377 research outputs found
Determination of Boiling Range of Xylene Mixed in PX Device Using Artificial Neural Networks
Determination of boiling range of xylene mixed in PX device is currently a
crucial topic in the practical applications because of the recent disputes of
PX project in China. In our study, instead of determining the boiling range of
xylene mixed by traditional approach in laboratory or industry, we successfully
established two Artificial Neural Networks (ANNs) models to determine the
initial boiling point and final boiling point respectively. Results show that
the Multilayer Feedforward Neural Networks (MLFN) model with 7 nodes (MLFN-7)
is the best model to determine the initial boiling point of xylene mixed, with
the RMS error 0.18; while the MLFN model with 4 nodes (MLFN-4) is the best
model to determine the final boiling point of xylene mixed, with the RMS error
0.75. The training and testing processes both indicate that the models we
developed are robust and precise. Our research can effectively avoid the damage
of the PX device to human body and environment
On Generalized Sub-Gaussian Canonical Processes and Their Applications
We obtain the tail probability of generalized sub-Gaussian canonical
processes. It can be viewed as a variant of the Bernstein-type inequality in
the i.i.d case, and we further get a tighter bound of concentration inequality
through uniformly randomized techniques. A concentration inequality for general
functions involving independent random variables is also derived as an
extension. As for applications, we derive convergence results for principal
component analysis and the Rademacher complexities method.Comment: 25page
The optimal connection model for blood vessels segmentation and the MEA-Net
Vascular diseases have long been regarded as a significant health concern.
Accurately detecting the location, shape, and afflicted regions of blood
vessels from a diverse range of medical images has proven to be a major
challenge. Obtaining blood vessels that retain their correct topological
structures is currently a crucial research issue. Numerous efforts have sought
to reinforce neural networks' learning of vascular geometric features,
including measures to ensure the correct topological structure of the
segmentation result's vessel centerline. Typically, these methods extract
topological features from the network's segmentation result and then apply
regular constraints to reinforce the accuracy of critical components and the
overall topological structure. However, as blood vessels are three-dimensional
structures, it is essential to achieve complete local vessel segmentation,
which necessitates enhancing the segmentation of vessel boundaries.
Furthermore, current methods are limited to handling 2D blood vessel
fragmentation cases. Our proposed boundary attention module directly extracts
boundary voxels from the network's segmentation result. Additionally, we have
established an optimal connection model based on minimal surfaces to determine
the connection order between blood vessels. Our method achieves
state-of-the-art performance in 3D multi-class vascular segmentation tasks, as
evidenced by the high values of Dice Similarity Coefficient (DSC) and
Normalized Surface Dice (NSD) metrics. Furthermore, our approach improves the
Betti error, LR error, and BR error indicators of vessel richness and
structural integrity by more than 10% compared to other methods, and
effectively addresses vessel fragmentation and yields blood vessels with a more
precise topological structure.Comment: 19 page
A Survey on Explainable Anomaly Detection
In the past two decades, most research on anomaly detection has focused on
improving the accuracy of the detection, while largely ignoring the
explainability of the corresponding methods and thus leaving the explanation of
outcomes to practitioners. As anomaly detection algorithms are increasingly
used in safety-critical domains, providing explanations for the high-stakes
decisions made in those domains has become an ethical and regulatory
requirement. Therefore, this work provides a comprehensive and structured
survey on state-of-the-art explainable anomaly detection techniques. We propose
a taxonomy based on the main aspects that characterize each explainable anomaly
detection technique, aiming to help practitioners and researchers find the
explainable anomaly detection method that best suits their needs.Comment: Paper accepted by the ACM Transactions on Knowledge Discovery from
Data (TKDD) for publication (preprint version
Test-Time Training for Semantic Segmentation with Output Contrastive Loss
Although deep learning-based segmentation models have achieved impressive
performance on public benchmarks, generalizing well to unseen environments
remains a major challenge. To improve the model's generalization ability to the
new domain during evaluation, the test-time training (TTT) is a challenging
paradigm that adapts the source-pretrained model in an online fashion. Early
efforts on TTT mainly focus on the image classification task. Directly
