Using an integrated fuzzy inference system and artificial neural network to forecast daily discharge

Given the nonlinearity and uncertainty in the rainfall-runoff process, estimating or predicting hydrologic data often encounters tremendous difficulty. This study applied fuzzy theory to create a daily flow forecasting modeL To improve the time-consuming definition process of membership function, which is usually concluded by a trial-and-error approach, this study designated the membership function by artificial neural network {ANN} with either a supervised or unsupervised learning procedure. The supervised learning was processed by the adaptive network based fuzzy inference system {ANFIS}, while the unsupervised learning was created by fuzzy and self-organizing map {SOMFIS}. The results indicate that the ANFIS method under increment flow data could provide more precise results for daily flow forecasting

Exploring Chain-of-Thought Style Prompting for Text-to-SQL

In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. However, its performance on text-to-SQL parsing still has much room for improvement. In this paper, we hypothesize that a crucial aspect of LLMs to improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we systematically study how to enhance LLMs' reasoning ability through chain of thought (CoT) style prompting, including the original chain-of-thought prompting (Wei et al., 2022b) and least-to-most prompting (Zhou et al., 2023). Our experiments demonstrate that iterative prompting as in Zhou et al. (2023) may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps tends to have more error propagation issues. Based on these findings, we propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gains, compared to the least-to-most prompting method.Comment: EMNLP 2023 main; long pape

SimMatchV2: Semi-Supervised Learning with Graph Consistency

Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor. In this paper, we introduce a new semi-supervised learning algorithm - SimMatchV2, which formulates various consistency regularizations between labeled and unlabeled data from the graph perspective. In SimMatchV2, we regard the augmented view of a sample as a node, which consists of a label and its corresponding representation. Different nodes are connected with the edges, which are measured by the similarity of the node representations. Inspired by the message passing and node classification in graph theory, we propose four types of consistencies, namely 1) node-node consistency, 2) node-edge consistency, 3) edge-edge consistency, and 4) edge-node consistency. We also uncover that a simple feature normalization can reduce the gaps of the feature norm between different augmented views, significantly improving the performance of SimMatchV2. Our SimMatchV2 has been validated on multiple semi-supervised learning benchmarks. Notably, with ResNet-50 as our backbone and 300 epochs of training, SimMatchV2 achieves 71.9\% and 76.2\% Top-1 Accuracy with 1\% and 10\% labeled examples on ImageNet, which significantly outperforms the previous methods and achieves state-of-the-art performance. Code and pre-trained models are available at \href{https://github.com/mingkai-zheng/SimMatchV2}{https://github.com/mingkai-zheng/SimMatchV2}

DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures

Diffusion models have recently exhibited remarkable performance on synthetic data. After a diffusion path is selected, a base model, such as UNet, operates as a denoising autoencoder, primarily predicting noises that need to be eliminated step by step. Consequently, it is crucial to employ a model that aligns with the expected budgets to facilitate superior synthetic performance. In this paper, we meticulously analyze the diffusion model and engineer a base model search approach, denoted "DiffNAS". Specifically, we leverage GPT-4 as a supernet to expedite the search, supplemented with a search memory to enhance the results. Moreover, we employ RFID as a proxy to promptly rank the experimental outcomes produced by GPT-4. We also adopt a rapid-convergence training strategy to boost search efficiency. Rigorous experimentation corroborates that our algorithm can augment the search efficiency by 2 times under GPT-based scenarios, while also attaining a performance of 2.82 with 0.37 improvement in FID on CIFAR10 relative to the benchmark IDDPM algorithm

Uniqueness and structure of solutions to the Dirichlet problem for an elliptic system

AbstractIn this paper, we consider the Dirichlet problem for an elliptic system on a ball in R2. By investigating the properties for the corresponding linearized equations of solutions, and adopting the Pohozaev identity and Implicit Function Theorem, we show the uniqueness and the structure of solutions

Outcomes of treatment of sudden deafness using different Protocols: a retrospective analysis of 104 cases

AbstractObjectiveTo compare different treatment protocols for sudden deafness(SD), for the purpose of identifying an appropriate approach to SD.MethodsA total of 104 patients with diagnosis of sudden hearing loss treated from Jan 2006 to December 2008 were included in this study, of which 31 received the typical pharmaceutical treatment(group I), 40 received the typical pharmaceutical treatment plus polarized liquid(Group II) and 33 received the hyperbaric oxygen in addition to the treatment included in Group II (Group III).ResultsThe total improvement rate(67.74%, 62.50% and 75.76% for Groups I, II and III respectively) was not statistically different between the three groups (P>0.05).ConclusionThe three treatment protocols are similar when judged by the treatment outcomes in SD, neither being superior to the others. The two important factors that appear to influence treatment outcomes are the audiogram pattern and duration of hearing loss before seeking treatment. Patients with upsloping or peak–type audiograms and treated within 7 days from the onset have better prognosis than others

Mycobacterium tuberculosis and M. bovis infection in Feedlot Deer (Cervus unicolor swinhoei and C. nippon taiouanus) in Taiwan

Background/purposeMycobacterium bovis frequently infects wild and farm deer species with tuberculosis. This study investigated mycobacterial infection in two native deer species Cervus unicolor swinhoei (Formosan Sambar, Sambar) and C. nippon taiouanus (Formasan Sika, Sika).MethodsBased on different sampling sources of 19 intradermal tuberculin test (ITT) Sambar, mycobacterial infection and/or species were detected by acid-fast stain, duplex polymerase chain reaction (PCR) and multiplex nested PCR (mnPCR) methods, traditional mycobacterial culture and gross lesion. Blood samples of 167 Sambar deer and 147 Sika deer were then tested by duplex PCR and mnPCR methods to investigate the prevalence of mycobacterial infection. Sequence variations of these mycobacterial species were analyzed as well.ResultsDuplex PCR and mnPCR assays could differentiate between MTBC (M. bovis and M. tuberculosis) and M. avium, as well as between M. bovis and M. tuberculosis, respectively. These PCR methods showed a higher detection rate than traditional culture and matched the gross lesions examined in 19 ITT-examined Sambar. Therefore, the mycobacterial infection in blood samples of 314 deer samples was detected using these PCR methods. Duplex PCR and mnPCR showed an identical prevalence of 16.1% in Sambar and 8.2% in Sika and a significant difference in prevalence between these two deer species. M. bovis and M. tuberculosis were the species detected in feedlot Sambar and Sika. M. tuberculosis was found only and first in Sambar fed in central Taiwan. Sequence analysis revealed diverse genetic variations in M. bovis and M. tuberculosis associated with deer subspecies.ConclusionMultiplex PCR methods were established, and M. bovis and M. tuberculosis were identified in feedlot deer in Taiwan. Sequence variations indicated diverse sources of both mycobacterial species