Unbiased Optimal Stopping via the MUSE

We propose a new unbiased estimator for estimating the utility of the optimal stopping problem. The MUSE, short for Multilevel Unbiased Stopping Estimator, constructs the unbiased Multilevel Monte Carlo (MLMC) estimator at every stage of the optimal stopping problem in a backward recursive way. In contrast to traditional sequential methods, the MUSE can be implemented in parallel. We prove the MUSE has finite variance, finite computational complexity, and achieves $\epsilon$-accuracy with $O(1/\epsilon^2)$ computational cost under mild conditions. We demonstrate MUSE empirically in an option pricing problem involving a high-dimensional input and the use of many parallel processors.Comment: 39 pages, add several numerical experiments and technical results, accepted by Stochastic Processes and their Application

Zero-Shot Aerial Object Detection with Visual Description Regularization

Existing object detection models are mainly trained on large-scale labeled datasets. However, annotating data for novel aerial object classes is expensive since it is time-consuming and may require expert knowledge. Thus, it is desirable to study label-efficient object detection methods on aerial images. In this work, we propose a zero-shot method for aerial object detection named visual Description Regularization, or DescReg. Concretely, we identify the weak semantic-visual correlation of the aerial objects and aim to address the challenge with prior descriptions of their visual appearance. Instead of directly encoding the descriptions into class embedding space which suffers from the representation gap problem, we propose to infuse the prior inter-class visual similarity conveyed in the descriptions into the embedding learning. The infusion process is accomplished with a newly designed similarity-aware triplet loss which incorporates structured regularization on the representation space. We conduct extensive experiments with three challenging aerial object detection datasets, including DIOR, xView, and DOTA. The results demonstrate that DescReg significantly outperforms the state-of-the-art ZSD methods with complex projection designs and generative frameworks, e.g., DescReg outperforms best reported ZSD method on DIOR by 4.5 mAP on unseen classes and 8.1 in HM. We further show the generalizability of DescReg by integrating it into generative ZSD methods as well as varying the detection architecture.Comment: 13 pages, 3 figure

ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4

In recent years, large language models (LLMs) have made significant progress in natural language processing (NLP), with models like ChatGPT and GPT-4 achieving impressive capabilities in various linguistic tasks. However, training models on such a large scale is challenging, and finding datasets that match the model's scale is often difficult. Fine-tuning and training models with fewer parameters using novel methods have emerged as promising approaches to overcome these challenges. One such model is MiniGPT-4, which achieves comparable vision-language understanding to GPT-4 by leveraging novel pre-training models and innovative training strategies. However, the model still faces some challenges in image understanding, particularly in artistic pictures. A novel multimodal model called ArtGPT-4 has been proposed to address these limitations. ArtGPT-4 was trained on image-text pairs using a Tesla A100 device in just 2 hours, using only about 200 GB of data. The model can depict images with an artistic flair and generate visual code, including aesthetically pleasing HTML/CSS web pages. Furthermore, the article proposes novel benchmarks for evaluating the performance of vision-language models. In the subsequent evaluation methods, ArtGPT-4 scored more than 1 point higher than the current \textbf{state-of-the-art} model and was only 0.25 points lower than artists on a 6-point scale. Our code and pre-trained model are available at \url{https://huggingface.co/Tyrannosaurus/ArtGPT-4}.Comment: 16 page

Mutation rate analysis via parent– progeny sequencing of the perennial peach. I. A low rate in woody perennials and a higher mutagenicity in hybrids

Mutation rates vary between species, between strains within species and between regions within a genome. What are the determinants of these forms of variation? Here, via parent-offspring sequencing of the peach we ask whether (i) woody perennials tend to have lower per unit time mutation rates compared to annuals, and (ii) hybrid strains have high mutation rates. Between a leaf from a low heterozygosity individual, derived from an intraspecific cross, to a leaf of its selfed progeny, the mutation rate is 7.77 × 10-9 point mutations per bp per generation, similar to Arabidopsis thaliana (7.0-7.4 × 10-9 point mutations per bp per generation). This suggests a low per unit time mutation rate as the generation time is much longer in peach. This is supported by our estimate of 9.48 × 10-9 point mutations per bp per generation from a 200-year-old low heterozygosity peach to its progeny. From a more highly heterozygous individual derived from an interspecific cross to its selfed progeny, the mutation rate is 1.38 × 10-8 mutations per site per generation, consistent with raised rates in hybrids. Our data thus suggest that (i) peach has an approximately order of magnitude lower mutation rate per unit time than Arabidopsis, consistent with reports of low evolutionary rates in woody perennials, and (ii) hybridization may, indeed, be associated with increased mutation rates as considered over a century ago.</p

RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language Understanding

This paper presents a new data augmentation algorithm for natural understanding tasks, called RPN:Random Position Noise algorithm.Due to the relative paucity of current text augmentation methods. Few of the extant methods apply to natural language understanding tasks for all sentence-level tasks.RPN applies the traditional augmentation on the original text to the word vector level. The RPN algorithm makes a substitution in one or several dimensions of some word vectors. As a result, the RPN can introduce a certain degree of perturbation to the sample and can adjust the range of perturbation on different tasks. The augmented samples are then used to give the model training.This makes the model more robust. In subsequent experiments, we found that adding RPN to the training or fine-tuning model resulted in a stable boost on all 8 natural language processing tasks, including TweetEval, CoLA, and SST-2 datasets, and more significant improvements than other data augmentation algorithms.The RPN algorithm applies to all sentence-level tasks for language understanding and is used in any deep learning model with a word embedding layer.Comment: 10 pages, 4 figure

VCL Challenges 2023 at ICCV 2023 Technical Report: Bi-level Adaptation Method for Test-time Adaptive Object Detection

This report outlines our team's participation in VCL Challenges B Continual Test_time Adaptation, focusing on the technical details of our approach. Our primary focus is Testtime Adaptation using bi_level adaptations, encompassing image_level and detector_level adaptations. At the image level, we employ adjustable parameterbased image filters, while at the detector level, we leverage adjustable parameterbased mean teacher modules. Ultimately, through the utilization of these bi_level adaptations, we have achieved a remarkable 38.3% mAP on the target domain of the test set within VCL Challenges B. It is worth noting that the minimal drop in mAP, is mearly 4.2%, and the overall performance is 32.5% mAP

Advancing Agro-Based Research

Taking the next sums up Universiti Putra Malaysia (UPM) approach to research. The university now aims to create an environment that inspires innovative research following its selection as a research university by the Higher Education Ministry in November 2006

Mutation rate analysis via parent– progeny sequencing of the perennial peach. I. A low rate in woody perennials and a higher mutagenicity in hybrids

Mutation rates vary between species, between strains within species and between regions within a genome. What are the determinants of these forms of variation? Here, via parent–offspring sequencing of the peach we ask whether (i) woody perennials tend to have lower per unit time mutation rates compared to annuals, and (ii) hybrid strains have high mutation rates. Between a leaf from a low heterozygosity individual, derived from an intraspecific cross, to a leaf of its selfed progeny, the mutation rate is 7.77 × 10(−9) point mutations per bp per generation, similar to Arabidopsis thaliana (7.0–7.4 × 10(−9) point mutations per bp per generation). This suggests a low per unit time mutation rate as the generation time is much longer in peach. This is supported by our estimate of 9.48 × 10(−9) point mutations per bp per generation from a 200-year-old low heterozygosity peach to its progeny. From a more highly heterozygous individual derived from an interspecific cross to its selfed progeny, the mutation rate is 1.38 × 10(−8) mutations per site per generation, consistent with raised rates in hybrids. Our data thus suggest that (i) peach has an approximately order of magnitude lower mutation rate per unit time than Arabidopsis, consistent with reports of low evolutionary rates in woody perennials, and (ii) hybridization may, indeed, be associated with increased mutation rates as considered over a century ago

CLEC16A variants conferred a decreased risk to allergic rhinitis in the Chinese population

Background: Allergic rhinitis (AR) is a chronic respiratory disease. Hereditary factors played a key role in the pathogenesis of the AR. This study investigated the association between CLEC16A variants and AR risk in the Chinese population.Methods: We applied Agena MassARRAY technology platform to genotype five single nucleotide polymorphisms (SNPs) located in CLEC16A in 1004 controls and 995 cases. The association between CLEC16A SNPs (rs2286973, rs887864, rs12935657, rs11645657 and rs36045143) and AR risk were calculated by logistic regression analysis, with odds ratio (OR) and 95% confidence interval (CI). False-positive report probability (FPRP) was also used to assess the significant results to reduce false positives. Multifactor dimensionality reduction (MDR) was completed to assess the interaction between CLEC16A variants to predict AR risk.Results: Totally, CLEC16A (rs887864, rs12935657, rs2286973, rs11645657 and rs36045143) were significantly associated with AR risk. Therein, rs2286973, rs11645657 and rs36045143 were related to a decreased risk of AR in the people ≤ 43 years old, females and the people with BMI≤24, respectively. And rs887864 and rs12935657 were also associated with a decreased susceptibility of AR in the people &gt;43 years old. Meanwhile, in the results of region stratification, rs887864 conferred a reduced risk to AR in the people from loess hilly area.Conclusion:CLEC16A variants conferred a decreased risk to AR in the Chinese population