BIPED
发表于 2025-3-26 22:03:19
Imagery in Physics Learning - from Physicists’ Practice to Naive Students’ Understandingof physics, cognitive science aspects of imagery, and educational research on physics learning with pictorial representations. A double analysis is used. The first analysis is focused on imagery in classical test cases in the history of physics, such as Faraday’s work on magnetism and Einstein’s tho
无目标
发表于 2025-3-27 02:53:11
http://reply.papertrans.cn/99/9839/983858/983858_32.png
叙述
发表于 2025-3-27 06:45:25
http://reply.papertrans.cn/99/9839/983858/983858_33.png
轮流
发表于 2025-3-27 09:58:37
Visualizing the Science of Genomicsprovides the equivalent of a complete genetic map; yet, knowledge of the sequence itself does not reveal how this map manifests itself into the physical characteristics or phenotypes observed for an organism. Genomics research is dependent upon comparative analyses of extraordinary volumes of data.
fibula
发表于 2025-3-27 16:46:15
Visualization in Undergraduate Geology Coursese shape of the land from topographic maps, the three-dimensional geometry of geologic structures from limited exposures, and the geologic history recorded in sequences of layers and in natural landscapes. Interactive animations have proven successful in helping college students visualize the three-d
Overstate
发表于 2025-3-27 18:18:56
http://reply.papertrans.cn/99/9839/983858/983858_36.png
crockery
发表于 2025-3-27 23:41:32
http://reply.papertrans.cn/99/9839/983858/983858_37.png
季雨
发表于 2025-3-28 05:21:45
http://reply.papertrans.cn/99/9839/983858/983858_38.png
cacophony
发表于 2025-3-28 06:17:32
John K Gilbert and onset-level F1 scores over the best performing baseline respectively. We also release an annotated dataset of monophonic Beatbox sequences along with their corresponding MIDI labels, the first of its kind comprising Beatbox samples with different variations such as time-stretches, pitch shifts, and added noise.
可耕种
发表于 2025-3-28 10:46:01
Barbara Tverskyuage inference tasks. This QA network enables our model to acquire better sentence encodings by modeling adjacent sentences in a dialogue as question-answer pairs. We evaluate our model in the benchmark EmotionX datasets provided by SocialNLP2018 and our model achieves the state-of-the-art performance.