Pharmacy students' perceptions toward peer assessment and its use in teaching patient presentation skills.

Background and purposeConducting peer assessment has been associated with positive learning outcomes in higher education. The primary objective was to evaluate pharmacy students' perceptions of using peer assessment as a pedagogical strategy in learning patient presentation skills. Secondary objectives were to determine helpful factors for providing and/or receiving peer assessment and to compare students' perceptions of peer assessment relative to receiving feedback from teaching assistants (TAs).Educational activity and settingPatient presentation skills were taught to third-year pharmacy students in three sessions (session 1: didactic lecture, session 2: faculty-led patient presentation workshops followed by peer assessment, session 3: one-on-one patient presentations to TAs). An anonymous survey instrument consisting of five-point Likert scale, yes/no, and open-ended questions was administered.FindingsA total of 187 students (98%) completed the survey. Peer assessment was perceived as a useful way to obtain feedback on patient presentations (87%). It facilitated higher level thinking and a self-reflection of students' own patient presentations. Most students felt that they received constructive feedback from peers (82%) that helped them improve their patient presentation skills (72%). However, students were more trusting of TAs' skills in assessing patient presentations (76% versus 93%, p < 0.001). Some students were concerned about the specificity and criticalness of feedback they received from peers.SummaryPeer assessment is a useful pedagogical strategy for providing formative feedback to students in learning patient presentations skills in the classroom setting. Students may benefit from additional training to improve the quality of feedback in peer assessment

Pinning dynamic systems of networks with Markovian switching couplings and controller-node set

In this paper, we study pinning control problem of coupled dynamical systems with stochastically switching couplings and stochastically selected controller-node set. Here, the coupling matrices and the controller-node sets change with time, induced by a continuous-time Markovian chain. By constructing Lyapunov functions, we establish tractable sufficient conditions for exponentially stability of the coupled system. Two scenarios are considered here. First, we prove that if each subsystem in the switching system, i.e. with the fixed coupling, can be stabilized by the fixed pinning controller-node set, and in addition, the Markovian switching is sufficiently slow, then the time-varying dynamical system is stabilized. Second, in particular, for the problem of spatial pinning control of network with mobile agents, we conclude that if the system with the average coupling and pinning gains can be stabilized and the switching is sufficiently fast, the time-varying system is stabilized. Two numerical examples are provided to demonstrate the validity of these theoretical results, including a switching dynamical system between several stable sub-systems, and a dynamical system with mobile nodes and spatial pinning control towards the nodes when these nodes are being in a pre-designed region.Comment: 9 pages; 3 figure

Muti-Scale And Token Mergence: Make Your ViT More Efficient

Since its inception, Vision Transformer (ViT) has emerged as a prevalent model in the computer vision domain. Nonetheless, the multi-head self-attention (MHSA) mechanism in ViT is computationally expensive due to its calculation of relationships among all tokens. Although some techniques mitigate computational overhead by discarding tokens, this also results in the loss of potential information from those tokens. To tackle these issues, we propose a novel token pruning method that retains information from non-crucial tokens by merging them with more crucial tokens, thereby mitigating the impact of pruning on model performance. Crucial and non-crucial tokens are identified by their importance scores and merged based on similarity scores. Furthermore, multi-scale features are exploited to represent images, which are fused prior to token pruning to produce richer feature representations. Importantly, our method can be seamlessly integrated with various ViTs, enhancing their adaptability. Experimental evidence substantiates the efficacy of our approach in reducing the influence of token pruning on model performance. For instance, on the ImageNet dataset, it achieves a remarkable 33% reduction in computational costs while only incurring a 0.1% decrease in accuracy on DeiT-S

Serre functors and complete torsion pairs

Given a torsion pair $(\mathcal{T},\mathcal{F})$ in an abelian category $\mathcal{A}$, there is a t-structure $(\mathcal{U}_\mathcal{T},\mathcal{V}_\mathcal{T})$ determined by $\mathcal{T}$ on the derived category $D^b(\mathcal{A})$. The existence of derived equivalence between heart $\mathcal{B}$ of the t-structure and $\mathcal{A}$ which naturally extends the embedding $\mathcal{B}\to D^b(\mathcal{A})$ is determined by the completeness of the torsion pair [6]. When $\mathcal{A}$ is the module category of a finite-dimensional hereditary algebra and $\mathcal{U}_\mathcal{T}$ is closed under Serre functor, then there exists a triangle equivalence $D^b(\mathcal{B})\to D^b(\mathcal{A})$ [21]. In this case, we give a straightforward proof of the fact torsion pair $(\mathcal{T},\mathcal{F})$ is complete if and only if $\mathcal{U}_\mathcal{T}$ is closed under the Serre functor.Comment: 18page

VITON: An Image-based Virtual Try-on Network

We present an image-based VIirtual Try-On Network (VITON) without using 3D information in any form, which seamlessly transfers a desired clothing item onto the corresponding region of a person using a coarse-to-fine strategy. Conditioned upon a new clothing-agnostic yet descriptive person representation, our framework first generates a coarse synthesized image with the target clothing item overlaid on that same person in the same pose. We further enhance the initial blurry clothing area with a refinement network. The network is trained to learn how much detail to utilize from the target clothing item, and where to apply to the person in order to synthesize a photo-realistic image in which the target item deforms naturally with clear visual patterns. Experiments on our newly collected Zalando dataset demonstrate its promise in the image-based virtual try-on task over state-of-the-art generative models