Leading from equity: Changing and organizing for deeper learning

Purpose – This study aims to explore how educational leaders in South Korea adopted equity mindsets and how they organized changes to support students’ deeper learning during COVID-19. Design/methodology/approach – The developed a comprehensive framework of Equity Leadership for Deeper Learning, by revising the existing model of Darling-Hammond and Darling-Hammond (2022) and synthesizing equity leadership literature. Drawing upon this framework, this study analyzed data collected from individual interviews and a focus group with school and district administrators in the K-12 Korean education system. Findings – The participants prioritized an equity stance of their leadership by critically understanding sociopolitical conditions, challenging unjust policies, and envisioning the big picture of equity-centered education. This led them to operationalize equity leadership in practice and create a more inclusive and supportive environment for student-centered deeper learning. District leaders established well-resourced systems by creating/developing instructional resources and making policies more useful. School leaders promoted quality teaching by strengthening access, developing student-centered curricula, and establishing individualized programs for more equitable deeper learning. Research limitations/implications – This study builds on scholarship of deeper learning and equity leadership by adding evidence from Korean educational leaders during COVID-19. First, the findings highlight the significance of leaders’ equity mindsets in creating a safe and inclusive environment for deeper learning. This study further suggests that sharing an equity stance as a collective norm at the system level, spanning across districts and schools is important, which is instrumental to scale up innovation and reform initiatives. Second, this research also extends comparative, culturally informed perspectives to understand educational leadership. Most contemporary leadership theories originated from and are informed by Western and English-speaking contexts despite being widely applied to other contexts across the culture. This study’s analysis underscores the importance of contextualizing leadership practices within the socio-historical contexts that influence how education systems are established and operate. Practical implications – Leaders’ adopting equity mindsets, utilizing bureaucratic resources in creative ways and implementing a school-wide quality curriculum are crucial to supporting students’ deeper learning. District leaders can leverage existing vertical and horizontal networks to effectively communicate with teachers and local communities to establish well-resourced systems. As deeper learning is timeless and requires high levels of student engagement, school leaders’ efforts to establish school-wide curricula is critical to facilitate deeper learning for students. Originality/value – The study provides a nuanced understanding of how equity focused leaders responded to difficulties caused by the pandemic and strategized to support students’ deeper learning. Existing studies tend to prioritize teacher effects on student learning, positing leadership effects as secondary or indirect. Alternatively, the authors argue that, without leadership supporting an inclusive environment, resourceful systems and student-centered school culture, deeper learning cannot be fully achieved in equitable ways

Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation

Vessel segmentation in medical images is one of the important tasks in the diagnosis of vascular diseases and therapy planning. Although learning-based segmentation approaches have been extensively studied, a large amount of ground-truth labels are required in supervised methods and confusing background structures make neural networks hard to segment vessels in an unsupervised manner. To address this, here we introduce a novel diffusion adversarial representation learning (DARL) model that leverages a denoising diffusion probabilistic model with adversarial learning, and apply it to vessel segmentation. In particular, for self-supervised vessel segmentation, DARL learns the background signal using a diffusion module, which lets a generation module effectively provide vessel representations. Also, by adversarial learning based on the proposed switchable spatially-adaptive denormalization, our model estimates synthetic fake vessel images as well as vessel segmentation masks, which further makes the model capture vessel-relevant semantic information. Once the proposed model is trained, the model generates segmentation masks in a single step and can be applied to general vascular structure segmentation of coronary angiography and retinal images. Experimental results on various datasets show that our method significantly outperforms existing unsupervised and self-supervised vessel segmentation methods.Comment: Accepted at ICLR 202

ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts

Recent success of large-scale Contrastive Language-Image Pre-training (CLIP) has led to great promise in zero-shot semantic segmentation by transferring image-text aligned knowledge to pixel-level classification. However, existing methods usually require an additional image encoder or retraining/tuning the CLIP module. Here, we propose a novel Zero-shot segmentation with Optimal Transport (ZegOT) method that matches multiple text prompts with frozen image embeddings through optimal transport. In particular, we introduce a novel Multiple Prompt Optimal Transport Solver (MPOT), which is designed to learn an optimal mapping between multiple text prompts and visual feature maps of the frozen image encoder hidden layers. This unique mapping method facilitates each of the multiple text prompts to effectively focus on distinct visual semantic attributes. Through extensive experiments on benchmark datasets, we show that our method achieves the state-of-the-art (SOTA) performance over existing Zero-shot Semantic Segmentation (ZS3) approaches.Comment: 18pages, 8 figure

Teaching with resilience during the COVID-19 pandemic: Korean teachers and collective professionalism

