Disorder (ADHD)
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects approximately 8%–12% of children worldwide. Throughout an individual’s lifetime, ADHD can significantly increase risk for other psychiatric disorders, educational and occupational failure, accidents, criminality, social disability and addictions. No single risk factor is necessary or sufficient to cause ADHD. The multifactorial causation of ADHD is reflected in the heterogeneity of this disorder, as indicated by its diversity of psychiatric comorbidities, varied clinical profiles, patterns of neurocognitive impairment and developmental trajectories, and the wide range of structural and functional brain anomalies. Although evidence-based treatments can reduce ADHD symptoms in a substantial portion of affected individuals, there is yet no curative treatment for ADHD. A number of theoretical models of the emergence and developmental trajectories of ADHD have been proposed, aimed at providing systematic guides for clinical research and practice. We conducted a comprehensive review of the current status of research in understanding the heterogeneity of ADHD in terms of etiology, clinical profiles and trajectories, and neurobiological mechanisms. We suggest that further research focus on investigating the impact of the etiological risk factors and their interactions with developmental neural mechanisms and clinical profiles in ADHD. Such research would have heuristic value for identifying biologically homogeneous subgroups and could facilitate the development of novel and more tailored interventions that target underlying neural anomalies characteristic of more homogeneous subgroups
The Impact of Callous-Unemotional Traits and Externalizing Tendencies on Neural Responsivity to Reward and Punishment in Healthy Adolescents
Both externalizing behavior and callous-unemotional (CU) traits in youth are precursors to later criminal offending in adulthood. It is posited that disruptions in reward and punishment processes may engender problematic behavior, such that CU traits and externalizing behavior may be linked to a dominant reward response style (e.g., heightened responsivity to rewards) and deficient punishment-processing. However, prior research has generated mixed findings and work examining both the sole and joint contribution of CU traits and externalizing problems related to functional brain alterations is lacking. In this pilot functional magnetic resonance imaging study, we measured externalizing behavior and CU traits in a community sample of adolescents (n = 29) and examined their impacts on brain activity associated with anticipation and receipt of reward and punishment using the Modified Monetary Incentive Delay task. We found that CU traits were associated with greater activation of the ventral striatum (VST) during reward anticipation. However, this effect became non-significant after controlling for externalizing behavior, indicating substantial overlap between the CU and externalizing measures in explaining VST activation when anticipating reward. In addition, externalizing behavior (but not CU) was significantly negatively associated with amygdala activation during punishment receipt, even after controlling for CU traits. The present findings extend previous evidence of hyper-responsivity to reward and hyporesponsivity to punishment in relation to psychopathic traits and antisocial behavior to non-clinical, non-incarcerated youths
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