Towards Effective Exact Algorithms for the Maximum Balanced Biclique Problem

The Maximum Balanced Biclique Problem (MBBP) is a prominent model with numerous applications. Yet, the problem is NP-hard and thus computationally challenging. We propose novel ideas for designing effective exact algorithms for MBBP. Firstly, we introduce an Upper Bound Propagation procedure to pre-compute an upper bound involving each vertex. Then we extend an existing branch-and-bound algorithm by integrating the pre-computed upper bounds. We also present a set of new valid inequalities induced from the upper bounds to tighten an existing mathematical formulation for MBBP. Lastly, we investigate another exact algorithm scheme which enumerates a subset of balanced bicliques based on our upper bounds. Experiments show that compared to existing approaches, the proposed algorithms and formulations are more efficient in solving a set of random graphs and large real-life instances

Transformer-based Image Compression with Variable Image Quality Objectives

This paper presents a Transformer-based image compression system that allows for a variable image quality objective according to the user's preference. Optimizing a learned codec for different quality objectives leads to reconstructed images with varying visual characteristics. Our method provides the user with the flexibility to choose a trade-off between two image quality objectives using a single, shared model. Motivated by the success of prompt-tuning techniques, we introduce prompt tokens to condition our Transformer-based autoencoder. These prompt tokens are generated adaptively based on the user's preference and input image through learning a prompt generation network. Extensive experiments on commonly used quality metrics demonstrate the effectiveness of our method in adapting the encoding and/or decoding processes to a variable quality objective. While offering the additional flexibility, our proposed method performs comparably to the single-objective methods in terms of rate-distortion performance

Development of a short and universal learning self-efficacy scale for clinical skills

Background Learning self-efficacy, defined as learners’ confidence in their capability to learn specific subjects, is crucial for the enhancement of academic progress, because it is positively correlated with academic achievements and effective learning strategy use. In this study, we developed a universal scale called the Learning Self-Efficacy Scale (L-SES) for Clinical Skills for undergraduate medical students and validated it through item analysis and content validity index (CVI) calculation. Design The L-SES was developed based on the framework of Bloom’s taxonomy, and the questions were generated through expert consensus and CVI calculation. A pilot version of the L-SES was administered to 235 medical students attending a basic clinical skills course. The collected data were then examined through item analysis. Results The first draft of the L-SES comprised 15 questions. After expert consensus and CVI calculation, 3 questions were eliminated; hence, the pilot version comprised 12 questions. The CVI values of the 12 questions were between .88 and 1, indicating high content validity. Moreover, the item analysis indicated that the quality of L-SES reached the qualified threshold. The results showed that the L-SES scores were unaffected by gender (t = −0.049; 95% confidence interval [−.115, .109], p > .05). Conclusion The L-SES is a short, well-developed scale that can serve as a generic assessment tool for measuring medical students’ learning self-efficacy for clinical skills. Moreover, the L-SES is unaffected by gender differences. However, additional analyses in relevant educational settings are needed

Transformer-based Variable-rate Image Compression with Region-of-interest Control

This paper proposes a transformer-based learned image compression system. It is capable of achieving variable-rate compression with a single model while supporting the region-of-interest (ROI) functionality. Inspired by prompt tuning, we introduce prompt generation networks to condition the transformer-based autoencoder of compression. Our prompt generation networks generate content-adaptive tokens according to the input image, an ROI mask, and a rate parameter. The separation of the ROI mask and the rate parameter allows an intuitive way to achieve variable-rate and ROI coding simultaneously. Extensive experiments validate the effectiveness of our proposed method and confirm its superiority over the other competing methods.Comment: Accepted to IEEE ICIP 202

TransTIC: Transferring Transformer-based Image Compression from Human Visualization to Machine Perception

This work aims for transferring a Transformer-based image compression codec from human vision to machine perception without fine-tuning the codec. We propose a transferable Transformer-based image compression framework, termed TransTIC. Inspired by visual prompt tuning, we propose an instance-specific prompt generator to inject instance-specific prompts to the encoder and task-specific prompts to the decoder. Extensive experiments show that our proposed method is capable of transferring the codec to various machine tasks and outshining the competing methods significantly. To our best knowledge, this work is the first attempt to utilize prompting on the low-level image compression task