This study applies system-focused resilience and collaborative professionalism to examine how teachers in Korea collectively developed resilience and transformed teaching during COVID-19. Using qualitative data from seven individual interviews and four focus groups, we found Korean teachers navigated complex challenges (rapidly changing policies, online teaching, exacerbated learning gaps, and excessive social pressure) and utilized contextual resources (collective autonomy and flexibility, solidity and solidarity, and collective responsibility) to develop strategies (collaborative inquiry, timely communication, and envisioning the future of schooling). The study extends teacher resilience toward more collective and communal, from the individual level, by linking resilience to collaborative systemic changes

Optimized Quantum Implementation of SEED

With the advancement of quantum computers, it has been demonstrated that Shor\u27s algorithm enables public key cryptographic attacks to be performed in polynomial time. In response, NIST conducted a Post-Quantum Cryptography Standardization competition. Additionally, due to the potential reduction in the complexity of symmetric key cryptographic attacks to square root with Grover\u27s algorithm, it is increasingly challenging to consider symmetric key cryptography as secure. In order to establish secure post-quantum cryptographic systems, there is a need for quantum post-quantum security evaluations of cryptographic algorithms. Consequently, NIST is estimating the strength of post-quantum security, driving active research in quantum cryptographic analysis for the establishment of secure post-quantum cryptographic systems. In this regard, this paper presents a depth-optimized quantum circuit implementation for SEED, a symmetric key encryption algorithm included in the Korean Cryptographic Module Validation Program (KCMVP). Building upon our implementation, we conduct a thorough assessment of the post-quantum security for SEED. Our implementation for SEED represents the first quantum circuit implementation for this cipher

Impact of Changes in Maternal Age and Parity Distribution on the Increasing Trends in the Low Birth Weight and Very Low Birth Weight Rates in South Korea, 2005-2015

Objectives The aim of this study was to evaluate the impact of shifts in maternal age and parity on the increasing trends in the low birth weight (LBW) and very low birth weight (VLBW) rates from 2005 to 2015 in South Korea. Methods Data from 4 993 041 live births registered with Statistics Korea during the period between 2005 and 2015 were analyzed. Applying a modified standardization method, we partitioned the total increment in the LBW and VLBW rates into (1) the increase in the LBW and VLBW rates due to changes in the maternal age and parity distribution (AP-dis) and (2) the increase due to changes in the age-specific and parity-specific rates (AP-spe) of LBW and VLBW for singleton and multiple births, respectively. Results During the study period, the total increment in the LBW and VLBW rates was 1.43%p and 0.25%p, respectively. Among singleton births, changes in the AP-dis accounted for 79% (0.34%p) and 50% (0.06%p) of the total increment in the LBW and VLBW rates, respectively. Meanwhile, among multiple births, changes in the AP-dis did not contribute to the increase in the LBW and VLBW rates, with 100% of the increase in the LBW (1.00%p) and VLBW (0.13%p) rates being attributed to changes in the AP-spe. Conclusions This study demonstrated that shifts in maternal age and parity were prominent contributors to the increase in the LBW and VLBW rates among singleton births between 2005 and 2015 in South Korea

Depth-Optimized Quantum Implementation of ARIA

The advancement of large-scale quantum computers poses a threat to the security of current encryption systems. In particular, symmetric-key cryptography significantly is impacted by general attacks using the Grover\u27s search algorithm. In recent years, studies have been presented to estimate the complexity of Grover\u27s key search for symmetric-key ciphers and assess post-quantum security. In this paper, we propose a depth-optimized quantum circuit implementation for ARIA, which is a symmetric key cipher included as a validation target the Korean Cryptographic Module Validation Program (KCMVP). Our quantum circuit implementation for ARIA improves the depth by more than 88.2% and Toffoli depth by more than 98.7% compared to the implementation presented in Chauhan et al.\u27s SPACE\u2720 paper. Finally, we present the cost of Grover\u27s key search for our circuit and evaluate the post-quantum security strength of ARIA according to relevant evaluation criteria provided NIST

Quantum Implementation of AIM: Aiming for Low-Depth

Security vulnerabilities in the symmetric-key primitives of a cipher can undermine the overall security claims of the cipher. With the rapid advancement of quantum computing in recent years, there is an increasing effort to evaluate the security of symmetric-key cryptography against potential quantum attacks. This paper focuses on analyzing the quantum attack resistance of AIM, a symmetric-key primitive used in the AIMer digital signature scheme. We presents the first quantum circuit implementation of AIM and estimates its complexity (such as qubit count, gate count, and circuit depth) with respect to Grover\u27s search algorithm. For Grover\u27s key search, the most important optimization metric is the depth, especially when considering parallel search. Our implementation gathers multiple methods for a low-depth quantum circuit of AIM in order to reduce the Toffoli depth and full depth