505 research outputs found
The ternary Goldbach problem with the Piatetski-Shapiro primes
With the help of the transference principle, we prove that for any
, every sufficiently large odd can be represented
as the sum of three primes , and , where for each , is of the form .Comment: This is a very preliminary manuscript, which maybe contains some
mistake
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning
Recently, multi-modal vision-language foundation models have gained
significant attention in the medical field. While these models offer great
opportunities, they still face a number of challenges, such as the requirement
for fine-grained knowledge understanding in computer-aided diagnosis and
capability of utilizing very limited or no task-specific labeled data in
real-world clinical applications. In this study, we present MaCo, a novel
multi-modal medical foundation model that explores masked contrastive learning
to achieve granular alignment and zero-shot learning for a variety of medical
imaging tasks. MaCo incorporates a correlation weighting mechanism to adjust
the correlation between masked image patches and their corresponding reports,
thereby enhancing the representation learning capabilities. We evaluate MaCo on
six well-known open-source X-ray datasets, and the experimental results show it
outperforms seven state-of-the-art approaches for classification, segmentation,
and zero-shot phase grounding, demonstrating its great potential to promote a
wide range of medical image analysis tasks
Addressless: A New Internet Server Model to Prevent Network Scanning
Eliminating unnecessary exposure is a principle of server security. The huge
IPv6 address space enhances security by making scanning infeasible, however,
with recent advances of IPv6 scanning technologies, network scanning is again
threatening server security. In this paper, we propose a new model named
addressless server, which separates the server into an entrance module and a
main service module, and assigns an IPv6 prefix instead of an IPv6 address to
the main service module. The entrance module generates a legitimate IPv6
address under this prefix by encrypting the client address, so that the client
can access the main server on a destination address that is different in each
connection. In this way, the model provides isolation to the main server,
prevents network scanning, and minimizes exposure. Moreover it provides a novel
framework that supports flexible load balancing, high-availability, and other
desirable features. The model is simple and does not require any modification
to the client or the network. We implement a prototype and experiments show
that our model can prevent the main server from being scanned at a slight
performance cost
CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke
Segmenting stroke lesions from T1-weighted MR images is of great value for
large-scale stroke rehabilitation neuroimaging analyses. Nevertheless, there
are great challenges with this task, such as large range of stroke lesion
scales and the tissue intensity similarity. The famous encoder-decoder
convolutional neural network, which although has made great achievements in
medical image segmentation areas, may fail to address these challenges due to
the insufficient uses of multi-scale features and context information. To
address these challenges, this paper proposes a Cross-Level fusion and Context
Inference Network (CLCI-Net) for the chronic stroke lesion segmentation from
T1-weighted MR images. Specifically, a Cross-Level feature Fusion (CLF)
strategy was developed to make full use of different scale features across
different levels; Extending Atrous Spatial Pyramid Pooling (ASPP) with CLF, we
have enriched multi-scale features to handle the different lesion sizes; In
addition, convolutional long short-term memory (ConvLSTM) is employed to infer
context information and thus capture fine structures to address the intensity
similarity issue. The proposed approach was evaluated on an open-source
dataset, the Anatomical Tracings of Lesions After Stroke (ATLAS) with the
results showing that our network outperforms five state-of-the-art methods. We
make our code and models available at https://github.com/YH0517/CLCI_Net
Highly enhanced catalytic stability of copper by the synergistic effect of porous hierarchy and alloying for selective hydrogenation reaction
Supported copper has a great potential for replacing the commercial palladium-based catalysts in the field of selective alkynes/alkadienes hydrogenation due to its excellent alkene selectivity and relatively high activity. However, fatally, it has a low catalytic stability owing to the rapid oligomerization of alkenes on the copper surface. In this study, 2.5 wt% Cu catalysts with various Cu:Zn ratios and supported on hierarchically porous alumina (HA) were designed and synthesized by deposition–precipitation with urea. Macropores (with diameters of 1 μm) and mesopores (with diameters of 3.5 nm) were introduced by the hydrolysis of metal alkoxides. After in situ activation at 350 °C, the catalytic stability of Cu was highly enhanced, with a limited effect on the catalytic activity and alkene selectivity. The time needed for losing 10% butadiene conversion for Cu1Zn3/HA was ~40 h, which is 20 times higher than that found for Cu/HA (~2 h), and 160 times higher than that found for Cu/bulky alumina (0.25 h). It was found that this type of enhancement in catalytic stability was mainly due to the rapid mass transportation in hierarchically porous structure (i.e., four times higher than that in bulky commercial alumina) and the well-dispersed copper active site modified by Zn, with identification by STEM–HAADF coupled with EDX. This study offers a universal way to optimize the catalytic stability of selective hydrogenation reactions
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