391 research outputs found
DNA Methylation and Non-small Cell Lung Cancer
Genomic DNA methylation is a major form of epigenetic modification. Hypermethylation could affect the binding of transcription factors to DNA and change the structure of chromatin resulting in silence of tumor suppressor genes, which plays an important role in cancer initiation and progression. In recent years, the study of DNA methylation in lung cancer, mostly in non-small cell lung cancer, has made great progress and become a new target for early detection, risk assessment, prognosis and cancer therapy
DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs
A ChatGPT-like system for drug compounds could be a game-changer in
pharmaceutical research, accelerating drug discovery, enhancing our
understanding of structure-activity relationships, guiding lead optimization,
aiding drug repurposing, reducing the failure rate, and streamlining clinical
trials. In this work, we make an initial attempt towards enabling ChatGPT-like
capabilities on drug molecule graphs, by developing a prototype system
DrugChat. DrugChat works in a similar way as ChatGPT. Users upload a compound
molecule graph and ask various questions about this compound. DrugChat will
answer these questions in a multi-turn, interactive manner. The DrugChat system
consists of a graph neural network (GNN), a large language model (LLM), and an
adaptor. The GNN takes a compound molecule graph as input and learns a
representation for this graph. The adaptor transforms the graph representation
produced by the GNN into another representation that is acceptable to the LLM.
The LLM takes the compound representation transformed by the adaptor and users'
questions about this compound as inputs and generates answers. All these
components are trained end-to-end. To train DrugChat, we collected instruction
tuning datasets which contain 10,834 drug compounds and 143,517 question-answer
pairs. The code and data is available at
\url{https://github.com/UCSD-AI4H/drugchat
Sustainable decisions on product upgrade confrontations with remanufacturing operations
In recent decades, remanufacturing is perceived to be an environmentally friendly option due to the reduced consumption of materials, energy etc. It should be noted that whether the remanufacturing operations are undertaken by the original equipment manufacturers (OEMs) or outsourced to the remanufacturers, given the size and the growth of remanufactured products, many OEMs intend to fend off the potential cannibalization of new products sales through differentiating their quality levels from those of remanufactured ones by launching upgraded versions. To understand whether and how the product upgrading strategy impacts on optimal outcomes in the context of the remanufacturing operations undertaken by OEMs or third-party remanufacturers (TPRs), in this paper, we develop two models that highlight the OEM’s product upgrading strategy under the scenarios where (1) the OEM owns its remanufacturing operations in-house (Model O) or (2) remanufacturing operations are undertaken by a TPR (Model T). Among other results, we find that, from an economic performance perspective, it is more beneficial for the OEM to perform remanufacturing operations in-house; however, from an environmental sustainability perspective, such behavior is not always good for our environment. In particular, when the level of product upgrading is pronounced, the remanufacturing operations undertaken by the OEM are always detrimental to our environment, due to indulging in remanufacturing, as seen in Model O
BLO-SAM: Bi-level Optimization Based Overfitting-Preventing Finetuning of SAM
The Segment Anything Model (SAM), a foundation model pretrained on millions
of images and segmentation masks, has significantly advanced semantic
segmentation, a fundamental task in computer vision. Despite its strengths, SAM
encounters two major challenges. Firstly, it struggles with segmenting specific
objects autonomously, as it relies on users to manually input prompts like
points or bounding boxes to identify targeted objects. Secondly, SAM faces
challenges in excelling at specific downstream tasks, like medical imaging, due
to a disparity between the distribution of its pretraining data, which
predominantly consists of general-domain images, and the data used in
downstream tasks. Current solutions to these problems, which involve finetuning
SAM, often lead to overfitting, a notable issue in scenarios with very limited
data, like in medical imaging. To overcome these limitations, we introduce
BLO-SAM, which finetunes SAM based on bi-level optimization (BLO). Our approach
allows for automatic image segmentation without the need for manual prompts, by
optimizing a learnable prompt embedding. Furthermore, it significantly reduces
the risk of overfitting by training the model's weight parameters and the
prompt embedding on two separate subsets of the training dataset, each at a
different level of optimization. We apply BLO-SAM to diverse semantic
segmentation tasks in general and medical domains. The results demonstrate
BLO-SAM's superior performance over various state-of-the-art image semantic
segmentation methods
SmartIntentNN: Towards Smart Contract Intent Detection
Researchers currently have been focusing on smart contract vulnerability
detection, but we find that developers' intent to write smart contracts is a
more noteworthy security concern because smart contracts with malicious intent
have caused significant financial loss to users. A more unfortunate fact is
that we can only rely on manual audits to check for unfriendly smart contracts.
In this paper, we propose \textsc{SmartIntentNN}, Smart Contract Intent Neural
Network, a deep learning-based tool that aims to automate the process of
developers' intent detection in smart contracts, saving human resources and
overhead.
The demo video is available on \url{https://youtu.be/ho1SMtYm-wI}.Comment: 4 pages, 3 figures, conference tool track. arXiv admin note:
substantial text overlap with arXiv:2211.1072
Deep Smart Contract Intent Detection
Nowadays, security activities in smart contracts concentrate on vulnerability
detection. Despite early success, we find that developers' intent to write
smart contracts is a more noteworthy security concern because smart contracts
with malicious intent have caused significant users' financial loss.
Unfortunately, current approaches to identify the aforementioned malicious
smart contracts rely on smart contract security audits, which entail huge
manpower consumption and financial expenditure. To resolve this issue, we
propose a novel deep learning-based approach, SmartIntentNN, to conduct
automated smart contract intent detection. SmartIntentNN consists of three
primary parts: a pre-trained sentence encoder to generate the contextual
representations of smart contracts, a K-means clustering method to highlight
intent-related representations, and a bidirectional LSTM-based (long-short term
memory) multi-label classification network to predict the intents in smart
contracts. To evaluate the performance of SmartIntentNN, we collect more than
40,000 real smart contracts and perform a series of comparison experiments with
our selected baseline approaches. The experimental results demonstrate that
SmartIntentNN outperforms all baselines by up to 0.8212 in terms of the
f1-score metric.Comment: 12 pages, 9 figures, conferenc
ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary Transformer
Deep learning (DL) has advanced the field of dense prediction, while
gradually dissolving the inherent barriers between different tasks. However,
most existing works focus on designing architectures and constructing visual
cues only for the specific task, which ignores the potential uniformity
introduced by the DL paradigm. In this paper, we attempt to construct a novel
\underline{ComP}lementary \underline{tr}ansformer, \textbf{ComPtr}, for diverse
bi-source dense prediction tasks. Specifically, unlike existing methods that
over-specialize in a single task or a subset of tasks, ComPtr starts from the
more general concept of bi-source dense prediction. Based on the basic
dependence on information complementarity, we propose consistency enhancement
and difference awareness components with which ComPtr can evacuate and collect
important visual semantic cues from different image sources for diverse tasks,
respectively. ComPtr treats different inputs equally and builds an efficient
dense interaction model in the form of sequence-to-sequence on top of the
transformer. This task-generic design provides a smooth foundation for
constructing the unified model that can simultaneously deal with various
bi-source information. In extensive experiments across several representative
vision tasks, i.e. remote sensing change detection, RGB-T crowd counting,
RGB-D/T salient object detection, and RGB-D semantic segmentation, the proposed
method consistently obtains favorable performance. The code will be available
at \url{https://github.com/lartpang/ComPtr}
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