72 research outputs found
Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images.
Although extreme learning machines (ELM) have been successfully applied for the classification of hyperspectral images (HSIs), they still suffer from three main drawbacks. These include: 1) ineffective feature extraction (FE) in HSIs due to a single hidden layer neuron network used; 2) ill-posed problems caused by the random input weights and biases; and 3) lack of spatial information for HSIs classification. To tackle the first problem, we construct a multilayer ELM for effective FE from HSIs. The sparse representation is adopted with the multilayer ELM to tackle the ill-posed problem of ELM, which can be solved by the alternative direction method of multipliers. This has resulted in the proposed multilayer sparse ELM (MSELM) model. Considering that the neighboring pixels are more likely from the same class, a local block extension is introduced for MSELM to extract the local spatial information, leading to the local block MSELM (LBMSELM). The loopy belief propagation is also applied to the proposed MSELM and LBMSELM approaches to further utilize the rich spectral and spatial information for improving the classification. Experimental results show that the proposed methods have outperformed the ELM and other state-of-the-art approaches
Second-Harmonic and Sum-Frequency Imaging of Organic Nanocrystals with Photon Scanning Tunneling Microscope
Second-harmonic generation and sum-frequency generation with photon scanning tunneling microscopy and shear-force detection are used to map the nonlinear optical response and the surface topograph of N-(4-nitrophenyl)-(L)-prolinol crystals with a subdiffraction-limited resolution. The domain-size dependence of the spatial feature is obtained, which shows the local orientational distribution of the optical near field radiated by nonlinear nanocrystals and reveals the difference between nanoscopic and macroscopic second-order optical nonlinearities of molecular crystals
Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics
We present the first study to investigate Large Language Models (LLMs) in
answering radiation oncology physics questions. Because popular exams like AP
Physics, LSAT, and GRE have large test-taker populations and ample test
preparation resources in circulation, they may not allow for accurately
assessing the true potential of LLMs. This paper proposes evaluating LLMs on a
highly-specialized topic, radiation oncology physics, which may be more
pertinent to scientific and medical communities in addition to being a valuable
benchmark of LLMs. We developed an exam consisting of 100 radiation oncology
physics questions based on our expertise at Mayo Clinic. Four LLMs, ChatGPT
(GPT-3.5), ChatGPT (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated against
medical physicists and non-experts. ChatGPT (GPT-4) outperformed all other LLMs
as well as medical physicists, on average. The performance of ChatGPT (GPT-4)
was further improved when prompted to explain first, then answer. ChatGPT
(GPT-3.5 and GPT-4) showed a high level of consistency in its answer choices
across a number of trials, whether correct or incorrect, a characteristic that
was not observed in the human test groups. In evaluating ChatGPTs (GPT-4)
deductive reasoning ability using a novel approach (substituting the correct
answer with "None of the above choices is the correct answer."), ChatGPT
(GPT-4) demonstrated surprising accuracy, suggesting the potential presence of
an emergent ability. Finally, although ChatGPT (GPT-4) performed well overall,
its intrinsic properties did not allow for further improvement when scoring
based on a majority vote across trials. In contrast, a team of medical
physicists were able to greatly outperform ChatGPT (GPT-4) using a majority
vote. This study suggests a great potential for LLMs to work alongside
radiation oncology experts as highly knowledgeable assistants
Artificial General Intelligence for Radiation Oncology
The emergence of artificial general intelligence (AGI) is transforming
radiation oncology. As prominent vanguards of AGI, large language models (LLMs)
such as GPT-4 and PaLM 2 can process extensive texts and large vision models
(LVMs) such as the Segment Anything Model (SAM) can process extensive imaging
data to enhance the efficiency and precision of radiation therapy. This paper
explores full-spectrum applications of AGI across radiation oncology including
initial consultation, simulation, treatment planning, treatment delivery,
treatment verification, and patient follow-up. The fusion of vision data with
LLMs also creates powerful multimodal models that elucidate nuanced clinical
patterns. Together, AGI promises to catalyze a shift towards data-driven,
personalized radiation therapy. However, these models should complement human
expertise and care. This paper provides an overview of how AGI can transform
radiation oncology to elevate the standard of patient care in radiation
oncology, with the key insight being AGI's ability to exploit multimodal
clinical data at scale
Large expert-curated database for benchmarking document similarity detection in biomedical literature search
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe
The Ninth Visual Object Tracking VOT2021 Challenge Results
acceptedVersionPeer reviewe
A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
Machine learning algorithms are increasingly used in various remote sensing applications due to their ability to identify nonlinear correlations. Ensemble algorithms have been included in many practical applications to improve prediction accuracy. We provide an overview of three widely used ensemble techniques: bagging, boosting, and stacking. We first identify the underlying principles of the algorithms and present an analysis of current literature. We summarize some typical applications of ensemble algorithms, which include predicting crop yield, estimating forest structure parameters, mapping natural hazards, and spatial downscaling of climate parameters and land surface temperature. Finally, we suggest future directions for using ensemble algorithms in practical applications
The First Passage Time Problem for Mixed-Exponential Jump Processes with Applications in Insurance and Finance
This paper stidies the first passage times to constant boundaries for
mixed-exponential jump diffusion processes. Explicit solutions of the Laplace
transforms of the distribution of the first passage times, the joint
distribution of the first passage times and undershoot (overshoot) are
obtained. As applications, we present explicit expression of the Gerber-Shiu
functions for surplus processes with two-sided jumps, present the analytical
solutions for popular path-dependent options such as lookback and barrier
options in terms of Laplace transforms and give a closed-form expression on the
price of the zero-coupon bond under a structural credit risk model with jumps.Comment: Abstract and Applied Analysis (To appear
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