CFEVER: A Chinese Fact Extraction and VERification Dataset

We present CFEVER, a Chinese dataset designed for Fact Extraction and VERification. CFEVER comprises 30,012 manually created claims based on content in Chinese Wikipedia. Each claim in CFEVER is labeled as "Supports", "Refutes", or "Not Enough Info" to depict its degree of factualness. Similar to the FEVER dataset, claims in the "Supports" and "Refutes" categories are also annotated with corresponding evidence sentences sourced from single or multiple pages in Chinese Wikipedia. Our labeled dataset holds a Fleiss' kappa value of 0.7934 for five-way inter-annotator agreement. In addition, through the experiments with the state-of-the-art approaches developed on the FEVER dataset and a simple baseline for CFEVER, we demonstrate that our dataset is a new rigorous benchmark for factual extraction and verification, which can be further used for developing automated systems to alleviate human fact-checking efforts. CFEVER is available at https://ikmlab.github.io/CFEVER.Comment: AAAI-2

Expression Profile of Cationic Amino Acid Transporters in Rats with Endotoxin-Induced Uveitis

Purpose. The transcellular arginine transportation via cationic amino acid transporter (CAT) is the rate-limiting step in nitric oxide (NO) synthesis, which is crucial in intraocular inflammation. In this study, CAT isoforms and inducible nitric oxide synthase (iNOS) expression was investigated in endotoxin-induced uveitis (EIU). Methods. EIU was induced in Lewis rats by lipopolysaccharide (LPS) injection. In the treatment group, the rats were injected intraperitoneally with the proteasome inhibitor bortezomib before EIU induction. After 24 hours, leukocyte quantification, NO measurement of the aqueous humor, and histopathological examination were evaluated. The expression of CAT isoforms and iNOS was determined by reverse transcription-polymerase chain reaction, western blotting, and immunofluorescence staining. Nuclear factor-kappa B (NF-κB) binding activity was evaluated by electrophoretic mobility shift assay. The mouse macrophage cell line RAW 264.7 was used to validate the in vivo findings. Results. LPS significantly stimulated iNOS, CAT-2A, and CAT-2B mRNA and protein expression but did not affect CAT-1 in EIU rats and RAW 264.7 cells. Bortezomib attenuated inflammation and inhibited iNOS, CAT-2A, and CAT-2B expression through NF-κB inhibition. Conclusions. CAT-2 and iNOS, but not CAT-1, are specifically involved in EIU. NF-κB is essential in the induction of CAT-2 and iNOS in EIU

Improved support vector machine classification for imbalanced medical datasets by novel hybrid sampling combining modified mega-trend-diffusion and bagging extreme learning machine model

To handle imbalanced datasets in machine learning or deep learning models, some studies suggest sampling techniques to generate virtual examples of minority classes to improve the models' prediction accuracy. However, for kernel-based support vector machines (SVM), some sampling methods suggest generating synthetic examples in an original data space rather than in a high-dimensional feature space. This may be ineffective in improving SVM classification for imbalanced datasets. To address this problem, we propose a novel hybrid sampling technique termed modified mega-trend-diffusion-extreme learning machine (MMTD-ELM) to effectively move the SVM decision boundary toward a region of the majority class. By this movement, the prediction of SVM for minority class examples can be improved. The proposed method combines α-cut fuzzy number method for screening representative examples of majority class and MMTD method for creating new examples of the minority class. Furthermore, we construct a bagging ELM model to monitor the similarity between new examples and original data. In this paper, four datasets are used to test the efficiency of the proposed MMTD-ELM method in imbalanced data prediction. Additionally, we deployed two SVM models to compare prediction performance of the proposed MMTD-ELM method with three state-of-the-art sampling techniques in terms of geometric mean (G-mean), F-measure (F1), index of balanced accuracy (IBA) and area under curve (AUC) metrics. Furthermore, paired t-test is used to elucidate whether the suggested method has statistically significant differences from the other sampling techniques in terms of the four evaluation metrics. The experimental results demonstrated that the proposed method achieves the best average values in terms of G-mean, F1, IBA and AUC. Overall, the suggested MMTD-ELM method outperforms these sampling methods for imbalanced datasets