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

A preliminary study of applying interpreting skills to teaching English reading

Even though the established literature has proven that translation actually plays a significant role in English Language Teaching (ELT) as well as in Second Language Acquisition (SLA), there is lack of empirical evidence showing the correlation between the use of sight translation skills and learners' acquisition of English proficiency. This preliminary study recruited 14 English learners and investigated the potential effect of sight translation on the learners' learning outcomes. By comparing the frequencies of ambiguity and significant features appeared in the learners' reading performance in two different genres of texts, the results suggest that the skills of sight translation can successfully improve English learners' reading comprehension. Implications and potential research directions are further addressed

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

Predictors of intra-abdominal coagulopathic hemorrhage after living donor liver transplantation

AbstractBackgroundResults of preoperative conventional coagulation assays are a poor predictor of hemorrhage after liver transplantation. In this study, we evaluated the factors that are predictive of intra-abdominal coagulopathic hemorrhage after living donor liver transplantation surgery.MethodsDuring the period from January 2009 to December 2012, 118 adults underwent living donor liver transplantation (LDLT) in our institution. Of those patients, 18 (15.3%) developed intra-abdominal coagulopathic hemorrhage (n = 7) or hemorrhage due to non-coagulopathic causes (n = 11) that required emergency medical, radiological, or surgical intervention within the first month after LDLT. Possible predictors of postoperative coagulopathic hemorrhage included donor-related factors, age, body mass index, MELD score, INR value, intra-operative blood transfusion, graft/recipient weight ratio, anhepatic phase, cold ischemia time, operative time, APACHE II score, onset of re-bleeding, and hemoglobin levels during rebleeding episodes.ResultsThere were no differences in any of the variables between the two groups (coagulopathic and noncoagulopathic hemorrhage) except for cold ischemia time. We found that cold ischemia time was significantly longer in patients with postoperative coagulopathic hemorrhage (160.50 ± 45.02 min) than in patients with hemorrhage due to non-coagulopathic causes (113.55 ± 29.31 min; P = 0.027).ConclusionProlonged cold ischemia time is associated with postoperative intra-abdominal coagulopathic hemorrhage in patients after LDLT. It is, therefore, necessary to shorten the cold ischemia time in order to reduce the risk of postoperative intra-abdominal hemorrhage due to coagulopathic causes

On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting

User-defined keyword spotting is a task to detect new spoken terms defined by users. This can be viewed as a few-shot learning problem since it is unreasonable for users to define their desired keywords by providing many examples. To solve this problem, previous works try to incorporate self-supervised learning models or apply meta-learning algorithms. But it is unclear whether self-supervised learning and meta-learning are complementary and which combination of the two types of approaches is most effective for few-shot keyword discovery. In this work, we systematically study these questions by utilizing various self-supervised learning models and combining them with a wide variety of meta-learning algorithms. Our result shows that HuBERT combined with Matching network achieves the best result and is robust to the changes of few-shot examples.Comment: Submitted to Interspeech 202

Evolution of long centromeres in fire ants

Background: Centromeres are essential for accurate chromosome segregation, yet sequence conservation is low even among closely related species. Centromere drive predicts rapid turnover because some centromeric sequences may compete better than others during female meiosis. In addition to sequence composition, longer centromeres may have a transmission advantage. Results: We report the first observations of extremely long centromeres, covering on average 34 % of the chromosomes, in the red imported fire ant Solenopsis invicta. By comparison, cytological examination of Solenopsis geminata revealed typical small centromeric constrictions. Bioinformatics and molecular analyses identified CenSol, the major centromeric satellite DNA repeat. We found that CenSol sequences are very similar between the two species but the CenSol copy number in S. invicta is much greater than that in S. geminata. In addition, centromere expansion in S. invicta is not correlated with the duplication of CenH3. Comparative analyses revealed that several closely related fire ant species also possess long centromeres. Conclusions: Our results are consistent with a model of simple runaway centromere expansion due to centromere drive. We suggest expanded centromeres may be more prevalent in hymenopteran insects, which use haplodiploid sex determination, than previously considered

Sitagliptin and Fractures in Type 2 Diabetes: A Nationwide Population-Based Propensity-Matching Study

Background: Sitagliptin, a dipeptidyl peptidase-4 inhibitor possibly affects bone turnover. We conducted this cohort study to determine whether sitagliptin is associated with an increased risk of fracture.Methods: The sitagliptin cohort included 1,578 patients aged 20 years and above. The nonsitagliptin cohort comprised propensity-score matched patients at a ratio of 1:1. The primary outcome was the incidence of fractures, which was evaluated using Kaplan–Meier survival analysis and proportional hazards modeling.Results: The mean age of patients in the sitagliptin and nonsitagliptin cohorts was 63.1 and 63.3 years, respectively. The incidence of fractures in the sitagliptin cohort was 46 per 1,000 person-years and that in the nonsitagliptin cohort was 40.8 per 1,000 person-years. Compared with patients in the nonsitagliptin cohort, those in the sitagliptin cohort who received sitagliptin for ≥250 days had a higher risk of fracture (aHR = 1.32, 95% CI = 1.06–1.64).Conclusion: Using sitaglipin ≥250 days was associated with an increased risk of fracture

Maximizing Science Outreach on Facebook: An Analysis of Scientists’ Communication Strategies in Taiwan

The internet, and especially social media platforms, offer scientists new opportunities to connect with a broader public. While many studies have focused on science communication on Twitter, surprisingly few have analyzed how scientists use Facebook, even though it is an essential platform for the general public in many countries. A possible explanation for this lack of research is that scientists keep their Facebook profiles separate from their work life and are more active on Twitter in their professional roles. Our study challenges this assumption by focusing on Taiwan as a peculiar case. Due to the local culture, Twitter is less popular there, and scientists are more active on Facebook, even in their professional roles. In our study, we analyzed 35 public pages of scientists on Facebook and assessed the factors explaining the reach of their communication using content analysis in combination with a multilevel model that allowed us to test predictors on the page level, such as the number of fans, in combination with predictors on the post level, such as the complexity of the language used. Our study shows that Facebook can play an influential role in science outreach. To effectively communicate with the audience on Facebook, it is best to use strategies that appeal to new and existing followers. Posts that address current issues and include opinions are likely to be shared widely, while humor or personal self-disclosure is likely to engage the existing audience. Our study contributes to the current debate about alternatives to Twitter in science communication