Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users’ trust and drain liquidity from token pools on Decentralized ...
This literature review aimed to compare various time-series analysis approaches utilized in forecasting COVID-19 cases in Africa. The study involved a methodical search for English-language research papers published b...
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There are around 1.8 million new cases of colorectal cancer diagnosed every year, making it one of the most prevalent forms of the disease. Therefore, the infection and mortality rates can be reduced with early identi...
There are around 1.8 million new cases of colorectal cancer diagnosed every year, making it one of the most prevalent forms of the disease. Therefore, the infection and mortality rates can be reduced with early identification of this cancer. Most diagnostic colonoscopy systems are currently incorporating artificial intelligence approaches validated for predicting advanced cancers. It is common practice to combine convolutional neural network-based patterns with image patches and preprocesses. The purpose of this paper was to develop a novel lightweight deep learning Convolutional Neural Network (CNN) that uses Adam optimizer for reliable colon cancer detection. Applying a database of publicly available histopathology images, the proposed approach is analyzed in the context of the existing best practices for colon cancer detection analysis. Based on the results, it is clear that the proposed deep neural network for colon cancer diagnosis achieves an accuracy of 98.40 percent, which is the highest accuracy achieved by any of the existing deep learning methods.
Soft electronics,known for their bendable,stretchable,and flexible properties,are revolutionizing fields such as biomedical sensing,consumer electronics,and robotics.A primary challenge in this domain is achieving low...
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Soft electronics,known for their bendable,stretchable,and flexible properties,are revolutionizing fields such as biomedical sensing,consumer electronics,and robotics.A primary challenge in this domain is achieving low power consumption,often hampered by the limitations of the conventional von Neumann *** response,the development of soft artificial synapses(SASs)has gained substantial *** synapses seek to replicate the signal transmission properties of biological synapses,offering an innovative solution to this *** review explores the materials and device architectures integral to SAS fabrication,emphasizing flexibility and stability under mechanical *** architectures,including floating-gate dielectric,ferroelectric-gate dielectric,and electrolyte-gate dielectric,are analyzed for effective weight control in *** utilization of organic and low-dimensional materials is highlighted,showcasing their plasticity and energy-efficient ***,the paper investigates the integration of functionality into SASs,particularly focusing on devices that autonomously sense external *** SASs,capable of recognizing optical,mechanical,chemical,olfactory,and auditory cues,demonstrate promising applications in computing and sensing.A detailed examination of photo-functionalized,tactile-functionalized,and chemoreception-functionalized SASs reveals their potential in image recognition,tactile sensing,and chemosensory applications,*** study highlights that SASs and functionalized SAS devices hold transformative potential for bioelectronics and sensing for soft-robotics applications;however,further research is necessary to address scalability,longtime stability,and utilizing functionalized SASs for prosthetics and in vivo applications through clinical *** providing a comprehensive overview,this paper contributes to the understanding of SASs,bridging research gaps and paving the way tow
As AI offers a suitable response to various challenges associated with this disease, it plays a crucial role in mental health. A fundamental concept of the AI-based mental health remedy and its impact on directly affe...
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Inferring the 3D structure of a scene from a single image is an ill-posed and challenging problem in the field of vision-centric autonomous driving. Existing methods usually employ neural radiance fields to produce vo...
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In this paper, by targeting low-level code optimization, an instruction scheduler is designed and experimented with a synergistic processor unit (SPU) to show its effectiveness on a basic block and data dependency gra...
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Heterogeneous integration of voltage regulators in power delivery networks is a growing trend that employs embedded inductor as a key component in significantly improving the power distribution. In this work, we propo...
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ISBN:
(数字)9781665450751
ISBN:
(纸本)9781665450751
Heterogeneous integration of voltage regulators in power delivery networks is a growing trend that employs embedded inductor as a key component in significantly improving the power distribution. In this work, we propose a neural network framework called the hierarchical invertible neural transport for the inverse design of an embedded inductor. With this invertible method, we obtain the probability distributions of the parameters of the embedded inductor design space that most likely satisfy the desired specifications. We also learn the impedance response for free in the forward design. In the forward design, our results show a 2.14% normalized mean square error when compared with the output response of a fullwave EM simulator.
Those interested in artificial intelligence technologies, especially supervised and unsupervised learning in education, know they need considerable data for well-modeled training and high-quality accuracy. However, da...
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The rapid advancement of large language models has opened new avenues for automating complex problem-solving tasks such as algorithmic coding and competitive programming. This paper introduces a novel evaluation techn...
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ISBN:
(数字)9798331526153
ISBN:
(纸本)9798331526160
The rapid advancement of large language models has opened new avenues for automating complex problem-solving tasks such as algorithmic coding and competitive programming. This paper introduces a novel evaluation technique, LLM-ProS, to assess the performance of state-of-the-art LLMs on International Collegiate Programming Contest (ICPC) problems. Using a curated dataset of 166 World Finals problems from 2011 to 2024, we benchmark the models’ reasoning, accuracy, and efficiency. We evaluated the five models-GPT-4o, Mistral Large, Llama-3.1-405B, and the o1 family, consisting of o1-mini and o1-preview, across critical metrics like correctness, resource utilization, and response calibration. Our results reveal significant differences in the models’ abilities to generalize, adapt, and solve novel problems. We also investigated the impact of training methodologies, dataset contamination, and chain-of-thought reasoning on model performance. The findings provide new insights into optimizing LLMs for algorithmic tasks, highlighting both strengths and limitations of current models.
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