探討不同科學認識觀的八年級學生在社會性科學議題上論證能力的表現

[[abstract]]This study aimed to explore the development of eighth graders’ argumentation skills and scientific epistemological views through socioscientific issues-based instruction, and the relationship between their argumentation skills and scientific epistemological views. After selecting 42 participators from a remote middle school using convenience sampling, we used the one-group and pre-posttest experimental design to examine the effect of socioscientific issues-based instruction. The results showed that students’ generating arguments, counterarguments, backings, evidences, and qualifiers were not improved significantly after the instruction. Moreover, their scientific epistemological views in the theory-laden exploration of science had changed significantly and were closer to the view of logical positivism. Furthermore, we found that there was a significant difference in argumentation skills between students with different scientific epistemological views, especially in generating backings. Students whose scientific epistemological views tended to constructivism epistemological views would have better argumentation skills after the socioscientific issues-based instruction.

Synthesis of thioesters through copper-catalyzed coupling of aldehydeswith thiols in water

Copper-catalyzed C–S bond formation between aldehydes and thiols in the presence of TBHP as an oxidant is described. Functional groups including chloro, trifluoromethyl, bromo, iodo, nitrile, ester and thiophene are all tolerated by the reaction conditions employed. This reaction is performed in water without the use of a surfactant. Both aryl and alkyl aldehydes couple suitably with aryl- and alkyl thiols, affording the corresponding thioesters in moderate to good yields

Exposing the Functionalities of Neurons for Gated Recurrent Unit Based Sequence-to-Sequence Model

The goal of this paper is to report certain scientific discoveries about a Seq2Seq model. It is known that analyzing the behavior of RNN-based models at the neuron level is considered a more challenging task than analyzing a DNN or CNN models due to their recursive mechanism in nature. This paper aims to provide neuron-level analysis to explain why a vanilla GRU-based Seq2Seq model without attention can achieve token-positioning. We found four different types of neurons: storing, counting, triggering, and outputting and further uncover the mechanism for these neurons to work together in order to produce the right token in the right position.Comment: 9 pages (excluding reference), 10 figure

Progressive Transformation Learning for Leveraging Virtual Images in Training

To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data curation, we introduce Progressive Transformation Learning (PTL), which gradually augments a training dataset by adding transformed virtual images with enhanced realism. Generally, a virtual2real transformation generator in the conditional GAN framework suffers from quality degradation when a large domain gap exists between real and virtual images. To deal with the domain gap, PTL takes a novel approach that progressively iterates the following three steps: 1) select a subset from a pool of virtual images according to the domain gap, 2) transform the selected virtual images to enhance realism, and 3) add the transformed virtual images to the training set while removing them from the pool. In PTL, accurately quantifying the domain gap is critical. To do that, we theoretically demonstrate that the feature representation space of a given object detector can be modeled as a multivariate Gaussian distribution from which the Mahalanobis distance between a virtual object and the Gaussian distribution of each object category in the representation space can be readily computed. Experiments show that PTL results in a substantial performance increase over the baseline, especially in the small data and the cross-domain regime.Comment: CVPR 2023 (Selected as Highlight

Interaction induced ferro-electricity in the rotational states of polar molecules

We show that a ferro-electric quantum phase transition can be driven by the dipolar interaction of polar molecules in the presence a micro-wave field. The obtained ferro-electricity crucially depends on the harmonic confinement potential, and the resulting dipole moment persists even when the external field is turned off adiabatically. The transition is shown to be second order for fermions and for bosons of a smaller permanent dipole moment, but is first order for bosons of a larger moment. Our results suggest the possibility of manipulating the microscopic rotational state of polar molecules by tuning the trap's aspect ratio (and other mesoscopic parameters), even though the later's energy scale is smaller than the former's by six orders of magnitude.Comment: 4 pages and 4 figure