extending these methods to semantic segmentation easily experiences unstable
adaption due to segmentation's inherent characteristics, such as extreme class
imbalance and complex decision spaces. To stabilize the adaptation process, we
introduce contrastive loss (CL), known for its capability to learn robust and
generalized representations. Nevertheless, the traditional CL operates in the
representation space and cannot directly enhance predictions. In this paper, we
resolve this limitation by adapting the CL to the output space, employing a
high temperature, and simplifying the formulation, resulting in a
straightforward yet effective loss function called Output Contrastive Loss
(OCL). Our comprehensive experiments validate the efficacy of our approach
across diverse evaluation scenarios. Notably, our method excels even when
applied to models initially pre-trained using domain adaptation methods on test
domain data, showcasing its resilience and adaptability.\footnote{Code and more
information could be found at~ \url{https://github.com/dazhangyu123/OCL}
General synthesis of porous mixed metal oxide hollow spheres with enhanced supercapacitive properties
Porous mixed metal oxide (MMO) hollow spheres present high specific surface areas, abundant electrochemically active sites, and outstanding electrochemical properties, showing potential applications in energy storage. A hydro/solvothermal process, followed by a calcination process, can be a viable method for producing uniform porous metal oxide hollow spheres. Unfortunately, this method usually involves harsh synthetic conditions such as high temperature and intricate processing. Herein, we report a general and facile ion adsorption-annealing approach for the fabrication of uniform porous MMO hollow spheres. The size and shell thickness of the as-obtained hollow spheres can be adjusted by the carbohydrate sphere templates and the solution concentration. Electrochemical measurements of the MMO hollow spheres demonstrate excellent supercapacitive properties, which may be due to the small size, ultrathin shells, and fine porous structure
Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples
Deep neural networks (DNNs) have been demonstrated to be vulnerable to
well-crafted \emph{adversarial examples}, which are generated through either
well-conceived -norm restricted or unrestricted attacks.
Nevertheless, the majority of those approaches assume that adversaries can
modify any features as they wish, and neglect the causal generating process of
the data, which is unreasonable and unpractical. For instance, a modification
in income would inevitably impact features like the debt-to-income ratio within
a banking system. By considering the underappreciated causal generating
process, first, we pinpoint the source of the vulnerability of DNNs via the
lens of causality, then give theoretical results to answer \emph{where to
attack}. Second, considering the consequences of the attack interventions on
the current state of the examples to generate more realistic adversarial
examples, we propose CADE, a framework that can generate
\textbf{C}ounterfactual \textbf{AD}versarial \textbf{E}xamples to answer
\emph{how to attack}. The empirical results demonstrate CADE's effectiveness,
as evidenced by its competitive performance across diverse attack scenarios,
including white-box, transfer-based, and random intervention attacks.Comment: Accepted by AAAI-202
LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion
As an emerging technology and a relatively affordable device, the 4D imaging
radar has already been confirmed effective in performing 3D object detection in
autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point
clouds hinder further performance improvement, and in-depth studies about its
fusion with other modalities are lacking. On the other hand, most of the
camera-based perception methods transform the extracted image perspective view
features into the bird's-eye view geometrically via "depth-based splatting"
proposed in Lift-Splat-Shoot (LSS), and some researchers exploit other modals
such as LiDARs or ordinary automotive radars for enhancement. Recently, a few
works have applied the "sampling" strategy for image view transformation,
showing that it outperforms "splatting" even without image depth prediction.
However, the potential of "sampling" is not fully unleashed. In this paper, we
investigate the "sampling" view transformation strategy on the camera and 4D
imaging radar fusion-based 3D object detection. In the proposed model, LXL,
predicted image depth distribution maps and radar 3D occupancy grids are
utilized to aid image view transformation, called "radar occupancy-assisted
depth-based sampling". Experiments on VoD and TJ4DRadSet datasets show that the
proposed method outperforms existing 3D object detection methods by a
significant margin without bells and whistles. Ablation studies demonstrate
that our method performs the best among different enhancement settings
Plasma-catalytic removal of formaldehyde over Cu-Ce catalysts in a dielectric barrier discharge reactor
- …