267 research outputs found
Examining Teaching Charisma and Its Relation to Student Engagement
This study focuses on the factor of teaching charisma which comprises four key constructs: knowledge, character traits, teaching techniques, and humor. Participants were collected from 17 regular education classrooms within 6 colleges or universities in central Taiwan. The results revealed that the Inventory of Teaching Charisma in the College Classroom (ITCCC) is a psychometrically valid instrument which can accurately assess students’ perceptions of the quality of a teacher’s teaching in a professional course. Furthermore, a strong positive relationship between teacher’s charisma and student engagement was found and three factors of the teaching charisma can jointly predict student engagement in the professional subject. The importance of the teacher’s charisma in enhancing student engagement is confirmed
General Greedy De-bias Learning
Neural networks often make predictions relying on the spurious correlations
from the datasets rather than the intrinsic properties of the task of interest,
facing sharp degradation on out-of-distribution (OOD) test data. Existing
de-bias learning frameworks try to capture specific dataset bias by annotations
but they fail to handle complicated OOD scenarios. Others implicitly identify
the dataset bias by special design low capability biased models or losses, but
they degrade when the training and testing data are from the same distribution.
In this paper, we propose a General Greedy De-bias learning framework (GGD),
which greedily trains the biased models and the base model. The base model is
encouraged to focus on examples that are hard to solve with biased models, thus
remaining robust against spurious correlations in the test stage. GGD largely
improves models' OOD generalization ability on various tasks, but sometimes
over-estimates the bias level and degrades on the in-distribution test. We
further re-analyze the ensemble process of GGD and introduce the Curriculum
Regularization inspired by curriculum learning, which achieves a good trade-off
between in-distribution and out-of-distribution performance. Extensive
experiments on image classification, adversarial question answering, and visual
question answering demonstrate the effectiveness of our method. GGD can learn a
more robust base model under the settings of both task-specific biased models
with prior knowledge and self-ensemble biased model without prior knowledge.Comment: This work has been submitted to IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
ALID: Scalable Dominant Cluster Detection
Detecting dominant clusters is important in many analytic applications. The
state-of-the-art methods find dense subgraphs on the affinity graph as the
dominant clusters. However, the time and space complexity of those methods are
dominated by the construction of the affinity graph, which is quadratic with
respect to the number of data points, and thus impractical on large data sets.
To tackle the challenge, in this paper, we apply Evolutionary Game Theory (EGT)
and develop a scalable algorithm, Approximate Localized Infection Immunization
Dynamics (ALID). The major idea is to perform Localized Infection Immunization
Dynamics (LID) to find dense subgraph within local range of the affinity graph.
LID is further scaled up with guaranteed high efficiency and detection quality
by an estimated Region of Interest (ROI) and a carefully designed Candidate
Infective Vertex Search method (CIVS). ALID only constructs small local
affinity graphs and has a time complexity of O(C(a^*+ {\delta})n) and a space
complexity of O(a^*(a^*+ {\delta})), where a^* is the size of the largest
dominant cluster and C << n and {\delta} << n are small constants. We
demonstrate by extensive experiments on both synthetic data and real world data
that ALID achieves state-of-the-art detection quality with much lower time and
space cost on single machine. We also demonstrate the encouraging
parallelization performance of ALID by implementing the Parallel ALID (PALID)
on Apache Spark. PALID processes 50 million SIFT data points in 2.29 hours,
achieving a speedup ratio of 7.51 with 8 executors
R&B: Region and Boundary Aware Zero-shot Grounded Text-to-image Generation
Recent text-to-image (T2I) diffusion models have achieved remarkable progress
in generating high-quality images given text-prompts as input. However, these
models fail to convey appropriate spatial composition specified by a layout
instruction. In this work, we probe into zero-shot grounded T2I generation with
diffusion models, that is, generating images corresponding to the input layout
information without training auxiliary modules or finetuning diffusion models.
We propose a Region and Boundary (R&B) aware cross-attention guidance approach
that gradually modulates the attention maps of diffusion model during
generative process, and assists the model to synthesize images (1) with high
fidelity, (2) highly compatible with textual input, and (3) interpreting layout
instructions accurately. Specifically, we leverage the discrete sampling to
bridge the gap between consecutive attention maps and discrete layout
constraints, and design a region-aware loss to refine the generative layout
during diffusion process. We further propose a boundary-aware loss to
strengthen object discriminability within the corresponding regions.
Experimental results show that our method outperforms existing state-of-the-art
zero-shot grounded T2I generation methods by a large margin both qualitatively
and quantitatively on several benchmarks.Comment: Preprint. Under review. Project page:
https://sagileo.github.io/Region-and-Boundar
Uniaxial Tension Simulation Using Real Microstructure-based Representative Volume Elements Model of Dual Phase Steel Plate
AbstractDual-phase steels have become a favored material for car bodies. In this study, the deformation behavior of dual-phase steels under uniaxial tension is investigated by means of 2D Representative Volume Elements (RVE) model. The real metallographic graphs including particle geometry, distribution and morphology are considered in this RVE model. Stress and strain distributions between martensite and ferrite are analyzed. The results show that martensite undertakes most stress without significant strain while ferrite shares the most strain. The tensile failure is the result of the deforming inhomogeneity between martensite phase and ferrite phase, which is the key factor triggering the plastic strain localization on specimen section during the tensile test
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