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State Estimation of a Powered Exoskeleton with Dynamic Motion Using Kinematic and Inertial Odometry
No full textFocus Where It Matters: LLM-Guided Regional Identification for Instruction-based Image Editing
No full textDesign Opportunities for Explainable AI Paraphrasing Tools: A User Study with Non-native English Speakers
No full textWe investigate how non-native English speakers (NNESs) interact with diverse information aids to assess and select AI-generated paraphrases. We develop ParaScope, an AI paraphrasing assistant that integrates diverse information aids, such as back-translation, explanations, and usage examples, and logs user interaction data. Our in-lab study with 22 NNESs reveals that user preferences for information aids vary by language proficiency, with workflows progressing from global to more detailed information. While back-translation was the most frequently used aid, it was not a decisive factor in suggestion acceptance; users combined multiple information aids to make informed decisions. Our findings demonstrate the potential of explainable AI paraphrasing tools to enhance NNESs' confidence, autonomy, and writing efficiency, while also emphasizing the importance of thoughtful design to prevent information overload. Based on these findings, we offer design implications for explainable AI paraphrasing tools that support NNESs in making informed decisions when using AI writing systems
Catalytic and selective chemical recycling of post-consumer rubbers into cycloalkenes
No full textDespite the invaluable properties and broad applications of synthetic elastomers, the management of waste rubbers and end-of-life tires typically involves mechanical recycling or conversion into low-grade fuel. This study introduces an effective and selective chemical recycling method for synthetic elastomers, including polybutadiene, polycycloalkenamers, and their copolymers, utilizing a synergistic tandem catalysis approach that combines isomerization and ring-closing metathesis. By exploiting the ring-chain equilibrium of oligomers, we have demonstrated the selective depolymerization of rubbers into C5-C7 cycloalkenes. Importantly, this method effectively depolymerizes post-consumer vulcanized rubbers, such as disposable rubbers and tires, converting them into highly valuable chemical feedstocks. These results highlight the significant potential of tandem dual catalysis for the selective chemical recycling of synthetic rubbers.
메로시아닌 화합물 및 이의 이성질체를 포함하는 DSRNA 검출 조성물, 및 DSRNA 발현 분석을 사용하여 암 진단을 위한 정보 제공 방법
No full textThe merocyanine compound of the present invention causes a spectral change of a hypochromic shift in the vicinity of the wavelength of 512 nm by intercalation between dsRNA nucleotide pairs, and the spectral change shows high accuracy and reproducibility, so the merocyanine compound of the present invention can be effectively used for detecting dsRNA and comparing the expression levels between samples. In addition, the present invention relates to a method for providing information for the diagnosis of cancer comprising the steps of 1) measuring the expression level of dsRNA in a sample of a test subject; and 2) comparing the expression level of dsRNA measured in step 1) with that of the normal control group, and a composition for detecting dsRNA comprising a merocyanine compound, a salt thereof or an isomer thereof. The present invention can provide information for the diagnosis of cancer by measuring the expression level of dsRNA separated from a sample of a test subject and comparing it with that of the normal control group. Further, the responsiveness of a patient to the treated drug can be predicted by treating the sample with a drug and measuring the expression level of dsRNA
Difficulty-aware Balancing Margin Loss for Long-tailed Recognition
No full text<jats:p>When trained with severely imbalanced data, deep neural networks often struggle to accurately recognize classes with few samples. Previous studies in long-tailed recognition have attempted to rebalance biased learning using known sample distributions, primarily addressing different classification difficulties at the class level. However, these approaches often overlook the instance difficulty variation within each class. In this paper, we propose a difficulty-aware balancing margin (DBM) loss, which considers both class imbalance and instance difficulty. DBM loss comprises two components: a class-wise margin to mitigate learning bias caused by imbalanced class frequencies, and an instance-wise margin assigned to hard positive samples based on their individual difficulty. DBM loss improves class discriminativity by assigning larger margins to more difficult samples. Our method effortlessly combine with existing approaches and consistently improves performance across various long-tailed recognition benchmarks.</jats:p>
